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array:23 [ "pii" => "S0300289624000024" "issn" => "03002896" "doi" => "10.1016/j.arbres.2024.01.001" "estado" => "S300" "fechaPublicacion" => "2024-03-01" "aid" => "3464" "copyright" => "SEPAR" "copyrightAnyo" => "2024" "documento" => "article" "crossmark" => 1 "subdocumento" => "fla" "cita" => "Arch Bronconeumol. 2024;60:153-60" "abierto" => array:3 [ "ES" => false "ES2" => false "LATM" => false ] "gratuito" => false "lecturas" => array:1 [ "total" => 0 ] "itemSiguiente" => array:18 [ "pii" => "S0300289624000061" "issn" => "03002896" "doi" => "10.1016/j.arbres.2024.01.004" "estado" => "S300" "fechaPublicacion" => "2024-03-01" "aid" => "3468" "copyright" => "The Authors" "documento" => "article" "crossmark" => 1 "subdocumento" => "rev" "cita" => "Arch Bronconeumol. 2024;60:161-70" "abierto" => array:3 [ "ES" => false "ES2" => false "LATM" => false ] "gratuito" => false "lecturas" => array:1 [ "total" => 0 ] "en" => array:12 [ "idiomaDefecto" => true "cabecera" => "<span class="elsevierStyleTextfn">Review Article</span>" "titulo" => "Respiratory Syncytial Virus Vaccination Recommendations for Adults Aged 60 Years and Older: The NeumoExperts Prevention Group Position Paper" "tienePdf" => "en" "tieneTextoCompleto" => "en" "tieneResumen" => "en" "paginas" => array:1 [ 0 => array:2 [ "paginaInicial" => "161" "paginaFinal" => "170" ] ] "contieneResumen" => array:1 [ "en" => true ] "contieneTextoCompleto" => array:1 [ "en" => true ] "contienePdf" => array:1 [ "en" => true ] "resumenGrafico" => array:2 [ "original" => 0 "multimedia" => array:7 [ "identificador" => "fig0005" "etiqueta" => "Fig. 1" "tipo" => "MULTIMEDIAFIGURA" "mostrarFloat" => true "mostrarDisplay" => false "figura" => array:1 [ 0 => array:4 [ "imagen" => "gr1.jpeg" "Alto" => 3266 "Ancho" => 3341 "Tamanyo" => 1066911 ] ] "descripcion" => array:1 [ "en" => "<p id="spar0005" class="elsevierStyleSimplePara elsevierViewall">Vaccination guide against community-acquired pneumonia in adults caused by vaccine-preventable diseases. CSF: cerebrospinal fluid; IPD: invasive pneumococcal disease.</p> <p id="spar0075" class="elsevierStyleSimplePara elsevierViewall">Adapted from Redondo et al.<a class="elsevierStyleCrossRef" href="#bib0715"><span class="elsevierStyleSup">70</span></a></p>" ] ] ] "autores" => array:1 [ 0 => array:2 [ "autoresLista" => "Esther Redondo, Irene Rivero-Calle, Enrique Mascarós, Daniel Ocaña, Isabel Jimeno, Ángel Gil, Manuel Linares, María Ángeles Onieva-García, Fernando González-Romo, José Yuste, Federico Martinón-Torres" "autores" => array:11 [ 0 => array:2 [ "nombre" => "Esther" "apellidos" => "Redondo" ] 1 => array:2 [ "nombre" => "Irene" "apellidos" => "Rivero-Calle" ] 2 => array:2 [ "nombre" => "Enrique" "apellidos" => "Mascarós" ] 3 => array:2 [ "nombre" => "Daniel" "apellidos" => "Ocaña" ] 4 => array:2 [ "nombre" => "Isabel" "apellidos" => "Jimeno" ] 5 => array:2 [ "nombre" => "Ángel" "apellidos" => "Gil" ] 6 => array:2 [ "nombre" => "Manuel" "apellidos" => "Linares" ] 7 => array:2 [ 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"idiomaDefecto" => true "cabecera" => "<span class="elsevierStyleTextfn">Original Article</span>" "titulo" => "Age-Related Differences in the Presentation, Management, and Clinical Outcomes of 100,000 Patients With Venous Thromboembolism in the RIETE Registry" "tienePdf" => "en" "tieneTextoCompleto" => "en" "tieneResumen" => array:2 [ 0 => "en" 1 => "en" ] "paginas" => array:1 [ 0 => array:2 [ "paginaInicial" => "143" "paginaFinal" => "152" ] ] "contieneResumen" => array:1 [ "en" => true ] "contieneTextoCompleto" => array:1 [ "en" => true ] "contienePdf" => array:1 [ "en" => true ] "resumenGrafico" => array:2 [ "original" => 1 "multimedia" => array:5 [ "identificador" => "fig0010" "tipo" => "MULTIMEDIAFIGURA" "mostrarFloat" => false "mostrarDisplay" => true "figura" => array:1 [ 0 => array:4 [ "imagen" => "fx1.jpeg" "Alto" => 703 "Ancho" => 1333 "Tamanyo" => 71180 ] ] ] ] "autores" => array:1 [ 0 => array:2 [ "autoresLista" => "Alberto García Ortega, David Jiménez, Ana Pedro-Tudela, Cristina Pérez-Ductor, Carmen Fernández-Capitán, Conxita Falgá, Andris Skride, Carmine Siniscalchi, Ido Weinberg, Manuel Monreal" "autores" => array:11 [ 0 => array:2 [ "nombre" => "Alberto García" "apellidos" => "Ortega" ] 1 => array:2 [ "nombre" => "David" "apellidos" => "Jiménez" ] 2 => array:2 [ "nombre" => "Ana" "apellidos" => "Pedro-Tudela" ] 3 => array:2 [ "nombre" => "Cristina" "apellidos" => "Pérez-Ductor" ] 4 => array:2 [ "nombre" => "Carmen" "apellidos" => "Fernández-Capitán" ] 5 => array:2 [ "nombre" => "Conxita" "apellidos" => "Falgá" ] 6 => array:2 [ "nombre" => "Andris" "apellidos" => "Skride" ] 7 => array:2 [ "nombre" => "Carmine" "apellidos" => "Siniscalchi" ] 8 => array:2 [ "nombre" => "Ido" "apellidos" => "Weinberg" ] 9 => array:2 [ "nombre" => "Manuel" "apellidos" => "Monreal" ] 10 => array:1 [ "colaborador" => "the RIETE investigators" ] ] ] ] "resumen" => array:1 [ 0 => array:3 [ "titulo" => "Graphical abstract" "clase" => "graphical" "resumen" => "<span id="abst0005" class="elsevierStyleSection elsevierViewall"><p id="spar0005" class="elsevierStyleSimplePara elsevierViewall"><elsevierMultimedia ident="fig0010"></elsevierMultimedia></p></span>" ] ] ] "idiomaDefecto" => "en" "EPUB" => "https://multimedia.elsevier.es/PublicationsMultimediaV1/item/epub/S0300289623004210?idApp=UINPBA00003Z" "url" => "/03002896/0000006000000003/v3_202406110502/S0300289623004210/v3_202406110502/en/main.assets" ] "en" => array:20 [ "idiomaDefecto" => true "cabecera" => "<span class="elsevierStyleTextfn">Original article</span>" "titulo" => "Predicting Response to In-Hospital Pulmonary Rehabilitation in Individuals Recovering From Exacerbations of Chronic Obstructive Pulmonary Disease" "tieneTextoCompleto" => true "paginas" => array:1 [ 0 => array:2 [ "paginaInicial" => "153" "paginaFinal" => "160" ] ] "autores" => array:1 [ 0 => array:4 [ "autoresLista" => "Michele Vitacca, Alberto Malovini, Mara Paneroni, Antonio Spanevello, Piero Ceriana, Armando Capelli, Rodolfo Murgia, Nicolino Ambrosino" "autores" => array:8 [ 0 => array:4 [ "nombre" => "Michele" "apellidos" => "Vitacca" "email" => array:1 [ 0 => "michele.vitacca@icsmaugeri.it" ] "referencia" => array:2 [ 0 => array:2 [ "etiqueta" => "<span class="elsevierStyleSup">a</span>" "identificador" => "aff0005" ] 1 => array:2 [ "etiqueta" => "<span class="elsevierStyleSup">*</span>" "identificador" => "cor0005" ] ] ] 1 => array:3 [ "nombre" => "Alberto" "apellidos" => "Malovini" "referencia" => array:1 [ 0 => array:2 [ "etiqueta" => "<span class="elsevierStyleSup">b</span>" "identificador" => "aff0010" ] ] ] 2 => array:3 [ "nombre" => "Mara" "apellidos" => "Paneroni" "referencia" => array:1 [ 0 => array:2 [ "etiqueta" => "<span class="elsevierStyleSup">a</span>" "identificador" => "aff0005" ] ] ] 3 => array:3 [ "nombre" => "Antonio" "apellidos" => "Spanevello" "referencia" => array:2 [ 0 => array:2 [ "etiqueta" => "<span class="elsevierStyleSup">c</span>" "identificador" => "aff0015" ] 1 => array:2 [ "etiqueta" => "<span class="elsevierStyleSup">d</span>" "identificador" => "aff0020" ] ] ] 4 => array:3 [ "nombre" => "Piero" "apellidos" => "Ceriana" "referencia" => array:1 [ 0 => array:2 [ "etiqueta" => "<span class="elsevierStyleSup">e</span>" "identificador" => "aff0025" ] ] ] 5 => array:3 [ "nombre" => "Armando" "apellidos" => "Capelli" "referencia" => array:1 [ 0 => array:2 [ "etiqueta" => "<span class="elsevierStyleSup">f</span>" "identificador" => "aff0030" ] ] ] 6 => array:3 [ "nombre" => "Rodolfo" "apellidos" => "Murgia" "referencia" => array:1 [ 0 => array:2 [ "etiqueta" => "<span class="elsevierStyleSup">g</span>" "identificador" => "aff0035" ] ] ] 7 => array:3 [ "nombre" => "Nicolino" "apellidos" => "Ambrosino" "referencia" => array:1 [ 0 => array:2 [ "etiqueta" => "<span class="elsevierStyleSup">g</span>" "identificador" => "aff0035" ] ] ] ] "afiliaciones" => array:7 [ 0 => array:3 [ "entidad" => "Respiratory Rehabilitation of the Institute of Lumezzane, Istituti Clinici Scientifici Maugeri IRCCS, Brescia, Italy" "etiqueta" => "a" "identificador" => "aff0005" ] 1 => array:3 [ "entidad" => "Laboratory of Informatics and Systems Engineering for Clinical Research, Istituti Clinici Scientifici Maugeri IRCCS, Pavia, Italy" "etiqueta" => "b" "identificador" => "aff0010" ] 2 => array:3 [ "entidad" => "Respiratory Rehabilitation of the Institute of Tradate, Istituti Clinici Scientifici Maugeri IRCCS, Varese, Italy" "etiqueta" => "c" "identificador" => "aff0015" ] 3 => array:3 [ "entidad" => "Department of Medicine and Surgery, University of Insubria, Varese, Italy" "etiqueta" => "d" "identificador" => "aff0020" ] 4 => array:3 [ "entidad" => "Respiratory Rehabilitation of the Institute of Pavia, Istituti Clinici Scientifici Maugeri IRCCS, Pavia, Italy" "etiqueta" => "e" "identificador" => "aff0025" ] 5 => array:3 [ "entidad" => "Respiratory Rehabilitation of the Institute of Veruno, Istituti Clinici Scientifici Maugeri IRCCS, Novara, Italy" "etiqueta" => "f" "identificador" => "aff0030" ] 6 => array:3 [ "entidad" => "Respiratory Rehabilitation of the Institute of Montescano, Istituti Clinici Scientifici Maugeri IRCCS, Pavia, Italy" "etiqueta" => "g" "identificador" => "aff0035" ] ] "correspondencia" => array:1 [ 0 => array:3 [ "identificador" => "cor0005" "etiqueta" => "⁎" "correspondencia" => "Corresponding author." ] ] ] ] "resumenGrafico" => array:2 [ "original" => 1 "multimedia" => array:5 [ "identificador" => "fig0015" "tipo" => "MULTIMEDIAFIGURA" "mostrarFloat" => false "mostrarDisplay" => true "figura" => array:1 [ 0 => array:4 [ "imagen" => "fx1.jpeg" "Alto" => 754 "Ancho" => 1333 "Tamanyo" => 140422 ] ] ] ] "textoCompleto" => "<span class="elsevierStyleSections"><span id="sec0005" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0045">Introduction</span><p id="par0005" class="elsevierStylePara elsevierViewall">Pulmonary rehabilitation (PR) is a recognized component of management of chronic obstructive pulmonary disease (COPD). It improves dyspnea, fatigue, exercise capacity, and health-related quality of life (HRQL). Guidelines recommend PR for individuals experiencing persistent breathlessness and/or exercise limitation with impaired HRQL.<a class="elsevierStyleCrossRefs" href="#bib0200"><span class="elsevierStyleSup">1,2</span></a></p><p id="par0010" class="elsevierStylePara elsevierViewall">Exacerbations of COPD (ECOPD) have effects on lung function, new ECOPD, mortality and on economic and symptom burden, HRQL, exercise capacity, and nutritional status.<a class="elsevierStyleCrossRefs" href="#bib0210"><span class="elsevierStyleSup">3,4</span></a> Early PR following an ECOPD is associated to reduced prevalence of new ECOPD, longer survival, and cost-effectiveness.