Damon C Scales1, Jun Guan, Claudio M Martin, Donald A Redelmeier. 1. Department of Critical Care, Sunnybrook and Women's College Health Sciences Centre, University of Toronto, G1 06, 2075 Bayview Avenue, Toronto, Ontario, Canada. damon.scales@utoronto.ca
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.
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.
Authors: Ziv Harel; Ron Wald; Eric McArthur; Glenn M Chertow; Shai Harel; Andrea Gruneir; Hadas D Fischer; Amit X Garg; Jeffrey Perl; Danielle M Nash; Samuel Silver; Chaim M Bell Journal: J Am Soc Nephrol Date: 2015-04-08 Impact factor: 10.121
Authors: Chaim M Bell; Hadas D Fischer; Sudeep S Gill; Brandon Zagorski; Kathy Sykora; Walter P Wodchis; Nathan Herrmann; Susan E Bronskill; Phil E Lee; Geoff M Anderson; Paula A Rochon Journal: J Gen Intern Med Date: 2007-04-24 Impact factor: 5.128