  Existing prognostic tools mainly focus on predicting the risk of mortality
among patients with coronavirus disease 2019. However, clinical evidence
suggests that COVID-19 can result in non-mortal complications that affect
patient prognosis. To support patient risk stratification, we aimed to develop
a prognostic system that predicts complications common to COVID-19. In this
retrospective study, we used data collected from 3,352 COVID-19 patient
encounters admitted to 18 facilities between April 1 and April 30, 2020, in Abu
Dhabi (AD), UAE. The hospitals were split based on geographical proximity to
assess for our proposed system's learning generalizability, AD Middle region
and AD Western & Eastern regions, A and B, respectively. Using data collected
during the first 24 hours of admission, the machine learning-based prognostic
system predicts the risk of developing any of seven complications during the
hospital stay. The complications include secondary bacterial infection, AKI,
ARDS, and elevated biomarkers linked to increased patient severity, including
d-dimer, interleukin-6, aminotransferases, and troponin. During training, the
system applies an exclusion criteria, hyperparameter tuning, and model
selection for each complication-specific model. The system achieves good
accuracy across all complications and both regions. In test set A (587 patient
encounters), the system achieves 0.91 AUROC for AKI and >0.80 AUROC for most of
the other complications. In test set B (225 patient encounters), the respective
system achieves 0.90 AUROC for AKI, elevated troponin, and elevated
interleukin-6, and >0.80 AUROC for most of the other complications. The best
performing models, as selected by our system, were mainly gradient boosting
models and logistic regression. Our results show that a data-driven approach
using machine learning can predict the risk of such complications with high
accuracy.
