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aiming to classify individual conditions or forecast outcomes when applied to the medical context and has been widely applied in clinical research&#46;<a class="elsevierStyleCrossRefs" href="#bib0240"><span class="elsevierStyleSup">9&#8211;11</span></a> Machine learning has been employed in early diagnosis of ECOPD&#46;<a class="elsevierStyleCrossRefs" href="#bib0255"><span class="elsevierStyleSup">12&#8211;14</span></a></p><p id="par0020" class="elsevierStylePara elsevierViewall">This large&#44; multicentre&#44; 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&#46;</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&#44; admitted to an in-hospital PR program and was approved by the Istituti Clinici Scientifici &#40;ICS&#41; Maugeri Ethics Committee &#40;2555 CE 8 June 2021&#41;&#46; As a retrospective study&#44; participants had not provided any specific written informed consent&#44; however&#44; at admission to hospitals&#44; they had given &#8211; in advance &#8211; informed consent for the scientific use of their data&#46; As a retrospective analysis&#44; the study was not registered&#46;</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&#44; either cared for in acute hospitals and transferred to Rehabilitation Hospitals or cared for at home by their general practitioners &#91;GP&#93;&#44; and admitted after an out-patient clinic visit&#44; between July&#44; 1st&#44; 2018 to December&#44; 31st&#44; 2021 to hospitals of the network previously described&#46;<a class="elsevierStyleCrossRef" href="#bib0270"><span class="elsevierStyleSup">15</span></a> These hospitals share common indications&#44; evaluation&#44; diagnostic and management tools and protocols for PR&#46; During the pandemic &#40;March 2020&#8211;December 2021&#41;&#44; only participants with negative swab tests were admitted&#46; It&#39;s worth noting that this study utilized the same dataset analyzed and published elsewhere&#46;<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&#58;<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&#46;<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&#47;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&#46;<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&#44; defined as absence of acute worsening in symptoms &#40;i&#46;e&#46; no change in dyspnea&#44; cough&#44; and&#47;or sputum beyond the day-to-day variability&#41; that would have required a change in management compared to the conditions reported at home or at discharge from the referring acute care hospital&#46;<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&#46;</p></li><li class="elsevierStyleListItem" id="lsti0035"><span class="elsevierStyleLabel">-</span><p id="par0325" class="elsevierStylePara elsevierViewall">Participating in at least 12 PR sessions&#44; a threshold chosen according to our previous published experience&#46;<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&#58; severe comorbidities such as oncological&#44; neurological disorders&#44; heart failure&#44; or recent &#40;less than 4 months&#41; acute ischemic cardiovascular diseases with an unstable status and individuals unable or refusing to perform PR&#46;</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&#58; demographics&#44; anthropometrics&#44; history of ECOPD in previous 12 months&#44; Comorbidity Index of the Cumulative Illness Rating Scale &#40;CIRS&#41;&#44; BMI &#8211; airflow obstruction &#8211; dyspnea&#44; and exercise capacity &#40;BODE&#41; index&#44; provenience &#40;hospital or home&#41;&#44; length of rehabilitation hospital stay &#40;LoS&#41;&#44; occurrence of chronic respiratory failure &#40;CRF&#41;&#44; distribution in GOLD stages&#44; drug therapy in stable state&#46;</p><p id="par0055" class="elsevierStylePara elsevierViewall">Before the program the following assessments had been performed&#58;<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&#46;<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 &#40;BI&#41;&#46;<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&#44; the following outcome measures had been assessed&#58;<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 &#40;6MWT&#41;&#46;<a class="elsevierStyleCrossRefs" href="#bib0295"><span class="elsevierStyleSup">20&#44;21</span></a> The minimal clinically important difference &#40;MCID&#41; has been reported as an improvement by at least 30<span class="elsevierStyleHsp" style=""></span>m&#46;<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 &#40;MRC&#41; scale&#46;<a class="elsevierStyleCrossRef" href="#bib0305"><span class="elsevierStyleSup">22</span></a> A one-point reduction is considered equivalent to MCID&#46;<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 &#40;BId&#41;&#46; The MCID has been defined as a 9-point reduction for individuals without and as a 12-point reduction for individuals with CRF&#44; respectively&#46;<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 &#40;CAT&#41;&#46; A two-point reduction in score has been reported as the MCID&#46;<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&#44; nurses&#44; physical therapists&#44; dieticians&#44; and psychologists full-time dedicated to PR&#46; It starts within 2 days from admission&#44; after baseline evaluations and includes daily supervised sessions &#40;6 weekly days&#41; of cycle training according to Maltais et al&#46;&#44;<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&#8211;70&#37; of maximal load&#44; &#40;calculated on baseline 6MWT according to Luxton et al&#46;<a class="elsevierStyleCrossRef" href="#bib0330"><span class="elsevierStyleSup">27</span></a>&#41;&#46; Workload was increased by 5 Watts when subjects scored their dyspnea or leg fatigue as &#60;3 on a 10-point Borg Scale&#44; remained unchanged if score was 4 or 5 and was reduced for scores of &#62;5&#46; Pulse oximetry&#44; arterial blood pressure&#44; and heart rate are monitored during sessions&#46;</p><p id="par0070" class="elsevierStylePara elsevierViewall">The program also includes optimization of medications&#44; education&#44; nutritional programs&#44; and psychosocial counseling when appropriate&#44; abdominal&#44; upper&#44; and lower limb muscle activities lifting weights progressively&#46; The duration of daily activities is 2&#8211;3<span class="elsevierStyleHsp" style=""></span>h&#46; The program is performed in Gymn Room with full availability of safety tools &#40;e&#46;g&#46; CPR&#41;&#46;</p><p id="par0075" class="elsevierStylePara elsevierViewall">During pandemic&#44; protective measures were adopted&#44; such as personal protective equipment&#44; distance among individuals not less than 2<span class="elsevierStyleHsp" style=""></span>m&#44; disinfection of materials&#44; frequent air changes&#44; execution of a swab at first harmful signs&#46;</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 &#40;25th&#44; 75th percentiles&#41; since most of them deviated from the normality assumptions&#44; as determined by visual inspection of histograms&#46; Categorical variable distribution is described as absolute and relative &#40;&#37;&#41; frequencies&#46; There were no missing values in the data&#46; The whole dataset was randomly split into a <span class="elsevierStyleItalic">training</span> &#40;70&#37; of the whole dataset&#41; and a <span class="elsevierStyleItalic">test</span> set &#40;30&#37;&#41;&#46; Statistical and ML methods for regression &#40;linear regression&#44; quantile regression &#91;quantile<span class="elsevierStyleHsp" style=""></span>&#61;<span class="elsevierStyleHsp" style=""></span>0&#46;5&#93;&#44; regression trees&#44; and conditional inference trees&#41; 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 &#8211; Supplementary Methods</a> section&#46; Post PR changes in outcomes were computed as value at discharge &#8211; value at admission and were dichotomized based on the corresponding MCID&#46; Predictive performances were expressed as mean absolute error &#40;MAE&#41;&#44; sensitivity&#44; specificity&#44; positive predictive value &#40;PPV&#41; and negative predictive value &#40;NPV&#41; &#40;<a class="elsevierStyleCrossRef" href="#sec0110">Supplementary