Modeling the recovery time of patients with coronavirus disease 2019
using an accelerated failure time model |
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| Authors: | Gayathri Thiruvengadam Ravanan Ramanujam Lakshmi Marappa |
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| Institution: | 1.Faculty of Allied Health Sciences, Sri Ramachandra Institute of Higher Education and Research, Porur, Chennai, India;2.Collegiate Education, Chennai Region, Chennai, India;3.Department of General Medicine, Sri Ramachandra Institute of Higher Education and Research, Porur, Chennai, India |
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| Abstract: | ObjectiveTo identify factors associated with recovery time from coronavirus disease 2019 (COVID-19).MethodsIn this retrospective study, data for patients with COVID-19 were obtained between 21 June and 30 August 2020. An accelerated failure time (AFT) model was used to identify covariates associated with recovery time (days from hospital admission to discharge). AFT models with different distributions (exponential, log-normal, Weibull, generalized gamma, and log-logistic) were generated. Akaike’s information criterion (AIC) was used to identify the most suitable model.ResultsA total of 730 patients with COVID-19 were included (92.5% recovered and 7.5% censored). Based on its low AIC value, the log-logistic AFT model was the best fit for the data. The covariates affecting length of hospital stay were oxygen saturation, lactate dehydrogenase, neutrophil-lymphocyte ratio, D-dimer, ferritin, creatinine, total leucocyte count, age > 80 years, and coronary artery disease.ConclusionsThe log-logistic AFT model accurately described the recovery time of patients with COVID-19. |
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| Keywords: | Accelerated failure time model Akaike’ s information criterion coronavirus disease-19 length of stay recovery time modeling |
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