Machine learning−derived multivariable predictors of postcardiotomy cardiogenic shock in high-risk cardiac surgery patientsCentral MessagePerspective

Abstract

Objective: To develop a model for preoperatively predicting postcardiotomy cardiogenic shock (PCCS) in patients with poor left ventricular (LV) function undergoing cardiac surgery. Methods: From the Society of Thoracic Surgeons Adult Cardiac Database, 11,493 patients with LV ejection fraction ≤35% underwent isolated on-pump surgery from 2018 through 2019, of whom 3428 experienced PCCS. In total, 68 preoperative clinical variables were considered in machine-learning algorithms trained and optimized using scikit-learn software. Results: Compared with patients with ideal recovery, those that did were younger (65 vs 67 years), more likely female, Black, with low LV ejection fraction (26.5 vs 28.9%), previous myocardial infarction, chronic lung disease, diabetes, reoperation, or advanced heart failure. Among those with PCCS versus ideal recovery, operative mortality was 27% (925/3428) versus 0.1% (5/8065). PCCS occurred more often after coronary artery bypass grafting with concomitant mitral valve repair or after longer perfusion and clamp times. Reliable preoperative PCCS predictors were more advanced cardiac, liver, and renal failure; frailty; and greater white cell count. Out of sample test set receiver operating curve achieved an area under the curve of 0.74 with acceptable calibration Hosmer-Lemeshow statistic χ2 = 1.33, P = .25. Conclusions: In patients with severe LV dysfunction undergoing cardiac surgery, risk of PCCS is elevated by preoperative failure of other organ systems and complexity of the planned operation that prolongs myocardial ischemia and cardiopulmonary bypass. This risk calculator could serve as an important tool to preoperatively identify patients in need of advanced levels of support.

Keywords

• CABG
• mitral valve surgery
• LV dysfunction
• machine learning
• postcardiotomy shock
• STS ACSD