Key result
Adding CPET to machine learning models improves postoperative morbidity prediction ~20% versus clinical data alone.
Why the study?
Existing preoperative risk scores often lack local calibration, input data diversity, or individual precision, whereas machine learning models may overcome these limitations.
Does integrating cardiorespiratory fitness data from CPET into machine learning models improve the prediction of postoperative morbidity in elderly patients undergoing major elective surgery?
Observational (n=1,190)
Does integrating cardiorespiratory fitness data from CPET into machine learning models improve the prediction of postoperative morbidity in elderly patients undergoing major elective surgery?
Integrating CPET-derived cardiorespiratory fitness data into advanced machine learning models significantly improves the accuracy of predicting early postoperative morbidity in elderly surgical patients.
May aid preoperative risk stratification in elderly surgery patients; hypothesis-generating for CPET-augmented ML models pending validation.
Accurate preoperative risk assessment is of great value to both patients and clinical teams. Several risk scores have been developed but are often not calibrated to the local institution, limited in terms of data input into the underlying models, and/or lack individual precision. Machine Learning (ML) models have the potential to address limitations in existing scoring systems. A database of 1190 elderly patients who underwent major elective surgery was analyzed retrospectively. Preoperative cardiorespiratory fitness data from cardiopulmonary exercise testing (CPET), demographic and clinical data were extracted and integrated into advanced machine learning (ML) algorithms. Multi-Objective-Symbolic-Regression (MOSR), a novel algorithm utilizing Genetic Programming to generate mathematical formulae for learning tasks, was employed to predict patient morbidity at Postoperative Day 3, as defined by the PostOperative Morbidity Survey (POMS). Shapley-Additive-exPlanations (SHAP) was subsequently used to analyze feature contributions. Model performance was benchmarked against existing risk prediction scores, namely the Portsmouth-Physiological-and-Operative-Severity-Score-for-the-Enumeration-of-Mortality-and-Morbidity (PPOSSUM) and the Duke-Activity-Status-Index, as well as linear regression using CPET features. A model was also developed for the same task using data directly extracted from the CPET time-series. The incorporation of cardiorespiratory fitness data enhanced the performance of all models for predicting postoperative morbidity by 20% compared to sole reliance on clinical data. Cardiorespiratory fitness features demonstrated greater importance than clinical features in the SHAP analysis. Models utilizing data taken directly from the CPET time-series demonstrated a 12% improvement over the cardiorespiratory fitness models. MOSR model surpassed all other models in every experiment, demonstrating excellent robustness and generalization capabilities. Integrating cardiorespiratory fitness data with ML models enables improved preoperative prediction of postoperative morbidity in elective surgical patients. The MOSR model stands out for its capacity to pinpoint essential features and build models that are both simple and accurate, showing excellent generalizability.
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Arina et al. (2025) conducted an observational in Elderly patients undergoing major elective surgery (n=1,190). Machine learning models incorporating cardiorespiratory fitness data from CPET vs. Models relying solely on clinical data was evaluated on Patient morbidity at Postoperative Day 3 (defined by POMS). Incorporating cardiorespiratory fitness data from CPET into machine learning models enhanced the prediction of postoperative morbidity by 20% compared to sole reliance on clinical data.
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