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Many countries are now experiencing the third wave of the COVID-19 pandemicstraining the healthcare resources with an acute shortage of hospital beds andventilators for the critically ill patients. This situation is especially worsein India with the second largest load of COVID-19 cases and a relativelyresource-scarce medical infrastructure. Therefore, it becomes essential totriage the patients based on the severity of their disease and devote resourcestowards critically ill patients. Yan et al. 1 have published a very pertinentresearch that uses Machine learning (ML) methods to predict the outcome ofCOVID-19 patients based on their clinical parameters at the day of admission.They used the XGBoost algorithm, a type of ensemble model, to build themortality prediction model. The final classifier is built through thesequential addition of multiple weak classifiers. The clinically operabledecision rule was obtained from a 'single-tree XGBoost' and used lacticdehydrogenase (LDH), lymphocyte and high-sensitivity C-reactive protein(hs-CRP) values. This decision tree achieved a 100% survival prediction and 81%mortality prediction. However, these models have several technical challengesand do not provide an out of the box solution that can be deployed for otherpopulations as has been reported in the "Matters Arising" section of Yan et al.Here, we show the limitations of this model by deploying it on one of thelargest datasets of COVID-19 patients containing detailed clinical parameterscollected from India.

Challenges in the application of a mortality prediction model for COVID-19 patients on an Indian cohort