Abstract
BackgroundNarrowing a large set of features to a smaller one can improve our understanding of the main risk factors for in-hospital mortality in patients with COVID-19. This study aimed to derive a parsimonious model for predicting overall survival (OS) among re-infected COVID-19 patients using machine-learning algorithms.MethodsThe retrospective data of 283 re-infected COVID-19 patients admitted to twenty-six medical centers (affiliated with Shiraz University of Medical Sciences) from 10 June to 26 December 2020 were reviewed and analyzed. An elastic-net regularized Cox proportional hazards (PH) regression and model approximation via backward elimination were utilized to optimize a predictive model of time to in-hospital death. The model was further reduced to its core features to maximize simplicity and generalizability.ResultsThe empirical in-hospital mortality rate among the re-infected COVID-19 patients was 9.5%. In addition, the mortality rate among the intubated patients was 83.5%. Using the Kaplan-Meier approach, the OS (95% CI) rates for days 7, 14, and 21 were 87.5% (81.6-91.6%), 78.3% (65.0-87.0%), and 52.2% (20.3-76.7%), respectively. The elastic-net Cox PH regression retained 8 out of 35 candidate features of death. Transfer by Emergency Medical Services (EMS) (HR=3.90, 95% CI: 1.63-9.48), SpO2≤85% (HR=8.10, 95% CI: 2.97-22.00), increased serum creatinine (HR=1.85, 95% CI: 1.48-2.30), and increased white blood cells (WBC) count (HR=1.10, 95% CI: 1.03-1.15) were associated with higher in-hospital mortality rates in the re-infected COVID-19 patients.ConclusionThe results of the machine-learning analysis demonstrated that transfer by EMS, profound hypoxemia (SpO2≤85%), increased serum creatinine (more than 1.6 mg/dL), and increased WBC count (more than 8.5 (×109 cells/L)) reduced the OS of the re-infected COVID-19 patients. We recommend that future machine-learning studies should further investigate these relationships and the associated factors in these patients for a better prediction of OS.
Highlights
Narrowing a large set of features to a smaller one can improve our understanding of the main risk fac‐ tors for in-hospital mortality in patients with COVID-19
Design and study population This retrospective cohort study was conducted on all inpatients with confirmed COVID-19 who were referred to 26 medical centers (affiliated with Shiraz University of Medical Sciences (SUMS)) from 10 June to 26 December 2020
More details of the baseline demographic characteristics and clinical and laboratory test findings are presented in Tables 1 and 2
Summary
Narrowing a large set of features to a smaller one can improve our understanding of the main risk fac‐ tors for in-hospital mortality in patients with COVID-19. Severe acute respiratory syndrome coronavirus 2 (SARSCoV-2) disease 2019 (COVID-19) first started in China in December 2019. It rapidly spread around the world and became a pandemic and a major health issue. The median time from symptom onset to the detection of immunoglobulin (Ig) M antibody was reported twelve days and was determined as fourteen days for IgG antibodies. It is not clear how long the protection will last [4]
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