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Development and Evaluation of a Prediction Model for Ascertaining Rheumatic Heart Disease Status in Administrative Data

Authors Bond-Smith D, Seth R, de Klerk N, Nedkoff L, Anderson M, Hung J, Cannon J, Griffiths K, Katzenellenbogen JM

Received 9 December 2019

Accepted for publication 16 May 2020

Published 9 July 2020 Volume 2020:12 Pages 717—730

DOI https://doi.org/10.2147/CLEP.S241588

Checked for plagiarism Yes

Review by Single-blind

Peer reviewer comments 2

Editor who approved publication: Professor Vera Ehrenstein


D Bond-Smith,1 R Seth,1 N de Klerk,1,2 L Nedkoff,1 M Anderson,3 J Hung,1 J Cannon,1,2 K Griffiths,4,5 JM Katzenellenbogen1,2

1School of Population and Global Health, The University of Western Australia, Perth, Australia; 2Telethon Kids Institute, Perth, Australia; 3Queensland Health, Brisbane, Australia; 4Centre for Big Data Research, The University of New South Wales, Sydney, Australia; 5Menzies School of Health Research, Charles Darwin University, Darwin, Australia

Correspondence: D Bond-Smith Email daniela.bond-smith@uwa.edu.au

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.

Keywords: rheumatic heart disease, international classification of diseases, prediction, case ascertainment, acute rheumatic fever, non-rheumatic valvular heart disease, administrative data, validation, discrimination, receiver operating curve, Australia

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