ORCID

Abstract

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.

Publication Date

2025-05-16

Publication Title

PLOS Digital Health

Volume

4

Issue

5

Acceptance Date

2025-04-05

Deposit Date

2025-10-14

Funding

Pietro Arina is supported by funds from the Cleveland Clinic London Hospital, London, United Kingdom and by the Mittal Fund at Cleveland Clinic Philanthropy (UK). Evangelos B. Mazomenos is supported by the Wellcome Trust and Engineering and Physical Sciences Research Council under grant Nos. EP/Z534754/1, 203145Z/16/Z and NS/A000050/1. Davide Ferrari is funded and supported by King’s College London and DRIVE-Health, KCL funded Centre for Doctoral Training (CDT) in Data-Driven Health. John Whittle is supported by funds from the University College London Hospitals National Institute of Health Research Biomedical Research Centre Critical and Perioperative Care theme and in part by an International Anesthesia Research Society Mentored Research Grant. Funding: This work was supported by the Cleveland Clinic London Hospital, London, UK, and the Mittal Fund at Cleveland Clinic Philanthropy (UK) (to PA); by King’s College London and DRIVE-Health, a KCL-funded Centre for Doctoral Training in Data-Driven Health (to DF); by the University College London Hospitals National Institute of Health Research Biomedical Research Centre Critical and Perioperative Care theme and in part by an International Anaesthesia Research Society Mentored Research Grant (to JW); and by the Wellcome/EPSRC Centre for Interventional and Surgical Sciences at University College London (to EM). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. This work was supported by the Cleveland Clinic London Hospital, London, UK, and the Mittal Fund at Cleveland Clinic Philanthropy (UK) (to PA); by King’s College London and DRIVE-Health, a KCL-funded Centre for Doctoral Training in Data-Driven Health (to DF); by the University College London Hospitals National Institute of Health Research Biomedical Research Centre Critical and Perioperative Care theme and in part by an International Anaesthesia Research Society Mentored Research Grant (to JW); and by the Wellcome/EPSRC Centre for Interventional and Surgical Sciences at University College London (to EM). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Pietro Arina is supported by funds from the Cleveland Clinic London Hospital, London, United Kingdom and by the Mittal Fund at Cleveland Clinic Philanthropy (UK). Evangelos B. Mazomenos is supported by the Wellcome Trust and Engineering and Physical Sciences Research Council under grant Nos. EP/Z534754/1, 203145Z/16/Z and NS/A000050/1. Davide Ferrari is funded and supported by King’s College London and DRIVE-Health, KCL funded Centre for Doctoral Training (CDT) in Data-Driven Health. John Whittle is supported by funds from the University College London Hospitals National Institute of Health Research Biomedical Research Centre Critical and Perioperative Care theme and in part by an International Anesthesia Research Society Mentored Research Grant.

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