<a class="elsevierStyleCrossRef" href="#bib0220"><span class="elsevierStyleSup">5</span></a> Unfortunately, there are barriers to PR programs, such as a high number of candidates, transportation, costs, and geographical obstacles.<a class="elsevierStyleCrossRef" href="#bib0225"><span class="elsevierStyleSup">6</span></a> To improve access to PR, governments should increase resources and quality of services, and practitioners should explore modalities such as personalization and telerehabilitation.<a class="elsevierStyleCrossRefs" href="#bib0230"><span class="elsevierStyleSup">7,8</span></a> An additional approach might involve identifying characteristics of potential responders to optimize resources.</p><p id="par0015" class="elsevierStylePara elsevierViewall">Machine learning (ML), a subfield of artificial intelligence (AI), consists of algorithms capable of learning and improving from experience, aiming to classify individual conditions or forecast outcomes when applied to the medical context and has been widely applied in clinical research.<a class="elsevierStyleCrossRefs" href="#bib0240"><span class="elsevierStyleSup">9–11</span></a> Machine learning has been employed in early diagnosis of ECOPD.<a class="elsevierStyleCrossRefs" href="#bib0255"><span class="elsevierStyleSup">12–14</span></a></p><p id="par0020" class="elsevierStylePara elsevierViewall">This large, multicentre, retrospective study aimed to explore the feasibility of integrating information from clinical practice to predict individual-level post-PR responses in commonly used outcomes of PR by statistical and ML methods.</p></span><span id="sec0010" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0050">Material and methods</span><p id="par0025" class="elsevierStylePara elsevierViewall">This study analyzed data from hospital medical records of individuals recovering from ECOPD, admitted to an in-hospital PR program and was approved by the Istituti Clinici Scientifici (ICS) Maugeri Ethics Committee (2555 CE 8 June 2021). As a retrospective study, participants had not provided any specific written informed consent, however, at admission to hospitals, they had given – in advance – informed consent for the scientific use of their data. As a retrospective analysis, the study was not registered.</p><span id="sec0015" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0055">Participants</span><p id="par0030" class="elsevierStylePara elsevierViewall">The study was conducted on data from individuals recovering from an ECOPD, either cared for in acute hospitals and transferred to Rehabilitation Hospitals or cared for at home by their general practitioners [GP], and admitted after an out-patient clinic visit, between July, 1st, 2018 to December, 31st, 2021 to hospitals of the network previously described.<a class="elsevierStyleCrossRef" href="#bib0270"><span class="elsevierStyleSup">15</span></a> These hospitals share common indications, evaluation, diagnostic and management tools and protocols for PR. During the pandemic (March 2020–December 2021), only participants with negative swab tests were admitted. It's worth noting that this study utilized the same dataset analyzed and published elsewhere.<a class="elsevierStyleCrossRef" href="#bib0270"><span class="elsevierStyleSup">15</span></a></p><p id="par0035" class="elsevierStylePara elsevierViewall">The study included data from individuals with:<ul class="elsevierStyleList" id="lis0015"><li class="elsevierStyleListItem" id="lsti0005"><span class="elsevierStyleLabel">-</span><p id="par0040" class="elsevierStylePara elsevierViewall">Diagnosis of COPD according to the GOLD guidelines.<a class="elsevierStyleCrossRef" href="#bib0205"><span class="elsevierStyleSup">2</span></a></p></li><li class="elsevierStyleListItem" id="lsti0020"><span class="elsevierStyleLabel">-</span><p id="par0310" class="elsevierStylePara elsevierViewall">Persistent breathlessness and/or exercise limitation within the previous 30 days after an ECOPD managed in acute care hospitals or within the previous 4 weeks after an ECOPD managed at home by the GP.<a class="elsevierStyleCrossRef" href="#bib0275"><span class="elsevierStyleSup">16</span></a></p></li><li class="elsevierStyleListItem" id="lsti0025"><span class="elsevierStyleLabel">-</span><p id="par0315" class="elsevierStylePara elsevierViewall">Present stable conditions, defined as absence of acute worsening in symptoms (i.e. no change in dyspnea, cough, and/or sputum beyond the day-to-day variability) that would have required a change in management compared to the conditions reported at home or at discharge from the referring acute care hospital.<a class="elsevierStyleCrossRef" href="#bib0275"><span class="elsevierStyleSup">16</span></a></p></li><li class="elsevierStyleListItem" id="lsti0030"><span class="elsevierStyleLabel">-</span><p id="par0320" class="elsevierStylePara elsevierViewall">Availability of data on lung function and paired pre and post-PR data of outcome measures.</p></li><li class="elsevierStyleListItem" id="lsti0035"><span class="elsevierStyleLabel">-</span><p id="par0325" class="elsevierStylePara elsevierViewall">Participating in at least 12 PR sessions, a threshold chosen according to our previous published experience.<a class="elsevierStyleCrossRef" href="#bib0280"><span class="elsevierStyleSup">17</span></a></p></li></ul></p><p id="par0045" class="elsevierStylePara elsevierViewall">Exclusion criteria from PR had been: severe comorbidities such as oncological, neurological disorders, heart failure, or recent (less than 4 months) acute ischemic cardiovascular diseases with an unstable status and individuals unable or refusing to perform PR.</p></span><span id="sec0020" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0060">Measurements</span><p id="par0050" class="elsevierStylePara elsevierViewall">The following data were recorded at admission: demographics, anthropometrics, history of ECOPD in previous 12 months, Comorbidity Index of the Cumulative Illness Rating Scale (CIRS), BMI – airflow obstruction – dyspnea, and exercise capacity (BODE) index, provenience (hospital or home), length of rehabilitation hospital stay (LoS), occurrence of chronic respiratory failure (CRF), distribution in GOLD stages, drug therapy in stable state.</p><p id="par0055" class="elsevierStylePara elsevierViewall">Before the program the following assessments had been performed:<ul class="elsevierStyleList" id="lis0020"><li class="elsevierStyleListItem" id="lsit0020"><span class="elsevierStyleLabel">-</span><p id="par0330" class="elsevierStylePara elsevierViewall">Forced expiratory volumes according to standards.<a class="elsevierStyleCrossRef" href="#bib0285"><span class="elsevierStyleSup">18</span></a></p></li><li class="elsevierStyleListItem" id="lsit0025"><span class="elsevierStyleLabel">-</span><p id="par0335" class="elsevierStylePara elsevierViewall">Functional disability by the Barthel index (BI).<a class="elsevierStyleCrossRef" href="#bib0290"><span class="elsevierStyleSup">19</span></a></p></li></ul></p><p id="par0060" class="elsevierStylePara elsevierViewall">Before and after the program, the following outcome measures had been assessed:<ul class="elsevierStyleList" id="lis0025"><li class="elsevierStyleListItem" id="lsit0030"><span class="elsevierStyleLabel">-</span><p id="par0340" class="elsevierStylePara elsevierViewall">Six-minute walking distance test (6MWT).<a class="elsevierStyleCrossRefs" href="#bib0295"><span class="elsevierStyleSup">20,21</span></a> The minimal clinically important difference (MCID) has been reported as an improvement by at least 30<span class="elsevierStyleHsp" style=""></span>m.<a class="elsevierStyleCrossRef" href="#bib0295"><span class="elsevierStyleSup">20</span></a></p></li><li class="elsevierStyleListItem" id="lsit0035"><span class="elsevierStyleLabel">-</span><p id="par0345" class="elsevierStylePara elsevierViewall">Dyspnea by the Medical Research Council (MRC) scale.<a class="elsevierStyleCrossRef" href="#bib0305"><span class="elsevierStyleSup">22</span></a> A one-point reduction is considered equivalent to MCID.<a class="elsevierStyleCrossRef" href="#bib0310"><span class="elsevierStyleSup">23</span></a></p></li><li class="elsevierStyleListItem" id="lsit0040"><span class="elsevierStyleLabel">-</span><p id="par0350" class="elsevierStylePara elsevierViewall">Dyspnea by the Barthel Index dyspnea (BId). The MCID has been defined as a 9-point reduction for individuals without and as a 12-point reduction for individuals with CRF, respectively.<a class="elsevierStyleCrossRef" href="#bib0315"><span class="elsevierStyleSup">24</span></a></p></li><li class="elsevierStyleListItem" id="lsit0045"><span class="elsevierStyleLabel">-</span><p id="par0355" class="elsevierStylePara elsevierViewall">COPD assessment test (CAT). A two-point reduction in score has been reported as the MCID.<a class="elsevierStyleCrossRef" href="#bib0320"><span class="elsevierStyleSup">25</span></a></p></li></ul></p></span><span id="sec0025" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0065">Pulmonary rehabilitation</span><p id="par0065" class="elsevierStylePara elsevierViewall">Our program is supervised by multidisciplinary teams of trained and experienced chest physicians, nurses, physical therapists, dieticians, and psychologists full-time dedicated to PR. It starts within 2 days from admission, after baseline evaluations and includes daily supervised sessions (6 weekly days) of cycle training according to Maltais et al.,<a class="elsevierStyleCrossRef" href="#bib0325"><span class="elsevierStyleSup">26</span></a> until performing 30<span class="elsevierStyleHsp" style=""></span>min of continuous cycling at 50–70% of maximal load, (calculated on baseline 6MWT according to Luxton et al.<a class="elsevierStyleCrossRef" href="#bib0330"><span class="elsevierStyleSup">27</span></a>). Workload was increased by 5 Watts when subjects scored their dyspnea or leg fatigue as <3 on a 10-point Borg Scale, remained unchanged if score was 4 or 5 and was reduced for scores of >5. Pulse oximetry, arterial blood pressure, and heart rate are monitored during sessions.</p><p id="par0070" class="elsevierStylePara elsevierViewall">The program also includes optimization of medications, education, nutritional programs, and psychosocial counseling when appropriate, abdominal, upper, and lower limb muscle activities lifting weights progressively. The duration of daily activities is 2–3<span class="elsevierStyleHsp" style=""></span>h. The program is performed in Gymn Room with full availability of safety tools (e.g. CPR).</p><p id="par0075" class="elsevierStylePara elsevierViewall">During pandemic, protective measures were adopted, such as personal protective equipment, distance among individuals not less than 2<span class="elsevierStyleHsp" style=""></span>m, disinfection of materials, frequent air changes, execution of a swab at first harmful signs.