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Development and Evaluation of a Prediction Model for Ascertaining Rheumatic Heart Disease Status in Administrative
D Bond-Smith1, R Seth1, N de Klerk1,2
1School of Population and Global Health, The University of Western Australia, Perth, Australia.
Insights
A new model accurately identifies rheumatic heart disease (RHD) in hospital data, significantly reducing misclassification errors from other heart conditions. This improves RHD surveillance and policy evaluation.
Area of Science:
- Cardiology
- Public Health
- Health Informatics
Background:
- Concerns exist regarding the accuracy of International Statistical Classification of Diseases (ICD) codes for rheumatic heart disease (RHD).
- Misclassification of non-rheumatic valvular disease (non-rheumatic VHD) as RHD is a significant issue in administrative hospital data.
- A validated, quantitative method for RHD case ascertainment in hospital records is lacking.
Purpose of the Study:
- To develop and validate a predictive model for accurate RHD case ascertainment in administrative hospital data.
- To address and reduce false-positive rates caused by misclassified non-rheumatic VHD and acute rheumatic fever (ARF).
Main Methods:
- A dataset of validated Australian RHD cases was linked to inpatient hospital records (2000-2018).
- A generalized linear mixed model was developed using demographic and clinical variables.
- Internal and external validation was performed, calculating conditional optimal probability cutpoints.
Main Results:
- The model reduced the false-positive rate for non-rheumatic VHD misclassified as RHD from 0.77 to 0.22.
- It also reduced the false-positive rate for acute rheumatic fever (ARF) misclassified as RHD from 0.59 to 0.27.
- The model demonstrated strong discriminant capacity (AUC: 0.93 internally, 0.88 externally) and can function with basic data.
Conclusions:
- Misclassification of non-rheumatic VHD and ARF as RHD leads to substantial false-positive rates.
- The proposed model effectively addresses these biases, offering a reliable solution for RHD case ascertainment.
- This facilitates improved epidemiological disease monitoring and policy evaluation for RHD.
Background:
Previous research has raised substantial concerns regarding the validity of the International Statistical Classification of Diseases and Related Health Problems (ICD) codes (ICD-10 I05-I09) for rheumatic heart disease (RHD) due to likely misclassification of non-rheumatic valvular disease (non-rheumatic VHD) as RHD. There is currently no validated, quantitative approach for reliable case ascertainment of RHD in administrative hospital data.
Methods:
A comprehensive dataset of validated Australian RHD cases was compiled and linked to inpatient hospital records with an RHD ICD code (2000-2018, n=7555). A prediction model was developed based on a generalized linear mixed model structure considering an extensive range of demographic and clinical variables. It was validated internally using randomly selected cross-validation samples and externally. Conditional optimal probability cutpoints were calculated, maximising discrimination separately for high-risk versus low-risk populations.
Results:
The proposed model reduced the false-positive rate (FPR) from acute rheumatic fever (ARF) cases misclassified as RHD from 0.59 to 0.27; similarly for non-rheumatic VHD from 0.77 to 0.22. Overall, the model achieved strong discriminant capacity (AUC: 0.93) and maintained a similar robust performance during external validation (AUC: 0.88). It can also be used when only basic demographic and diagnosis data are available.
Conclusion:
This paper is the first to show that not only misclassification of non-rheumatic VHD but also of ARF as RHD yields substantial FPRs. Both sources of bias can be successfully addressed with the proposed model which provides an effective solution for reliable RHD case ascertainment from hospital data for epidemiological disease monitoring and policy evaluation.
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Initial Enquiry
Ask the patient about their primary concern and thoroughly explore all reported symptoms.
Medical History
Investigate past illnesses affecting the cardiovascular system, such as angina, anemia, rheumatic fever, congenital heart disease, stroke, thrombophlebitis, dysrhythmias, varicosities
Inquire about symptoms...