基于性别分层构建儿童免疫性血小板减少症慢性化风险预测模型OA

Objective:The impact of sex on the prognosis of childhood immune thrombocytopenia(ITP)remains controversial,and the view that female sex is a high-risk factor for chronicity has not reached a universal consensus.This study aimed to construct sex-specific prediction models for chronic ITP through sex stratification,providing a basis for individualized prognosis assessment and early clinical intervention.Methods:This retrospective study enrolled 224 children initially diagnosed with ITP who were hospitalized in the Department of Pediatrics at The Affiliated Huai'an No.1 People's Hospital of Nanjing Medical University,between 1 January 2019 and 1 January 2023.The cohort included 128 males and 96 females.Demographic and clinical data were collected,with a follow-up period of at least one year.Multivariate logistic regression analysis was used to develop prediction models for chronic ITP separately for males and females.The performance of the models was evaluated using receiver operating characteristic(ROC)curves,calibration curves,and clinical decision curve analysis.Results:This study found that sex was an independent factor influencing the prognosis of childhood ITP(P<0.05).Sex-stratified prediction models were constructed.In the male model,absolute neutrophil count,neutrophil-to-lymphocyte ratio,platelet count on day 7 of treatment,complement C4,and bleeding grade were independent predictors of chronicity(P<0.05).The area under the ROC curve(AUC)for the combination of these five indicators was 0.879(95%CI:0.819-0.938).However,in the female model,absolute lymphocyte count,complement C3,platelet count on day 7 of treatment,and immunoglobulin G were independent predictors of chronicity(P<0.05),with a combined AUC of 0.902(95%CI:0.842-0.961).The calibration curves for both models were close to the ideal curve,and the clinical decision curves indicated a positive net clinical benefit within a threshold probability range of 0.10-0.70.Conclusion:Predictive factors for chronicity in childhood ITP showed significant sex differences.The sex-stratified prediction models demonstrated good predictive performance.