material &#8211; Supplementary Methods</a>&#41;&#46; Significance level was set to <span class="elsevierStyleItalic">&#945;</span><span class="elsevierStyleHsp" style=""></span>&#61;<span class="elsevierStyleHsp" style=""></span>0&#46;05&#46; Statistical and ML analyses were performed by the R software tool version 4&#46;2&#46;2 &#40;<a href="http://www.r-project.org/">www&#46;r-project&#46;org</a>&#41;&#46;</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&#47;or exercise limitation after an ECOPD&#44; data from 4582 individuals were excluded&#46; Causes of exclusion were&#58; 4142 lacking lung function data or with unconfirmed COPD&#59; 293 lacking pre and&#47;or post PR data of outcomes&#44; 28 had severe comorbidities&#44; 89 unable to perform PR&#44; 12 transferred to an acute care hospital or deceased and 18 performing less than 12 sessions&#46;<a class="elsevierStyleCrossRef" href="#bib0270"><span class="elsevierStyleSup">15</span></a></p><span id="sec0040" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0080">Participants&#8217; characteristics</span><p id="par0090" class="elsevierStylePara elsevierViewall">Characteristics of 1159 individuals included are in <a class="elsevierStyleCrossRef" href="#tbl0005">Table 1</a>&#46;<a class="elsevierStyleCrossRef" href="#bib0270"><span class="elsevierStyleSup">15</span></a> The majority of participants were males&#44; were admitted from home&#44; used triple &#40;Long acting muscarinic antagonists<span class="elsevierStyleHsp" style=""></span>&#43;<span class="elsevierStyleHsp" style=""></span>Long acting beta agonists<span class="elsevierStyleHsp" style=""></span>&#43;<span class="elsevierStyleHsp" style=""></span>inhaled corticosteroids&#58; LAMA<span class="elsevierStyleHsp" style=""></span>&#43;<span class="elsevierStyleHsp" style=""></span>LABA<span class="elsevierStyleHsp" style=""></span>&#43;<span class="elsevierStyleHsp" style=""></span>ICS&#41; inhaled therapy and were included in most severe GOLD stages &#40;1 and 2 or C and D&#41; but did not suffer from CRF&#46;</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 &#40;reported in <a class="elsevierStyleCrossRef" href="#sec0110">Supplementary Table 1</a>&#41; were assessed and compared on training set data &#40;<span class="elsevierStyleItalic">n</span><span class="elsevierStyleHsp" style=""></span>&#61;<span class="elsevierStyleHsp" style=""></span>811&#41; as described in <a class="elsevierStyleCrossRef" href="#sec0110">Supplementary material</a>&#46; The evaluated approaches showed similar median prediction error estimates &#40;<a class="elsevierStyleCrossRef" href="#sec0110">Supplementary material &#8211; Supplementary Fig&#46; 1</a>&#41;&#46;</p><p id="par0100" class="elsevierStylePara elsevierViewall"><span class="elsevierStyleItalic">Quantile regression</span> coupled with a backward features selection approach based on statistical significance &#40;<span class="elsevierStyleItalic">QR-p</span>&#41; was selected as the most suitable method to predict changes &#40;<a class="elsevierStyleCrossRef" href="#sec0110">Supplementary material &#8211; Supplementary Fig&#46; 2</a>&#41;&#46;</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>&#46;</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&#46;17<span class="elsevierStyleHsp" style=""></span>m reduction in median 6MWT change for every meter of 6MWT at admission &#40;<span class="elsevierStyleItalic">p</span>-value<span class="elsevierStyleHsp" style=""></span>&#60;<span class="elsevierStyleHsp" style=""></span>0&#46;0001&#41;&#46; Similarly&#44; for every year increase in age and every point in admission BId&#44; we expected a reduction in median post PR 6MWT change by &#8722;0&#46;71 and &#8722;0&#46;35<span class="elsevierStyleHsp" style=""></span>m respectively &#40;<span class="elsevierStyleItalic">p</span><span class="elsevierStyleHsp" style=""></span>&#60;<span class="elsevierStyleHsp" style=""></span>0&#46;05&#41;&#46; On the opposite&#44; for each unit FEV<span class="elsevierStyleInf">1</span>&#47;FVC&#44; &#37; a 0&#46;34-m increase in post-PR median 6MWT change was predicted &#40;<span class="elsevierStyleItalic">p</span><span class="elsevierStyleHsp" style=""></span>&#61;<span class="elsevierStyleHsp" style=""></span>0&#46;0191&#41;&#46; Also&#44; individuals from hospitals showed a 24&#46;85-m increase in median post-PR change in 6MWT compared to individuals from home &#40;<span class="elsevierStyleItalic">p</span>-value<span class="elsevierStyleHsp" style=""></span>&#60;<span class="elsevierStyleHsp" style=""></span>0&#46;0001&#41;&#46;</p><p id="par0115" class="elsevierStylePara elsevierViewall">The corresponding equation to be used to predict 6MWT post PR change is the following&#58; <span class="elsevierStyleUnderline">6MWT post PR change &#40;meters&#41;</span><span class="elsevierStyleHsp" style=""></span>&#61;<span class="elsevierStyleHsp" style=""></span>135&#46;124<span class="elsevierStyleHsp" style=""></span>&#8722;<span class="elsevierStyleHsp" style=""></span>0&#46;171<span class="elsevierStyleHsp" style=""></span>&#215;<span class="elsevierStyleHsp" style=""></span>6MWT at admission &#40;m&#41;<span class="elsevierStyleHsp" style=""></span>&#43;<span class="elsevierStyleHsp" style=""></span>24&#46;849<span class="elsevierStyleHsp" style=""></span>&#215;<span class="elsevierStyleHsp" style=""></span>Provenience &#40;Hospital<span class="elsevierStyleHsp" style=""></span>&#61;<span class="elsevierStyleHsp" style=""></span>1&#44; Home<span class="elsevierStyleHsp" style=""></span>&#61;<span class="elsevierStyleHsp" style=""></span>0&#41;<span class="elsevierStyleHsp" style=""></span>&#8722;<span class="elsevierStyleHsp" style=""></span>0&#46;707<span class="elsevierStyleHsp" style=""></span>&#215;<span class="elsevierStyleHsp" style=""></span>Age &#40;years&#41;<span class="elsevierStyleHsp" style=""></span>&#8722;<span class="elsevierStyleHsp" style=""></span>0&#46;351<span class="elsevierStyleHsp" style=""></span>&#215;<span class="elsevierStyleHsp" style=""></span>BId at admission &#40;points&#41;<span class="elsevierStyleHsp" style=""></span>&#43;<span class="elsevierStyleHsp" style=""></span>0&#46;339<span class="elsevierStyleHsp" style=""></span>&#215;<span class="elsevierStyleHsp" style=""></span>FEV<span class="elsevierStyleInf">1</span>&#47;FVC &#37;&#46;</p><p id="par0120" class="elsevierStylePara elsevierViewall">Taken together&#44; these results indicate that the higher is the baseline exercise limitation&#44; the higher is the improvement&#46; Aged as well as dyspnoeic individuals share a lower predicted improvement&#44; while individuals with less severe airway obstruction as well as those admitted from hospitals have larger potential improvements in exercise capacity&#46; Multivariate explainability analyses identified 6MWT at admission &#40;meters&#41; as the strongest predictor of post-PR changes in 6MWT&#44; followed by provenience from the hospital&#44; age&#44; BId&#44; and FEV<span class="elsevierStyleInf">1</span>&#47;FVC&#44; &#37; at admission&#46;</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 &#8722;0&#46;41&#44; &#8722;0&#46;47&#44; and &#8722;0&#46;01 points respectively &#40;<span class="elsevierStyleItalic">p</span><span class="elsevierStyleHsp" style=""></span>&#60;<span class="elsevierStyleHsp" style=""></span>0&#46;05&#44; <a class="elsevierStyleCrossRef" href="#tbl0010">Table 2</a>&#41;&#46;</p><p id="par0130" class="elsevierStylePara elsevierViewall">The equation is the following&#58; <span class="elsevierStyleUnderline">CAT post PR change &#40;points&#41;</span><span class="elsevierStyleHsp" style=""></span>&#61;<span class="elsevierStyleHsp" style=""></span>5&#46;263<span class="elsevierStyleHsp" style=""></span>&#8722;<span class="elsevierStyleHsp" style=""></span>0&#46;407 &#42; CAT at admission &#40;points&#41;<span class="elsevierStyleHsp" style=""></span>&#8722;<span class="elsevierStyleHsp" style=""></span>0&#46;469<span class="elsevierStyleHsp" style=""></span>&#42;<span class="elsevierStyleHsp" style=""></span>MRC at admission &#40;points&#41;<span class="elsevierStyleHsp" style=""></span>&#8722;<span