Preoperative hemorrhagic risk stratification in pediatric moyamoya disease: a multi-institutional propensity score-matched analysis

Background: Pediatric hemorrhagic moyamoya disease (MMD) is rare, and currently, no risk model exists for predicting preoperative bleeding. We aimed to develop a nomogram to predict the preoperative bleeding risk in children with MMD. Methods: We retrospectively analyzed data from 1,350 children diagnosed with MMD from January 2004 to December 2022 at our institution. After applying propensity score matching (PSM), 392 patients were selected for analysis, comprising 98 with hemorrhagic MMD and 294 with non-hemorrhagic MMD. The cohort was divided into training and internal validation cohorts. To construct the nomogram, variable selection was performed using the least absolute shrinkage and selection operator (LASSO), and the model was externally validated with an independent cohort of 70 children. We utilized multivariate logistic regression to determine odds ratios and 95% confidence intervals for preoperative bleeding risk. A predictive nomogram was then developed from the logistic model, with polynomial equations to quantify risk. The model’s effectiveness was evaluated using receiver operating characteristic (ROC) curves, calibration plots, and decision curve analyses (DCA). Inflection points for continuous variables were identified using restricted cubic spline (RCS) analysis. Results: The LASSO model demonstrated superior discriminative performance compared to six alternative models, achieving area under the curve (AUC) values of 91.5% in the training cohort, 78.4% in the internal validation cohort, and 91.2% in the external validation cohort. Based on variables selected through the LASSO model, we developed a nomogram incorporating three critical factors: age at onset (P = 0.001), anterior choroidal artery grades 1 (P = 0.047) and 2 (P<0.001), and posterior communicating artery grades 1 (P = 0.002) and 2 (P = 0.032). Calibration plots indicated strong concordance between predicted and observed outcomes across both training and validation cohorts (Hosmer-Lemeshow P = 0.503), affirming the model’s accuracy. Additionally, decision curve analysis (DCA) highlighted the nomogram’s clinical utility by effectively identifying patients at high risk. Restricted cubic spline (RCS) analysis revealed age 8 as a pivotal inflection point (P<0.05), marking a significant increase in the risk of preoperative bleeding beyond this age. Conclusion: The nomogram demonstrated high accuracy in predicting preoperative bleeding risk in pediatric patients with MMD. This predictive accuracy may enhance preoperative evaluation by surgeons, allowing for more proactive intervention and intensified monitoring of children at elevated risk of bleeding, thereby improving patient outcomes.