Development of a prediction model integrating cardiac ultrasound parameters for cardiac complications after distal cholangiocarcinoma surgery: a retrospective cohort study

Patients undergoing pancreaticoduodenectomy for distal cholangiocarcinoma (dCCA) face a substantial risk of major postoperative cardiac complications (MPCC), which significantly impact mortality and recovery. Existing risk assessment tools lack objective cardiac functional parameters. This study aimed to develop and validate a novel prediction model integrating preoperative cardiac ultrasound parameters to individually predict the risk of MPCC within 30 days after dCCA surgery. A retrospective cohort study was conducted on 154 dCCA patients who underwent radical pancreaticoduodenectomy. Univariate and multivariate binary logistic regression analyses were performed to identify independent predictors of MPCC from clinical variables and preoperative transthoracic echocardiography parameters. A nomogram model was constructed based on the identified independent predictors. The model’s discrimination, calibration, and clinical utility were assessed using the area under the receiver operating characteristic curve (AUC), calibration curves, and decision curve analysis (DCA), with internal validation via bootstrapping. The incidence of MPCC was 34.4% (53/154). Multivariate analysis identified preoperative B-type natriuretic peptide (BNP), left ventricular ejection fraction (LVEF), left ventricular mass index (LVMI), and left atrial volume index (LAVI) as independent predictors. A nomogram incorporating these four factors was developed. The model demonstrated excellent discrimination, with an AUC of 0.894 (95% CI: 0.838–0.950). Calibration curves showed good agreement between predicted and observed probabilities. DCA confirmed the model’s clinical net benefit across a wide range of threshold probabilities. This study presents a robust nomogram that effectively integrates cardiac ultrasound parameters (LVEF, LVMI, LAVI) and BNP to preoperatively predict the risk of cardiac complications following dCCA surgery. The model offers superior individualized risk stratification compared to traditional tools, potentially facilitating optimized perioperative management for high-risk patients.