Key result
Perioperative risk scores and machine learning show promise but require further validation for clinical utility.
Why the study?
Established perioperative risk scores often fail to generalize well to new data, and the clinical utility of recent machine learning advances has yet to be consistently demonstrated.
This review highlights that while newer perioperative risk scores and machine learning models show improved predictive performance, robust validation and interpretability are needed for clinical utility.
May inform perioperative risk model development; leaves open clinical adoption pending prospective validation.
PURPOSE OF REVIEW: Perioperative risk scores aim to risk-stratify patients to guide their evaluation and management. Several scores are established in clinical practice, but often do not generalize well to new data and require ongoing updates to improve their reliability. Recent advances in machine learning have the potential to handle multidimensional data and associated interactions, however their clinical utility has yet to be consistently demonstrated. In this review, we introduce key model performance metrics, highlight pitfalls in model development, and examine current perioperative risk scores, their limitations, and future directions in risk modelling. RECENT FINDINGS: Newer perioperative risk scores developed in larger cohorts appear to outperform older tools. Recent updates have further improved their performance. Machine learning techniques show promise in leveraging multidimensional data, but integrating these complex tools into clinical practice requires further validation, and a focus on implementation principles to ensure these tools are trusted and usable. SUMMARY: All perioperative risk scores have some limitations, highlighting the need for robust model development and validation. Advancements in machine learning present promising opportunities to enhance this field, particularly through the integration of diverse data sources that may improve predictive performance. Future work should focus on improving model interpretability and incorporating continuous learning mechanisms to increase their clinical utility.
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Bedford et al. (2024) conducted a review in Perioperative risk. Perioperative risk scores and machine learning models was evaluated. Perioperative risk scores and machine learning techniques show promise for risk stratification, but require further validation and focus on implementation principles to ensure clinical utility.
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