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.
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.
Authors: Steven Kleinman; Tim Caulfield; Penny Chan; Robertson Davenport; Janice McFarland; Susan McPhedran; Maureen Meade; Douglas Morrison; Thomas Pinsent; Pierre Robillard; Peter Slinger Journal: Transfusion Date: 2004-12 Impact factor: 3.157
Authors: Pearl Toy; Mark A Popovsky; Edward Abraham; Daniel R Ambruso; Leslie G Holness; Patricia M Kopko; Janice G McFarland; Avery B Nathens; Christopher C Silliman; David Stroncek Journal: Crit Care Med Date: 2005-04 Impact factor: 7.598
Authors: Anne F Eder; Ross Herron; Annie Strupp; Beth Dy; Edward P Notari; Linda A Chambers; Roger Y Dodd; Richard J Benjamin Journal: Transfusion Date: 2007-04 Impact factor: 3.157
Authors: Mariell Jessup; William T Abraham; Donald E Casey; Arthur M Feldman; Gary S Francis; Theodore G Ganiats; Marvin A Konstam; Donna M Mancini; Peter S Rahko; Marc A Silver; Lynne Warner Stevenson; Clyde W Yancy Journal: Circulation Date: 2009-03-26 Impact factor: 29.690
Authors: R S Evans; R A Larsen; J P Burke; R M Gardner; F A Meier; J A Jacobson; M T Conti; J T Jacobson; R K Hulse Journal: JAMA Date: 1986 Aug 22-29 Impact factor: 56.272
Authors: Nareg H Roubinian; Jeanne E Hendrickson; Darrell J Triulzi; Jerome L Gottschall; Michael Michalkiewicz; Dhuly Chowdhury; Daryl J Kor; Mark R Looney; Michael A Matthay; Steven H Kleinman; Donald Brambilla; Edward L Murphy Journal: Crit Care Med Date: 2018-04 Impact factor: 7.598
Authors: N H Roubinian; J E Hendrickson; D J Triulzi; J L Gottschall; D Chowdhury; D J Kor; M R Looney; M A Matthay; S H Kleinman; D Brambilla; E L Murphy Journal: Vox Sang Date: 2016-12-21 Impact factor: 2.144
Authors: Nareg H Roubinian; Mark R Looney; Sheila Keating; Daryl J Kor; Clifford A Lowell; Ognjen Gajic; Rolf Hubmayr; Michael Gropper; Monique Koenigsberg; Gregory A Wilson; Michael A Matthay; Pearl Toy; Edward L Murphy Journal: Transfusion Date: 2017-05-03 Impact factor: 3.157
Authors: N J Smischney; V M Velagapudi; J A Onigkeit; B W Pickering; V Herasevich; R Kashyap Journal: Appl Clin Inform Date: 2013-09-04 Impact factor: 2.342
Authors: Nareg H Roubinian; Mark R Looney; Daryl J Kor; Clifford A Lowell; Ognjen Gajic; Rolf D Hubmayr; Michael A Gropper; Monique Koenigsberg; Gregory A Wilson; Michael A Matthay; Pearl Toy; Edward L Murphy Journal: Transfusion Date: 2015-02-23 Impact factor: 3.157
Authors: Leanne Clifford; Qing Jia; Hemang Yadav; Arun Subramanian; Gregory A Wilson; Sean P Murphy; Jyotishman Pathak; Darrell R Schroeder; Mark H Ereth; Daryl J Kor Journal: Anesthesiology Date: 2015-01 Impact factor: 7.892
Authors: Jeanne E Hendrickson; Nareg H Roubinian; Dhuly Chowdhury; Don Brambilla; Edward L Murphy; Yanyun Wu; Paul M Ness; Eric A Gehrie; Edward L Snyder; R George Hauser; Jerome L Gottschall; Steve Kleinman; Ram Kakaiya; Ronald G Strauss Journal: Transfusion Date: 2016-07-26 Impact factor: 3.157
Authors: Steven Kleinman; Michael P Busch; Edward L Murphy; Hua Shan; Paul Ness; Simone A Glynn Journal: Transfusion Date: 2013-11-04 Impact factor: 3.157