External Validation and Recalibration of the CURB-65 and PSI for Predicting 30-Day Mortality and Critical Care Intervention in Multiethnic Patients with COVID-19.

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.

Mortality Prediction Utilizing Blood Biomarkers to Predict the Severity of COVID-19 Using Machine Learning Technique.

Healthcare researchers have been working on mortality prediction for COVID-19 patients with differing levels of severity. A rapid and reliable clinical evaluation of disease intensity will assist in the allocation and prioritization of mortality mitigation resources. The novelty of the work proposed in this paper is an early prediction model of high mortality risk for both COVID-19 and non-COVID-19 patients, which provides state-of-the-art performance, in an external validation cohort from a different population. Retrospective research was performed on two separate hospital datasets from two different countries for model development and validation. In the first dataset, COVID-19 and non-COVID-19 patients were admitted to the emergency department in Boston (24 March 2020 to 30 April 2020), and in the second dataset, 375 COVID-19 patients were admitted to Tongji Hospital in China (10 January 2020 to 18 February 2020). The key parameters to predict the risk of mortality for COVID-19 and non-COVID-19 patients were identified and a nomogram-based scoring technique was developed using the top-ranked five parameters. Age, Lymphocyte count, D-dimer, CRP, and Creatinine (ALDCC), information acquired at hospital admission, were identified by the logistic regression model as the primary predictors of hospital death. For the development cohort, and internal and external validation cohorts, the area under the curves (AUCs) were 0.987, 0.999, and 0.992, respectively. All the patients are categorized into three groups using ALDCC score and death probability: Low (probability < 5%), Moderate (5% < probability < 50%), and High (probability > 50%) risk groups. The prognostic model, nomogram, and ALDCC score will be able to assist in the early identification of both COVID-19 and non-COVID-19 patients with high mortality risk, helping physicians to improve patient management.