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Predicting the Risk of 1-Year Mortality in Incident Dialysis Patients: Accounting for Case-Mix Severity in Studies Using Administrative Data

Quinn, Robert R. MD, PhD*†‡; Laupacis, Andreas MD, MSc§∥; Hux, Janet E. MD, SMS‡§¶**; Oliver, Matthew J. MD, MHS§¶**; Austin, Peter C. PhD‡¶††

Medical Care:
doi: 10.1097/MLR.0b013e318202aa0b
Original Article
Abstract

Background: Administrative databases are increasingly being used to study the incident dialysis population and have important advantages. However, traditional methods of risk adjustment have limitations in this patient population.

Objective: Our objective was to develop a prognostic index for 1-year mortality in incident dialysis patients using administrative data that was applicable to ambulatory patients, used objective definitions of candidate predictor variables, and was easily replicated in other environments.

Research Design: Anonymized, administrative health data housed at the Institute for Clinical Evaluative Sciences in Toronto, Canada were used to identify a population-based sample of 16,205 patients who initiated dialysis between July 1, 1998 and March 31, 2005. The cohort was divided into derivation, validation, and testing samples and 4 different strategies were used to derive candidate logistic regression models for 1-year mortality. The final risk prediction model was selected based on discriminatory ability (as measured by the c-statistic) and a risk prediction score was derived using methods adopted from the Framingham Heart Study. Calibration of the predictive model was assessed graphically.

Results: The risk of death during the first year of dialysis therapy was 16.4% in the derivation sample. The final model had a c-statistic of 0.765, 0.763, and 0.756 in the derivation, validation, and testing samples, respectively. Plots of actual versus predicted risk of death at 1-year showed good calibration.

Conclusion: The prognostic index and summary risk score accurately predict 1-year mortality in incident dialysis patients and can be used for the purposes of risk adjustment.

Author Information

From the *Faculty of Medicine, University of Calgary, Calgary, Alberta, Canada; †Department of Medicine, Foothills Medical Centre, Calgary, Alberta Canada; ‡Institute for Clinical Evaluative Sciences, Toronto, Ontario, Canada; §Department of Medicine, University of Toronto, Toronto, Ontario, Canada; ∥Keenan Research Centre, Li Ka Shing Knowledge Institute of St. Michael's Hospital, Toronto, Ontario, Canada; ¶Department of Health Policy, Management and Evaluation, University of Toronto, Toronto, Ontario, Canada; **Sunnybrook Health Sciences Centre, Toronto, Ontario, Canada; and ††Dalla Lana School of Public Health, University of Toronto, Toronto, Ontario, Canada.

Supported by the Institute for Clinical Evaluative Sciences (ICES), which is funded by an annual grant from the Ontario Ministry of Health and Long-Term Care (MOHLTC); and supported by a Canadian Institutes for Health Research (CIHR)–Institute for Health Services and Policy Research (IHSPR) Fellowship award (to R.Q.).

The opinions, results and conclusions reported in this paper are those of the authors and are independent from the funding sources. No endorsement by ICES or the Ontario MOHLTC is intended or should be inferred. The MOHLTC had no role in the design and conduct of the study, collection, management, analysis or interpretation of the data, preparation, review, or approval of the manuscript.

Reprints: Robert R. Quinn, MD, PhD, University of Calgary, Foothills Medical Centre, Room C202C, 1403 29 St NW, Calgary, Alberta, Canada. E-mail: rrquinn@gmail.com.

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© 2011 Lippincott Williams & Wilkins, Inc.