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Development of a Clinical Predictive Score for Bracing Outcomes in Children with Pectus Carinatum: A Single-center
Haonan Wang1, Yunyu He1, Minhua Lin1
1Department of Pediatric Surgery, Guangzhou Women and Children's Medical Center, Guangzhou Medical University, Guangzhou, China.
Insights
A new predictive model helps forecast chest wall deformity improvement in pediatric patients undergoing compressive orthotic bracing therapy. Key factors include initial chest width, age, compliance, and morphology, guiding treatment decisions.
Area of Science:
- Orthopedics
- Biomedical Engineering
- Pediatric Surgery
Background:
- Pectus carinatum (PC) is a prevalent chest wall deformity in children.
- Current methods lack predictive tools for assessing Compressive Orthotic Bracing (COB) therapy outcomes.
- Developing predictive models is crucial for evidence-based treatment recommendations.
Purpose of the Study:
- To identify factors influencing PC improvement with COB therapy.
- To develop and validate a predictive efficacy scoring tool for PC.
- To integrate the tool into a clinical decision pathway for pediatric PC patients.
Main Methods:
- Retrospective analysis of 182 pediatric PC patients using 3D scanning.
- Development and validation of a multiple linear regression model for a predictive scoring tool.
- Establishment of a treatment decision pathway based on the scoring tool.
Main Results:
- Smaller initial thoracic width, older age, good compliance, and favorable chest wall morphology independently predicted PC improvement.
- The predictive efficacy scoring tool demonstrated excellent discriminative ability between good and poor response groups.
- The model was optimized into a functional scoring tool (Score = Expected %dEHI + 4.761).
Conclusions:
- The developed scoring tool and decision pathway offer evidence-based recommendations for PC treatment.
- Recommendations vary based on the score, compliance, age, and parental preferences, guiding bracing, supervision, or surgical consideration.
- Further prospective multicenter studies are needed to validate the model's generalizability.
Background And Objectives:
Pectus carinatum (PC) is a common chest wall deformity, but there is currently a lack of predictive models and tools for forecasting deformity improvement with compressive orthotic bracing (COB) therapy. This study aimed to identify key factors influencing treatment outcomes, and to develop and integrate a predictive efficacy scoring tool with a clinical decision pathway to provide evidence-based treatment recommendations for pediatric PC patients.
Methods:
In this retrospective study, 182 pediatric PC patients evaluated and followed using three-dimensional scanning were enrolled. A multiple linear regression model was developed to create and validate a predictive efficacy scoring tool, which subsequently informed the establishment of a treatment decision pathway.
Results:
The predictive model identified smaller initial external thoracic width (p < 0.001, β = - 0.644), older age at treatment initiation (p = 0.04, β = 0.271), good compliance (p = 0.008, β = 0.188), and favorable chest wall morphology (p = 0.033, β = 0.152) as independent predictors of deformity improvement. The model was optimized into a predictive efficacy scoring tool (Score = Expected %dEHI + 4.761). Comparison between the good response (score > 0) and poor response (score ≤ 0) groups confirmed the tool's excellent discriminative ability (8.348 vs. 15.320, p < 0.001).
Conclusion:
Treatment recommendations were derived by integrating the scoring tool with the decision pathway: children with score > 0 are strongly recommended to continue bracing; those with score ≤ 0 due to poor compliance should receive intensified supervision, while those with score ≤ 0 due to other factors should be counseled on possible surgery; children under 4 years require personalized recommendations integrating the score, symptoms, and parental preferences. However, as a single-center retrospective study without a control group and with subjective compliance assessment, future prospective multicenter studies are required to validate the model's generalizability.
