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Machine learning-based predictive model for postoperative delirium of elderly patients with coronary heart disease
Wenjie Kong1, Jing Jiang2, Yuanlong Wang1,2
1The Second School of Clinical Medicine of Binzhou Medical University, Yantai, China.
Insights
This study developed a machine learning model to predict postoperative delirium (POD) in elderly patients with coronary heart disease (CHD). The gradient boosting model accurately identifies patients at high risk, aiding clinical decision-making.
Area of Science:
- Geriatric Medicine
- Cardiology
- Data Science in Healthcare
Background:
- Elderly patients with coronary heart disease (CHD) face a high risk of postoperative delirium (POD).
- Current methods for predicting POD in this specific population are lacking.
- This study addresses the critical need for a predictive tool in this vulnerable group.
Purpose of the Study:
- To develop and validate a machine learning model for predicting POD in elderly patients with CHD.
- To identify key predictors of POD in this patient cohort.
- To create an accessible tool for clinical risk assessment.
Main Methods:
- Utilized data from elderly patients with CHD undergoing non-cardiac surgery.
- Employed Boruta algorithm, LASSO regression, and multiple logistic regression for feature selection.
- Constructed and evaluated ten machine learning models, including gradient boosting, using metrics like ROC curve, decision curve, and calibration plots.
Main Results:
- Identified seven key features predicting POD, with an incidence of 16.6% in 861 patients.
- The gradient boosting model (GBM) demonstrated superior predictive performance with an AUC of 0.856.
- Clinical Frailty Scale (CFS), Mini-mental State Examination (MMSE), and Athens Insomnia Scale (AIS) scores were significant predictors.
Conclusions:
- A reliable GBM model was developed for predicting POD in elderly CHD patients.
- Higher CFS grade, lower MMSE score, and higher AIS score significantly increase POD risk.
- External validation is recommended prior to clinical implementation.
Background:
Patients with advanced age and coronary heart disease (CHD) are at significantly increased risk for postoperative delirium (POD). However, there is no method to predict POD in elderly patients with CHD.
Methods:
Date from elderly patients with CHD who underwent non-cardiac surgery was collected. The dataset is subdivided into training and validation sets at a ratio of 7:3. Boruta algorithm, least absolute shrinkage and selection operator (LASSO) regression and multiple logistic regression analysis were used to select features. Machine learning method was used to construct a model for predicting the occurrence of POD. Receiver operating characteristic (ROC) curve, decision curve, calibration curve, specificity, sensitivity, accuracy, F1 score and Brier score were used to compare the predictive performance of these machine learning models, and the interpretability of the models was evaluated by Shapley additive interpretation (SHAP).
Results:
A total of 861 patients were included in the study. The incidence of POD was 16.6% (143/861). Seven key features were identified. Ten machine learning models were constructed. Among the models, gradient boosting model (GBM) performed better. The area under the ROC curve (AUC) is 0.856 (95% confidence interval [CI]: 0.796-0.916). The decision curve, calibration curve, specificity, sensitivity, accuracy, F1 score and Brier score were also relatively good. SHAP plots of GBM showed that Clinical Frailty Scale (CFS) grade, Mini-mental State Examination (MMSE) score, and Athens Insomnia Scale (AIS) score were significant predictors of POD in elderly CHD patients, and an easy-to-use calculator for predicting the risk of POD was developed based on the GBM model.
Conclusion:
This study developed a reliable GBM model for predicting the occurrence of POD in elderly patients with CHD. Higher CFS grade, lower MMSE score and higher AIS score significantly enhanced the predictive ability of the model. External validation of our model is needed before it can be applied in a clinical setting.
Trial Registration:
Registration number of the Chinese Clinical Trial Registry: ChiCTR2500097325, Registration Date: 17/02/2025.
