National and regional registries for congenital heart diseases: Strengths, weaknesses and opportunities

Chami, Jason; Nicholson, C.; Strange, Geoff; Cordina, Rachael; Celermajer, David S.

doi:10.1016/j.ijcard.2021.05.007

Cited by 7 publications

(3 citation statements)

References 32 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This algorithm could therefore be used to instantly calculate ESC complexity for patients in the world's largest CHD databases, most of which already include procedural information. [5][6][7] This could be used in research, for example, where having comprehensive complexity scoring allows for stratified sampling (as is currently being performed in the CHAANZ database), and can be used to group diagnoses together in order to generate sufficient statistical power-a vital function given the rarity and heterogeneity of CHDs. In large databases, manual classification is infeasible; this algorithm can be used instead, and easily modified as guidelines are updated.…”

Section: Overall Resultsmentioning

confidence: 99%

“…Our review of the world's largest CHD databases 5 showed that most, including the Belgian Congenital Heart Disease Database 6 and the Dutch CONCOR Database, 7 already contain procedural information. The CHAANZ Registry is no exception, with surgical and procedural data recently added from two children's hospitals and two expert adult CHD (ACHD) centers.…”

Section: (Which Was Not Certified By Peer Review)mentioning

confidence: 99%

See 1 more Smart Citation

Improved Complexity Stratification in Congenital Heart Disease; the Impact of Including Procedural Data on Accuracy and Reliability

Chami,

Nicholson,

Baker

et al. 2024

Preprint

Self Cite

View full text Add to dashboard Cite

Introduction–In order to manage a class of diseases as broad as congenital heart disease (CHD), multiple "manually generated" classification systems defining CHDs as mild, moderate and severe have been developed and used to good effect. As databases have grown, however, such ?manual? complexity scoring has become infeasible. Though past attempts have been made to determine CHD complexity algorithmically using a list of diagnoses alone, missing data and lack of procedural information have been significant limitations. Methods–We built an algorithm that can stratify the complexity of patients with CHD by integrating their diagnoses with a list of their previous procedures. Specific procedures which address a missing diagnosis or imply a certain operative status were used to supplement the diagnosis list. To verify this algorithm, CHD specialists manually checked the classification of 100 children and 100 adults across four hospitals in Australia. Results–Our algorithm was 99.5% accurate in the manually checked cohort (100% in children and 99% in adults), and was able to automatically classify more than 90% of a cohort of over 24,000 CHD patients, including 92.5% of children (vs 84.4% without procedures, p < 0.0001) and 91.1% of adults (vs 70.4% without procedures; p < 0.0001). Conclusions–CHD complexity scoring is significantly improved by access to procedural history and can be automatically calculated with high accuracy.

show abstract

Section: Overall Resultsmentioning

confidence: 99%

Section: (Which Was Not Certified By Peer Review)mentioning

confidence: 99%

Improved Complexity Stratification in Congenital Heart Disease; the Impact of Including Procedural Data on Accuracy and Reliability

Chami,

Nicholson,

Baker

et al. 2024

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

“…This clinic planning tool allows for this to be determined in NSW, although scaling this to the whole of Australia would benefit future resource planning. The current development of a national registry for CHD patients in Australia and New Zealand will provide a resource to expand the current project to include both countries [ 48 – 50 ].…”

Section: Discussionmentioning

confidence: 99%

An interactive geographic information system to inform optimal locations for healthcare services

2023

Self Cite

View full text Add to dashboard Cite

Large health datasets can provide evidence for the equitable allocation of healthcare resources and access to care. Geographic information systems (GIS) can help to present this data in a useful way, aiding in health service delivery. An interactive GIS was developed for the adult congenital heart disease service (ACHD) in New South Wales, Australia to demonstrate its feasibility for health service planning. Datasets describing geographic boundaries, area-level demographics, hospital driving times, and the current ACHD patient population were collected, linked, and displayed in an interactive clinic planning tool. The current ACHD service locations were mapped, and tools to compare current and potential locations were provided. Three locations for new clinics in rural areas were selected to demonstrate the application. Introducing new clinics changed the number of rural patients within a 1-hour drive of their nearest clinic from 44·38% to 55.07% (79 patients) and reduced the average driving time from rural areas to the nearest clinic from 2·4 hours to 1·8 hours. The longest driving time was changed from 10·9 hours to 8·9 hours. A de-identified public version of the GIS clinic planning tool is deployed at https://cbdrh.shinyapps.io/ACHD_Dashboard/. This application demonstrates how a freely available and interactive GIS can be used to aid in health service planning. In the context of ACHD, GIS research has shown that adherence to best practice care is impacted by patients’ accessibility to specialist services. This project builds on this research by providing opensource tools to build more accessible healthcare services.

show abstract

Congenital heart disease registries: Vast room for improvement

Perčin

Brida

2021

International Journal of Cardiology

View full text Add to dashboard Cite

National and regional registries for congenital heart diseases: Strengths, weaknesses and opportunities

Cited by 7 publications

References 32 publications

Improved Complexity Stratification in Congenital Heart Disease; the Impact of Including Procedural Data on Accuracy and Reliability

Improved Complexity Stratification in Congenital Heart Disease; the Impact of Including Procedural Data on Accuracy and Reliability

An interactive geographic information system to inform optimal locations for healthcare services

Congenital heart disease registries: Vast room for improvement

Contact Info

Product

Resources

About