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Vol. 57. Issue 11.
Pages 717-719 (November 2021)
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Vol. 57. Issue 11.
Pages 717-719 (November 2021)
Scientific Letter
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Health Indicators in Hospitalized Patients With SARS-CoV-2 Pneumonia: A Comparison Between the First and Second Wave
Indicadores sanitarios en pacientes hospitalizados por neumonía SARS-COV-2: comparación entre la primera y segunda ola
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Nuria Rodríguez-Núñeza, Francisco Gudeb,
Corresponding author
, Adriana Lamaa, Carlos Rábadea, Alfonso Varelac, Romina Abelleiraa, Ana Casala, Vanessa Riveiroa, Manuel Taboadad, Antonio Posee, Luis Valdésa,f
a Pulmonology Department, University Clinical Hospital of Santiago, Santiago de Compostela, Spain
b Epidemiology Department, Research Methods Group, University Clinical Hospital of Santiago, Health Research Institute of Santiago de Compostela (IDIS), Santiago de Compostela, Spain
c Directorate of Healthcare Processes, University Clinical Hospital of Santiago, Santiago de Compostela, Spain
d Anesthesiology Department, University Clinical Hospital of Santiago, Santiago de Compostela, Spain
e Internal Medicine Department, University Hospital Complex of Santiago de Compostela, Santiago de Compostela, Spain
f Multidisciplinary Research Group on Pulmonology, Health Research Institute of Santiago de Compostela (IDIS), Spain
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Table 1. Clinical Characteristics of the Study Patients.
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Dear Editor,

The first cases of coronavirus disease 2019 (COVID-19) were identified in Wuhan, China1 a year ago. However, few studies have been published comparing the characteristics and clinical outcomes of hospitalized patients with pneumonia secondary to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) of the first and second wave of the pandemic in Europe. Moreover, it has not been investigated whether patient management and health indicators improved during the second wave as a result of the experience acquired during the first. The objective of this study is to compare the characteristics and outcomes of hospitalized patients with COVID-19 of the first and second wave of the pandemic.

Data were collected from the medical reports of patients diagnosed with Covid-19 and admitted to our hospital, from February 2020 (date of first Covid-19 diagnosis) to December 31 (first wave until June 30; second wave since July 1). The study was approved by the Institutional Review Board (#2020/194). A case of Covid-19 was confirmed in the presence of a positive result in the reverse transcription polymerase chain reaction test on samples obtained from nasal or throat swabs, or a positive antigen test performed in accordance with the Spanish Ministry of Health recommendations.2 Only laboratory-confirmed cases were considered for analysis. All patients diagnosed with Covid-19 pneumonia3 were hospitalized and included in the study. All data were recorded at admission (+1 day). Patient were eligible for intensive care unit (ICU) admission if required mechanical ventilation or had a fraction of inspired oxygen (FiO2) of at least 60% or more. Radiological anomalies were collected from reports of the Unit of Radiology.

We assumed missing data occurred at random depending on the clinical variables and performed multiple imputations using chained equations. Missing values were predicted from other outcome predictors. We created 100 datasets with identical known information but with differences in imputed values reflecting the uncertainty associated with imputations. In total <1% clinical data items were imputed (see Table 1). We used Chi-square test to compare proportions and Mann–Whitney U test for comparison of quantitative variables. Logistic regression analyses were performed to investigate the effects of the two waves on outcomes (risk of death, ICU admission and the risk for mechanical ventilation). For this purpose, regression models were tested using non-parametric techniques, adjusted for the predictors of the DALSH score (diabetes, age, lymphocytes, oxygen saturation, pH).4 Results are presented as Odds Ratio (OR) with 95% confidence intervals (95%CI). Statistical analyses were carried out in R using the mice and mgcv packages. These packages are freely available at http://cran.r-project.org.

Table 1.

Clinical Characteristics of the Study Patients.

