A Validation Study: How Predictive Is a Diagnostic Coding Algorithm at Identifying Rheumatic Heart Disease in Western Australian Hospital Data?

Abstract Aims Rheumatic Heart Disease (RHD) is a major contributor to cardiac morbidity and mortality globally. We aimed to estimate the probability and predictors of progressing to non-fatal cardiovascular complications and death in young Australians after first RHD diagnosis. Methods and Results This retrospective cohort study used linked RHD register, hospital and death data from five Australian states and territories (covering 70% of the whole population and 86% of the Indigenous population). Progression from uncomplicated RHD to all-cause death and non-fatal cardiovascular complications (surgical intervention, heart failure, atrial fibrillation, infective endocarditis, stroke) was estimated for people aged <35years with first-ever RHD diagnosis between 2010 and 2018, identified from register and hospital data. The study cohort comprised 1718 initially uncomplicated RHD cases (84.6% Indigenous; 10.9% migrant; 63.2% women; 40.3% aged 5-14-years; 76.4% non-metropolitan). The composite outcome of death/cardiovascular complication was experienced by 23.3% (95% CI: 19.5-26.9) within 8 years. Older age and metropolitan residence were independent positive predictors of the composite outcome; history of acute rheumatic fever (ARF) was a negative predictor. Population group (Indigenous/migrant/other Australian) and sex were not predictive of outcome after multivariable-adjustment. Conclusion This study provides the most definitive and contemporary estimates of progression to major cardiovascular complication or death in young Australians with RHD. Despite access to the publically-funded universal Australian healthcare system, one-fifth of initially uncomplicated RHD cases will experience one of the major complications of RHD within 8 years supporting the need for programs to eradicate RHD.

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Case Ascertainment on Australian Registers for Acute Rheumatic Fever and Rheumatic Heart Disease

International Journal of Environmental Research and Public Health ◽

10.3390/ijerph17155505 ◽

2020 ◽

Vol 17 (15) ◽

pp. 5505

Author(s):

Treasure Agenson ◽

Judith M. Katzenellenbogen ◽

Rebecca Seth ◽

Karen Dempsey ◽

Mellise Anderson ◽

...

Keyword(s):

Heart Disease ◽

Rheumatic Fever ◽

Rheumatic Heart Disease ◽

Disease Surveillance ◽

Acute Rheumatic Fever ◽

South Australia ◽

Hospital Data ◽

Indigenous Status ◽

Case Ascertainment ◽

Rheumatic Heart

In Australia, disease registers for acute rheumatic fever (ARF) and rheumatic heart disease (RHD) were previously established to facilitate disease surveillance and control, yet little is known about the extent of case-ascertainment. We compared ARF/RHD case ascertainment based on Australian ARF/RHD register records with administrative hospital data from the Northern Territory (NT), South Australia (SA), Queensland (QLD) and Western Australia (WA) for cases 3–59 years of age. Agreement across data sources was compared for persons with an ARF episode or first-ever RHD diagnosis. ARF/RHD registers from the different jurisdictions were missing 26% of Indigenous hospitalised ARF/RHD cases overall (ranging 17–40% by jurisdiction) and 10% of non-Indigenous hospitalised ARF/RHD cases (3–28%). The proportion of hospitalised RHD cases (36%) was half the proportion of hospitalised ARF cases (70%) notified to the ARF/RHD registers. The registers were found to capture few RHD cases in metropolitan areas (SA Metro: 13%, QLD Metro: 35%, WA Metro: 14%). Indigenous status, older age, comorbidities, drug/alcohol abuse and disease severity were predictors of cases appearing in the hospital data only (p < 0.05); sex was not a determinant. This analysis confirms that there are biases associated with the epidemiological analysis of single sources of case ascertainment for ARF/RHD using Australian data.

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A response to the short communication ‘Rheumatic heart disease in pregnancy: Profile of women admitted to a Western Australian tertiary obstetric hospital’ by Ongzalima et al

Australian and New Zealand Journal of Obstetrics and Gynaecology ◽

10.1111/ajo.13143 ◽

2020 ◽

Vol 60 (4) ◽

Author(s):

Sophie Lesseps ◽

Jack Hewett ◽

Sumona Mandal ◽

Katie Green

Keyword(s):

Heart Disease ◽

Rheumatic Heart Disease ◽

Short Communication ◽

Heart Disease In Pregnancy ◽

Western Australian ◽

Rheumatic Heart ◽

In Pregnancy

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Pioneering Methods Developed to Investigate the Burden of Acute Rheumatic Fever and Rheumatic Heart Disease Using Multiple Linked Data Sources

International Journal for Population Data Science ◽

10.23889/ijpds.v5i5.1632 ◽

2020 ◽

Vol 5 (5) ◽

Author(s):

Rebecca Seth ◽

Daniela Bond-Smith ◽

Judith Katzenellenbogan

Keyword(s):

Heart Disease ◽

Rheumatic Fever ◽

Rheumatic Heart Disease ◽

Linked Data ◽

Hospital Admissions ◽

Data Sources ◽

Health Concern ◽

Prediction Algorithm ◽

Hospital Data ◽

Rheumatic Heart

IntroductionAcute Rheumatic Fever (ARF) and Rheumatic Heart Disease (RHD) remain a major public health concern in Australia. Government action requires reliable burden estimates, however data from single or unlinked sources are only partial and likely to be skewed, exacerbated by systemic problems with ICD-10 codes for RHD. Linked data provide an opportunity to address these shortcomings. Objectives and ApproachObjectives: to develop a methodology using harmonised linked data across five Australian jurisdictions to determine the burden of ARF and RHD <55 years, in particular robust case definitions for calculating incidence and prevalence. For identifying RHD in hospital-only patients, validated case and non-cases from non-hospital sources were used with linked inpatient hospital admissions to develop a RHD prediction model. Additional data sources (register and surgery databases) were used to identify cases for reporting RHD prevalence. A unique ARF episode was defined as an ARF record >90 days from the previous one across both register and hospital data. For first-ever episodes we applied a lookback to mid-2001 for both ARF and RHD. For Western Australia, we evaluated the effect of look-back period on prevalence pooling. ResultsFor total ARF incidence over 3 years (2015-2017), there was 1425 episodes compared to 1027 episodes for first-ever ARF. There was an annual average of 5241 cases of RHD identified using our new methods (0-54yrs) – substantially higher than 2634 and 4255 RHD cases reported by Global Burden of Disease Study and Australian Institute of Welfare estimates respectively for 2017. Increased lookback had no effect on first-ever ARF but increased RHD prevalence >25 years. Conclusion / ImplicationsBy using multiple sources and cross-jurisdictional data we were able to provide contemporary and robust estimates for the burden of ARF and RHD in Australia. The prediction algorithm we developed can also be used in other countries, where only hospital data is available, to quantify RHD burden.

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