Administrative data accurately identified intensive care unit admissions in Ontario

Scales, Damon C.; Guan, Jun; Martin, Claudio M.; Redelmeier, Donald A.

doi:10.1016/j.jclinepi.2005.11.015

« Previous Next »Journal of Clinical Epidemiology
Volume 59, Issue 8 , Pages 802-807, August 2006

Administrative data accurately identified intensive care unit admissions in Ontario

Damon C. Scales
- Department of Critical Care, Sunnybrook & Women's College Health Sciences Centre, University of Toronto, G1 06, 2075 Bayview Avenue, Toronto, Ontario, Canada, M4N 3M5
- Interdepartmental Division of Critical Care, University of Toronto, G1 06, 2075 Bayview Avenue, Toronto, Ontario, Canada, M4N 3M5
- Institute for Clinical Evaluative Sciences, Toronto, Ontario, Canada
- Corresponding author. Tel.: 416-480-5291; fax: 416-480-4999.
Jun Guan
- Institute for Clinical Evaluative Sciences, Toronto, Ontario, Canada
Claudio M. Martin
- Department of Medicine, University of Western Ontario, London, Ontario, Canada
- Centre for Critical Illness Research, Lawson Health Research Institute, London, Ontario, Canada
Donald A. Redelmeier
- Institute for Clinical Evaluative Sciences, Toronto, Ontario, Canada
- Department of Medicine, Sunnybrook & Women's College Health Sciences Centre, Toronto, Ontario, Canada

Accepted 27 November 2005. published online 27 March 2006.

Abstract

Background and Objectives

To evaluate the accuracy of Ontario administrative health data for identifying intensive care unit (ICU) patients.

Materials and Methods

Records from the Critical Care Research Network patient registry (CCR-Net) were linked to the Ontario Health Insurance Program (OHIP) database and the Canadian Institute for Health Information (CIHI) database. The CCR-Net was considered the criterion standard for assessing the accuracy of different OHIP or CIHI codes for identifying ICU admission.

Results

The highest positive predictive value (PPV) for ICU admission (91%) was obtained using a CIHI special care unit (SCU) code, but its sensitivity was poor (26%). A strategy based on a combination of CIHI SCU codes yielded a lower PPV (84%) but a higher sensitivity (92%). A strategy based purely on OHIP claims yielded further reductions in PPV (73%), gains in specificity (99%), and moderate sensitivity (56%). The highest sensitivity (100%) was obtained using a combination of CIHI and OHIP codes in exchange for poor PPV (32%).

Conclusions

Administrative databases can be used to identify ICU patients, but no single strategy simultaneously provided high sensitivity, specificity, and PPV. Researchers should consider the study purpose when selecting a strategy for health services research on ICU patients.

Keywords: Claims analysis, Critical care, Databases, Health services research, Predictive value of tests, Sensitivity and specificity