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Updated: May 9, 2025
Establishing a Competing Risk Regression Nomogram Model for Survival Data
Published on: October 23, 2020
Development and validation of a nomogram for predicting severe adenovirus pneumonia in children
1Department of Pediatric Respiratory Medicine, Tianjin Children's Hospital (Children's Hospital, Tianjin University), Tianjin Key Laboratory of Birth Defects for Prevention and Treatment, Tianjin, China.
Insights
A new nomogram effectively predicts severe adenovirus pneumonia (SAP) in children, identifying atelectasis, LDH, and IL-6 as key risk factors. This tool aids in early diagnosis and clinical management of pediatric adenoviral pneumonia.
Area of Science:
- Pediatric Respiratory Medicine
- Infectious Diseases
- Clinical Diagnostics
Background:
- Adenovirus is a significant respiratory pathogen in children, with severe cases potentially leading to serious complications.
- Severe Adenovirus Pneumonia (SAP) poses a considerable risk to pediatric patients.
- Current assessment methods for adenoviral pneumonia severity are often empirical and lack precision.
Purpose of the Study:
- To develop and validate a nomogram-based predictive model for quantifying the risk of Severe Adenovirus Pneumonia (SAP) in children.
- To identify independent predictors associated with the development of SAP.
- To provide a simple, intuitive, and effective tool for clinical decision-making in pediatric adenoviral pneumonia cases.
Main Methods:
- A retrospective study of 228 children with adenoviral pneumonia was conducted.
- Patients were categorized into SAP and general adenoviral pneumonia (GAP) groups based on clinical manifestations.
- Univariate and multivariate logistic regression analyses were employed to identify significant predictors, followed by nomogram construction and validation using calibration curves, ROC curves, and clinical decision curves.
Main Results:
- Patients with SAP exhibited longer durations of fever and complications compared to the GAP group.
- Independent predictors for SAP included atelectasis (OR=2.757), FER (OR=2.232), IL-6 (OR=2.001), and LDH (OR=2.860).
- The predictive model demonstrated good performance with an AUC of 0.873 in the training set and 0.738 in the validation set, indicating high clinical practicability.
Conclusions:
- Atelectasis, lactate dehydrogenase (LDH), and interleukin-6 (IL-6) are significant predictive factors for Severe Adenovirus Pneumonia (SAP).
- The developed clinical predictive model, presented as a nomogram, offers a valuable tool for the early and efficient assessment of SAP.
- This nomogram can aid clinicians in guiding treatment strategies for pediatric adenoviral pneumonia.
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.
