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Clinical Epidemiology Volume 12, 2020 - Issue
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Original Research

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

D Bond-Smith1 School of Population and Global Health, The University of Western Australia, Perth, AustraliaCorrespondence[email protected]
ORCID Iconhttps://orcid.org/0000-0002-9415-1314View further author information
ORCID Icon,
R Seth1 School of Population and Global Health, The University of Western Australia, Perth, AustraliaORCID Iconhttps://orcid.org/0000-0002-3551-6205View further author information
ORCID Icon,
N de Klerk1 School of Population and Global Health, The University of Western Australia, Perth, Australia;2 Telethon Kids Institute, Perth, AustraliaORCID Iconhttps://orcid.org/0000-0001-9223-0767View further author information
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L Nedkoff1 School of Population and Global Health, The University of Western Australia, Perth, AustraliaView further author information
,
M Anderson3 Queensland Health, Brisbane, AustraliaView further author information
,
J Hung1 School of Population and Global Health, The University of Western Australia, Perth, AustraliaORCID Iconhttps://orcid.org/0000-0002-4468-6097View further author information
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J Cannon1 School of Population and Global Health, The University of Western Australia, Perth, Australia;2 Telethon Kids Institute, Perth, AustraliaView further author information
,
K Griffiths4 Centre for Big Data Research, The University of New South Wales, Sydney, Australia;5 Menzies School of Health Research, Charles Darwin University, Darwin, AustraliaView further author information
&
J M Katzenellenbogen1 School of Population and Global Health, The University of Western Australia, Perth, Australia;2 Telethon Kids Institute, Perth, AustraliaORCID Iconhttps://orcid.org/0000-0001-5287-5819View further author information
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Pages 717-730 | Published online: 09 Jul 2020

References

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  • Carapetis JR, Beaton A, Cunningham MW, et al. Acute rheumatic fever and rheumatic heart disease. Nat Rev Dis Primers. 2016;2(1):15084. doi:10.1038/nrdp.2015.8427188830
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  • Katzenellenbogen JM, Nedkoff L, Canon J, et al. Low positive predictive value of ICD-10 codes in relation to rheumatic heart disease: a challenge for global surveillance. Int Med J. 2019;49(3):400–403. doi:10.1111/imj.14221
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  • Katzenellenbogen JM, Bond-Smith D, Cunneen R, et al. The End Rheumatic Heart Disease in Australia Study of Epidemiology (ERASE) project: data sources, case ascertainment and cohort profile. Clin Epidemiol. 2019;11:997.31814772
  • Fitz-Gerald JA, Ng A, Ongzalima CO, et al.editors. International Classification of Diseases (ICD) coding for rheumatic heart disease: a validation study. Australasian Epidemiology Association Annual Scientific Meeting; 2018; Perth: AEA.
  • Australian and New Zealand Society of Cardiac and Thoracic Surgeons (ANZSCTS). National cardiac surgery database melbourne: CCRE, Monash University; 2010 Available from: https://anzscts.org/national-database/.
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  • Searle SR, McCulloch CE. Generalized, Linear, and Mixed Models. John Wiley & Sons, Inc.; 2001.
  • Colby E, Bair E. Cross-validation for nonlinear mixed effects models. J Pharmacokinet Pharmacodyn. 2013;40(2):243–252. doi:10.1007/s10928-013-9313-523532511
  • Reményi B, Wilson N, Steer A, et al. World heart federation criteria for echocardiographic diagnosis of rheumatic heart disease–an evidence-based guideline. Nat Rev Cardiol. 2012;9(5):297–309. doi:10.1038/nrcardio.2012.722371105
  • Australia Bureau of Statistics. Australian Statistical Geography Standard (ASGS): Volume 5 - Remoteness Structure, July 2016. Canberra; 2018.
  • Adhikari P. Socio-economic indexes for areas: introduction, use and future directions Australian Bureau of Statistics Research Paper 1351.0.55.015. Canberra;2006.

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