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Updated: Feb 25, 2026
An Experimental Paradigm for the Prediction of Post-Operative Pain PPOP
Published on: January 27, 2010
El valor predictivo del aprendizaje automático para el delirio posoperatorio en cirugía cardíaca: revisión
Yi Guo1, Hong Xu2, Ankui Wang1
1Department of Anesthesiology, Norinco General Hospital, Xi'an, China.
Background:
Postoperative delirium (POD) following cardiac surgery is a severe complication, and early identification of delirium risk remains a challenge in clinical practice. While machine learning (ML) has garnered increasing attention in health care applications, effective early prediction tools remain limited in current clinical practice. Recent investigations have explored the effectiveness of ML-based methods for identifying the risk of POD in patients undergoing cardiac surgery. However, more evidence is required to validate the feasibility of these methods.
Objectives:
This study aims to ascertain the performance of ML in identifying the risk of POD following cardiac surgery, providing evidence for the development or updating of future ML-based assessment tools.
Methods:
A comprehensive literature search was conducted across 4 databases-PubMed, the Cochrane Library, Embase, and Web of Science-through August 30, 2024, to identify studies investigating individual POD risk prediction using ML approaches and nomograms. The risk of bias of the models in the included studies was assessed leveraging the Prediction Model Bias Risk Assessment Tool. Subgroup analyses were performed based on datasets, validation methods, study types, risk of bias, and model types.
Results:
The analysis incorporated 28 original studies comprising 80,143 patients undergoing cardiac surgery, of whom 6326 developed POD. Meta-analysis revealed that, in validation datasets, the c-index, sensitivity, and specificity for delirium prediction reached 0.805 (95% CI 0.759-0.852), 0.72 (95% CI 0.65-0.79), and 0.78 (95% CI 0.71-0.83), respectively. Logistic regression was the primary modeling method. In validation datasets, the c-index, sensitivity, and specificity reached 0.773 (95% CI 0.724-0.823), 0.73 (95% CI 0.64-0.80), and 0.70 (95% CI 0.65-0.74), respectively.
Conclusions:
ML-based prediction tools for POD following cardiac surgery demonstrate promising performance. However, the limited number of studies and validation approaches necessitate cautious interpretation of these findings. Future multicenter studies are warranted to develop more robust ML-based prediction tools, enabling precise risk stratification and targeted preventive interventions for POD.