class="elsevierStyleHsp" style=""></span>0&#46;009<span class="elsevierStyleHsp" style=""></span>&#42;<span class="elsevierStyleHsp" style=""></span>6MWT at admission &#40;m&#41;&#46;</p><p id="par0135" class="elsevierStylePara elsevierViewall">Thus&#44; individuals with more disease impact&#44; more dyspnea&#44; and more exercise limitation have a greater potential improvement in CAT&#46; Multivariate explainability analyses revealed that CAT at admission was the strongest predictor of post-PR changes in CAT&#44; followed by 6MWT and MRC at admission&#46;</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&#44; each point increase in BId and MRC at admission corresponds to a post-PR decrease by &#8722;0&#46;19 and &#8722;1 points in median BId and MRC&#44; respectively &#40;<span class="elsevierStyleItalic">p</span>-value<span class="elsevierStyleHsp" style=""></span>&#60;<span class="elsevierStyleHsp" style=""></span>0&#46;0001&#44; <a class="elsevierStyleCrossRef" href="#tbl0010">Table 2</a>&#41;&#46;</p><p id="par0145" class="elsevierStylePara elsevierViewall">The corresponding equations are the following&#58;<ul class="elsevierStyleList" id="lis0010"><li class="elsevierStyleListItem" id="lsti0010"><span class="elsevierStyleLabel">a&#41;</span><p id="par0150" class="elsevierStylePara elsevierViewall"><span class="elsevierStyleUnderline">BId post PR change &#40;points&#41;</span><span class="elsevierStyleHsp" style=""></span>&#61;<span class="elsevierStyleHsp" style=""></span>&#8722;4&#46;038<span class="elsevierStyleHsp" style=""></span>&#8722;<span class="elsevierStyleHsp" style=""></span>0&#46;192<span class="elsevierStyleHsp" style=""></span>&#42;<span class="elsevierStyleHsp" style=""></span>BId at admission &#40;points&#41;</p></li><li class="elsevierStyleListItem" id="lsti0015"><span class="elsevierStyleLabel">b&#41;</span><p id="par0155" class="elsevierStylePara elsevierViewall"><span class="elsevierStyleUnderline">MRC post PR change &#40;points&#41;</span><span class="elsevierStyleHsp" style=""></span>&#61;<span class="elsevierStyleHsp" style=""></span>2<span class="elsevierStyleHsp" style=""></span>&#8722;<span class="elsevierStyleHsp" style=""></span>1<span class="elsevierStyleHsp" style=""></span>&#42;<span class="elsevierStyleHsp" style=""></span>MRC at admission &#40;points&#41;</p></li></ul></p><p id="par0160" class="elsevierStylePara elsevierViewall">Individuals with more severe dyspnea during Activity daily life &#40;ADL&#41; at admission are more prone to improve&#46;</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 &#40;<span class="elsevierStyleItalic">n</span><span class="elsevierStyleHsp" style=""></span>&#61;<span class="elsevierStyleHsp" style=""></span>348&#41;&#46; Mean absolute error estimates of the four models were 44&#46;70<span class="elsevierStyleHsp" style=""></span>m&#44; 3&#46;22 points&#44; 5&#46;35 points&#44; and 0&#46;32 points for 6MWT&#44; CAT&#44; BId and MRC&#46;</p><p id="par0170" class="elsevierStylePara elsevierViewall"><a class="elsevierStyleCrossRef" href="#fig0005">Fig&#46; 1</a> shows remarkable discrepancies between predicted and observed post PR changes in outcomes&#44; especially for low and high observed outcome distribution values&#44; particularly for 6MWT and BId&#46; In detail&#44; in comparison to the observed post-PR changes in 6MWT ranging between &#8722;243 and &#43;348<span class="elsevierStyleHsp" style=""></span>m&#44; the spectrum of predicted values of change in 6MWT is narrower&#44; ranging between &#8722;8 and &#43;139<span class="elsevierStyleHsp" style=""></span>m&#46; Similarly&#44; observed changes in BId ranged between &#8722;68 and &#43;11 points&#44; whereas predicted changes ranged between &#8722;21 and &#8722;4 points&#46; Post-PR changes in CAT ranged between &#8722;27 and &#43;4 points&#44; as compared to predicted values between &#8722;15 and &#43;1 points&#46; Predicted post-PR changes in MRC ranged between &#8722;3 and &#43;1 points&#44; whereas observed changes ranged between &#8722;2 and &#43;2 points&#46;</p><elsevierMultimedia ident="fig0005"></elsevierMultimedia><p id="par0175" class="elsevierStylePara elsevierViewall"><a class="elsevierStyleCrossRef" href="#fig0010">Fig&#46; 2</a> shows MAE and outcome changes by five intervals characterized by approximately equal frequency of participants &#40;quintiles&#41; based on outcome variables distribution at admission&#46; Prediction error tends to be higher for participants with lower 6MWT&#44; as well as higher CAT and BId values at admission&#44; with these subsets of participants characterized by the largest median post-PR changes&#46;</p><elsevierMultimedia ident="fig0010"></elsevierMultimedia><p id="par0180" class="elsevierStylePara elsevierViewall">In detail&#44; MAE characterizing quantile regression predicting 6MWT at discharge was 67&#46;59<span class="elsevierStyleHsp" style=""></span>m in individuals with admission 6MWT<span class="elsevierStyleHsp" style=""></span>&#60;<span class="elsevierStyleHsp" style=""></span>173<span class="elsevierStyleHsp" style=""></span>m &#40;median 6MWT change<span class="elsevierStyleHsp" style=""></span>&#61;<span class="elsevierStyleHsp" style=""></span>&#43;<span class="elsevierStyleHsp" style=""></span>92&#46;5<span class="elsevierStyleHsp" style=""></span>m&#41; whereas 30&#46;47<span class="elsevierStyleHsp" style=""></span>m in those with admission 6MWT between 423 and 600<span class="elsevierStyleHsp" style=""></span>m &#40;median 6MWT change<span class="elsevierStyleHsp" style=""></span>&#61;<span class="elsevierStyleHsp" style=""></span>&#43;25&#46;5<span class="elsevierStyleHsp" style=""></span>m&#41; &#40;<a class="elsevierStyleCrossRef" href="#fig0010">Fig&#46; 2</a>A and E&#41;&#46;</p><p id="par0185" class="elsevierStylePara elsevierViewall">When predicting CAT at discharge&#44; MAE was 2&#46;11 points in individuals with admission CAT<span class="elsevierStyleHsp" style=""></span>&#60;<span class="elsevierStyleHsp" style=""></span>11 &#40;median CAT change<span class="elsevierStyleHsp" style=""></span>&#61;<span class="elsevierStyleHsp" style=""></span>&#8722;2&#46;5 points&#41;&#44; increasing up to 4&#46;98 points in those with CAT ranging from 25 to 35 &#40;median CAT change<span class="elsevierStyleHsp" style=""></span>&#61;<span class="elsevierStyleHsp" style=""></span>&#8722;10 points&#41; &#40;<a class="elsevierStyleCrossRef" href="#fig0010">Fig&#46; 2</a>B and F&#41;&#46;</p><p id="par0190" class="elsevierStylePara elsevierViewall">Similarly&#44; MAE was 3&#46;01 points in people with admission BId<span class="elsevierStyleHsp" style=""></span>&#60;<span class="elsevierStyleHsp" style=""></span>13 &#40;median BId change<span class="elsevierStyleHsp" style=""></span>&#61;<span class="elsevierStyleHsp" style=""></span>&#8722;3 points&#41;&#44; increasing up to 10&#46;35 points in those with admission BId ranging from 43 to 89 &#40;median BId change<span class="elsevierStyleHsp" style=""></span>&#61;<span class="elsevierStyleHsp" style=""></span>&#8722;12 points&#41; &#40;<a class="elsevierStyleCrossRef" href="#fig0010">Fig&#46; 2</a>C and G&#41;&#46;</p><p id="par0195" class="elsevierStylePara elsevierViewall">No interpretable trend in MAE was observed when analyzing predictions for MRC &#40;<a class="elsevierStyleCrossRef" href="#fig0010">Fig&#46; 2</a>D and H&#41;&#46;</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&#44; indicating whether a participant reached the MCID or not&#46; 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&#46;78&#37; to 98&#46;99&#37;&#44; PPV ranging from 63&#46;82&#37; to 93&#46;99&#37;&#44; specificity ranging from 14&#46;00&#37; to 71&#46;20&#37; and NPV ranging from 52&#46;31&#37; to 70&#46;00&#37;&#46; The ability of quantitative predictions of post-PR changes to correctly identify participants reaching the MCID for the combination of outcomes was also assessed &#40;<a class="elsevierStyleCrossRef" href="#tbl0015">Table 3</a>&#41;&#46;</p><elsevierMultimedia ident="tbl0015"></elsevierMultimedia><p id="par0205" class="elsevierStylePara elsevierViewall">Sensitivity&#44; PPV&#44; specificity and NPV in predicting the combined 6MWT-CAT-BId outcomes were 