Romero-Velez Gustavo, Dang Jerry, Barajas-Gamboa Juan S, Lee-St John Terrence, Strong Andrew T, Navarrete Salvador, Corcelles Ricard, Rodriguez John, Fares Maan, Kroh Matthew
Endocrine and Metabolism Institute, Cleveland Clinic, Cleveland, OH, USA.
Digestive Disease and Surgery Institute, Cleveland Clinic, 9500 Euclid Avenue, Desk A100, Cleveland, OH, 44195, USA.
Surg Endosc. 2024 Jan;38(1):319-326. doi: 10.1007/s00464-023-10429-8. Epub 2023 Sep 25.
Machine learning (ML) is an emerging technology with the potential to predict and improve clinical outcomes including adverse events, based on complex pattern recognition. Major adverse cardiac events (MACE) after bariatric surgery have an incidence of 0.1% but carry significant morbidity and mortality. Prior studies have investigated these events using traditional statistical methods, however, studies reporting ML for MACE prediction in bariatric surgery remain limited. As such, the objective of this study was to evaluate and compare MACE prediction models in bariatric surgery using traditional statistical methods and ML.
Cross-sectional study of the MBSAQIP database, from 2015 to 2019. A binary-outcome MACE prediction model was generated using three different modeling methods: (1) main-effects-only logistic regression, (2) neural network with a single hidden layer, and (3) XGBoost model with a max depth of 3. The same set of predictor variables and random split of the total data (50/50) were used to train and validate each model. Overall performance was compared based on the area under the receiver operating curve (AUC).
A total of 755,506 patients were included, of which 0.1% experienced MACE. Of the total sample, 79.6% were female, 47.8% had hypertension, 26.2% had diabetes, 23.7% had hyperlipidemia, 8.4% used tobacco within 1 year, 1.9% had previous percutaneous cardiac intervention, 1.2% had a history of myocardial infarction, 1.1% had previous cardiac surgery, and 0.6% had renal insufficiency. The AUC for the three different MACE prediction models was: 0.790 for logistic regression, 0.798 for neural network and 0.787 for XGBoost. While the AUC implies similar discriminant function, the risk prediction histogram for the neural network shifted in a smoother fashion.
The ML models developed achieved good discriminant function in predicting MACE. ML can help clinicians with patient selection and identify individuals who may be at elevated risk for MACE after bariatric surgery.
机器学习（ML）是一项新兴技术，有潜力基于复杂的模式识别来预测和改善包括不良事件在内的临床结局。减肥手术后的主要不良心脏事件（MACE）发生率为0.1%，但具有显著的发病率和死亡率。先前的研究使用传统统计方法对这些事件进行了调查，然而，报道ML用于减肥手术中MACE预测的研究仍然有限。因此，本研究的目的是使用传统统计方法和ML评估和比较减肥手术中的MACE预测模型。
对2015年至2019年的MBSAQIP数据库进行横断面研究。使用三种不同的建模方法生成二元结局MACE预测模型：（1）仅主效应逻辑回归，（2）具有单个隐藏层的神经网络，以及（3）最大深度为3的XGBoost模型。使用同一组预测变量和总数据的随机分割（50/50）来训练和验证每个模型。基于受试者工作特征曲线（AUC）下的面积比较总体性能。
共纳入755,506例患者，其中0.1%发生MACE。在总样本中，79.6%为女性，47.8%患有高血压，26.2%患有糖尿病，23.7%患有高脂血症，8.4%在1年内吸烟，1.9%曾接受经皮心脏介入治疗，1.2%有心肌梗死病史，1.1%曾接受心脏手术，0.6%有肾功能不全。三种不同MACE预测模型的AUC分别为：逻辑回归为0.790，神经网络为0.798，XGBoost为0.