</p></span><span id="sec0030" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0070">Statistical and ML analyses</span><p id="par0080" class="elsevierStylePara elsevierViewall">Quantitative variable distribution is described as median (25th, 75th percentiles) since most of them deviated from the normality assumptions, as determined by visual inspection of histograms. Categorical variable distribution is described as absolute and relative (%) frequencies. There were no missing values in the data. The whole dataset was randomly split into a <span class="elsevierStyleItalic">training</span> (70% of the whole dataset) and a <span class="elsevierStyleItalic">test</span> set (30%). Statistical and ML methods for regression (linear regression, quantile regression [quantile<span class="elsevierStyleHsp" style=""></span>=<span class="elsevierStyleHsp" style=""></span>0.5], regression trees, and conditional inference trees) were trained to predict quantitative pre to post PR changes in outcomes and tested as described in the <a class="elsevierStyleCrossRef" href="#sec0110">Supplementary material – Supplementary Methods</a> section. Post PR changes in outcomes were computed as value at discharge – value at admission and were dichotomized based on the corresponding MCID. Predictive performances were expressed as mean absolute error (MAE), sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV) (<a class="elsevierStyleCrossRef" href="#sec0110">Supplementary material – Supplementary Methods</a>). Significance level was set to <span class="elsevierStyleItalic">α</span><span class="elsevierStyleHsp" style=""></span>=<span class="elsevierStyleHsp" style=""></span>0.05. Statistical and ML analyses were performed by the R software tool version 4.2.2 (<a href="http://www.r-project.org/">www.r-project.org</a>).</p></span></span><span id="sec0035" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0075">Results</span><p id="par0085" class="elsevierStylePara elsevierViewall">Out of 5741 individuals admitted with breathlessness and/or exercise limitation after an ECOPD, data from 4582 individuals were excluded. Causes of exclusion were: 4142 lacking lung function data or with unconfirmed COPD; 293 lacking pre and/or post PR data of outcomes, 28 had severe comorbidities, 89 unable to perform PR, 12 transferred to an acute care hospital or deceased and 18 performing less than 12 sessions.<a class="elsevierStyleCrossRef" href="#bib0270"><span class="elsevierStyleSup">15</span></a></p><span id="sec0040" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0080">Participants’ characteristics</span><p id="par0090" class="elsevierStylePara elsevierViewall">Characteristics of 1159 individuals included are in <a class="elsevierStyleCrossRef" href="#tbl0005">Table 1</a>.<a class="elsevierStyleCrossRef" href="#bib0270"><span class="elsevierStyleSup">15</span></a> The majority of participants were males, were admitted from home, used triple (Long acting muscarinic antagonists<span class="elsevierStyleHsp" style=""></span>+<span class="elsevierStyleHsp" style=""></span>Long acting beta agonists<span class="elsevierStyleHsp" style=""></span>+<span class="elsevierStyleHsp" style=""></span>inhaled corticosteroids: LAMA<span class="elsevierStyleHsp" style=""></span>+<span class="elsevierStyleHsp" style=""></span>LABA<span class="elsevierStyleHsp" style=""></span>+<span class="elsevierStyleHsp" style=""></span>ICS) inhaled therapy and were included in most severe GOLD stages (1 and 2 or C and D) but did not suffer from CRF.</p><elsevierMultimedia ident="tbl0005"></elsevierMultimedia></span><span id="sec0045" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0085">Multivariate models to predict outcome variables values at discharge</span><p id="par0095" class="elsevierStylePara elsevierViewall">Performances of ML methods in predicting post-PR changes in outcome measures using values at admission of explanatory variables (reported in <a class="elsevierStyleCrossRef" href="#sec0110">Supplementary Table 1</a>) were assessed and compared on training set data (<span class="elsevierStyleItalic">n</span><span class="elsevierStyleHsp" style=""></span>=<span class="elsevierStyleHsp" style=""></span>811) as described in <a class="elsevierStyleCrossRef" href="#sec0110">Supplementary material</a>. The evaluated approaches showed similar median prediction error estimates (<a class="elsevierStyleCrossRef" href="#sec0110">Supplementary material – Supplementary Fig. 1</a>).</p><p id="par0100" class="elsevierStylePara elsevierViewall"><span class="elsevierStyleItalic">Quantile regression</span> coupled with a backward features selection approach based on statistical significance (<span class="elsevierStyleItalic">QR-p</span>) was selected as the most suitable method to predict changes (<a class="elsevierStyleCrossRef" href="#sec0110">Supplementary material – Supplementary Fig. 2</a>).</p><p id="par0105" class="elsevierStylePara elsevierViewall">Regression coefficients estimated by the four outcome-specific multivariate regressions on the whole <span class="elsevierStyleItalic">training</span> set using post-PR changes in outcomes are reported in <a class="elsevierStyleCrossRef" href="#tbl0010">Table 2</a>.</p><elsevierMultimedia ident="tbl0010"></elsevierMultimedia></span><span id="sec0050" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0090">Prediction of changes in 6MWT</span><p id="par0110" class="elsevierStylePara elsevierViewall">Multivariate regression allowed estimating a post-PR 0.17<span class="elsevierStyleHsp" style=""></span>m reduction in median 6MWT change for every meter of 6MWT at admission (<span class="elsevierStyleItalic">p</span>-value<span class="elsevierStyleHsp" style=""></span><<span class="elsevierStyleHsp" style=""></span>0.0001). Similarly, for every year increase in age and every point in admission BId, we expected a reduction in median post PR 6MWT change by −0.71 and −0.35<span class="elsevierStyleHsp" style=""></span>m respectively (<span class="elsevierStyleItalic">p</span><span class="elsevierStyleHsp" style=""></span><<span class="elsevierStyleHsp" style=""></span>0.05). On the opposite, for each unit FEV<span class="elsevierStyleInf">1</span>/FVC, % a 0.34-m increase in post-PR median 6MWT change was predicted (<span class="elsevierStyleItalic">p</span><span class="elsevierStyleHsp" style=""></span>=<span class="elsevierStyleHsp" style=""></span>0.0191). Also, individuals from hospitals showed a 24.85-m increase in median post-PR change in 6MWT compared to individuals from home (<span class="elsevierStyleItalic">p</span>-value<span class="elsevierStyleHsp" style=""></span><<span class="elsevierStyleHsp" style=""></span>0.0001).</p><p id="par0115" class="elsevierStylePara elsevierViewall">The corresponding equation to be used to predict 6MWT post PR change is the following: <span class="elsevierStyleUnderline">6MWT post PR change (meters)</span><span class="elsevierStyleHsp" style=""></span>=<span class="elsevierStyleHsp" style=""></span>135.124<span class="elsevierStyleHsp" style=""></span>−<span class="elsevierStyleHsp" style=""></span>0.171<span class="elsevierStyleHsp" style=""></span>×<span class="elsevierStyleHsp" style=""></span>6MWT at admission (m)<span class="elsevierStyleHsp" style=""></span>+<span class="elsevierStyleHsp" style=""></span>24.849<span class="elsevierStyleHsp" style=""></span>×<span class="elsevierStyleHsp" style=""></span>Provenience (Hospital<span class="elsevierStyleHsp" style=""></span>=<span class="elsevierStyleHsp" style=""></span>1, Home<span class="elsevierStyleHsp" style=""></span>=<span class="elsevierStyleHsp" style=""></span>0)<span class="elsevierStyleHsp" style=""></span>−<span class="elsevierStyleHsp" style=""></span>0.707<span class="elsevierStyleHsp" style=""></span>×<span class="elsevierStyleHsp" style=""></span>Age (years)<span class="elsevierStyleHsp" style=""></span>−<span class="elsevierStyleHsp" style=""></span>0.351<span class="elsevierStyleHsp" style=""></span>×<span class="elsevierStyleHsp" style=""></span>BId at admission (points)<span class="elsevierStyleHsp" style=""></span>+<span class="elsevierStyleHsp" style=""></span>0.339<span class="elsevierStyleHsp" style=""></span>×<span class="elsevierStyleHsp" style=""></span>FEV<span class="elsevierStyleInf">1</span>/FVC %.</p><p id="par0120" class="elsevierStylePara elsevierViewall">Taken together, these results indicate that the higher is the baseline exercise limitation, the higher is the improvement. Aged as well as dyspnoeic individuals share a lower predicted improvement, while individuals with less severe airway obstruction as well as those admitted from hospitals have larger potential improvements in exercise capacity. Multivariate explainability analyses identified 6MWT at admission (meters) as the strongest predictor of post-PR changes in 6MWT, followed by provenience from the hospital, age, BId, and FEV<span class="elsevierStyleInf">1</span>/FVC, % at admission.</p></span><span id="sec0055" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0095">Prediction of changes in CAT</span><p id="par0125" class="elsevierStylePara elsevierViewall">Each point of CAT and MRC and each meter of 6MWT at admission correspond to a decrease in the post-program median change in CAT by −0.41, −0.47, and −0.01 points respectively (<span class="elsevierStyleItalic">p</span><span class="elsevierStyleHsp" style=""></span><<span class="elsevierStyleHsp" style=""></span>0.05, <a class="elsevierStyleCrossRef" href="#tbl0010">Table 2</a>).</p><p id="par0130" class="elsevierStylePara elsevierViewall">The equation is the following: <span class="elsevierStyleUnderline">CAT post PR change (points)</span><span class="elsevierStyleHsp" style=""></span>=<span class="elsevierStyleHsp" style=""></span>5.263<span class="elsevierStyleHsp" style=""></span>−<span class="elsevierStyleHsp" style=""></span>0.407 * CAT at admission (points)<span class="elsevierStyleHsp" style=""></span>−<span class="elsevierStyleHsp" style=""></span>0.469<span class="elsevierStyleHsp" style=""></span>*<span class="elsevierStyleHsp" style=""></span>MRC at admission (points)<span class="elsevierStyleHsp" style=""></span>−<span class="elsevierStyleHsp" style=""></span>0.009<span class="elsevierStyleHsp" style=""></span>*<span class="elsevierStyleHsp" style=""></span>6MWT at admission (m).</p><p id="par0135" class="elsevierStylePara elsevierViewall">Thus, individuals with more disease impact, more dyspnea, and more exercise limitation have a greater potential improvement in CAT. Multivariate explainability analyses revealed that CAT at admission was the strongest predictor of post-PR changes in CAT, followed by 6MWT and MRC at admission.