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Development and Validation of a Predictive Screening Model for Congenital Heart Disease in High-Altitude Children
Xulin Hu1, Yangyan Liu2, Bo Li1
1Department of Neonatology, Shanghai Tongren Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, People's Republic of China.
Insights
This study developed a cost-effective three-tier screening model for early congenital heart disease (CHD) detection in high-altitude children. The model prioritizes risk factors like drug use, respiratory infections, and age for accurate diagnosis.
Area of Science:
- Pediatric Cardiology
- Public Health
- Biostatistics
Background:
- Standard congenital heart disease (CHD) screening faces challenges in high-altitude hypoxic environments, leading to missed or incorrect diagnoses.
- Plateau regions present unique physiological conditions impacting CHD detection rates.
Purpose of the Study:
- To develop and validate a predictive model for early CHD screening in school-aged children residing in high-altitude areas.
- To evaluate the cost-effectiveness of various screening strategies for CHD in this population.
Main Methods:
- A logistic regression model was developed using cross-sectional data from 7315 school-aged children.
- Model performance was assessed using decision curve analysis, calibration curves, and ROC analysis.
- Cost-effectiveness was evaluated using a Markov model comparing different screening strategies.
Main Results:
- Logistic regression identified maternal drug use (OR=4.368) and respiratory infections (OR=5.795) as significant predictors of CHD.
- The predictive model demonstrated excellent discrimination with an AUC of 0.867.
- The three-tier screening model was the most cost-effective, with lower costs and higher health benefits (0.060 QALYs).
Conclusions:
- A three-tier screening strategy, incorporating maternal history of drug use, smoking exposure, younger age, and respiratory infections, is a cost-effective approach for early CHD detection in high-altitude settings.
- This tailored approach improves diagnostic accuracy and resource allocation in challenging environments.
Background And Purpose:
Current congenital heart disease (CHD) screening protocols adopt plain-standard criteria, yet they predispose to missed diagnoses and misdiagnoses in high-altitude hypoxic environments. This study aimed to develop and validate a predictive model for early CHD screening in plateau-region school-aged children and to evaluate the cost-effectiveness of different screening strategies.
Methods:
Cross-sectional data from 7315 school-age children undergoing initial school screening in high-altitude areas were analyzed using R 4.2.0 and SPSS 26.0. A logistic regression model predicting CHD detection rate was built via stepwise selection. Model performance was assessed through decision curve analysis (DCA), calibration curves, and ROC analysis. Variable importance was visualized via random forest plots. Cost-effectiveness of three screening strategies was evaluated using a Markov model.
Results:
Logistic regression model identified drug use (OR=4.368) and respiratory infections (OR=5.795) were independently associated with CHD diagnosis, followed by age (OR=0.680) and smoking (OR=1.476). The model showed excellent discrimination (AUC=0.867). Random forest analysis confirmed respiratory tract infections as the significant associated factor. Cost-effectiveness analysis identified the three-tier screening model as dominant, offering the lowest cost (¥2604) and highest health benefit (0.060 QALYs), with the favorable incremental cost-utility ratio. Single-stage diagnosis was the least cost-effective (¥15,400).
Conclusion:
The three-tier screening strategy, prioritizing children with maternal history of drug use or smoking exposure, younger age, and history of respiratory tract infections, shows a promising cost-effective approach for early CHD detection in high-altitude settings.