47&#46;78&#37;&#44; 59&#46;80&#37;&#44; 75&#46;58&#37;&#44; and 80&#46;58&#37; respectively&#44; with a 25&#46;86&#37; proportion of participants reaching MCID for 6MWT&#44; CAT&#44; and BId&#46;</p><p id="par0210" class="elsevierStylePara elsevierViewall">Sensitivity&#44; PPV&#44; specificity&#44; and NPV in predicting the combined 6MWT-CAT-MRC outcome were 73&#46;94&#37;&#44; 59&#46;8&#37;&#44; 55&#46;19&#37; and 70&#46;14&#37; respectively&#44; with a 47&#46;41&#37; proportion of participants reaching MCID for 6MWT&#44; CAT&#44; and MRC&#46;</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&#46; Consequently&#44; there is a higher probability of correctly detecting individuals reaching the MCID for the combined 6MWT-CAT-MRC concerning the 6MWT-CAT-BId outcome&#46;</p></span></span><span id="sec0075" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0115">Discussion</span><p id="par0220" class="elsevierStylePara elsevierViewall">Our large&#44; multicentric&#44; retrospective study explored multivariate approaches to predict the responses of common outcomes to an in-hospital PR program in individuals recovering from an ECOPD&#46; The response was assessed using simple and interpretable equations to predict quantitative changes in outcomes and&#44; indirectly&#44; the individual level of capability to reach the MCID&#46; Results confirm that individuals with worse baseline conditions are better responders to PR&#46; Despite assessed models not reaching sufficiently high predictive performances to be recommended as a &#8220;screening&#8221; tool in a clinical setting&#44; our study suggests a potential methodology to predict the response to define priority criteria for admission to PR&#46;<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&#46;<a class="elsevierStyleCrossRef" href="#bib0240"><span class="elsevierStyleSup">9</span></a> However&#44; the predictions obtained in our study demonstrated poor performances deserving a comment&#46; This result might be due to at least three factors&#58; &#40;1&#41; heterogeneity of participants&#8217; condition&#44; corresponding to different probabilities of successful PR&#59; &#40;2&#41; potential incomplete information provided by the set of variables commonly collected at admission&#59; &#40;3&#41; limited spectrum of ML methods tested&#46; Only ML methods generating decisional rules &#40;i&#46;e&#46;&#44; tree-like algorithms&#41; or models that can be easily interpreted as regression equations &#40;i&#46;e&#46;&#44; linear and quantile regression methods&#41; were evaluated&#44; to generate rules easily implementable and interpretable in daily clinical practice&#46; More complex and promising models &#40;e&#46;g&#46;&#44; Random Forests&#44; Support Vector Machines&#44; and Neural Networks&#41; were not tested in our study and we cannot exclude that using different approaches the performance in prediction might be higher&#46;</p><p id="par0230" class="elsevierStylePara elsevierViewall">In an era where health economic resources are shrinking&#44; and there is a growing recognition of the need for appropriateness and personalization of care&#44; the development of prediction models becomes crucial&#46; Tailoring programs and optimizing resources for individuals with higher probabilities of success are essential considerations&#46;<a class="elsevierStyleCrossRefs" href="#bib0335"><span class="elsevierStyleSup">28&#44;29</span></a> In addition&#44; the effectiveness of PR&#44; as shown in our study &#40;at least for the assessed program&#41;&#44; is related to the characteristics of candidates&#46; Prediction models might assist clinicians in defining the priority of PR prescription based on objective and measurable parameters of the disease&#46; The development of a dedicated equation tool could provide specialists&#44; GPs&#44; and healthcare providers with a common language for both clinical and administrative purposes&#44; enabling them to prioritize PR access&#46;</p><p id="par0235" class="elsevierStylePara elsevierViewall">While we did not evaluate physical activity&#44;<a class="elsevierStyleCrossRef" href="#bib0345"><span class="elsevierStyleSup">30</span></a> the outcome measures assessed in this study &#40;dyspnea&#44; exercise capacity&#44; disease impact&#41; target the goals of PR with evidence of effectiveness&#46; 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&#46;<a class="elsevierStyleCrossRefs" href="#bib0350"><span class="elsevierStyleSup">31&#44;32</span></a></p><p id="par0240" class="elsevierStylePara elsevierViewall">We assessed responses in physiological outcomes&#44; just a component of program success and used the MCID to define responders&#46; Our findings suggest that baseline presentation should be considered when assessing the efficacy of PR&#46; However&#44; the clinical significance of the variation underpinning MCID is yet to be determined&#46;<a class="elsevierStyleCrossRef" href="#bib0360"><span class="elsevierStyleSup">33</span></a> In most studies&#44; responders have been defined as individuals showing a meaningful response in one specific outcome such as exercise capacity&#46;<a class="elsevierStyleCrossRef" href="#bib0365"><span class="elsevierStyleSup">34</span></a> Nevertheless&#44; response in one particular dimension does not guarantee to be a responder in another one&#46; Multidimensional response outcomes have been proposed&#46;<a class="elsevierStyleCrossRefs" href="#bib0370"><span class="elsevierStyleSup">35&#44;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&#44; including the proportion of responders&#46;<a class="elsevierStyleCrossRef" href="#bib0270"><span class="elsevierStyleSup">15</span></a> Despite relevant improvements in management&#44; the natural course of ECOPD remains unchanged with effects beyond lung function&#44; highlighting the importance of PR&#46;<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&#58; a shorter &#40;10&#8211;12 sessions&#41; in-patient program resulted in improvement in exercise tolerance and symptoms similar to a longer out-patient program&#46;<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&#44; were too few for any further analysis&#46; The retrospective design prevented any assessment of causes of the negligible number of individuals not completing the program&#46; Lack of motivation would have been improbable in individuals admitted to hospitals specialized in PR&#46; The in-hospital program excludes lack of accessibility among the causes of non-completion&#46;</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&#46;<a class="elsevierStyleCrossRefs" href="#bib0380"><span class="elsevierStyleSup">37&#44;38</span></a> The main limitations of our study are related to its retrospective design&#46; However&#44; in addition to the relatively new approach of analysis&#44; our study represents a real-life condition&#44; and its results are supported by the large sample size at a time when even randomized controlled trials are being questioned&#46;<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 &#40;also&#41; time-dependent&#46; However&#44; given recognized benefits and mission of our hospitals&#44; not performing PR would have been unaethical&#46;</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&#44; information from clinical practice and predictive equations might help in predicting response to PR in individuals recovering from an ECOPD&#46; Larger studies should confirm methodology&#44; variables&#44; and utility&#46; Therefore our study should be interpreted as preliminary&#44; offering potential hypotheses for future research rather than providing a ready-to-use &#8220;screening&#8221; clinical tool&#46;</p></span><span id="sec0090" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0130">Authors&#8217; contributions</span><p id="par0270" class="elsevierStylePara elsevierViewall">MV and NA conceived and designed the study&#46; MV&#44; AM&#44; and NA contributed to the writing of the manuscript&#46; AM performed formal analysis and visualization&#46; MV&#44; AS&#44; PC&#44; AC&#44; and RM were responsible for investigations&#46; MV&#44; AM&#44; MP&#44; and NA participated in the analysis and discussion of the data&#46; All the authors revised the article critically and approved the final version&#46;</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&#46;</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&#44; commercial&#44; or not-for-profit sectors&#46;</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&#46;</p></span></span>"
