External validation of six COVID-19 prognostic models for predicting mortality risk in older populations in a hospital, primary care, and nursing home setting

Abstract

Objective: To systematically evaluate the performance of COVID-19 prognostic models and scores for mortality risk in older populations across three healthcare settings: hospitals, primary care, and nursing homes. Study Design and setting: This retrospective external validation study included fourteen thousand and ninety-two older individuals of ≥70 years of age with a clinical or PCR-confirmed COVID-19 diagnosis from March 2020 to December 2020. The six validation cohorts include three hospital-based (CliniCo, COVID-OLD, COVID-PREDICT), two primary care-based (JGPN/ANH/AHA, PHARMO), and one nursing home cohort (YSIS) in the Netherlands. Based on a living systematic review of COVID-19 prediction models using PROBAST for quality and risk of bias assessment and considering predictor availability in validation cohorts, we selected six prognostic models predicting mortality risk in adults with COVID-19 infection (GAL-COVID-19 mortality, 4C Mortality Score, NEWS2-extended model, Xie model, Wang clinical model, and CURB65 score). All six prognostic models were validated in the hospital cohorts and the GAL-COVID-19 mortality model was validated in all three healthcare settings. The primary outcome was in-hospital mortality for hospitals and 28-day mortality for primary care and nursing home settings. Model performance was evaluated in each validation cohort separately in terms of discrimination, calibration, and decision curves. An intercept update was performed in models indicating miscalibration followed by predictive performance re-evaluation. Results: All six prognostic models performed poorly and showed miscalibration in the older population cohorts. In the hospital settings, model performance ranged from calibration-in-the-large -1.45 to 7.46, calibration slopes 0.24 to 0.81, and c-statistic 0.55 to 0.71 with 4C Mortality Score performing as the most discriminative and well-calibrated model. Performance across healthcare settings was similar for the GAL-COVID-19 model, with a calibration-in-the-large in the range of -2.35 to -0.15 indicating overestimation, calibration slopes of 0.24 to 0.81 indicating signs of overfitting, and c-statistic of 0.55 to 0.71. Conclusions: Our results show that most prognostic models for predicting mortality risk performed poorly in the older population with COVID-19, in each healthcare setting: hospital, primary care, and nursing home settings. Insights into factors influencing predictive model performance in the older population are needed for pandemic preparedness and reliable prognostication of health-related outcomes in this demographic.