Establishment and validation of a prediction model for apnea on bronchiolitis

Abstract

ObjectiveThe objective of this study is to examine the risk factors associated with apnea in hospitalized patients diagnosed with bronchiolitis and to develop a nomogram prediction model for the early identification of patients who are at risk of developing apnea.MethodsThe clinical data of patients diagnosed with acute bronchiolitis and hospitalized at the Children's Hospital of Nanjing Medical University between February 2018 and May 2021 were retrospectively analyzed. LASSO regression and logistic regression analysis were used to determine the risk factors for apnea in these patients. A nomogram was constructed based on variables selected through multivariable logistic regression analysis. Receiver operating characteristic (ROC) curve and calibration curve were used to assess the accuracy and discriminative ability of the nomogram model, and decision curve analysis (DCA) was performed to evaluate the model's performance and clinical effectiveness.ResultsA retrospective analysis was conducted on 613 children hospitalized with bronchiolitis, among whom 53 (8.6%) experienced apnea. The results of Lasso regression and Logistic regression analyses showed that underlying diseases, feeding difficulties, tachypnea, WBC count, and lung consolidation were independent risk factors for apnea. A nomogram prediction model was constructed based on the five predictors mentioned above. After internal validation, the nomogram model demonstrated an AUC of 0.969 (95% CI 0.951–0.987), indicating strong predictive performance for apnea in bronchiolitis. Calibration curve analysis confirmed that the nomogram prediction model had good calibration, and the clinical decision curve analysis (DCA) indicated that the nomogram was clinically useful in estimating the net benefit to patients.ConclusionIn this study, a nomogram model was developed to predict the risk of apnea in hospitalized children with bronchiolitis. The model showed good predictive performance and clinical applicability, allowing for timely identification and intensified monitoring and treatment of high-risk patients to improve overall clinical prognosis.