Development and internal validation of a predictive model of cognitive decline 36 months following elective surgery

Abstract

Abstract Introduction Our goal was to determine if features of surgical patients, easily obtained from the medical chart or brief interview, could be used to predict those likely to experience more rapid cognitive decline following surgery. Methods We analyzed data from an observational study of 560 older adults (≥70 years) without dementia undergoing major elective non‐cardiac surgery. Cognitive decline was measured using change in a global composite over 2 to 36 months following surgery. Predictive features were identified as variables readily obtained from chart review or a brief patient assessment. We developed predictive models for cognitive decline (slope) and predicting dichotomized cognitive decline at a clinically determined cut. Results In a hold‐out testing set, the regularized regression predictive model achieved a root mean squared error (RMSE) of 0.146 and a model r‐square (R2) of .31. Prediction of “rapid” decliners as a group achieved an area under the curve (AUC) of .75. Conclusion Some of our models could predict persons with increased risk for accelerated cognitive decline with greater accuracy than relying upon chance, and this result might be useful for stratification of surgical patients for inclusion in future clinical trials.

Keywords

• cognitive decline
• delirium
• machine learning
• model prediction
• post‐operative
• statistical learning