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          "titulo" => "Introduction"
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          "titulo" => "Material and methods"
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              "titulo" => "Pulmonary rehabilitation"
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              "titulo" => "Participants&#8217; characteristics"
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              "titulo" => "Multivariate models to predict outcome variables values at discharge"
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              "titulo" => "Prediction of changes in 6MWT"
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              "identificador" => "sec0055"
              "titulo" => "Prediction of changes in CAT"
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              "titulo" => "Prediction of changes in BId and MRC"
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              "titulo" => "Predicting quantitative outcomes after PR"
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              "titulo" => "Predicting the reaching of MCID after PR"
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    "fechaRecibido" => "2023-09-13"
    "fechaAceptado" => "2024-01-06"
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          "clase" => "keyword"
          "titulo" => "Keywords"
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          "palabras" => array:6 [
            0 => "COPD exacerbations"
            1 => "Pulmonary rehabilitation"
            2 => "Exercise"
            3 => "Respiratory measurement"
            4 => "Dyspnea"
            5 => "Machine learning"
          ]
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        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"
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      "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 &#40;PR&#41; could be valuable in defining admission priorities&#46; We aimed to investigate whether the response of individuals recovering from a COPD exacerbation &#40;ECOPD&#41; could be forecasted using machine learning approaches&#46;</p></span> <span id="abst0015" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0020">Method</span><p id="spar0015" class="elsevierStyleSimplePara elsevierViewall">This multicenter&#44; retrospective study recorded data on anthropometrics&#44; demographics&#44; physiological characteristics&#44; post-PR changes in six-minute walking distance test &#40;6MWT&#41;&#44; Medical Research Council scale for dyspnea &#40;MRC&#41;&#44; Barthel Index dyspnea &#40;BId&#41;&#44; COPD assessment test &#40;CAT&#41; and proportion of participants reaching the minimal clinically important difference &#40;MCID&#41;&#46; The ability of multivariate approaches &#40;linear regression&#44; quantile regression&#44; regression trees&#44; and conditional inference trees&#41; in predicting changes in each outcome measure has been assessed&#46;</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&#44; as well as those with less severe airway obstruction or admitted from acute care hospitals&#44; exhibited greater improvements in 6MWT&#44; whereas older as well as more dyspnoeic individuals had a lower forecasted improvement&#46; Individuals with more severe CAT and dyspnea&#44; and lower 6MWT had a greater potential improvement in CAT&#46; More dyspnoeic individuals were also more likely to show improvement in BId and MRC&#46; The Mean Absolute Error estimates of change prediction were 44&#46;70<span class="elsevierStyleHsp" style=""></span>m&#44; 3&#46;22 points&#44; 5&#46;35 points&#44; and 0&#46;32 points for 6MWT&#44; CAT&#44; BId&#44; and MRC respectively&#46; Sensitivity and specificity in discriminating individuals reaching the MCID of outcomes ranged from 61&#46;78&#37; to 98&#46;99&#37; and from 14&#46;00&#37; to 71&#46;20&#37;&#44; respectively&#46;</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&#44; predictive equations derived from clinical practice data might help in forecasting the response to PR in individuals recovering from an ECOPD&#46; Future larger studies will be essential to confirm the methodology&#44; variables&#44; and utility&#46;</p></span>"
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            "apendice" => "<p id="par0305" class="elsevierStylePara elsevierViewall">The following are the supplementary data to this article&#58;<elsevierMultimedia ident="upi0005"></elsevierMultimedia></p>"
            "etiqueta" => "Appendix B"
            "titulo" => "Supplementary data"
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          "en" => "<p id="spar0030" class="elsevierStyleSimplePara elsevierViewall">Comparison between observed and predicted outcome variables change on the test set&#46; <span class="elsevierStyleItalic">Legend</span>&#46; Black dots represent pointwise predictions while gray lines represent the corresponding 95&#37; confidence intervals&#59; diagonal dashed lines represent the theoretical condition of perfect agreement between observed and predicted values&#46; <span class="elsevierStyleItalic">Abbreviations</span>&#46; 6MWT&#58; six-minute walking distance test&#59; CAT&#58; COPD assessment test&#59; BId&#58; Barthel Index dyspnea&#59; MRC&#58; Medical Research Council&#59; m&#58; meters&#46;</p>"
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          "en" => "<p id="spar0035" class="elsevierStyleSimplePara elsevierViewall">Prediction error and outcome change from admission by intervals of outcome variables at admission&#46; <span class="elsevierStyleItalic">Legend</span>&#46; The three lines plots in panels A&#8211;D describe graphically the Mean Absolute Error &#40;MAE&#41; by quintile of outcome variables distribution at admission &#40;6MWT&#44; CAT&#44; and BId&#41; or by MRC value characterizing the evaluated machine learning methods&#46; 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&#46; The three boxplots in panels E&#8211;H describe the outcomes&#8217; change between admission and discharge distribution by quintile of outcome variables distribution at admission&#46; Each boxplot describes from bottom to top&#58; the lowest non-outlier value&#47;minimum value&#59; 25th percentile&#59; median value&#59; 75th percentile and the highest non-outlier value&#47;maximum value&#59; outliers are depicted as dots&#46; The black continuous horizontal line corresponds to the condition of no change between admission and discharge&#59; 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&#46; Abbreviations&#46; 6MWT&#58; six-minute walking distance test&#59; CAT&#58; COPD assessment test&#59; BId&#58; Barthel Index dyspnea&#59; MRC&#58; Medical Research Council&#59; m&#58; meters&#59; CRF&#58; chronic respiratory failure&#46;</p>"
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          "leyenda" => "<p id="spar0045" class="elsevierStyleSimplePara elsevierViewall"><span class="elsevierStyleItalic">Legend</span>&#58; Categorical variables distribution is described as absolute and relative frequency &#40;&#37;&#41;&#44; quantitative variables distribution by median &#40;25th&#44; 75th percentiles&#41;&#44; minimum and maximum values &#40;Min&#58;Max&#41;&#46;</p><p id="spar0050" class="elsevierStyleSimplePara elsevierViewall"><span class="elsevierStyleItalic">Abbreviations</span>&#46; BMI&#58; Body Mass Index&#59; BODE&#58; body-mass index&#44; airflow obstruction&#44; dyspnea&#44; and exercise capacity index&#59; LoS&#58; length of stay&#59; CRF&#58; chronic respiratory failure&#59; LAMA&#58; long acting muscarinic antagonist&#59; LABA&#58; long acting beta agonist&#59; ICS&#58; inhaled corticosteroids&#59; CIRS&#58; Comorbidity Index of Cumulative Illness Rating Scale&#59; BI&#58; Barthel Index&#59; BId&#58; Barthel Index dyspnea&#59; FEV<span class="elsevierStyleInf">1</span>&#58; forced expiratory volume at one second&#59; FVC&#58; forced vital capacity&#59; prd&#58; predicted&#59; GOLD&#58; Global Initiative for Obstructive Lung Disease&#59; CAT&#58; COPD assessment test&#59; MRC&#58; Medical Research Council&#59; 6MWT&#58; six-minute walking distance test&#46;</p>"
