Identifying cases of congestive heart failure from administrative data: a validation study using primary care patient records S. E. Schultz, MA, MSc (1); D. M. Rothwell, MSc (2); Z. Chen, MD (1); K. Tu, MD (1, 3, 4) This article has been peer reviewed. 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. Keywords: congestive heart failure, validation studies, epidemiologic methods, population prevalence Introduction Hospital discharge abstracts 1,2 have tradi- tionally been used to identify those patients with congestive heart failure (CHF) who present to hospital or who are hospitalized for other conditions but have CHF listed as a co-morbidity. In their recent systematic review of validation studies of algorithms to identify CHF from administrative data, Saczynski et al. 3 found this to be true for 25 of 35 studies listed. Compared with hospital records, the use of hospital discharge abstracts to identify patients with CHF has been found to be highly accurate. 4,5 However, with improve- ments in treatment and decreases in hospital resources, more patients with heart failure are being successfully mana- ged in the community. As a result, they may never show up in the hospital dis- charge data or else not until their disease is in the advanced stages. Thus, using hospi- tal data alone will probably underestimate the incidence and prevalence of CHF. Validated algorithms using combinations of physician billing data and hospital discharge abstracts have been developed to identify patients with chronic disease conditions that do not necessarily require hospitalization, for example, hyperten- sion, diabetes, ischemic heart disease and asthma. 6-9 However, of the 35 studies listed in the systematic review conducted by Saczynski et al., 3 only 9 used data from both hospital discharges and ambulatory claims data, and only 2 were also popula- tion-based, although the population was still limited to patients enrolled in a large managed-care organization. 10,11 The purpose of our study was to deter- mine the most suitable algorithm of administrative data to identify patients with CHF in Ontario, Canada. We used information within primary care physician outpatient electronic medical records (EMRs) and fee-for-service primary care physician charts to assess the validity and reliability of various combinations of physician billing data and hospital dis- charge data. Methods Data sources Validation cohort The validation cohort used in this study comprised data from two sources. The first was collected through the Canadian Cardiovascular Outcomes Research Team (CCORT) from 17 physicians using Practice SolutionsH Electronic Medical Author references: 1. Institute for Clinical Evaluative Sciences, Toronto, Ontario, Canada 2. Ottawa Hospital Research Institute, Ottawa, Ontario, Canada 3. Department of Family and Community Medicine – University of Toronto, Toronto, Ontario, Canada 4. Toronto Western Hospital Family Health Team – University Health Network, Toronto, Ontario, Canada Correspondence: Susan E. Schultz, Institute for Clinical Evaluative Sciences G1-06, 2075 Bayview Ave., Toronto, ON M4N 3M5; Tel.: 416-480-4055 ext. 1-3788; Fax: 416-480-6048; Email: sue.schultz@ices.on.ca Vol 33, No 3, June 2013 – Chronic Diseases and Injuries in Canada $ 160