Predicting Length of Hospital Stay of COVID-19 Patients Using Accelerated Failure Time Model

The objective of the study was to predict the length of hospital stay (LOS) of COVID-19 patients using accelerated failure time model (AFT). Estimates of LOS can be used to project demands and to allocate resources efficiently. Retrospective data of 131 recovered patients who were admitted at a secondary hospital in Quezon City, Philippines was used for model development while another set of 131 patients’ records was used for model validation. An AFT model using log-logistic distribution showed that age (TR = 0.996, 95% CI [0.992, 0.999], p = .010), temperature (TR = 0.909, 95% CI [0.832, 0.992], p = .033), and the interaction of gender and vaccination status (TR = 1.810, 95% CI [1.269, 2.582], p = .001) are significant predictors of LOS at .05 level of significance, with Adjusted R2 of 12.11%.