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Predicting Postoperative Cognitive Dysfunction in Older Cardiac Surgery Patients: An Integrated Machine Learning
Ming Sang1, Fengxia Weng1, Sirui Wang1
1Department of Nursing, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China.
Journal of Cardiothoracic and Vascular Anesthesia
|May 15, 2026
Summary
Machine learning identified key risk factors for postoperative cognitive dysfunction (POCD) in older cardiac surgery patients. A predictive nomogram aids in identifying high-risk individuals for targeted interventions.
Area of Science:
- Cardiology
- Geriatrics
- Data Science
- Anesthesiology
Background:
- Postoperative cognitive dysfunction (POCD) is a significant concern in older adults undergoing cardiac surgery.
- Early identification of patients at risk for POCD is crucial for implementing timely interventions.
Purpose of the Study:
- To leverage machine learning to pinpoint critical risk factors for POCD in elderly cardiac surgery patients.
- To create a predictive nomogram for clinicians to identify high-risk individuals and guide preventive strategies.
Main Methods:
- Prospective observational cohort study involving 353 patients aged 60 years and above undergoing cardiac surgery.
- Utilized Least Absolute Shrinkage and Selection Operator (LASSO) regression for predictor selection, followed by multivariate logistic regression.
- Collected comprehensive perioperative data including demographics, lab results, and clinical variables.
Main Results:
- The incidence of POCD was 49.86%.
- Seven independent predictors for POCD were identified: surgical approach, comorbidities, surgery duration, blood loss, ICU sleep quality, APACHE II score, and self-care ability.
- The developed nomogram demonstrated good predictive discrimination (AUC 0.786) and calibration.
Conclusions:
- A validated nomogram effectively predicts POCD in older cardiac surgery patients.
- The nomogram incorporates key clinical and perioperative factors, offering a valuable tool for risk stratification.
- This predictive model can assist healthcare providers in developing targeted prevention strategies for POCD.
