| Summary: | Objectives: To validate and recalibrate the CURB-65 and pneumonia severity index (PSI) in predicting 30-day mortality and critical care intervention (CCI) in a multiethnic population with COVID-19, along with evaluating both models in predicting CCI.Methods: Retrospective data was collected for 1181 patients admitted to the largest hospital in Qatar with COVID-19 pneumonia. The area under the curve (AUC), calibration curves, and other metrics were bootstrapped to examine the performance of the models. Variables constituting the CURB-65 and PSI scores underwent further analysis using the Least Absolute Shrinkage and Selection Operator (LASSO) along with logistic regression to develop a model predicting CCI. Complex machine learning models were built for comparative analysis.Results: The PSI performed better than CURB-65 in predicting 30-day mortality (AUC 0.83, 0.78 respectively), while CURB-65 outperformed PSI in predicting CCI (AUC 0.78, 0.70 respectively). The modified PSI/CURB-65 model (respiratory rate, oxygen saturation, hematocrit, age, sodium, and glucose) predicting CCI had excellent accuracy (AUC 0.823) and good calibration.Conclusions: Our study recalibrated, externally validated the PSI and CURB-65 for predicting 30-day mortality and CCI, and developed a model for predicting CCI. Our tool can potentially guide clinicians in Qatar to stratify patients with COVID-19 pneumonia.
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