S E Schultz1, D M Rothwell, Z Chen, K Tu. 1. Institute for Clinical Evaluative Sciences, Toronto, Ontario, Canada. sue.schultz@ices.on.ca
Abstract
INTRODUCTION: To determine if using a combination of hospital administrative data and ambulatory care physician billings can accurately identify patients with congestive heart failure (CHF), we tested 9 algorithms for identifying individuals with CHF from administrative data. METHODS: The validation cohort against which the 9 algorithms were tested combined data from a random sample of adult patients from EMRALD, an electronic medical record database of primary care physicians in Ontario, Canada, and data collected in 2004/05 from a random sample of primary care patients for a study of hypertension. Algorithms were evaluated on sensitivity, specificity, positive predictive value, area under the curve on the ROC graph and the combination of likelihood ratio positive and negative. RESULTS: We found that that one hospital record or one physician billing followed by a second record from either source within one year had the best result, with a sensitivity of 84.8% and a specificity of 97.0%. CONCLUSION: Population prevalence of CHF can be accurately measured using combined administrative data from hospitalization and ambulatory care.
INTRODUCTION: To determine if using a combination of hospital administrative data and ambulatory care physician billings can accurately identify patients with congestive heart failure (CHF), we tested 9 algorithms for identifying individuals with CHF from administrative data. METHODS: The validation cohort against which the 9 algorithms were tested combined data from a random sample of adult patients from EMRALD, an electronic medical record database of primary care physicians in Ontario, Canada, and data collected in 2004/05 from a random sample of primary care patients for a study of hypertension. Algorithms were evaluated on sensitivity, specificity, positive predictive value, area under the curve on the ROC graph and the combination of likelihood ratio positive and negative. RESULTS: We found that that one hospital record or one physician billing followed by a second record from either source within one year had the best result, with a sensitivity of 84.8% and a specificity of 97.0%. CONCLUSION: Population prevalence of CHF can be accurately measured using combined administrative data from hospitalization and ambulatory care.
Entities:
Keywords:
congestive heart failure; epidemiologic methods; population prevalence; validation studies
Authors: Ryan Ng; Claire E Kendall; Ann N Burchell; Ahmed M Bayoumi; Mona R Loutfy; Janet Raboud; Richard H Glazier; Sean Rourke; Tony Antoniou Journal: CMAJ Open Date: 2016-05-25
Authors: S D MacNeil; K Liu; S Z Shariff; A Thind; E Winquist; J Yoo; A Nichols; K Fung; S Hall; A X Garg Journal: Curr Oncol Date: 2015-04 Impact factor: 3.677
Authors: Geoffrey H Tison; Alanna M Chamberlain; Mark J Pletcher; Shannon M Dunlay; Susan A Weston; Jill M Killian; Jeffrey E Olgin; Véronique L Roger Journal: Int J Med Inform Date: 2018-09-19 Impact factor: 4.046
Authors: Tony Antoniou; Simon Hollands; Erin M Macdonald; Tara Gomes; Muhammad M Mamdani; David N Juurlink Journal: CMAJ Date: 2015-02-02 Impact factor: 8.262
Authors: Luke Mondor; Colleen J Maxwell; Susan E Bronskill; Andrea Gruneir; Walter P Wodchis Journal: Qual Life Res Date: 2016-04-06 Impact factor: 4.147
Authors: Clare L Atzema; Peter C Austin; Bing Yu; Michael J Schull; Cynthia A Jackevicius; Noah M Ivers; Paula A Rochon; Douglas S Lee Journal: CMAJ Date: 2018-12-17 Impact factor: 8.262
Authors: Kieran L Quinn; Erin M Macdonald; Tara Gomes; Muhammad M Mamdani; Anjie Huang; David N Juurlink Journal: Drug Saf Date: 2017-09 Impact factor: 5.606
Authors: Nicholas T Vozoris; Xuesong Wang; Peter C Austin; Douglas S Lee; Anne L Stephenson; Denis E O'Donnell; Sudeep S Gill; Paula A Rochon Journal: Eur J Clin Pharmacol Date: 2017-06-29 Impact factor: 2.953