An externally validated machine learning algorithm for predicting mental and physical health outcomes three months post-hospitalization for severe viral infection with SARS-CoV-2

Many individuals hospitalized due to severe viral infections develop mental and physical sequelae, which could potentially be prevented by targeted interventions for those at risk. Our goal was to develop and externally validate an algorithm for predicting mental and physical symptoms after SARS-CoV-2 hospitalization utilizing routinely collected clinical data. Participants were included from two independent samples of the German National Pandemic Cohort Network (NAPKON): a model development sample (SUEP; N = 451; mean age: 55.6 ± 15.3; 36.2% female) and an external validation sample (HAP: N = 158; mean age: 55.1 ± 12.1; 39.9% female). Machine learning models leveraging demographic, clinical and biological variables collected at the time of admission were employed to predict Patient-Reported Outcomes Measurement Information System scores (PROMIS) across 7 domains (physical function, anxiety, depression, fatigue, sleep disturbance, ability to participate in social roles and activities, and pain) three months after SARS-CoV-2 hospitalization. Shapley Additive exPlanation values were used to provide interpretable information about key predictive factors. Approximately 15-20% of participants reported moderate to severe impairment in at least one PROMIS domain three months after hospitalization. For the mental health composite score, the best-performing model achieved RMSE = 1.833 ± 0.341 and R² = 0.927 ± 0.031 in SUEP and RMSE = 3.131 and R² = 0.893 in HAP. For the physical health composite, the best-performing model achieved RMSE = 2.908 ± 0.703 and R² = 0.824 ± 0.052 in SUEP and RMSE = 3.019 and R² = 0.850 in HAP. Furthermore, the models achieved high predictive performance across all individual PROMIS domain scores in both samples. We provide an externally validated methodology for accurately predicting mental and physical symptomatology following hospitalization due to a severe viral infection. This approach may facilitate the development of a brief risk stratification tool at the point of hospitalization, enabling early identification of at-risk patients, improving the prediction accuracy of subsequent psychological and physical sequelae, and supporting timely preventive interventions.