External Validation of Mortality Prediction Models for Critical Illness Reveals Preserved Discrimination but Poor Calibration

Research output: Contribution to journalJournal articleResearchpeer-review

  • Eline G.M. Cox
  • Renske Wiersema
  • Ruben J. Eck
  • Thomas Kaufmann
  • Anders Granholm
  • Suvi T. Vaara
  • Møller, Morten Hylander
  • Bas C.T. Van Bussel
  • Harold Snieder
  • Rick G. Pleijhuis
  • Iwan C.C. Van Der Horst
  • Frederik Keus

OBJECTIVES: In a recent scoping review, we identified 43 mortality prediction models for critically ill patients. We aimed to assess the performances of these models through external validation. DESIGN: Multicenter study. SETTING: External validation of models was performed in the Simple Intensive Care Studies-I (SICS-I) and the Finnish Acute Kidney Injury (FINNAKI) study. PATIENTS: The SICS-I study consisted of 1,075 patients, and the FINNAKI study consisted of 2,901 critically ill patients. MEASUREMENTS AND MAIN RESULTS: For each model, we assessed: 1) the original publications for the data needed for model reconstruction, 2) availability of the variables, 3) model performance in two independent cohorts, and 4) the effects of recalibration on model performance. The models were recalibrated using data of the SICS-I and subsequently validated using data of the FINNAKI study. We evaluated overall model performance using various indexes, including the (scaled) Brier score, discrimination (area under the curve of the receiver operating characteristics), calibration (intercepts and slopes), and decision curves. Eleven models (26%) could be externally validated. The Acute Physiology And Chronic Health Evaluation (APACHE) II, APACHE IV, Simplified Acute Physiology Score (SAPS)-Reduced (SAPS-R)‚ and Simplified Mortality Score for the ICU models showed the best scaled Brier scores of 0.11‚ 0.10‚ 0.10‚ and 0.06‚ respectively. SAPS II, APACHE II, and APACHE IV discriminated best; overall discrimination of models ranged from area under the curve of the receiver operating characteristics of 0.63 (0.61-0.66) to 0.83 (0.81-0.85). We observed poor calibration in most models, which improved to at least moderate after recalibration of intercepts and slopes. The decision curve showed a positive net benefit in the 0-60% threshold probability range for APACHE IV and SAPS-R. CONCLUSIONS: In only 11 out of 43 available mortality prediction models, the performance could be studied using two cohorts of critically ill patients. External validation showed that the discriminative ability of APACHE II, APACHE IV, and SAPS II was acceptable to excellent, whereas calibration was poor.

Original languageEnglish
JournalCritical Care Medicine
Issue number1
Pages (from-to)80-90
Number of pages11
Publication statusPublished - 2023

Bibliographical note

Publisher Copyright:
© 2023 Lippincott Williams and Wilkins. All rights reserved.

    Research areas

  • critical care, critically ill patients, intensive care unit, mortality prediction model, performance, risk prediction

ID: 363555892