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

Anum Zahra*, Maarten van Smeden, KGM Moons, Kim Luijken, Brent Appelman, Evertine J. Abbink, Jesse M van den Berg, Marieke T. Blom, Carline van den Dries, Jacobijn Gussekloo, Fenne Wouters, Karlijn Joling, René J.F. Melis, Simon P. Mooijaart, Jeannette Peters, Harmke Polinder-Bos, Bas F M van Raaij, Hannah Marieke Teeuw, Kim Luijken

*Corresponding author for this work

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Abstract

Objectives

To systematically evaluate the performance of COVID-19 prognostic models and scores for mortality risk in older populations across three health-care settings: hospitals, primary care, and nursing homes.
Study Design and Setting

This retrospective external validation study included 14,092 older individuals of ≥70 years of age with a clinical or polymerase chain reaction-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 (Julius General Practitioners Network/Academisch network huisartsgeneeskunde/Network of Academic general Practitioners, PHARMO), and one nursing home cohort (YSIS) in the Netherlands. Based on a living systematic review of COVID-19 prediction models using Prediction model Risk Of Bias ASsessment Tool 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, National Early Warning Score 2-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.
Main Outcome Measure

In-hospital mortality for hospitals and 28-day mortality for primary care and nursing home setting.
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 0.24–0.81, and C-statistic 0.55–0.71 with 4C Mortality Score performing as the most discriminative and well-calibrated model. Performance across health-care 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 0.24–0.81 indicating signs of overfitting, and C-statistic of 0.55–0.71.
Conclusion

Our results show that most prognostic models for predicting mortality risk performed poorly in the older population with COVID-19, in each health-care 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.
Original languageEnglish
Article number111270
Number of pages14
JournalJournal of Clinical Epidemiology
Volume168
DOIs
Publication statusPublished - Apr 2024

Keywords

  • COVID-19
  • COVID-19-Related mortality
  • External validation
  • Older population
  • Prognostic models
  • clinical prediction models

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