Can predicting COVID-19 mortality in a European cohort using only demographic and comorbidity data surpass age-based prediction: An externally validated study

Avishek Chatterjee*, Guangyao Wu, Sergey Primakov, Cary Oberije, Henry Woodruff, Pieter Kubben, Ronald Henry, Marcel J. H. Aries, Martijn Beudel, Peter G. Noordzij, Tom Dormans, Niels C. Gritters van den Oever, Joop P. van den Bergh, Caroline E. Wyers, Suat Simsek, Renee Douma, Auke C. Reidinga, Martijn D. de Kruif, Julien Guiot, Anne-Noelle FrixRenaud Louis, Michel Moutschen, Pierre Lovinfosse, Philippe Lambin

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

8 Citations (Web of Science)



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.


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.


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.


When applied to an external validation set, we found that an age-only mortality model outperformed all modeling attempts (curated on ) using three feature selection methods on 22 demographic and comorbid features.

Original languageEnglish
Article number0249920
Number of pages15
Issue number4
Publication statusPublished - 15 Apr 2021


  • RISK

Cite this