Abstract
BackgroundThe identification of factors associated with Intensive Care Unit (ICU) mortality and derived clinical phenotypes in COVID-19 patients could help for a more tailored approach to clinical decision-making that improves prognostic outcomes.MethodsProspective, multicenter, observational study of critically ill patients with confirmed COVID-19 disease and acute respiratory failure admitted from 63 ICUs in Spain. The objective was to utilize an unsupervised clustering analysis to derive clinical COVID-19 phenotypes and to analyze patient’s factors associated with mortality risk. Patient features including demographics and clinical data at ICU admission were analyzed. Generalized linear models were used to determine ICU morality risk factors. The prognostic models were validated and their performance was measured using accuracy test, sensitivity, specificity and ROC curves.ResultsThe database included a total of 2022 patients (mean age 64 [IQR 5–71] years, 1423 (70.4%) male, median APACHE II score (13 [IQR 10–17]) and SOFA score (5 [IQR 3–7]) points. The ICU mortality rate was 32.6%. Of the 3 derived phenotypes, the A (mild) phenotype (537; 26.7%) included older age (< 65 years), fewer abnormal laboratory values and less development of complications, B (moderate) phenotype (623, 30.8%) had similar characteristics of A phenotype but were more likely to present shock. The C (severe) phenotype was the most common (857; 42.5%) and was characterized by the interplay of older age (> 65 years), high severity of illness and a higher likelihood of development shock. Crude ICU mortality was 20.3%, 25% and 45.4% for A, B and C phenotype respectively. The ICU mortality risk factors and model performance differed between whole population and phenotype classifications.ConclusionThe presented machine learning model identified three clinical phenotypes that significantly correlated with host-response patterns and ICU mortality. Different risk factors across the whole population and clinical phenotypes were observed which may limit the application of a “one-size-fits-all” model in practice.
Highlights
Since the outbreak of COVID-19 disease began in December 2019 in China, soaring cases of confirmed SARS-CoV-2 are pummeling the global health system
The presented machine learning model identified three clinical phenotypes that significantly correlated with host-response patterns and Intensive Care Unit (ICU) mortality
Forty percent of ICUs belonged to hospitals with more than 500 beds, 40% to hospitals between 200 and 500 beds and the remaining 20 percent to hospitals with fewer than 200 beds
Summary
Since the outbreak of COVID-19 disease began in December 2019 in China, soaring cases of confirmed SARS-CoV-2 are pummeling the global health system. As of January 18, 2021 more than 2.5 million people in Spain have been infected with SARS-CoV-2 and more than 53,000 have died [3]. The heterogeneity of patients that have been treated in China [4], Italy [8], USA [5,6,7] or Spain [9, 10] may explain the wide variation of mortality rate due to their population characteristics, presence of comorbidities and healthcare systems. A recent international survey [11] reported significant practice variations in the management of severe COVID-19 patients, including differences at the regional, hospital, and patient level. The identification of factors associated with Intensive Care Unit (ICU) mortality and derived clinical phenotypes in COVID-19 patients could help for a more tailored approach to clinical decision-making that improves prognostic outcomes
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