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                  \t\t\t\t\ttop\n
                  \t\t\t\t" scope="col">Values&nbsp;\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 &#40;<span class="elsevierStyleItalic">n</span><span class="elsevierStyleHsp" style=""></span>&#61;<span class="elsevierStyleHsp" style=""></span>1159&#41;</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">&nbsp;\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">&nbsp;\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 &#40;25th&#44; 75th percentiles&#41; or frequency &#40;&#37;&#41;&nbsp;\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&#58;Max&nbsp;\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&#44; years&nbsp;\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">&nbsp;\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 &#40;65&#44; 77&#41;&nbsp;\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&#58;93&nbsp;\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&nbsp;\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&nbsp;\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 &#40;33&#46;8&#37;&#41;&nbsp;\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">&nbsp;\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">&nbsp;\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&nbsp;\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 &#40;66&#46;2&#37;&#41;&nbsp;\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">&nbsp;\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&#44; kg&#47;m<span class="elsevierStyleSup">2</span>&nbsp;\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">&nbsp;\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&#46;2 &#40;22&#46;8&#44; 31&#46;0&#41;&nbsp;\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&#46;7&#58;64&#46;5&nbsp;\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&#44; score&nbsp;\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">&nbsp;\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 &#40;4&#44; 7&#41;&nbsp;\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&#58;10&nbsp;\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&#44; days&nbsp;\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">&nbsp;\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 &#40;21&#44; 32&#41;&nbsp;\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&#58;120&nbsp;\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&nbsp;\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&nbsp;\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 &#40;73&#46;4&#37;&#41;&nbsp;\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">&nbsp;\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">&nbsp;\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&nbsp;\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 &#40;26&#46;6&#37;&#41;&nbsp;\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">&nbsp;\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&nbsp;\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&nbsp;\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 &#40;66&#46;4&#37;&#41;&nbsp;\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">&nbsp;\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">&nbsp;\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&nbsp;\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 &#40;33&#46;6&#37;&#41;&nbsp;\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">&nbsp;\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&nbsp;\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&nbsp;\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 &#40;12&#46;1&#41;&nbsp;\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">&nbsp;\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">&nbsp;\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>&#43;<span class="elsevierStyleHsp" style=""></span>ICS&nbsp;\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 &#40;2&#46;1&#41;&nbsp;\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">&nbsp;\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">&nbsp;\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>&#43;<span class="elsevierStyleHsp" style=""></span>LAMA&nbsp;\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 &#40;26&#46;2&#41;&nbsp;\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">&nbsp;\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">&nbsp;\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>&#43;<span class="elsevierStyleHsp" style=""></span>LAMA<span class="elsevierStyleHsp" style=""></span>&#43;<span class="elsevierStyleHsp" style=""></span>ICS&nbsp;\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 &#40;59&#46;6&#41;&nbsp;\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">&nbsp;\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&#44; score&nbsp;\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">&nbsp;\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 &#40;2&#44; 5&#41;&nbsp;\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
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                  \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n
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                  \t\t\t\t">GOLD airflow stages&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n
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                  \t\t\t\t">GOLD quadrant stages&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
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                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">A&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t">106 &#40;9&#46;2&#37;&#41;&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t  " align="left" valign="\n
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                  \t\t\t\t">B&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t  " align="char" valign="\n
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                  \t\t\t\t">260 &#40;22&#46;4&#37;&#41;&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">C&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t  " align="char" valign="\n
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                  \t\t\t\t">115 &#40;9&#46;9&#37;&#41;&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">D&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t  " align="char" valign="\n
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                  \t\t\t\t">678 &#40;58&#46;5&#37;&#41;&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">CAT&#44; score&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
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                  \t\t\t\t  " align="" valign="\n
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                  \t\t\t\t">18 &#40;12&#44; 24&#41;&nbsp;\t\t\t\t\t\t\n
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                  \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&#44; score&nbsp;\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
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                  \t\t\t\t">3 &#40;3&#44; 3&#41;&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t">0&#58;4&nbsp;\t\t\t\t\t\t\n
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                  \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&#44; meters&nbsp;\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
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                  \t\t\t\t">300 &#40;200&#44; 400&#41;&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t">0&#58;635&nbsp;\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 &#37; prd&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t">64&#46;1 &#40;43&#46;1&#44; 83&#41;&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t">0&#58;174&#46;3&nbsp;\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&#8217; characteristics&#46;<a class="elsevierStyleCrossRef" href="#bib0310"><span class="elsevierStyleSup">23</span></a></p>"
