Objective: To evaluate probabilistic matching for linking a cohort of cardiac arrest (CA) patients identified in the Metro Toronto Ambulance (MTA) database in Toronto, Ontario, Canada, to their appropriate record in either the Vital Statistics Information System (VSIS) or the Canadian Institute of Health Information (CIHI) databases and thus establish their clinical outcomes.
Methods: A linkage of a large administrative database was performed. A cohort of patients who suffered an out-of-hospital CA during the calendar years 1988-1993 was identified. To determine the patients' outcomes, the cohort was probabilistically linked to patient records in the VSIS and CIHI databases. Identifying variables used during the process of linking records included: names (first and last); New York State Identification and Intelligence System (NYSIIS) code; date of event; date of death; city; admitting hospital number; mode of admission to hospital; age; and sex.
Results: A cohort of 7,079 CA patients was identified from the MTA database; 6,448 (91%) patients were accurately linked to records in 1 of the 2 outcome databases (CIHI, VSIS). Missing data for > or = 1 of the linking variables were responsible for unlinked records. Using these longitudinal data, it was possible to determine the number of patients surviving their out-of-hospital CAs to be admitted to hospital (n = 833) (16%). No differences in survival rates (p = 0.06) or median lengths of hospital stay among the survivors (p = 0.15) were observed between admitting hospitals.
Conclusions: Probabilistic matching is an effective method by which researchers can use existing administrative data to determine outcomes of population cohorts. This is especially valuable in situations where controlled intervention studies are not feasible or may be inappropriate. In this analysis, in-hospital management of admitted CA patients, as determined by hospital-specific survival rates and length of stay, suggests no measurable differences in the care provided to these patients by hospitals in Toronto.