A Risk Prediction Model for Adverse Events after Surgical Valve Replacement

Abstract

Background. Although several risk-predictive models for patients undergoing surgical valve replacement (SVR) have been published, reports on composite endpoints of adverse events in these patients are limited. This study aimed to establish a novel, easy-to-use prognostic prediction model of composite endpoints in patients following SVR. Methods. According to the inclusion criteria, patients with successful SVR were enrolled. Adverse events, including heart failure hospitalization, stroke, major bleeding, uncontrolled infection, secondary surgery, postoperative arrhythmia, and all-cause mortality during follow-up, were tracked. All datasets were randomly divided into the derivation and validation cohorts at a ratio of 7 to 3. Logistic regression analysis was used to screen for independent predictors and construct a nomogram for adverse events. We further presented a calibration curve and decision curve analysis for evaluating prediction models. Results. According to the multivariate logistic regression analyses, three variables were selected for the final predictive model, including platelet-to-lymphocyte ratio, diabetes mellitus, and albumin. A nomogram was then constructed to present the results. The C-index of the model was 0.73 (95% confidence interval: 0.65–0.81) for the derivation cohort and 0.75 (95% confidence interval: 0.64–0.86) for the validation cohort. The calibration curve demonstrated that the results of the nomogram agreed with actual observations (Brier score = 0.09). Conclusions. We developed an effective nomogram to predict the occurrence of composite adverse events in patients following SVR. This model could be used to evaluate the mid-term risks of adverse events as well as provide clinicians and patients with a basis for decision-making.