        ]
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      3 => array:8 [
        "identificador" => "tbl0010"
        "etiqueta" => "Table 2"
        "tipo" => "MULTIMEDIATABLA"
        "mostrarFloat" => true
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        "detalles" => array:1 [
          0 => array:3 [
            "identificador" => "at2"
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          ]
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          "leyenda" => "<p id="spar0060" class="elsevierStyleSimplePara elsevierViewall"><span class="elsevierStyleItalic">Legend</span>&#46; Response variable<span class="elsevierStyleHsp" style=""></span>&#61;<span class="elsevierStyleHsp" style=""></span>response variable in each quantile regression model&#59; Explanatory variables<span class="elsevierStyleHsp" style=""></span>&#61;<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&#59; Coefficient<span class="elsevierStyleHsp" style=""></span>&#61;<span class="elsevierStyleHsp" style=""></span>quantile regression coefficient corresponding to each explanatory variable&#59; SE<span class="elsevierStyleHsp" style=""></span>&#61;<span class="elsevierStyleHsp" style=""></span>standard error corresponding to the regression coefficients&#59; <span class="elsevierStyleItalic">p</span>-value<span class="elsevierStyleHsp" style=""></span>&#61;<span class="elsevierStyleHsp" style=""></span><span class="elsevierStyleItalic">p</span>-value corresponding to the regression coefficients&#46; As an example&#44; regression coefficients can be used to predict 6MWT change for a patient characterized by 6MWT at admission<span class="elsevierStyleHsp" style=""></span>&#61;<span class="elsevierStyleHsp" style=""></span>192<span class="elsevierStyleHsp" style=""></span>m&#44; provenience<span class="elsevierStyleHsp" style=""></span>&#61;<span class="elsevierStyleHsp" style=""></span>hospital&#44; age<span class="elsevierStyleHsp" style=""></span>&#61;<span class="elsevierStyleHsp" style=""></span>59 years&#44; BId<span class="elsevierStyleHsp" style=""></span>&#61;<span class="elsevierStyleHsp" style=""></span>35 points and FEV<span class="elsevierStyleInf">1</span>&#47;FVC<span class="elsevierStyleHsp" style=""></span>&#61;<span class="elsevierStyleHsp" style=""></span>50&#37; by the following formula&#58; 135&#46;124 &#91;Intercept&#93;<span class="elsevierStyleHsp" style=""></span>&#8722;<span class="elsevierStyleHsp" style=""></span>0&#46;171<span class="elsevierStyleHsp" style=""></span>&#215;<span class="elsevierStyleHsp" style=""></span>192 &#91;6MWT at admission<span class="elsevierStyleHsp" style=""></span>&#61;<span class="elsevierStyleHsp" style=""></span>192<span class="elsevierStyleHsp" style=""></span>m&#93;<span class="elsevierStyleHsp" style=""></span>&#43;<span class="elsevierStyleHsp" style=""></span>24&#46;849<span class="elsevierStyleHsp" style=""></span>&#215;<span class="elsevierStyleHsp" style=""></span>1 &#91;Provenience<span class="elsevierStyleHsp" style=""></span>&#61;<span class="elsevierStyleHsp" style=""></span>Hospital&#93;<span class="elsevierStyleHsp" style=""></span>&#8722;<span class="elsevierStyleHsp" style=""></span>0&#46;707<span class="elsevierStyleHsp" style=""></span>&#215;<span class="elsevierStyleHsp" style=""></span>59 &#91;Age<span class="elsevierStyleHsp" style=""></span>&#61;<span class="elsevierStyleHsp" style=""></span>59 years&#93;<span class="elsevierStyleHsp" style=""></span>&#8722;<span class="elsevierStyleHsp" style=""></span>0&#46;351<span class="elsevierStyleHsp" style=""></span>&#215;<span class="elsevierStyleHsp" style=""></span>35 &#91;BId<span class="elsevierStyleHsp" style=""></span>&#61;<span class="elsevierStyleHsp" style=""></span>35 points&#93;<span class="elsevierStyleHsp" style=""></span>&#43;<span class="elsevierStyleHsp" style=""></span>0&#46;339<span class="elsevierStyleHsp" style=""></span>&#215;<span class="elsevierStyleHsp" style=""></span>50 &#91;FEV<span class="elsevierStyleInf">1</span>&#47;FVC<span class="elsevierStyleHsp" style=""></span>&#61;<span class="elsevierStyleHsp" style=""></span>50&#37;&#93;<span class="elsevierStyleHsp" style=""></span>&#61;<span class="elsevierStyleHsp" style=""></span>&#8764;<span class="elsevierStyleHsp" style=""></span>&#43;<span class="elsevierStyleHsp" style=""></span>90<span class="elsevierStyleHsp" style=""></span>m&#46; CAT&#44; BId&#44; and MRC change values can be predicted analogously by the corresponding coefficients and variables&#46; BId&#58; Barthel Index dyspnea&#59; CAT&#58; COPD assessment test&#59; MRC&#58; Medical Research Council&#59; 6MWT&#58; six-minute walking distance test&#59; FEV<span class="elsevierStyleInf">1</span>&#58; forced expiratory volume at one second&#59; FVC&#58; forced vital capacity&#59; m&#58; meters&#46;</p>"
          "tablatextoimagen" => array:1 [
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                  \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&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t\ttable-head\n
                  \t\t\t\t  " align="left" valign="\n
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                  \t\t\t\t" scope="col">Explanatory variables&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t" scope="col" style="border-bottom: 2px solid black">Training set &#40;<span class="elsevierStyleItalic">n</span><span class="elsevierStyleHsp" style=""></span>&#61;<span class="elsevierStyleHsp" style=""></span>811&#44; 70&#37;&#41;</th></tr><tr title="table-row"><th class="td" title="\n
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                  \t\t\t\t" scope="col" style="border-bottom: 2px solid black">&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t" scope="col" style="border-bottom: 2px solid black">&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t\ttable-head\n
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                  \t\t\t\t" scope="col" style="border-bottom: 2px solid black">SE&nbsp;\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
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                  \t\t\t\t" scope="col" style="border-bottom: 2px solid black"><span class="elsevierStyleItalic">p</span>-Value&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n
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                  \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
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                  \t\t\t\t">&#40;Intercept&#41;&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t  " align="char" valign="\n
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                  \t\t\t\t">135&#46;124&nbsp;\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
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                  \t\t\t\t">26&#46;66&nbsp;\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
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                  \t\t\t\t">&nbsp;\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
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                  \t\t\t\t">&nbsp;\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 &#40;m&#41;&nbsp;\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">&#8722;0&#46;171&nbsp;\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&#46;024&nbsp;\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">&#60;0&#46;0001&nbsp;\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">&nbsp;\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
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                  \t\t\t\t">Provenience &#40;Hospital&#41;&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="char" valign="\n
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                  \t\t\t\t">24&#46;849&nbsp;\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
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                  \t\t\t\t">4&#46;419&nbsp;\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">&#60;0&#46;0001&nbsp;\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