</p></span><span id="sec0060" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0100">Prediction of changes in BId and MRC</span><p id="par0140" class="elsevierStylePara elsevierViewall">Similarly, each point increase in BId and MRC at admission corresponds to a post-PR decrease by −0.19 and −1 points in median BId and MRC, respectively (<span class="elsevierStyleItalic">p</span>-value<span class="elsevierStyleHsp" style=""></span><<span class="elsevierStyleHsp" style=""></span>0.0001, <a class="elsevierStyleCrossRef" href="#tbl0010">Table 2</a>).</p><p id="par0145" class="elsevierStylePara elsevierViewall">The corresponding equations are the following:<ul class="elsevierStyleList" id="lis0010"><li class="elsevierStyleListItem" id="lsti0010"><span class="elsevierStyleLabel">a)</span><p id="par0150" class="elsevierStylePara elsevierViewall"><span class="elsevierStyleUnderline">BId post PR change (points)</span><span class="elsevierStyleHsp" style=""></span>=<span class="elsevierStyleHsp" style=""></span>−4.038<span class="elsevierStyleHsp" style=""></span>−<span class="elsevierStyleHsp" style=""></span>0.192<span class="elsevierStyleHsp" style=""></span>*<span class="elsevierStyleHsp" style=""></span>BId at admission (points)</p></li><li class="elsevierStyleListItem" id="lsti0015"><span class="elsevierStyleLabel">b)</span><p id="par0155" class="elsevierStylePara elsevierViewall"><span class="elsevierStyleUnderline">MRC post PR change (points)</span><span class="elsevierStyleHsp" style=""></span>=<span class="elsevierStyleHsp" style=""></span>2<span class="elsevierStyleHsp" style=""></span>−<span class="elsevierStyleHsp" style=""></span>1<span class="elsevierStyleHsp" style=""></span>*<span class="elsevierStyleHsp" style=""></span>MRC at admission (points)</p></li></ul></p><p id="par0160" class="elsevierStylePara elsevierViewall">Individuals with more severe dyspnea during Activity daily life (ADL) at admission are more prone to improve.</p></span><span id="sec0065" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0105">Predicting quantitative outcomes after PR</span><p id="par0165" class="elsevierStylePara elsevierViewall">The ability of the four regression models to correctly predict post-PR changes in outcomes was then assessed on the independent <span class="elsevierStyleItalic">test</span> set data (<span class="elsevierStyleItalic">n</span><span class="elsevierStyleHsp" style=""></span>=<span class="elsevierStyleHsp" style=""></span>348). Mean absolute error estimates of the four models were 44.70<span class="elsevierStyleHsp" style=""></span>m, 3.22 points, 5.35 points, and 0.32 points for 6MWT, CAT, BId and MRC.</p><p id="par0170" class="elsevierStylePara elsevierViewall"><a class="elsevierStyleCrossRef" href="#fig0005">Fig. 1</a> shows remarkable discrepancies between predicted and observed post PR changes in outcomes, especially for low and high observed outcome distribution values, particularly for 6MWT and BId. In detail, in comparison to the observed post-PR changes in 6MWT ranging between −243 and +348<span class="elsevierStyleHsp" style=""></span>m, the spectrum of predicted values of change in 6MWT is narrower, ranging between −8 and +139<span class="elsevierStyleHsp" style=""></span>m. Similarly, observed changes in BId ranged between −68 and +11 points, whereas predicted changes ranged between −21 and −4 points. Post-PR changes in CAT ranged between −27 and +4 points, as compared to predicted values between −15 and +1 points. Predicted post-PR changes in MRC ranged between −3 and +1 points, whereas observed changes ranged between −2 and +2 points.</p><elsevierMultimedia ident="fig0005"></elsevierMultimedia><p id="par0175" class="elsevierStylePara elsevierViewall"><a class="elsevierStyleCrossRef" href="#fig0010">Fig. 2</a> shows MAE and outcome changes by five intervals characterized by approximately equal frequency of participants (quintiles) based on outcome variables distribution at admission. Prediction error tends to be higher for participants with lower 6MWT, as well as higher CAT and BId values at admission, with these subsets of participants characterized by the largest median post-PR changes.</p><elsevierMultimedia ident="fig0010"></elsevierMultimedia><p id="par0180" class="elsevierStylePara elsevierViewall">In detail, MAE characterizing quantile regression predicting 6MWT at discharge was 67.59<span class="elsevierStyleHsp" style=""></span>m in individuals with admission 6MWT<span class="elsevierStyleHsp" style=""></span><<span class="elsevierStyleHsp" style=""></span>173<span class="elsevierStyleHsp" style=""></span>m (median 6MWT change<span class="elsevierStyleHsp" style=""></span>=<span class="elsevierStyleHsp" style=""></span>+<span class="elsevierStyleHsp" style=""></span>92.5<span class="elsevierStyleHsp" style=""></span>m) whereas 30.47<span class="elsevierStyleHsp" style=""></span>m in those with admission 6MWT between 423 and 600<span class="elsevierStyleHsp" style=""></span>m (median 6MWT change<span class="elsevierStyleHsp" style=""></span>=<span class="elsevierStyleHsp" style=""></span>+25.5<span class="elsevierStyleHsp" style=""></span>m) (<a class="elsevierStyleCrossRef" href="#fig0010">Fig. 2</a>A and E).</p><p id="par0185" class="elsevierStylePara elsevierViewall">When predicting CAT at discharge, MAE was 2.11 points in individuals with admission CAT<span class="elsevierStyleHsp" style=""></span><<span class="elsevierStyleHsp" style=""></span>11 (median CAT change<span class="elsevierStyleHsp" style=""></span>=<span class="elsevierStyleHsp" style=""></span>−2.5 points), increasing up to 4.98 points in those with CAT ranging from 25 to 35 (median CAT change<span class="elsevierStyleHsp" style=""></span>=<span class="elsevierStyleHsp" style=""></span>−10 points) (<a class="elsevierStyleCrossRef" href="#fig0010">Fig. 2</a>B and F).</p><p id="par0190" class="elsevierStylePara elsevierViewall">Similarly, MAE was 3.01 points in people with admission BId<span class="elsevierStyleHsp" style=""></span><<span class="elsevierStyleHsp" style=""></span>13 (median BId change<span class="elsevierStyleHsp" style=""></span>=<span class="elsevierStyleHsp" style=""></span>−3 points), increasing up to 10.35 points in those with admission BId ranging from 43 to 89 (median BId change<span class="elsevierStyleHsp" style=""></span>=<span class="elsevierStyleHsp" style=""></span>−12 points) (<a class="elsevierStyleCrossRef" href="#fig0010">Fig. 2</a>C and G).</p><p id="par0195" class="elsevierStylePara elsevierViewall">No interpretable trend in MAE was observed when analyzing predictions for MRC (<a class="elsevierStyleCrossRef" href="#fig0010">Fig. 2</a>D and H).</p></span><span id="sec0070" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0110">Predicting the reaching of MCID after PR</span><p id="par0200" class="elsevierStylePara elsevierViewall">Post PR changes in outcomes have been discretized into binary values, indicating whether a participant reached the MCID or not. Performances in discriminating participants reaching from those not reaching MCID for the four outcomes are depicted in <a class="elsevierStyleCrossRef" href="#tbl0015">Table 3</a> and show sensitivity ranging from 61.78% to 98.99%, PPV ranging from 63.82% to 93.99%, specificity ranging from 14.00% to 71.20% and NPV ranging from 52.31% to 70.00%. The ability of quantitative predictions of post-PR changes to correctly identify participants reaching the MCID for the combination of outcomes was also assessed (<a class="elsevierStyleCrossRef" href="#tbl0015">Table 3</a>).</p><elsevierMultimedia ident="tbl0015"></elsevierMultimedia><p id="par0205" class="elsevierStylePara elsevierViewall">Sensitivity, PPV, specificity and NPV in predicting the combined 6MWT-CAT-BId outcomes were 47.78%, 59.80%, 75.58%, and 80.58% respectively, with a 25.86% proportion of participants reaching MCID for 6MWT, CAT, and BId.</p><p id="par0210" class="elsevierStylePara elsevierViewall">Sensitivity, PPV, specificity, and NPV in predicting the combined 6MWT-CAT-MRC outcome were 73.94%, 59.8%, 55.19% and 70.14% respectively, with a 47.41% proportion of participants reaching MCID for 6MWT, CAT, and MRC.</p><p id="par0215" class="elsevierStylePara elsevierViewall">These results suggest a better capacity to accurately discriminate between individuals reaching or not the MCID of MRC as compared to BId. Consequently, there is a higher probability of correctly detecting individuals reaching the MCID for the combined 6MWT-CAT-MRC concerning the 6MWT-CAT-BId outcome.</p></span></span><span id="sec0075" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0115">Discussion</span><p id="par0220" class="elsevierStylePara elsevierViewall">Our large, multicentric, retrospective study explored multivariate approaches to predict the responses of common outcomes to an in-hospital PR program in individuals recovering from an ECOPD. The response was assessed using simple and interpretable equations to predict quantitative changes in outcomes and, indirectly, the individual level of capability to reach the MCID. Results confirm that individuals with worse baseline conditions are better responders to PR. Despite assessed models not reaching sufficiently high predictive performances to be recommended as a “screening” tool in a clinical setting, our study suggests a potential methodology to predict the response to define priority criteria for admission to PR.<a class="elsevierStyleCrossRef" href="#bib0270"><span class="elsevierStyleSup">15</span></a></p><p id="par0225" class="elsevierStylePara elsevierViewall">Machine learning offers the possibility to predict clinical outcomes of interest by interpreting heterogeneous data sources.<a class="elsevierStyleCrossRef" href="#bib0240"><span class="elsevierStyleSup">9</span></a> However, the predictions obtained in our study demonstrated poor performances deserving a comment. This result might be due to at least three factors: (1) heterogeneity of participants’ condition, corresponding to different probabilities of successful PR; (2) potential incomplete information provided by the set of variables commonly collected at admission; (3) limited spectrum of ML methods tested. Only ML methods generating decisional rules (i.e., tree-like algorithms) or models that can be easily interpreted as regression equations (i.e., linear and quantile regression methods) were evaluated, to generate rules easily implementable and interpretable in daily clinical practice. More complex and promising models (e.g., Random Forests, Support Vector Machines, and Neural Networks) were not tested in our study and we cannot exclude that using different approaches the performance in prediction might be higher.