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Existing prognostic tools mainly focus on predicting the risk of mortalityamong patients with coronavirus disease 2019. However, clinical evidencesuggests that COVID-19 can result in non-mortal complications that affectpatient prognosis. To support patient risk stratification, we aimed to developa prognostic system that predicts complications common to COVID-19. In thisretrospective study, we used data collected from 3,352 COVID-19 patientencounters admitted to 18 facilities between April 1 and April 30, 2020, in AbuDhabi (AD), UAE. The hospitals were split based on geographical proximity toassess for our proposed system's learning generalizability, AD Middle regionand AD Western & Eastern regions, A and B, respectively. Using data collectedduring the first 24 hours of admission, the machine learning-based prognosticsystem predicts the risk of developing any of seven complications during thehospital stay. The complications include secondary bacterial infection, AKI,ARDS, and elevated biomarkers linked to increased patient severity, includingd-dimer, interleukin-6, aminotransferases, and troponin. During training, thesystem applies an exclusion criteria, hyperparameter tuning, and modelselection for each complication-specific model. The system achieves goodaccuracy across all complications and both regions. In test set A (587 patientencounters), the system achieves 0.91 AUROC for AKI and >0.80 AUROC for most ofthe other complications. In test set B (225 patient encounters), the respectivesystem achieves 0.90 AUROC for AKI, elevated troponin, and elevatedinterleukin-6, and >0.80 AUROC for most of the other complications. The bestperforming models, as selected by our system, were mainly gradient boostingmodels and logistic regression. Our results show that a data-driven approachusing machine learning can predict the risk of such complications with highaccuracy.

Clinical prediction system of complications among COVID-19 patients: a development and validation retrospective multicentre study