Consistency of performance of adverse outcome prediction models for hospitalized COVID-19 patients
Abstract
Background
We previously reported and validated a risk prediction tool based on COVID-19 hospitalizations prior to June 2020. Here, we report performance of that model on subsequent data from 6 hospitals and among individual patient subgroups.
Method
We included individuals age 18 or older hospitalized at one of 2 academic medical centers and 4 community hospitals from 6/7/2020 through 1/22/2021 with positive PCR test for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) within 5 days of admission. Coefficients from our previously reported least absolute shrinkage and selection operator (Lasso) risk models were applied to estimate probability of a mortality, as well as a composite severe illness outcome, including admission to the ICU, mechanical ventilation or mortality.
Results
Overall model performance for mortality included AUC of 0.83 (95% CI:0.80-0.87) for mortality, with a PPV 0.55 and NPV of 0.95 when using a cutoff corresponding to the highest 20% of predicted risk derived in the training set. For all adverse outcomes, AUC was 0.79 (95% CI:0.75-0.81) and PPV 0.48 and NPV 0.98 in the top 20% risk group. Model discrimination was generally similar between genders and race/ethnicity groups, but markedly poorer for younger age groups.
Conclusion
Although the population of individuals hospitalized for COVID-19 has shifted and outcomes have improved overall, prediction models derived earlier in the pandemic may maintain utility.
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