OBJECTIVE: To establish whether one can build a mortality prediction model for COVID-19 patients based solely on demographics and comorbidity data that outperforms age alone . Such a model could be a precursor to implementing smart lockdowns and vaccine distribution strategies .
METHODS: The training cohort comprised 2337 COVID-19 inpatients from nine hospitals in The Netherlands . The clinical outcome was death within 21 days of being discharged . The features were derived from electronic health records collected during admission . Three feature selection methods were used: LASSO, univariate using a novel metric, and pairwise (age being half of each pair). 478 patients from Belgium were used to test the model . All modeling attempts were compared against an age-only model .
RESULTS: In the training cohort, the mortality group's median age was 77 years (interquartile range = 70-83), higher than the non-mortality group (median = 65, IQR = 55-75). The incidence of former/active smokers, male gender, hypertension, diabetes, dementia, cancer, chronic obstructive pulmonary disease, chronic cardiac disease, chronic neurological disease, and chronic kidney disease was higher in the mortality group . All stated differences were statistically significant after Bonferroni correction . LASSO selected eight features, novel univariate chose five, and pairwise chose none . No model was able to surpass an age-only model in the external validation set, where age had an AUC of 0.85 and a balanced accuracy of 0.77 .
CONCLUSION: When applied to an external validation set, we found that an age-only mortality model outperformed all modeling attempts (curated on www.covid19risk.ai) using three feature selection methods on 22 demographic and comorbid features.