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Establishing a Competing Risk Regression Nomogram Model for Survival Data
Published on: October 23, 2020
Establishment of a predictive nomogram and its validation for severe adenovirus pneumonia in children
Yang Shen1, Yao Zhou1, Cuian Ma1
1Department of Infectious Disease, Tianjin Children's Hospital/Tianjin University Children's Hospital, Tianjin, China.
Insights
A new nomogram can predict the severity of severe adenovirus pneumonia (SAP) in children. This tool aids in personalized treatment plans for better outcomes in pediatric SAP cases.
Area of Science:
- Pediatric Infectious Diseases
- Critical Care Medicine
- Biostatistics
Background:
- Severe adenovirus pneumonia (SAP) in children presents significant risks, including multi-system complications, high mortality, and long-term sequelae.
- Accurate severity prediction is crucial for tailoring individualized treatment strategies in pediatric SAP.
- This study aimed to develop and validate a predictive nomogram for children diagnosed with SAP.
Purpose of the Study:
- To develop a predictive nomogram for assessing the severity of severe adenovirus pneumonia in children.
- To evaluate the nomogram's discrimination, accuracy, and clinical utility for individualized risk stratification.
Main Methods:
- A retrospective observational study design was employed, with data split into training (70%) and validation (30%) sets.
- Lasso logistic regression was utilized for predictor selection, followed by nomogram construction.
- Nomogram performance was assessed using ROC curves, calibration curves, and decision curve analysis (DCA).
Main Results:
- Fever duration, interleukin-6 levels, and CD4+ T cell counts were identified as key predictors and incorporated into the nomogram.
- The nomogram demonstrated good discrimination, with an AUC of 0.79 in the training set and 0.76 in the test set.
- Decision curve analysis indicated potential clinical usefulness of the developed nomogram.
Conclusions:
- A novel nomogram has been developed for the individualized prediction of severe adenovirus pneumonia severity in children.
- This tool shows potential for effective clinical application in managing pediatric SAP.
- Further validation may enhance its role in personalized pediatric critical care.
Background:
Severe adenovirus pneumonia (SAP) of children is prone to multi-system complications, has the high mortality rate and high incidence of sequelae. Severity prediction can facilitate an adequate individualized treatment plan. Our study try to develop and evaluate a predictive nomogram for children with SAP.
Methods:
An observational study was designed and performed retrospectively. The data were categorized as training and validation datasets using the method of credible random split-sample (split ratio =0.7:0.3). The predictors were selected using Lasso (least absolute shrinkage and selection operator) logistic regression and the nomogram was developed. Nomogram discrimination was assessed using the receiver operating characteristic (ROC) curve, and the prediction accuracy was evaluated using a calibration curve. The nomogram was also evaluated for clinical effectiveness by the decision curve analysis (DCA). A P value of <0.05 was deemed statistically significant.
Results:
The identified predictors were fever duration, and interleukin-6 and CD4+ T cells and were assembled into the nomogram. The nomogram exhibited good discrimination with area under ROC curve in training dataset (0.79, 95% CI: 0.60-0.92) and test dataset (0.76, 95% CI: 0.63-0.87). The nomogram seems to be useful clinically as per DCA.
Conclusions:
A nomogram with a potentially effective application was developed to facilitate individualized prediction for SAP in children.
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