Journal of investigative surgery : the official journal of the Academy of Surgical Research · 2025
A Novel Nomogram for Preoperative Prediction of Early Postoperative Mortality in Patients Undergoing Surgical Revascularization for Acute Myocardial Infarction.
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Liu Yanyi, Yang Ning, Mei Ju, Wang Chao, Lin Zhengyu, Zou Yang, Qiu Shi, Ding Fangbao, Jiang Zhaolei
Аннотация
BACKGROUND: Despite advancements in surgical techniques, coronary artery bypass grafting (CABG) for patients with recent acute myocardial infarction (AMI) remains associated with relatively high mortality. Risk prediction in these patients is essential. The aim of this study was to develop a nomogram model to predict the early postoperative mortality in patients undergoing surgical revascularization for AMI based on preoperative clinical features.
METHOD: We retrospectively analyzed the clinical data of 332 consecutive patients who underwent CABG for AMI at our center from January 2018 to December 2024. Independent predictors for early postoperative death were identified by using univariate and multivariate logistic regression models. A nomogram prediction model was developed based on all independent predictors. Discriminative ability, calibration, and clinical utility of the model were evaluated. Internal validation was performed utilizing the bootstrapping method.
RESULTS: The nomogram model incorporated seven independent predictors: preoperative cardiac arrest, previous history of myocardial infarction(MI), left ventricular ejection fraction (LVEF) <50%, MI-to-CABG interval ≤ 3d, age > 75 years, serum albumin < 35g/L and serum creatinine > 2.0 mg/dL. The model achieved good discrimination with an area under the receiver operating characteristic curve (AUC) of 0.905 (95% CI: 0.832-0.978), and showed well-fitted calibration curves with Hosmer-Lemeshow test results (χ2 = 3.437, p = 0.944). Decision curve analysis indicated that the model can provide greater clinical net benefits compared to "operate-all" or "operate-none" strategies in a wide range of threshold probability.
CONCLUSIONS: The novel nomogram model combining seven preoperative clinical predictors can provide an accurate preoperative estimation of early postoperative death for AMI patients undergoing surgical revascularization, with satisfactory discrimination and calibration.
METHOD: We retrospectively analyzed the clinical data of 332 consecutive patients who underwent CABG for AMI at our center from January 2018 to December 2024. Independent predictors for early postoperative death were identified by using univariate and multivariate logistic regression models. A nomogram prediction model was developed based on all independent predictors. Discriminative ability, calibration, and clinical utility of the model were evaluated. Internal validation was performed utilizing the bootstrapping method.
RESULTS: The nomogram model incorporated seven independent predictors: preoperative cardiac arrest, previous history of myocardial infarction(MI), left ventricular ejection fraction (LVEF) <50%, MI-to-CABG interval ≤ 3d, age > 75 years, serum albumin < 35g/L and serum creatinine > 2.0 mg/dL. The model achieved good discrimination with an area under the receiver operating characteristic curve (AUC) of 0.905 (95% CI: 0.832-0.978), and showed well-fitted calibration curves with Hosmer-Lemeshow test results (χ2 = 3.437, p = 0.944). Decision curve analysis indicated that the model can provide greater clinical net benefits compared to "operate-all" or "operate-none" strategies in a wide range of threshold probability.
CONCLUSIONS: The novel nomogram model combining seven preoperative clinical predictors can provide an accurate preoperative estimation of early postoperative death for AMI patients undergoing surgical revascularization, with satisfactory discrimination and calibration.