Development of a Nomogram for Predicting Massive Necrotizing Pneumonia in Children.

Objective: This study aimed to develop a nomogram model for predicting massive necrotizing pneumonia (NP) in children. Methods: A total of 282 children with NP admitted to Kunming Children's Hospital from January 2014 to November 2022 were enrolled. The children with NP were divided into massive necrotizing pneumonia (MNP) group and non-MNP group according to the severity of the lung necrosis. The clinical data of the children were collected, and least absolute shrinkage and selection operator (LASSO) regression and multivariate logistic regression models were used to analyze the influencing factors of MNP. A nomogram model was constructed, and its predictive efficacy was evaluated. Results: The predictors selected by LASSO regression analysis were: haematogenous spread, white blood cell (WBC), hemoglobin (Hb), C-reactive protein (CRP), lactate dehydrogenase (LDH), and activated partial thromboplastin time (APTT) (P < 0.05). Based on the above independent influencing factors, a nomogram model for MNP was constructed. The bootstrap method was used to repeat sampling 1000 times. The results showed that the consistency index of the nomogram model in predicting MNP was 0.833 in the training set and 0.810 in the validation set. The results of ROC curve analysis showed that the area under the receiver-operating-characteristic curve (AUC) of the nomogram model for predicting MNP was 0.889 [95% CI (0.818, 0.959)] in the training set and 0.814 [95% CI (0.754, 0.874)] in the validation set. The calibration curve of the nomogram predicting MNP was basically close to the actual curve. The decision curve showed that the nomogram had good clinical utility. Conclusion: We developed a nomogram for predicting MNP, which can help clinicians identify the severity of lung necrosis early.