</p><p id="par0230" class="elsevierStylePara elsevierViewall">In an era where health economic resources are shrinking, and there is a growing recognition of the need for appropriateness and personalization of care, the development of prediction models becomes crucial. Tailoring programs and optimizing resources for individuals with higher probabilities of success are essential considerations.<a class="elsevierStyleCrossRefs" href="#bib0335"><span class="elsevierStyleSup">28,29</span></a> In addition, the effectiveness of PR, as shown in our study (at least for the assessed program), is related to the characteristics of candidates. Prediction models might assist clinicians in defining the priority of PR prescription based on objective and measurable parameters of the disease. The development of a dedicated equation tool could provide specialists, GPs, and healthcare providers with a common language for both clinical and administrative purposes, enabling them to prioritize PR access.</p><p id="par0235" class="elsevierStylePara elsevierViewall">While we did not evaluate physical activity,<a class="elsevierStyleCrossRef" href="#bib0345"><span class="elsevierStyleSup">30</span></a> the outcome measures assessed in this study (dyspnea, exercise capacity, disease impact) target the goals of PR with evidence of effectiveness. These measures are not only widely accepted for PR but are also recommended in an outcome set for clinical trials evaluating the management of ECOPD.<a class="elsevierStyleCrossRefs" href="#bib0350"><span class="elsevierStyleSup">31,32</span></a></p><p id="par0240" class="elsevierStylePara elsevierViewall">We assessed responses in physiological outcomes, just a component of program success and used the MCID to define responders. Our findings suggest that baseline presentation should be considered when assessing the efficacy of PR. However, the clinical significance of the variation underpinning MCID is yet to be determined.<a class="elsevierStyleCrossRef" href="#bib0360"><span class="elsevierStyleSup">33</span></a> In most studies, responders have been defined as individuals showing a meaningful response in one specific outcome such as exercise capacity.<a class="elsevierStyleCrossRef" href="#bib0365"><span class="elsevierStyleSup">34</span></a> Nevertheless, response in one particular dimension does not guarantee to be a responder in another one. Multidimensional response outcomes have been proposed.<a class="elsevierStyleCrossRefs" href="#bib0370"><span class="elsevierStyleSup">35,36</span></a></p><p id="par0245" class="elsevierStylePara elsevierViewall">Our study focused on individuals after an ECOPD confirming the short terms benefits of PR, including the proportion of responders.<a class="elsevierStyleCrossRef" href="#bib0270"><span class="elsevierStyleSup">15</span></a> Despite relevant improvements in management, the natural course of ECOPD remains unchanged with effects beyond lung function, highlighting the importance of PR.<a class="elsevierStyleCrossRef" href="#bib0220"><span class="elsevierStyleSup">5</span></a></p><p id="par0250" class="elsevierStylePara elsevierViewall">We defined 12 sessions as the minimum attendance rate for program completion based on our previous report: a shorter (10–12 sessions) in-patient program resulted in improvement in exercise tolerance and symptoms similar to a longer out-patient program.<a class="elsevierStyleCrossRef" href="#bib0280"><span class="elsevierStyleSup">17</span></a> Data of individuals excluded due to transfer to an acute hospital or death or performing less than 12 sessions, were too few for any further analysis. The retrospective design prevented any assessment of causes of the negligible number of individuals not completing the program. Lack of motivation would have been improbable in individuals admitted to hospitals specialized in PR. The in-hospital program excludes lack of accessibility among the causes of non-completion.</p><span id="sec0080" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0120">Limitations</span><p id="par0255" class="elsevierStylePara elsevierViewall">The prediction models of different issues in PR have been addressed also in a few prospective studies.<a class="elsevierStyleCrossRefs" href="#bib0380"><span class="elsevierStyleSup">37,38</span></a> The main limitations of our study are related to its retrospective design. However, in addition to the relatively new approach of analysis, our study represents a real-life condition, and its results are supported by the large sample size at a time when even randomized controlled trials are being questioned.<a class="elsevierStyleCrossRef" href="#bib0390"><span class="elsevierStyleSup">39</span></a></p><p id="par0260" class="elsevierStylePara elsevierViewall">A control population not performing PR would have clarified whether any improvement would have been (also) time-dependent. However, given recognized benefits and mission of our hospitals, not performing PR would have been unaethical.</p></span></span><span id="sec0085" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0125">Conclusions</span><p id="par0265" class="elsevierStylePara elsevierViewall">While assessed models did not achieve complete satisfaction, information from clinical practice and predictive equations might help in predicting response to PR in individuals recovering from an ECOPD. Larger studies should confirm methodology, variables, and utility. Therefore our study should be interpreted as preliminary, offering potential hypotheses for future research rather than providing a ready-to-use “screening” clinical tool.</p></span><span id="sec0090" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0130">Authors’ contributions</span><p id="par0270" class="elsevierStylePara elsevierViewall">MV and NA conceived and designed the study. MV, AM, and NA contributed to the writing of the manuscript. AM performed formal analysis and visualization. MV, AS, PC, AC, and RM were responsible for investigations. MV, AM, MP, and NA participated in the analysis and discussion of the data. All the authors revised the article critically and approved the final version.</p></span><span id="sec0095" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0135">Data availability statement</span><p id="par0275" class="elsevierStylePara elsevierViewall">Data are available from the corresponding author upon reasonable request.</p></span><span id="sec0100" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0140">Funding sources</span><p id="par0280" class="elsevierStylePara elsevierViewall">This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.</p></span><span id="sec0105" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0145">Conflict of interest</span><p id="par0285" class="elsevierStylePara elsevierViewall">The authors declare they have no conflict of interest.</p></span></span>" "textoCompletoSecciones" => array:1 [ "secciones" => array:15 [ 0 => array:3 [ "identificador" => "xres2163337" "titulo" => "Graphical abstract" "secciones" => array:1 [ 0 => array:1 [ "identificador" => "abst0005" ] ] ] 1 => array:3 [ "identificador" => "xres2163336" "titulo" => "Abstract" "secciones" => array:4 [ 0 => array:2 [ "identificador" => "abst0010" "titulo" => "Background" ] 1 => array:2 [ "identificador" => "abst0015" "titulo" => "Method" ] 2 => array:2 [ "identificador" => "abst0020" "titulo" => "Results" ] 3 => array:2 [ "identificador" => "abst0025" "titulo" => "Conclusion" ] ] ] 2 => array:2 [ "identificador" => "xpalclavsec1835075" "titulo" => "Keywords" ] 3 => array:2 [ "identificador" => "xpalclavsec1835074" "titulo" => "Abbreviations" ] 4 => array:2 [ "identificador" => "sec0005" "titulo" => "Introduction" ] 5 => array:3 [ "identificador" => "sec0010" "titulo" => "Material and methods" "secciones" => array:4 [ 0 => array:2 [ "identificador" => "sec0015" "titulo" => "Participants" ] 1 => array:2 [ "identificador" => "sec0020" "titulo" => "Measurements" ] 2 => array:2 [ "identificador" => "sec0025" "titulo" => "Pulmonary rehabilitation" ] 3 => array:2 [ "identificador" => "sec0030" "titulo" => "Statistical and ML analyses" ] ] ] 6 => array:3 [ "identificador" => "sec0035" "titulo" => "Results" "secciones" => array:7 [ 0 => array:2 [ "identificador" => "sec0040" "titulo" => "Participants’ characteristics" ] 1 => array:2 [ "identificador" => "sec0045" "titulo" => "Multivariate models to predict outcome variables values at discharge" ] 2 => array:2 [ "identificador" => "sec0050" "titulo" => "Prediction of changes in 6MWT" ] 3 => array:2 [ "identificador" => "sec0055" "titulo" => "Prediction of changes in CAT" ] 4 => array:2 [ "identificador" => "sec0060" "titulo" => "Prediction of changes in BId and MRC" ] 5 => array:2 [ "identificador" => "sec0065" "titulo" => "Predicting quantitative outcomes after PR" ] 6 => array:2 [ "identificador" => "sec0070" "titulo" => "Predicting the reaching of MCID after PR" ] ] ] 7 => array:3 [ "identificador" => "sec0075" "titulo" => "Discussion" "secciones" => array:1 [ 0 => array:2 [ "identificador" => "sec0080" "titulo" => "Limitations" ] ] ] 8 => array:2 [ "identificador" => "sec0085" "titulo" => "Conclusions" ] 9 => array:2 [ "identificador" => "sec0090" "titulo" => "Authors’ contributions" ] 10 => array:2 [ "identificador" => "sec0095" "titulo" => "Data availability statement" ] 11 => array:2 [ "identificador" => "sec0100" "titulo" => "Funding sources" ] 12 => array:2 [ "identificador" => "sec0105" "titulo" => "Conflict of interest" ] 13 => array:2 [ "identificador" => "xack750773" "titulo" => "Acknowledgments" ] 14 => array:1 [ "titulo" => "References" ] ] ] "pdfFichero" => "main.pdf" "tienePdf" => true "fechaRecibido" => "2023-09-13" "fechaAceptado" => "2024-01-06" "PalabrasClave" => array:1 [ "en" => array:2 [ 0 => array:4 [ "clase" => "keyword" "titulo" => "Keywords" "identificador" => "xpalclavsec1835075" "palabras" => array:6 [ 0 => "COPD exacerbations" 1 => "Pulmonary rehabilitation" 2 => "Exercise" 3 => "Respiratory measurement" 4 => "Dyspnea" 5 => "Machine learning" ] ] 1 => array:4 [ "clase" => "abr" "titulo" => "Abbreviations" "identificador" => "xpalclavsec1835074" "palabras" => array:24 [ 0 => "6MWT" 1 => "BI" 2 => "BId" 3 => "BMI" 4 => "BODE" 5 => "CAT" 6 => "CIRS" 7 => "COPD" 8 => "CRF" 9 => "ECOPD" 10 => "FEV<span class="elsevierStyleInf">1</span>" 11 => "FVC" 12 => "GOLD" 13 => "GPs" 14 => "HRQL" 15 => "ICS" 16 => "LoS" 17 => "MAE" 18 => "MCID" 19 => "ML" 20 => "MRC" 21 => "NPV" 22 => "PPV" 23 => "PR" ] ] ] ] "tieneResumen" => true "resumen" => array:1 [ "en" => array:3 [ "titulo" => "Abstract" "resumen" => "<span id="abst0010" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0015">Background</span><p id="spar0010" class="elsevierStyleSimplePara elsevierViewall">Predicting the response to pulmonary rehabilitation (PR) could be valuable in defining admission priorities. We aimed to investigate whether the response of individuals recovering from a COPD exacerbation (ECOPD) could be forecasted using machine learning approaches.