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                  \t\t\t\t">&nbsp;\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
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                  \t\t\t\t">Age &#40;years&#41;&nbsp;\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">&#8722;0&#46;707&nbsp;\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&#46;265&nbsp;\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&#46;0077&nbsp;\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
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                  \t\t\t\t">&nbsp;\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 &#40;points&#41;&nbsp;\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
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                  \t\t\t\t">&#8722;0&#46;351&nbsp;\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&#46;150&nbsp;\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&#46;0195&nbsp;\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">&nbsp;\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>&#47;FVC&#44; &#37;&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="char" valign="\n
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                  \t\t\t\t">0&#46;339&nbsp;\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&#46;144&nbsp;\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&#46;0191&nbsp;\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 &#40;points&#41;&nbsp;\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">&#40;Intercept&#41;&nbsp;\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&#46;263&nbsp;\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&#46;021&nbsp;\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">&nbsp;\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">&nbsp;\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 &#40;points&#41;&nbsp;\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">&#8722;0&#46;407&nbsp;\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&#46;031&nbsp;\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">&#60;0&#46;0001&nbsp;\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">&nbsp;\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 &#40;points&#41;&nbsp;\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">&#8722;0&#46;469&nbsp;\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&#46;236&nbsp;\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&#46;0469&nbsp;\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">&nbsp;\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 &#40;m&#41;&nbsp;\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
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                  \t\t\t\t">BId at admission &#40;points&#41;&nbsp;\t\t\t\t\t\t\n
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                  """
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        "etiqueta" => "Table 3"
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          "leyenda" => "<p id="spar0070" class="elsevierStyleSimplePara elsevierViewall"><span class="elsevierStyleItalic">Legend</span>&#46; Point estimate &#40;95&#37; confidence interval&#41; of Sensitivity&#44; Specificity&#44; Positive Predictive Value &#40;PPV&#41;&#44; and Negative Predictive Value &#40;NPV&#41; on the independent test set are reported for 6MWT&#44; CAT&#44; BId&#44; MRC&#44; and for the combination of 6MWT-CAT-BId and 6MWT-CAT-MRC outcomes&#46; Percentages within brackets in the title line correspond to the frequency of patients reaching MCID for the outcomes&#46; <span class="elsevierStyleItalic">Abbreviations</span>&#46; 6MWT&#58; six-minute walking distance test&#59; CAT&#58; COPD assessment test&#59; BId&#58; Barthel Index dyspnea&#59; MRC&#58; Medical Research Council&#59; MCID&#58; minimal clinically important difference&#46;</p>"
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                  <table border="0" frame="\n
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                  \t\t\t\t" scope="col">&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t  " align="left" valign="\n
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                  \t\t\t\t" scope="col">6MWT&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t" scope="col">CAT&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t" scope="col">6MWT-CAT-MRC&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t" scope="col" style="border-bottom: 2px solid black">&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t  " align="left" valign="\n
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                  \t\t\t\t" scope="col" style="border-bottom: 2px solid black">&#40;MCID&#58; 65&#46;52&#37;&#41;&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t" scope="col" style="border-bottom: 2px solid black">&#40;MCID&#58; 85&#46;63&#37;&#41;&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t\ttable-head\n
                  \t\t\t\t  " align="left" valign="\n
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                  \t\t\t\t" scope="col" style="border-bottom: 2px solid black">&#40;MCID&#58; 45&#46;11&#37;&#41;&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t" scope="col" style="border-bottom: 2px solid black">&#40;MCID&#58; 85&#46;34&#37;&#41;&nbsp;\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
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                  \t\t\t\t" scope="col" style="border-bottom: 2px solid black">&#40;MCID&#58; 25&#46;86&#37;&#41;&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t" scope="col" style="border-bottom: 2px solid black">&#40;MCID&#58; 47&#46;41&#37;&#41;&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t">Sensitivity &#40;95&#37; CI&#41;&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t">73&#46;94 &#40;66&#46;54&#8211;80&#46;45&#41;&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n
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                  \t\t\t\t">Specificity &#40;95&#37; CI&#41;&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t">66&#46;67 &#40;52&#46;08&#8211;79&#46;24&#41;&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t">75&#46;58 &#40;69&#46;87&#8211;80&#46;70&#41;&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t">55&#46;19 &#40;47&#46;68&#8211;62&#46;53&#41;&nbsp;\t\t\t\t\t\t\n
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                  \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">PPV &#40;95&#37; CI&#41;&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="char" valign="\n
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                  \t\t\t\t">73&#46;58 &#40;67&#46;60&#8211;78&#46;98&#41;&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t">87&#46;28 &#40;83&#46;25&#8211;90&#46;64&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
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                  \t\t\t\t</td><td class="td" title="\n
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                  \t\t\t\t\ttop\n
                  \t\t\t\t">93&#46;99 &#40;90&#46;56&#8211;96&#46;46&#41;&nbsp;\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">40&#46;57 &#40;31&#46;13&#8211;50&#46;54&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
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                  \t\t\t\t">59&#46;80 &#40;52&#46;73&#8211;66&#46;59&#41;&nbsp;\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">NPV &#40;95&#37; CI&#41;&nbsp;\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
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        "texto" => "<p id="par0290" class="elsevierStylePara elsevierViewall">The authors thank the Maugeri Respiratory Network for data collection&#47;evaluation and Adriana Olivares and Laura Comini for their technical assistance and editing&#46;</p><p id="par0295" class="elsevierStylePara elsevierViewall">This work was supported by the &#8220;Ricerca Corrente&#8221; Funding scheme of the Ministry of Health&#44; Italy</p>"
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