Multiparameter predictor model for Low Cardiac Output Syndrome After Pericardiectomy

Background Low cardiac output syndrome (LCOS) following pericardiectomy in patients with constrictive pericarditis significantly contributes to perioperative morbidity mortality. Predictive models specific to LCOS after pericardiectomy are currently lacking. This study aimed to identify independent risk factors for LCOS following pericardiectomy and develop a predictive model to guide clinical decision-making. Methods A retrospective cohort of 190 patients with constrictive pericarditis undergoing isolated pericardiectomy were divided into LCOS group (57 cases) and non-LCOS group (133 cases). Univariate and multivariate logistic regression analyses were performed to identify independent risk factors. A predictive model was developed and validated. Model performance was evaluated using receiver operating characteristic (ROC) curve, Hosmer-Lemeshow test, and 10-fold cross-validation. A visual nomogram was constructed for clinical application. Results Multivariate logistic regression identified preoperative New York Heart Association function (NYHA) classification, white blood cell counts (WBC), left ventricular end-diastolic dimension(LVEDD), and early diastolic trans-mitral velocity to mitral annular early diastolic velocity ratio (E/e' ratio) as independent risk factors. The model showed excellent performance (AUC = 0.951, P < 0.001; Hosmer-Lemeshow P = 0.176). Ten-fold cross-validation yielded 85.7% accuracy, 92.6% sensitivity, and 82.7% specificity. The nomogram demonstrated good calibration (bootstrap C-index = 0.951; corrected = 0.945). Conclusions Preoperative NYHA class, elevated WBC, reduced LVEDD, and decreased E/e' ratio predict LCOS after pericardiectomy in constrictive pericarditis. The model exhibits high clinical utility for early risk stratificaiton.