Characteristics  All PatientsWavesP Value 
  Missing  (n=754)  First (n=308)  Second (n=446)   
Age, yr  0  69 (58, 80)  68 (58, 77)  71 (58, 82)  .032 
Male sex  0  57%  59%  56%  .389 
Temperature37.5°C  1  28%  32%  28%  .031 
Systolic blood pressure, mmHg  2  130 (116, 144)  130 (119, 142)  130 (114, 145)  .966 
Diastolic blood pressure, mmHg  2  75 (68, 82)  74 (67, 81)  75 (69, 84)  .007 
Heart rate, beats/min  7  82 (74, 92)  81 (73, 90)  82 (74, 92)  .395 
Symptoms
Fever  1  65%  75%  59%  <.001 
Cough  1  71%  71%  71%  .963 
Shortness of breath  1  55%  52%  57%  .119 
Thoracic pain  3  8%  9%  8%  .593 
Diarrhea  4  21%  23%  19%  .200 
Anosmia  15  10%  8%  12%  .077 
Dysgeusia  15  13%  11%  15%  .094 
Confusion  3  4%  4%  4%  .707 
Treatment
ACEI  1  11%  11%  11%  .992 
ARAII  2  26%  26%  27%  .912 
Statins  1  39%  39%  40%  .704 
Corticosteroids  1  6%  7%  5%  .247 
Immunosupressors  1  4%  5%  3%  .055 
Anticoagulants  3  12%  10%  14%  .094 
Antiplatelet agents  8  13%  12%  14%  .509 
Medical history
COPD  0  7%  8%  7%  .577 
Arterial hypertension  0  51%  49%  53%  .248 
Diabetes mellitus  0  24%  23%  25%  .665 
Chronic renal disease  0  10%  9%  11%  .347 
Coronary heart disease  0  9%  9%  10%  .684 
Heart failure  2  10%  7%  12%  .035 
Cancer  1  8%  7%  9%  .343 
Systemic disease  2  7%  8%  7%  .589 
Pulmonary disease  1  15%  15%  15%  .974 
Laboratory
White-cell count, 103cells/mm3  6  5.8 (4.3, 7.9)  5.7 (4.3, 7.4)  6.0 (4.3, 8.2)  .089 
Lymphocyte count, 102cells/mm3  7  8.9 (6.0, 12.3)  9.7 (6.4, 14.0)  8.4 (5.9, 11.6)  <.001 
Neutrophil count, 103cells/mm3  7  4.2 (4.0, 6.0)  3.9 (2.8, 5.6)  4.4 (3.1, 6.3)  .004 
Platelet count, 103cells/mm3  6  188 (143, 256)  206 (153, 278)  177 (138, 235)  <.001 
Haemoglobin, g/dL  9  13.5 (12.1, 14.7)  13.0 (11.8, 14.0)  13.8 (12.5, 14.9)  <.001 
C-reactive protein, mg/dL  16  6.7 (2.9, 12.3)  6.6 (2.9, 12.8)  6.8 (3.0, 12.0)  .852 
Lactate dehydrogenase, U/L  42  388 (276, 530)  439 (320, 587)  346 (261, 517)  <.001 
Creatine kinase, UI/L  47  80 (49, 143)  71 (44, 131)  85 (52, 155)  .006 
Creatinine, mg/dL  10  0.9 (0.7, 1.2)  0.9 (0.7, 1.1)  0.9 (0.8, 1.2)  .005 
Urea, mg/dL  8  41 (31, 60)  40 (30, 59)  41 (31, 61)  .618 
D-dimer, ng/mL  24  754 (448, 1265)  707 (422, 1171)  807 (477, 1310)  .048 
Interleukin-6, pg/mL  121  19 (8, 43)  28 (12, 54)  16 (7, 36)  <.001 
Gasometry
pH  73  7.45 (7.43, 7.48)  7.46 (7.43, 7.48)  7.45 (7.42, 7.48)  .028 
PaCO2, mm Hg  74  33 (30, 37)  33 (30, 36)  33 (30, 37)  .108 
PaO2, mm Hg  75  65 (58, 74)  67 (59, 76)  64 (57, 73)  <.001 
HCO3, mmol/L  76  23 (21, 25)  23 (21, 25)  23 (21, 25)  .677 
SaO2, %  52  93 (91, 95)  94 (92, 95)  93 (91, 95)  .007 
PaO2/FiO2 ratio, mm Hg  75  309 (276, 353)  319 (283, 362)  303 (272, 346)  <.001 
SaO2/FiO2 ratio  52  443 (433, 452)  447 (436, 453)  443 (431,452)  .007 
Radiologic  0        .039 
Unilateral consolidation    41%  42%  40%   
Bilateral consolidation    46%  38%  52%   
Interstitial abnormalities    7%  11%  5%   
Consolidation+interstitial    6%  9%  3%   
Covid treatments
Hydroxychloroquine  0  37%  91%  5%  <.001 
Antibiotics  0  93%  93%  93%  .824 
Lopinavir/ritonavir  0  32%  77%  0%  <.001 
Costicosteroids  0  67%  37%  87%  <.001 
Tocilizumab  0  7%  10%  6%  .022 
Remdesivir  0  7%  1%  11%  .001 

ACEI, angiotensin-converting enzyme inhibitors; ARAII, angiotensin II receptor antagonists; COPD, chronic obstructive pulmonary disease.

