Development and Validation of a Nomogram Model to Predict the Risk of Severe Pneumonia in Children with Pneumococcal Infection

Duoduo Li,1 Xixia Guo,1 Xiaolu Zhao,2 Li Wang,1 Xinyan Jia,2 Lingchao Wang,1 Weihong Lu,1 Xiangtao Wu,1 Fenglian Zhu1 1Department of Pediatrics, the First Affiliated Hospital of Henan Medical University, Xinxiang, Henan Province, 453100, People’s Republic of China; 2Department of Nephrology, the First Affiliated Hospital of Henan Medical University, Xinxiang, Henan Province, 453100, People’s Republic of ChinaCorrespondence: Duoduo Li, Department of Pediatrics, the First Afliated Hospital of Henan Medical University, No. 88 of Jiankang Road, Weihui, Xinxiang, Henan Province, 453100, People’s Republic of China, Tel +86 0373-4403483, Fax +860373-4402573, Email li4duo2uo4uo5a@126.com Fenglian Zhu, Department of Pediatrics, the First Afliated Hospital of Henan Medical University, No. 88 of Jiankang Road, Weihui, Xinxiang, Henan Province, 453100, People’s Republic of China, Tel +86 0373-4403483, Fax +860373-4402573, Email fenglianzhu33@21cn.comBackground: Streptococcus pneumoniae is a leading cause of bacterial pneumonia in children, with severe cases rapidly progressing to respiratory failure and multiple organ dysfunction. Accurate early risk stratification tools are urgently needed. This study aimed to develop and validate a nomogram model to predict the risk of PICU admission in children with pneumococcal pneumonia.Methods: A retrospective cohort of 485 children diagnosed with pneumococcal pneumonia (August 2018–August 2023) was randomly divided into a training set (n=339) and a validation set (n=146) in a 7:3 ratio. Independent predictors of PICU admission were identified using univariate and multivariate logistic regression. A nomogram was constructed based on the training set and evaluated using ROC curves, calibration curves, Hosmer-Lemeshow tests, decision curve analysis (DCA), and SHAP analysis.Results: Multivariate analysis identified nine independent predictors: cardiovascular abnormalities, electrolyte disturbances, elevated neutrophil percentage, prolonged wheezing duration, decreased albumin, decreased hemoglobin, and elevated CT score were risk factors, while prolonged fever and cough duration were protective factors. The nomogram achieved an AUC of 0.92 (95% CI: 0.89– 0.95) in the training set and 0.87 (95% CI: 0.81– 0.93) in the validation set. Calibration was satisfactory on the Hosmer-Lemeshow test, and DCA demonstrated net clinical benefit across a 5%– 95% threshold probability range. SHAP analysis identified cough duration, albumin, and cardiovascular abnormalities as the top contributing features.Conclusion: This nine-variable nomogram demonstrates high accuracy, good calibration, and strong interpretability, providing clinicians with a practical tool for early identification of children at high risk for PICU admission, supporting risk stratification and treatment decisions.Keywords: Streptococcus pneumoniae, children, severe pneumonia, nomogram, predictive model