Development and Validation of a Nomogram for Predicting Heparin Resistance in Neonates and Young Infants Undergoing Cardiac Surgery: A Retrospective Study

Heparin resistance (HR) is a common finding in pediatric cardiac surgery and generally refers to decreased sensitivity to heparin. Antithrombin (AT) deficiency is considered the primary mechanism of HR; however, the etiology of HR may be multifactorial. Early identification of HR might help optimize heparin anticoagulation management. This study aimed to develop a predictive nomogram for HR in neonates and young infants undergoing cardiac surgery.From January 2020 to August 2022, a total of 296 pediatric patients 1 to 180 days of age were included in this retrospective study. The patients were randomly divided into development and validation cohorts in a 7:3 ratio. Univariable logistic regression and the Least Absolute Shrinkage and Selection Operator (LASSO) regularization were used for variable selection. A multivariable logistic regression was performed to identify predictors and establish a nomogram to predict HR risk. Discrimination, calibration, and clinical usefulness were assessed in the development and validation cohorts.After the multistep variable selection, AT activity, platelet count, and fibrinogen were predictors for HR in neonates and young infants. The prediction model constructed using these 3 factors achieved an area under the receiver operating characteristic curve (ROC-AUC) of 0.874 and 0.873 in the development and validation cohorts. The Hosmer-Lemeshow test did not find evidence of a lack of fit (P = .768). The calibration curve of the nomogram was close to the ideal diagonal line. Furthermore, the model performed well in neonate and infant subgroups.A nomogram based on preoperative variables was developed to predict the HR risk in neonates and young infants undergoing cardiac surgery. This provides clinicians with a simple tool for the early prediction of HR, which may help optimize heparin anticoagulation strategies in this vulnerable patient population.