title: Electronic health record surveillance algorithms facilitate the detection of transfusion-related pulmonary complications.
abstract: 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.
  
  
  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.affiliation: Mayo Clinic, Rochester, Minnesota 55905, USA.
authors.forename: Leanne
authors.initials: L
authors.lastname: Clifford
authors.forename: Amandeep
authors.initials: A
authors.lastname: Singh
authors.forename: Gregory A
authors.initials: GA
authors.lastname: Wilson
authors.forename: Pearl
authors.initials: P
authors.lastname: Toy
authors.forename: Ognjen
authors.initials: O
authors.lastname: Gajic
authors.forename: Michael
authors.initials: M
authors.lastname: Malinchoc
authors.forename: Vitaly
authors.initials: V
authors.lastname: Herasevich
authors.forename: Jyotishman
authors.initials: J
authors.lastname: Pathak
authors.forename: Daryl J
authors.initials: DJ
authors.lastname: Kor
mesh_terms: D055371:Acute Lung Injury / Q000453:epidemiology* / Q000209:etiology; D000368:Aged; D000465:Algorithms; D001784:Blood Gas Analysis; D001787:Blood Group Incompatibility / Q000150:complications / Q000453:epidemiology*; D057286:Electronic Health Records; D005260:Female; D006801:Humans; D000860:Hypoxia / Q000453:epidemiology / Q000209:etiology; D008137:Longitudinal Studies; D008297:Male; D008875:Middle Aged; D015999:Multivariate Analysis; D011159:Population Surveillance / Q000379:methods*; D011654:Pulmonary Edema / Q000453:epidemiology* / Q000209:etiology; D056152:Respiratory Rate; D012189:Retrospective Studies; D012307:Risk Factors; D012680:Sensitivity and Specificity