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Clinically Interpretable Machine Learning Models for Early Prediction of Mortality in Older Patients with Multiple Organ Dysfunction Syndrome (MODS): An International Multicenter Retrospective Study

BACKGROUND

Multiple organ dysfunction syndrome (MODS) is associated with a high risk of mortality among older patients. Current severity scores are limited in their ability to assist clinicians with triage and management decisions. We aim to develop mortality prediction models for older patients with MODS admitted to the ICU.

METHODS

The study analyzed older patients from 197 hospitals in the US and one hospital in the Netherlands. The cohort was divided into the young-old (65-80 years) and old-old (≥80 years), which were separately used to develop and evaluate models including internal, external and temporal validation. Demographic characteristics, comorbidities, vital signs, laboratory measurements, and treatments were used as predictors. We used the XGBoost algorithm to train models, and the SHAP method to interpret predictions.

RESULTS

34,497 young-old (11.3% mortality) and 21,330 old-old (15.7% mortality) patients were analyzed. Discrimination AUROC of internal validation models in 9,046 U.S. patients was as follows: 0.87 and 0.82, respectively; Discrimination of external validation models in 1,905 EUR patients was as follows: 0.86 and 0.85, respectively; and of temporal validation models in 8,690 U.S. patients: 0.85 and 0.78, respectively. These models outperformed standard clinical scores like SOFA and APSIII. The GCS, Charlson Comorbidity Index, and Code Status emerged as top predictors of mortality.

CONCLUSIONS

Our models integrate data spanning physiologic and geriatric-relevant variables that outperform existing scores used in older adults with MODS, which represents a proof of concept of how machine learning can streamline data analysis for busy ICU clinicians to potentially optimize prognostication and decision making.