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Establishing a Competing Risk Regression Nomogram Model for Survival Data
Published on: October 23, 2020
Development and validation of a nomogram to predict recurrence in children with Henoch-Schönlein purpura
Danyang Song1, Yajing Jiang2, Qiuju Zhao2
1Department of Children's Emergency, Cangzhou Central Hospital Cangzhou 061000, Hebei, China.
Insights
This study identifies key risk factors for Henoch-Schönlein Purpura (HSP) recurrence in children, including respiratory infections and abnormal urine. A validated nomogram aids in predicting and managing HSP recurrence.
Area of Science:
- Pediatric Nephrology
- Clinical Research
- Predictive Modeling
Background:
- Henoch-Schönlein Purpura (HSP) is a common vasculitis in children.
- Recurrence of HSP can lead to significant morbidity.
- Identifying predictors for HSP recurrence is crucial for effective management.
Purpose of the Study:
- To identify risk factors associated with HSP recurrence in pediatric patients.
- To develop and validate a predictive nomogram for HSP recurrence.
- To aid clinicians in risk stratification and patient counseling.
Main Methods:
- A cohort of 212 children with HSP was analyzed.
- Logistic regression models were used to identify risk factors.
- A nomogram was constructed and validated using ROC curves and calibration plots.
Main Results:
- Respiratory infection, lack of preventive medication/dietary restrictions, age, allergen sensitivity, and abnormal urine routine were significant risk factors for HSP recurrence.
- The developed nomogram demonstrated excellent accuracy in predicting recurrence.
- Calibration curves confirmed the model's reliability.
Conclusions:
- A validated clinical nomogram effectively predicts recurrence in children with HSP.
- Key independent risk factors for recurrence include respiratory infections and abnormal urine.
- The nomogram serves as a valuable tool for clinical decision-making in managing HSP.
Background:
To screen risk factors for the recurrence in children with Henoch-Schönlein Purpura (HSP) and to develop and validate a nomogram for recurrence in children with HSP.
Methods:
During September 2019 and September 2021, 212 children with HSP were selected in this study. The children were divided into two sets in a proportion of 7:3 using R language, with the first group as the training sets and the second as the internal validation sets. The related variables were analyzed by univariate and multivariate logistic regression analyses, and a nomogram for predicting the recurrence in HSP children was established. The nomogram was evaluated by ROC curve, calibration curve and decision curve, and 1000 times bootstrap resampling method was used to verify the model internally.
Results:
Univariate and multivariate regression analyses identified respiratory infection, without preventive medication and diet restriction, age, allergen positive and abnormal urine routine as risk factors for the recurrence in children with HSP. Those risk factors were used to construct a predictive nomogram. The calibration curves revealed excellent accuracy of the predictive nomogram model, internally and externally.
Conclusions:
We constructed and validated a clinical nomogram to predict the recurrence in children with HSP. We confirmed that respiratory tract infection, without preventive medication and diet restriction, age, allergen positive and abnormal urine routine were independent recurrence risk factors. This nomogram had a good performance in clinical decision-making.
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