Data are percentages or medians (percentil25, percentil75).

Fig. 1 shows the number of hospitalized patients by month during the two waves. Table 1 shows the characteristics of patients at baseline. As compared to the first wave, during the second wave, patients were significantly older, had fever less frequently, received the same medications and had similar comorbidities as patients of the first wave. Of note, the prevalence of heart failure was higher. Laboratory data show a lower inflammatory component (lower levels of lactate dehydrogenase and interleukin-6 and similar C-reactive protein levels) and lower concentrations of lymphocyte and platelets. In addition, respiratory distress (lower SaO2, PaO2/FiO2 and SaO2/FiO2 ratios) and bilateral consolidations on chest X-ray were more frequent in the second wave, whereas interstitial abnormalities were less frequent. A higher use of corticosteroids and remdesivir was observed in the second wave, whereas hydroxychloroquine was hardly administered (5%) and Lopinavir/ritonavir were no longer used.

Fig. 1.

Number of hospitalized patients by month during the two waves.

(0.08MB).

The median length of hospital stay was significantly higher during the first wave [10 (7, 19) vs 9 (6, 13) days; P<.001]. Health indicators were poorer in the first than in the second wave (admission to ICU, 13% vs 11%; mechanical ventilation, 11% vs 7%; deaths, 17% vs 15%).

After adjusting for diabetes, age, lymphocyte count, oxygen saturation and pH (DALSH score), the risk for mechanical ventilation (OR 0.45, 95%CI 0.26–0.79) and for death (OR 0.52, 95%CI 0.31–0.85) were significantly lower in the second wave than in the first wave. The risk of admission to ICU (0.65, 95%CI 0.40–1.05) was also lower but without reaching significantly statistical association.

According to the results obtained, the length of hospital stay, use of mechanical ventilation and mortality were lower in hospitalized patients with COVID-19 pneumonia during the second wave, as compared to the first. All despite the fact that patients in the second wave were older and their characteristics were associated with a higher risk for developing severe COVID-19 (lower SaO2, PaO2/FiO2 and SaO2/FiO2 ratios).5 These results persisted after adjustment for variables of severity (DALSH score).4

Previous studies with a different design also show that mortality decreased over time.6,7 This cannot be attributed to demographic changes or variations in disease severity at presentation. Thus, whereas COVID-19 was approached as other severe respiratory diseases during the first wave, during the second wave the disease was managed based on the clinical experience acquired. As a result, clinicians started to use new drugs, prevailingly remdesivir and corticosteroids. Remdesivir had demonstrated its effectiveness in reducing time to recovery in hospitalized adults with Covid-19 and improving lower respiratory tract infection, although survival did not seem to improve with this drug.8 Corticosteroids proved a benefit on survival in patients with respiratory insufficiency9 and their use increased in the second wave. New practices (prone position)10 were also incorporated in the second wave. In addition, during the first wave it was observed that clinical outcomes improved with the use of assisted ventilation or high-flow nasal cannula at the early stages of the disease, so they were more frequently used during the second wave.

The strengths of the study lie in that it is the only hospital of the Health District (450,000 population) where COVID-19 patients are hospitalized, and all associated clinical data are available on electronic medical records. The major limitation of this study is that it only includes patients from a single healthcare area.

Although COVID-19 may progress to severe disease in high-risk patients and patients of the second wave were older and respiratory distress was more frequent, health indicators improved in the second wave of the pandemic.

Authors’ Contributions

Conception of the study: Francisco Gude, Luis Valdés.

Study design: Nuria Rodríguez-Núñez, Francisco Gude, Luis Valdés.

Data collection: Nuria Rodríguez-Núñez, Adriana Lama, Carlos Rábade, Alfonso Varela, Romina Abelleira, Ana Casal, Vanessa Riveiro, Manuel Taboada.

Data analysis: Francisco Gude.

Drafting the manuscript: All authors helped to revise the draft of the manuscript.

Editing and approval of the manuscript: All authors.

Funding

Instituto de Salud Carlos III, Spain, Grant/Award Number: COV20/00404; Ministry of Economy and Competitiveness (SPAIN) and the European Regional Development Fund (FEDER).

Funding source had no involvement in study design, in the collection, analysis, and interpretation of data, in the writing of the report, and in the decision to submit the paper for publication.

Conflict of Interest

We declare no conflicts of interest associated with this publication. This project was funded by the Carlos III Health Institute, Spain, Grant/Award Number: COV20/00404; Ministry of Economy and Competitiveness (SPAIN) and the European Regional Development Fund (FEDER). This funding source did not have a role in the design of the study, analysis, interpretation of results, or decision to submit the manuscript for publication.

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