Early prediction of severe RSV-associated ALRTI in Asian pediatric patients: a simple nomogram.

OBJECT: An effective predictive model composed of common clinical indices for assessing the risk of severe respiratory syncytial virus (RSV) associated acute lower respiratory tract infection (ALRTI) is crucial for timely intervention and improving prognosis. However, currently, this kind of model is limited. METHOD: Total of 600 pediatric RSV participants, including 185 severe RSV-associated ALRTI cases, were enrolled. Univariate and multivariate logistic regressions identified independent predictors and developed a series of predictive models. The area under the receiver operating characteristic curve (AUROC), calibration plot, decision curve analysis, net reclassification index and integrated discrimination index were used to screen the best model, visualized as a nomogram. Stratification analysis by gender and RSV type was conducted to optimize the model’s clinical applicability, while both an internal and an external validation were used to verify its predictive efficiency. RESULT: Age, red blood cell count (RBC), alanine aminotransferase (ALT), and prematurity were identified as candidate predictors for severe RSV-associated ALRTI. Four predictive models were constructed in the training set, including AP (Age, Prematurity), APA (Age, Prematurity, ALT), ARA (Age, RBC, ALT), and ARAP (Age, RBC, ALT, Prematurity), with AUROC values of 0.827, 0.824, 0.826, and 0.828, respectively. ARAP achieved the highest AUROC in both internal (0.784) and external (0.770) validations. Using stratification analysis, the male RSV-A subgroup was identified as the optimum application scope of ARAP with the highest AUROC of 0.896.

CONCLUSION

ARAP is a simple and effective tool for assessing the risk of severe RSV-associated ALRTI, especially in the male RSV-A subgroup. CLINICAL TRIAL NUMBER: Not applicable.