Arina Pietro, Ferrari Davide, Kaczorek Maciej R, Tetlow Nicholas, Dewar Amy, Stephens Robert, Martin Daniel, Moonesinghe Ramani, Singer Mervyn, Whittle John, Mazomenos Evangelos B
Bloomsbury Institute of Intensive Care Medicine, University College London, London, United Kingdom.
Human Physiology and Performance Laboratory, Centre for Perioperative Medicine, Department of Targeted Intervention, University College London, London, United Kingdom.
PLOS Digit Health. 2025 May 16;4(5):e0000851. doi: 10.1371/journal.pdig.0000851. eCollection 2025 May.
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.
准确的术前风险评估对患者和临床团队都具有重要价值。已经开发了几种风险评分系统，但这些系统往往未针对当地机构进行校准，基础模型的数据输入有限，和/或缺乏个体精准度。机器学习（ML）模型有潜力解决现有评分系统的局限性。对1190例接受大型择期手术的老年患者数据库进行了回顾性分析。提取了心肺运动试验（CPET）的术前心肺适能数据、人口统计学和临床数据，并将其整合到先进的机器学习（ML）算法中。采用多目标符号回归（MOSR）算法，这是一种利用遗传编程为学习任务生成数学公式的新型算法，用于预测术后第3天的患者发病率，发病率由术后发病率调查（POMS）定义。随后使用Shapley值加法解释（SHAP）来分析特征贡献。将现有风险预测评分，即朴茨茅斯生理和手术严重程度评分系统（用于死亡率和发病率枚举，PPOSSUM）和杜克活动状态指数，以及使用CPET特征的线性回归作为基准，对模型进行评估。还使用直接从CPET时间序列中提取的数据针对相同任务开发了一个模型。与单纯依赖临床数据相比，纳入心肺适能数据可使所有预测术后发病率的模型性能提高20%。在SHAP分析中，心肺适能特征比临床特征更为重要。使用直接从CPET时间序列获取数据的模型比心肺适能模型表现出12%的提升。在每个实验中，MOSR模型都超越了所有其他模型，展现出出色的稳健性和泛化能力。将心肺适能数据与ML模型相结合，能够改善择期手术患者术后发病率的术前预测。MOSR模型因其能够精准确定关键特征并构建简单准确且具有出色泛化能力的模型而脱颖而出。