</p></span> <span id="abst0015" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0020">Method</span><p id="spar0015" class="elsevierStyleSimplePara elsevierViewall">This multicenter, retrospective study recorded data on anthropometrics, demographics, physiological characteristics, post-PR changes in six-minute walking distance test (6MWT), Medical Research Council scale for dyspnea (MRC), Barthel Index dyspnea (BId), COPD assessment test (CAT) and proportion of participants reaching the minimal clinically important difference (MCID). The ability of multivariate approaches (linear regression, quantile regression, regression trees, and conditional inference trees) in predicting changes in each outcome measure has been assessed.</p></span> <span id="abst0020" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0025">Results</span><p id="spar0020" class="elsevierStyleSimplePara elsevierViewall">Individuals with lower baseline 6MWT, as well as those with less severe airway obstruction or admitted from acute care hospitals, exhibited greater improvements in 6MWT, whereas older as well as more dyspnoeic individuals had a lower forecasted improvement. Individuals with more severe CAT and dyspnea, and lower 6MWT had a greater potential improvement in CAT. More dyspnoeic individuals were also more likely to show improvement in BId and MRC. The Mean Absolute Error estimates of change prediction were 44.70<span class="elsevierStyleHsp" style=""></span>m, 3.22 points, 5.35 points, and 0.32 points for 6MWT, CAT, BId, and MRC respectively. Sensitivity and specificity in discriminating individuals reaching the MCID of outcomes ranged from 61.78% to 98.99% and from 14.00% to 71.20%, respectively.</p></span> <span id="abst0025" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0030">Conclusion</span><p id="spar0025" class="elsevierStyleSimplePara elsevierViewall">While the assessed models were not entirely satisfactory, predictive equations derived from clinical practice data might help in forecasting the response to PR in individuals recovering from an ECOPD. Future larger studies will be essential to confirm the methodology, variables, and utility.</p></span>" "secciones" => array:4 [ 0 => array:2 [ "identificador" => "abst0010" "titulo" => "Background" ] 1 => array:2 [ "identificador" => "abst0015" "titulo" => "Method" ] 2 => array:2 [ "identificador" => "abst0020" "titulo" => "Results" ] 3 => array:2 [ "identificador" => "abst0025" "titulo" => "Conclusion" ] ] ] ] "apendice" => array:1 [ 0 => array:1 [ "seccion" => array:1 [ 0 => array:4 [ "apendice" => "<p id="par0305" class="elsevierStylePara elsevierViewall">The following are the supplementary data to this article:<elsevierMultimedia ident="upi0005"></elsevierMultimedia></p>" "etiqueta" => "Appendix B" "titulo" => "Supplementary data" "identificador" => "sec0115" ] ] ] ] "multimedia" => array:7 [ 0 => array:7 [ "identificador" => "fig0005" "etiqueta" => "Fig. 1" "tipo" => "MULTIMEDIAFIGURA" "mostrarFloat" => true "mostrarDisplay" => false "figura" => array:1 [ 0 => array:4 [ "imagen" => "gr1.jpeg" "Alto" => 2482 "Ancho" => 2508 "Tamanyo" => 377699 ] ] "descripcion" => array:1 [ "en" => "<p id="spar0030" class="elsevierStyleSimplePara elsevierViewall">Comparison between observed and predicted outcome variables change on the test set. <span class="elsevierStyleItalic">Legend</span>. Black dots represent pointwise predictions while gray lines represent the corresponding 95% confidence intervals; diagonal dashed lines represent the theoretical condition of perfect agreement between observed and predicted values. <span class="elsevierStyleItalic">Abbreviations</span>. 6MWT: six-minute walking distance test; CAT: COPD assessment test; BId: Barthel Index dyspnea; MRC: Medical Research Council; m: meters.</p>" ] ] 1 => array:7 [ "identificador" => "fig0010" "etiqueta" => "Fig. 2" "tipo" => "MULTIMEDIAFIGURA" "mostrarFloat" => true "mostrarDisplay" => false "figura" => array:1 [ 0 => array:4 [ "imagen" => "gr2.jpeg" "Alto" => 1644 "Ancho" => 3341 "Tamanyo" => 335250 ] ] "descripcion" => array:1 [ "en" => "<p id="spar0035" class="elsevierStyleSimplePara elsevierViewall">Prediction error and outcome change from admission by intervals of outcome variables at admission. <span class="elsevierStyleItalic">Legend</span>. The three lines plots in panels A–D describe graphically the Mean Absolute Error (MAE) by quintile of outcome variables distribution at admission (6MWT, CAT, and BId) or by MRC value characterizing the evaluated machine learning methods. MRC changes have been not analyzed by quintiles of MRC at admission but by pooling values at admission between 0 and 2 to define approximately balanced intervals. The three boxplots in panels E–H describe the outcomes’ change between admission and discharge distribution by quintile of outcome variables distribution at admission. Each boxplot describes from bottom to top: the lowest non-outlier value/minimum value; 25th percentile; median value; 75th percentile and the highest non-outlier value/maximum value; outliers are depicted as dots. The black continuous horizontal line corresponds to the condition of no change between admission and discharge; the black dashed horizontal line corresponds to the MCID value for each outcome variable and gray-shaded areas correspond to the condition of MCID achievement. Abbreviations. 6MWT: six-minute walking distance test; CAT: COPD assessment test; BId: Barthel Index dyspnea; MRC: Medical Research Council; m: meters; CRF: chronic respiratory failure.</p>" ] ] 2 => array:8 [ "identificador" => "tbl0005" "etiqueta" => "Table 1" "tipo" => "MULTIMEDIATABLA" "mostrarFloat" => true "mostrarDisplay" => false "detalles" => array:1 [ 0 => array:3 [ "identificador" => "at1" "detalle" => "Table " "rol" => "short" ] ] "tabla" => array:2 [ "leyenda" => "<p id="spar0045" class="elsevierStyleSimplePara elsevierViewall"><span class="elsevierStyleItalic">Legend</span>: Categorical variables distribution is described as absolute and relative frequency (%), quantitative variables distribution by median (25th, 75th percentiles), minimum and maximum values (Min:Max).</p><p id="spar0050" class="elsevierStyleSimplePara elsevierViewall"><span class="elsevierStyleItalic">Abbreviations</span>. BMI: Body Mass Index; BODE: body-mass index, airflow obstruction, dyspnea, and exercise capacity index; LoS: length of stay; CRF: chronic respiratory failure; LAMA: long acting muscarinic antagonist; LABA: long acting beta agonist; ICS: inhaled corticosteroids; CIRS: Comorbidity Index of Cumulative Illness Rating Scale; BI: Barthel Index; BId: Barthel Index dyspnea; FEV<span class="elsevierStyleInf">1</span>: forced expiratory volume at one second; FVC: forced vital capacity; prd: predicted; GOLD: Global Initiative for Obstructive Lung Disease; CAT: COPD assessment test; MRC: Medical Research Council; 6MWT: six-minute walking distance test.</p>" "tablatextoimagen" => array:1 [ 0 => array:2 [ "tabla" => array:1 [ 0 => """ <table border="0" frame="\n \t\t\t\t\tvoid\n \t\t\t\t" class=""><thead title="thead"><tr title="table-row"><th class="td-with-role" title="\n \t\t\t\t\ttable-head\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t" scope="col">Characteristic \t\t\t\t\t\t\n \t\t\t\t\t\t</th><th class="td" title="\n \t\t\t\t\ttable-head\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t" scope="col">Values \t\t\t\t\t\t\n \t\t\t\t\t\t</th><th class="td" title="\n \t\t\t\t\ttable-head\n \t\t\t\t " colspan="2" align="center" valign="\n \t\t\t\t\ttop\n \t\t\t\t" scope="col" style="border-bottom: 2px solid black">Whole sample (<span class="elsevierStyleItalic">n</span><span class="elsevierStyleHsp" style=""></span>=<span class="elsevierStyleHsp" style=""></span>1159)</th></tr><tr title="table-row"><th class="td" title="\n \t\t\t\t\ttable-head\n \t\t\t\t " align="" valign="\n \t\t\t\t\ttop\n \t\t\t\t" scope="col" style="border-bottom: 2px solid black"> \t\t\t\t\t\t\n \t\t\t\t\t\t</th><th class="td" title="\n \t\t\t\t\ttable-head\n \t\t\t\t " align="" valign="\n \t\t\t\t\ttop\n \t\t\t\t" scope="col" style="border-bottom: 2px solid black"> \t\t\t\t\t\t\n \t\t\t\t\t\t</th><th class="td" title="\n \t\t\t\t\ttable-head\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t" scope="col" style="border-bottom: 2px solid black">Median (25th, 75th percentiles) or frequency (%) \t\t\t\t\t\t\n \t\t\t\t\t\t</th><th class="td" title="\n \t\t\t\t\ttable-head\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t" scope="col" style="border-bottom: 2px solid black">Min:Max \t\t\t\t\t\t\n \t\t\t\t\t\t</th></tr></thead><tbody title="tbody"><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">Age, years \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="\n \t\t\t\t\ttop\n \t\t\t\t"> \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">72 (65, 77) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">34:93 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " colspan="4" align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t"><span class="elsevierStyleVsp" style="height:0.5px"></span></td></tr><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">Gender \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">Females \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">392 (33.8%) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="\n \t\t\t\t\ttop\n \t\t\t\t"> \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="\n \t\t\t\t\ttop\n \t\t\t\t"> \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">Males \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">767 (66.2%) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="\n \t\t\t\t\ttop\n \t\t\t\t"> \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " colspan="4" align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t"><span class="elsevierStyleVsp" style="height:0.5px"></span></td></tr><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">BMI, kg/m<span class="elsevierStyleSup">2</span> \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="\n \t\t\t\t\ttop\n \t\t\t\t"> \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">26.2 (22.8, 31.0) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">11.7:64.5 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">BODE index, score \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="\n \t\t\t\t\ttop\n \t\t\t\t"> \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">5 (4, 7) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">0:10 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">LOS, days \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="\n \t\t\t\t\ttop\n \t\t\t\t"> \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">25 (21, 32) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">10:120 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " colspan="4" align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t"><span class="elsevierStyleVsp" style="height:0.5px"></span></td></tr><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">Provenience \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">Home \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">851 (73.4%) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="\n \t\t\t\t\ttop\n \t\t\t\t"> \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="\n \t\t\t\t\ttop\n \t\t\t\t"> \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">Hospital \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">308 (26.6%) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="\n \t\t\t\t\ttop\n \t\t\t\t"> \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " colspan="4" align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t"><span class="elsevierStyleVsp" style="height:0.5px"></span></td></tr><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">CRF \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">No \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">770 (66.4%) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="\n \t\t\t\t\ttop\n \t\t\t\t"> \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="\n \t\t\t\t\ttop\n \t\t\t\t"> \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">Yes \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">389 (33.6%) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="\n \t\t\t\t\ttop\n \t\t\t\t"> \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " colspan="4" align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t"><span class="elsevierStyleVsp" style="height:0.5px"></span></td></tr><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">Inhaled therapy \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">LAMA \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">140 (12.1) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="\n \t\t\t\t\ttop\n \t\t\t\t"> \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="\n \t\t\t\t\ttop\n \t\t\t\t"> \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">LABA<span class="elsevierStyleHsp" style=""></span>+<span class="elsevierStyleHsp" style=""></span>ICS \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">24 (2.1) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="\n \t\t\t\t\ttop\n \t\t\t\t"> \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="\n \t\t\t\t\ttop\n \t\t\t\t"> \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">LABA<span class="elsevierStyleHsp" style=""></span>+<span class="elsevierStyleHsp" style=""></span>LAMA \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">305 (26.2) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="\n \t\t\t\t\ttop\n \t\t\t\t"> \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="\n \t\t\t\t\ttop\n \t\t\t\t"> \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">LABA<span class="elsevierStyleHsp" style=""></span>+<span class="elsevierStyleHsp" style=""></span>LAMA<span class="elsevierStyleHsp" style=""></span>+<span class="elsevierStyleHsp" style=""></span>ICS \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">690 (59.6) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="\n \t\t\t\t\ttop\n \t\t\t\t"> \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " colspan="4" align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t"><span class="elsevierStyleVsp" style="height:0.5px"></span></td></tr><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">CIRS, score \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="\n \t\t\t\t\ttop\n \t\t\t\t"> \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">4 (2, 5) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">0:12 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">BI, score \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="\n \t\t\t\t\ttop\n \t\t\t\t"> \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">100 (90, 100) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">0:100 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">BId score \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="\n \t\t\t\t\ttop\n \t\t\t\t"> \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">25 (14, 39) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">0:90 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">FEV<span class="elsevierStyleInf">1</span>, % prd \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="\n \t\t\t\t\ttop\n \t\t\t\t"> \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">44 (34, 56) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">12:86 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">FVC, % prd \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="\n \t\t\t\t\ttop\n \t\t\t\t"> \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">70 (58, 81) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">27:112 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">FEV<span class="elsevierStyleInf">1</span>/FVC, % \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="\n \t\t\t\t\ttop\n \t\t\t\t"> \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">46 (42, 56) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">21:69 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " colspan="4" align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t"><span class="elsevierStyleVsp" style="height:0.5px"></span></td></tr><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">GOLD airflow stages \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">1 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">29 (2.5%) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="\n \t\t\t\t\ttop\n \t\t\t\t"> \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="\n \t\t\t\t\ttop\n \t\t\t\t"> \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">2 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">337 (29.1%) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="\n \t\t\t\t\ttop\n \t\t\t\t"> \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="\n \t\t\t\t\ttop\n \t\t\t\t"> \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">3 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">437 (37.7%) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="\n \t\t\t\t\ttop\n \t\t\t\t"> \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="\n \t\t\t\t\ttop\n \t\t\t\t"> \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">4 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">356 (30.7%) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="\n \t\t\t\t\ttop\n \t\t\t\t"> \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " colspan="4" align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t"><span class="elsevierStyleVsp" style="height:0.5px"></span></td></tr><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">GOLD quadrant stages \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">A \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">106 (9.2%) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="\n \t\t\t\t\ttop\n \t\t\t\t"> \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="\n \t\t\t\t\ttop\n \t\t\t\t"> \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">B \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">260 (22.4%) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="\n \t\t\t\t\ttop\n \t\t\t\t"> \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="\n \t\t\t\t\ttop\n \t\t\t\t"> \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">C \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">115 (9.9%) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="\n \t\t\t\t\ttop\n \t\t\t\t"> \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="\n \t\t\t\t\ttop\n \t\t\t\t"> \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">D \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">678 (58.5%) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="\n \t\t\t\t\ttop\n \t\t\t\t"> \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " colspan="4" align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t"><span class="elsevierStyleVsp" style="height:0.5px"></span></td></tr><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">CAT, score \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="\n \t\t\t\t\ttop\n \t\t\t\t"> \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">18 (12, 24) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">0:37 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">MRC, score \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="\n \t\t\t\t\ttop\n \t\t\t\t"> \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">3 (3, 3) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">0:4 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">6MWT, meters \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="\n \t\t\t\t\ttop\n \t\t\t\t"> \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">300 (200, 400) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">0:635 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">6MWT % prd \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="\n \t\t\t\t\ttop\n \t\t\t\t"> \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">64.1 (43.1, 83) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">0:174.3 \t\t\t\t\t\t\n \t\t\t\t</td></tr></tbody></table> """ ] "imagenFichero" => array:1 [ 0 => "xTab3564706.png" ] ] ] ] "descripcion" => array:1 [ "en" => "<p id="spar0040" class="elsevierStyleSimplePara elsevierViewall">Baseline participants’ characteristics.