The Random Forest machine learning model outperformed a traditional nomogram in predicting postoperative delirium after heart valve replacement (AUROC 0.854, 95% CI 0.764-0.945 vs 0.754).
Cohort (n=1,357)
Does a Random Forest machine learning model improve the prediction of postoperative delirium compared to a traditional nomogram in adult patients undergoing heart valve replacement with cardiopulmonary bypass?
A Random Forest machine learning model outperforms traditional logistic regression nomograms in predicting postoperative delirium after heart valve replacement, identifying postoperative awakening time as a critical modifiable risk factor.
Absolute Event Rate: 0.854% vs 0.754%
Postoperative delirium (POD) is a common and severe complication following heart valve replacement (HVR) with cardiopulmonary bypass (CPB), associated with poor outcomes. Accurate early prediction is crucial for targeted prevention. This study aimed to develop and externally validate machine learning (ML) models for predicting POD in this high-risk population. We conducted a retrospective cohort study involving 1076 adult patients who underwent HVR with CPB between January 2018 and December 2022. POD was assessed using the CAM-ICU. Perioperative factors were analyzed to develop a traditional logistic regression-based nomogram and four ML models. The best-performing model was then tested on an independent external validation cohort of 281 patients. The incidence of POD was 25.1% (270/1076) and was associated with significantly increased mortality (11.9% vs. 0.2%, P < 0.001) and prolonged hospitalization. In the internal test set, the Random Forest (RF) model demonstrated the highest predictive performance with an AUROC of 0.854 (95% CI, 0.764–0.945), outperforming the traditional nomogram (AUROC: 0.754). The RF model maintained robust performance in the external validation cohort, achieving an AUROC of 0.793. Model interpretation using SHAP analysis identified postoperative awakening time (PAT), postoperative mechanical ventilation time (PMVT), and postoperative reintubation (POR) as the most influential predictors. Machine learning models, particularly Random Forest, provide a robust and generalizable tool for the early prediction of POD after HVR. These models outperform traditional statistical approaches and offer interpretable insights, highlighting PAT as a critical, modifiable target for perioperative management to mitigate POD risk. • Machine learning models, particularly random forest, outperformed traditional nomogram model in predicting postoperative delirium after HVR, offering enhanced accuracy for clinical risk stratification. • Eight independent risk factors (age, surgery type, minimum during CPB, PMVT, PAT, POR, postoperative IL-6 and AKI) and one protective factor (heart rate on admission to CSICU) were identified, with prolonged PAT most strongly associated with POD. • Postoperative awakening time was considered the key management target for high-risk patients of POD by both machine leaning and traditional statistical methods, guiding perioperative management to reduce complications following HVR.
Li et al. (Sat,) conducted a cohort in Postoperative delirium after heart valve replacement with cardiopulmonary bypass (n=1,357). Random Forest machine learning model vs. Traditional logistic regression-based nomogram was evaluated on Prediction of postoperative delirium (AUROC) (95% CI 0.764-0.945). The Random Forest machine learning model outperformed a traditional nomogram in predicting postoperative delirium after heart valve replacement (AUROC 0.854, 95% CI 0.764-0.945 vs 0.754).