Development and validation of a nomogram for predicting severe adenovirus pneumonia in children.

Background: Adenovirus is a common respiratory pathogen in children. Severe adenovirus pneumonia(SAP) can cause serious complications in children. In this study, The nomogram we developed quantifies the severity of adenoviral pneumonia into percentage risk in a scientific, simple, intuitive, and effective manner, showing unique advantages compared to current empirical assessments and chart evaluations. Methods: 228 children with adenoviral pneumonia admitted to the Respiratory Department of Tianjin Children's Hospital from January 2020 to January 2024 were collected. According to the clinical manifestations, the patients were divided into SAP (SAP) group and general adenoviral pneumonia (GAP) group. The clinical manifestations, laboratory indexes and some imaging data of the two groups were observed. Univariate and multivariate logical regression were used to select the variables of SAP. Select the prediction factor, construct the prediction model, and express the prediction factor with nomogram. Calibration curve, ROC curve and clinical decision curve were used to evaluate the performance and clinical practicability of the prediction model. Results: The time of fever and complications in SAP group were longer than those in GAP group. The data of diagnosis and prediction of adenoviral pneumonia and clinical significance were included in logical regression. Univariate logical regression was performed first, followed by multivariate logical regression, atelectasis (OR = 2.757; 95%CI, 1.454-5.34), FER (OR = 2.232; 95%CI, 1.442-3.536), IL-6 (OR = 2.001; 95%CI, 1.368-3.009), LDH (OR = 2.860; 95%CI, 1.839-4.680) were independent significant predictors of SAP. The probability of prediction is consistent with that of observation in the training queue (0.819) and the verification queue (0.317). The area under the ROC curve of the model group and verification group was 0.873 (95%CI: 0.82-0.926) and 0.738 (95%CI: 0.620-0.856), respectively. The clinical decision curve indicated that the prediction model had high clinical practicability. Conclusion: Atelectasis, LDH and IL-6 are predictive factors of SAP. The construction of clinical predictive model nomogram plays a key role in simple and efficient judgment of the occurrence and development of SAP, and has value in guiding clinical treatment.