<a class="elsevierStyleCrossRef" href="#bib0310"><span class="elsevierStyleSup">23</span></a></p>" ] ] 3 => array:8 [ "identificador" => "tbl0010" "etiqueta" => "Table 2" "tipo" => "MULTIMEDIATABLA" "mostrarFloat" => true "mostrarDisplay" => false "detalles" => array:1 [ 0 => array:3 [ "identificador" => "at2" "detalle" => "Table " "rol" => "short" ] ] "tabla" => array:2 [ "leyenda" => "<p id="spar0060" class="elsevierStyleSimplePara elsevierViewall"><span class="elsevierStyleItalic">Legend</span>. Response variable<span class="elsevierStyleHsp" style=""></span>=<span class="elsevierStyleHsp" style=""></span>response variable in each quantile regression model; Explanatory variables<span class="elsevierStyleHsp" style=""></span>=<span class="elsevierStyleHsp" style=""></span>independent variables measured at admission selected as informative with respect to each dependent variable by the backward features selection approach; Coefficient<span class="elsevierStyleHsp" style=""></span>=<span class="elsevierStyleHsp" style=""></span>quantile regression coefficient corresponding to each explanatory variable; SE<span class="elsevierStyleHsp" style=""></span>=<span class="elsevierStyleHsp" style=""></span>standard error corresponding to the regression coefficients; <span class="elsevierStyleItalic">p</span>-value<span class="elsevierStyleHsp" style=""></span>=<span class="elsevierStyleHsp" style=""></span><span class="elsevierStyleItalic">p</span>-value corresponding to the regression coefficients. As an example, regression coefficients can be used to predict 6MWT change for a patient characterized by 6MWT at admission<span class="elsevierStyleHsp" style=""></span>=<span class="elsevierStyleHsp" style=""></span>192<span class="elsevierStyleHsp" style=""></span>m, provenience<span class="elsevierStyleHsp" style=""></span>=<span class="elsevierStyleHsp" style=""></span>hospital, age<span class="elsevierStyleHsp" style=""></span>=<span class="elsevierStyleHsp" style=""></span>59 years, BId<span class="elsevierStyleHsp" style=""></span>=<span class="elsevierStyleHsp" style=""></span>35 points and FEV<span class="elsevierStyleInf">1</span>/FVC<span class="elsevierStyleHsp" style=""></span>=<span class="elsevierStyleHsp" style=""></span>50% by the following formula: 135.124 [Intercept]<span class="elsevierStyleHsp" style=""></span>−<span class="elsevierStyleHsp" style=""></span>0.171<span class="elsevierStyleHsp" style=""></span>×<span class="elsevierStyleHsp" style=""></span>192 [6MWT at admission<span class="elsevierStyleHsp" style=""></span>=<span class="elsevierStyleHsp" style=""></span>192<span class="elsevierStyleHsp" style=""></span>m]<span class="elsevierStyleHsp" style=""></span>+<span class="elsevierStyleHsp" style=""></span>24.849<span class="elsevierStyleHsp" style=""></span>×<span class="elsevierStyleHsp" style=""></span>1 [Provenience<span class="elsevierStyleHsp" style=""></span>=<span class="elsevierStyleHsp" style=""></span>Hospital]<span class="elsevierStyleHsp" style=""></span>−<span class="elsevierStyleHsp" style=""></span>0.707<span class="elsevierStyleHsp" style=""></span>×<span class="elsevierStyleHsp" style=""></span>59 [Age<span class="elsevierStyleHsp" style=""></span>=<span class="elsevierStyleHsp" style=""></span>59 years]<span class="elsevierStyleHsp" style=""></span>−<span class="elsevierStyleHsp" style=""></span>0.351<span class="elsevierStyleHsp" style=""></span>×<span class="elsevierStyleHsp" style=""></span>35 [BId<span class="elsevierStyleHsp" style=""></span>=<span class="elsevierStyleHsp" style=""></span>35 points]<span class="elsevierStyleHsp" style=""></span>+<span class="elsevierStyleHsp" style=""></span>0.339<span class="elsevierStyleHsp" style=""></span>×<span class="elsevierStyleHsp" style=""></span>50 [FEV<span class="elsevierStyleInf">1</span>/FVC<span class="elsevierStyleHsp" style=""></span>=<span class="elsevierStyleHsp" style=""></span>50%]<span class="elsevierStyleHsp" style=""></span>=<span class="elsevierStyleHsp" style=""></span>∼<span class="elsevierStyleHsp" style=""></span>+<span class="elsevierStyleHsp" style=""></span>90<span class="elsevierStyleHsp" style=""></span>m. CAT, BId, and MRC change values can be predicted analogously by the corresponding coefficients and variables. BId: Barthel Index dyspnea; CAT: COPD assessment test; MRC: Medical Research Council; 6MWT: six-minute walking distance test; FEV<span class="elsevierStyleInf">1</span>: forced expiratory volume at one second; FVC: forced vital capacity; m: meters.</p>" "tablatextoimagen" => array:1 [ 0 => array:2 [ "tabla" => array:1 [ 0 => """ <table border="0" frame="\n \t\t\t\t\tvoid\n \t\t\t\t" class=""><thead title="thead"><tr title="table-row"><th class="td-with-role" title="\n \t\t\t\t\ttable-head\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t" scope="col">Response variable \t\t\t\t\t\t\n \t\t\t\t\t\t</th><th class="td" title="\n \t\t\t\t\ttable-head\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t" scope="col">Explanatory variables \t\t\t\t\t\t\n \t\t\t\t\t\t</th><th class="td" title="\n \t\t\t\t\ttable-head\n \t\t\t\t " colspan="3" align="center" valign="\n \t\t\t\t\ttop\n \t\t\t\t" scope="col" style="border-bottom: 2px solid black">Training set (<span class="elsevierStyleItalic">n</span><span class="elsevierStyleHsp" style=""></span>=<span class="elsevierStyleHsp" style=""></span>811, 70%)</th></tr><tr title="table-row"><th class="td" title="\n \t\t\t\t\ttable-head\n \t\t\t\t " align="" valign="\n \t\t\t\t\ttop\n \t\t\t\t" scope="col" style="border-bottom: 2px solid black"> \t\t\t\t\t\t\n \t\t\t\t\t\t</th><th class="td" title="\n \t\t\t\t\ttable-head\n \t\t\t\t " align="" valign="\n \t\t\t\t\ttop\n \t\t\t\t" scope="col" style="border-bottom: 2px solid black"> \t\t\t\t\t\t\n \t\t\t\t\t\t</th><th class="td" title="\n \t\t\t\t\ttable-head\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t" scope="col" style="border-bottom: 2px solid black">Coefficient \t\t\t\t\t\t\n \t\t\t\t\t\t</th><th class="td" title="\n \t\t\t\t\ttable-head\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t" scope="col" style="border-bottom: 2px solid black">SE \t\t\t\t\t\t\n \t\t\t\t\t\t</th><th class="td" title="\n \t\t\t\t\ttable-head\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t" scope="col" style="border-bottom: 2px solid black"><span class="elsevierStyleItalic">p</span>-Value \t\t\t\t\t\t\n \t\t\t\t\t\t</th></tr></thead><tbody title="tbody"><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">6MWT change (m) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">(Intercept) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">135.124 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">26.66 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="\n \t\t\t\t\ttop\n \t\t\t\t"> \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="\n \t\t\t\t\ttop\n \t\t\t\t"> \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">6MWT at admission (m) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">−0.171 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">0.024 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t"><0.0001 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="\n \t\t\t\t\ttop\n \t\t\t\t"> \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">Provenience (Hospital) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">24.849 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">4.419 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t"><0.0001 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="\n \t\t\t\t\ttop\n \t\t\t\t"> \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">Age (years) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">−0.707 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">0.265 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">0.0077 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="\n \t\t\t\t\ttop\n \t\t\t\t"> \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">BId at admission (points) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">−0.351 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">0.150 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">0.0195 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="\n \t\t\t\t\ttop\n \t\t\t\t"> \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">FEV<span class="elsevierStyleInf">1</span>/FVC, % \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">0.339 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">0.144 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">0.0191 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " colspan="5" align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t"><span class="elsevierStyleVsp" style="height:0.5px"></span></td></tr><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">CAT change (points) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">(Intercept) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">5.263 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">1.021 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="\n \t\t\t\t\ttop\n \t\t\t\t"> \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="\n \t\t\t\t\ttop\n \t\t\t\t"> \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">CAT at admission (points) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">−0.407 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">0.031 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t"><0.0001 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="\n \t\t\t\t\ttop\n \t\t\t\t"> \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">MRC at admission (points) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">−0.469 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">0.236 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">0.0469 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="\n \t\t\t\t\ttop\n \t\t\t\t"> \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">6MWT at admission (m) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">−0.009 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">0.001 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t"><0.0001 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " colspan="5" align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t"><span class="elsevierStyleVsp" style="height:0.5px"></span></td></tr><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">BId change (points) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">(Intercept) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">−4.038 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">0.464 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="\n \t\t\t\t\ttop\n \t\t\t\t"> \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="\n \t\t\t\t\ttop\n \t\t\t\t"> \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">BId at admission (points) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">−0.192 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">0.022 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t"><0.0001 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " colspan="5" align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t"><span class="elsevierStyleVsp" style="height:0.5px"></span></td></tr><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">MRC change (points) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">(Intercept) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">2 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">0.274 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="\n \t\t\t\t\ttop\n \t\t\t\t"> \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="\n \t\t\t\t\ttop\n \t\t\t\t"> \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">MRC at admission (points) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">−1 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">0.091 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t"><0.0001 \t\t\t\t\t\t