Objective : Develop models to predict 30-day COVID-19 hospitalization and death in the Omicron era for clinical and research applications. Material and Methods : We used comprehensive electronic health records from a national cohort of patients in the Veterans Health Administration (VHA) who tested positive for SARS-CoV-2 between March 1, 2022, and March 31, 2023. Full models incorporated 84 predictors , including demographics, comorbidities, and receipt of COVID-19 vaccinations and anti-SARS-CoV-2 treatments. Parsimonious models included 19 predictors. We created models for 30-day hospitalization or death, 30-day hospitalization, and 30-day all-cause mortality. We used the Super Learner ensemble machine learning algorithm to model risks. Model performance was assessed with the area under the receiver operating characteristic curve (AUC), Brier scores, and calibration intercepts and slopes in a 20% holdout dataset. Results : Models were trained and tested on 198,174 patients, of whom 8% were hospitalized or died within 30 days of testing positive. AUCs for the full models ranged from 0.80 (hospitalization) to 0.91 (death). Brier scores were close to 0, with the lowest error in the mortality model (Brier score: 0.01). All three models were well calibrated with calibration intercepts <0.23 and slopes <1.05. Parsimonious models performed comparably to full models. Discussion : These models may be used for risk stratification to inform COVID-19 treatment and to identify high-risk patients for inclusion in clinical trials. Conclusions : We developed prediction models that accurately estimate COVID-19 hospitalization and mortality risk following emergence of the Omicron variant and in the setting of COVID-19 vaccinations and antiviral treatments.
Competing Interest Statement .
The authors have declared no competing interest.
Funding Statement .
Primary Funding Source: U.S. Department of Veterans Affairs
Author Declarations .
I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained.
The details of the IRB/oversight body that provided approval or exemption for the research described are given below:
The study was approved by the VA Central Institutional Review Board.
I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals.
I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance).
I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable.
Data Availability .
All data produced in the present study are available upon reasonable request to the authors .