Performance Analysis of a Medical Decision Algorithm to Mitigate Spread of SARS Due to Interfacility Patient Transfers

Abstract

Objective. To determine performance of a medical decision algorithm to mitigate spread of severe acute respiratory syndrome (SARS) from interfacility patient transfers during the Toronto SARS outbreak. Methods. Records from the Provincial Transfer Authorization Centre andToronto Public Health from April 1 to July 31, 2003, were linked using probabilistic methods. Authorization decision (transfer authorized or denied) andSARS status (probable case, suspect case, or patient under investigation for SARS; or non-SARS case) were obtained for linked records. Primary outcome was the number of patients where correct authorization decisions were made based on SARS status at the time of request. Secondary outcome was the number for whom, in retrospect, authorization decision was correct knowing final SARS status. Algorithm sensitivity, specificity, andpredictive values were determined. Results. There were 14,571 requests for transfer and2,132 patients investigated for SARS during the study period. The algorithm authorized 14,551 anddid not authorize 20 requests. Sensitivity andspecificity to make appropriate authorization decisions at the time of request were 100% (95% confidence interval [CI], 77.2%–100%) and99.95% (95% CI, 99.9–100%), respectively. Positive andnegative predictive values were 65% (95% CI, 44.1%–85.9%) and100% (95% CI, 98.4%–100%), respectively. Sensitivity andspecificity, in retrospect, within ten days of the transfer request were 100% (95% CI, 80.6%–100%) and99.97% (95% CI, 99.9%–100%), respectively. Positive andnegative predictive values were 80% (95% CI, 62.5%–97.5%) and100% (95% CI, 98.4%–100%), respectively. Seven of the 20 patients with nonauthorized requests were not known to have SARS at the time of request. Within ten days, three of seven were under investigation for, a suspect case of, or a probable case of SARS. Conclusions. The medical decision algorithm was highly sensitive andspecific in correctly authorizing transfers. Despite its highly sensitive andspecific algorithm, it did incorrectly deny authorization to a very small number of patients without SARS.

Key words:

• SARS
• emergency medical services
• risk management
• communication systems
• sentinel surveillance