Supported by the Department of Anesthesia, Mayo Clinic, Rochester, MN.
Electronic health record surveillance algorithms facilitate the detection of transfusion-related pulmonary complications
Article first published online: 30 AUG 2012
© 2012 American Association of Blood Banks
Volume 53, Issue 6, pages 1205–1216, June 2013
How to Cite
Clifford, L., Singh, A., Wilson, G. A., Toy, P., Gajic, O., Malinchoc, M., Herasevich, V., Pathak, J. and Kor, D. J. (2013), Electronic health record surveillance algorithms facilitate the detection of transfusion-related pulmonary complications. Transfusion, 53: 1205–1216. doi: 10.1111/j.1537-2995.2012.03886.x
Attribution for work: LC—study design, data acquisition, data analysis, data interpretation, and manuscript preparation; AS—data acquisition and manuscript revision; GAW—data acquisition and manuscript revision; PT—data acquisition, interpretation, and manuscript revision; OG—data acquisition, data interpretation, and manuscript revision; MM—data analysis, data interpretation, and manuscript revision; VH—data acquisition and manuscript revision; JP—data acquisition and manuscript revision; DJL—study conception, study design, data analysis, data interpretation, and manuscript revision.
There are no disclaimers.
- Issue published online: 10 JUN 2013
- Article first published online: 30 AUG 2012
- Received for publication May 23, 2012; revision received July 9, 2012, and accepted July 19, 2012.
BACKGROUND: Transfusion-related acute lung injury (TRALI) and transfusion-associated circulatory overload (TACO) are leading causes of transfusion-related mortality. Notably, poor syndrome recognition and underreporting likely result in an underestimate of their true attributable burden. We aimed to develop accurate electronic health record–based screening algorithms for improved detection of TRALI/transfused acute lung injury (ALI) and TACO.
STUDY DESIGN AND METHODS: This was a retrospective observational study. The study cohort, identified from a previous National Institutes of Health–sponsored prospective investigation, included 223 transfused patients with TRALI, transfused ALI, TACO, or complication-free controls. Optimal case detection algorithms were identified using classification and regression tree (CART) analyses. Algorithm performance was evaluated with sensitivities, specificities, likelihood ratios, and overall misclassification rates.
RESULTS: For TRALI/transfused ALI detection, CART analysis achieved a sensitivity and specificity of 83.9% (95% confidence interval [CI], 74.4%-90.4%) and 89.7% (95% CI, 80.3%-95.2%), respectively. For TACO, the sensitivity and specificity were 86.5% (95% CI, 73.6%-94.0%) and 92.3% (95% CI, 83.4%-96.8%), respectively. Reduced PaO2/FiO2 ratios and the acquisition of posttransfusion chest radiographs were the primary determinants of case versus control status for both syndromes. Of true-positive cases identified using the screening algorithms (TRALI/transfused ALI, n = 78; TACO, n = 45), only 11 (14.1%) and five (11.1%) were reported to the blood bank by physicians, respectively.
CONCLUSIONS: Electronic screening algorithms have shown good sensitivity and specificity for identifying patients with TRALI/transfused ALI and TACO at our institution. This supports the notion that active electronic surveillance may improve case identification, thereby providing a more accurate understanding of TRALI/transfused ALI and TACO epidemiology.