Rogers Michael P, Janjua Haroon, Fishberger Gregory, Harish Abhinav, Sujka Joseph, Toloza Eric M, DeSantis Anthony J, Hooker Robert L, Pietrobon Ricardo, Lozonschi Lucian, Kuo Paul C
Department of Surgery, University of South Florida Morsani College of Medicine, Tampa, Florida, USA.
Department of Oncologic Sciences, University of South Florida Morsani College of Medicine, Tampa, Florida, USA.
J Card Surg. 2022 Dec;37(12):4612-4620. doi: 10.1111/jocs.17110. Epub 2022 Nov 8.
In patients undergoing high-risk cardiac surgery, the uncertainty of outcome may complicate the decision process to intervene. To augment decision-making, a machine learning approach was used to determine weighted personalized factors contributing to mortality.
American College of Surgeons National Surgical Quality Improvement Program was queried for cardiac surgery patients with predicted mortality ≥10% between 2012 and 2019. Multiple machine learning models were investigated, with significant predictors ultimately used in gradient boosting machine (GBM) modeling. GBM-trained data were then used for local interpretable model-agnostic explanations (LIME) modeling to provide individual patient-specific mortality prediction.
A total of 194 patient deaths among 1291 high-risk cardiac surgeries were included. GBM performance was superior to other model approaches. The top five factors contributing to mortality in LIME modeling were preoperative dialysis, emergent cases, Hispanic ethnicity, steroid use, and ventilator dependence. LIME results individualized patient factors with model probability and explanation of fit.
The application of machine learning techniques provides individualized predicted mortality and identifies contributing factors in high-risk cardiac surgery. Employment of this modeling to the Society of Thoracic Surgeons database may provide individualized risk factors contributing to mortality.
在接受高风险心脏手术的患者中，预后的不确定性可能会使干预决策过程变得复杂。为了加强决策，采用了一种机器学习方法来确定导致死亡的加权个性化因素。
查询美国外科医师学会国家外科质量改进计划中2012年至2019年间预测死亡率≥10%的心脏手术患者。研究了多种机器学习模型，最终将显著预测因素用于梯度提升机（GBM）建模。然后，将经过GBM训练的数据用于局部可解释模型无关解释（LIME）建模，以提供个体患者特定的死亡率预测。
1291例高风险心脏手术中共有194例患者死亡。GBM的表现优于其他模型方法。LIME建模中导致死亡的前五个因素是术前透析、急诊病例、西班牙裔、使用类固醇和呼吸机依赖。LIME结果通过模型概率和拟合解释对患者因素进行了个体化。
机器学习技术的应用提供了个体化的预测死亡率，并确定了高风险心脏手术中的影响因素。将这种建模应用于胸外科医师协会数据库可能会提供导致死亡的个体化风险因素。
