Electronic health record surveillance algorithms facilitate the detection of transfusion-related pulmonary complications

Leanne Clifford, Amandeep Singh, Gregory A. Wilson, Pearl Toy, Ognjen Gajic, Michael Malinchoc, Vitaly D Herasevich, Jyotishman Pathak, Daryl J Kor

Research output: Contribution to journalArticle

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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. 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.

Original languageEnglish (US)
Pages (from-to)1205-1216
Number of pages12
JournalTransfusion
Volume53
Issue number6
DOIs
StatePublished - Jun 2013

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Acute Lung Injury
Electronic Health Records
Lung
Confidence Intervals
Sensitivity and Specificity
Regression Analysis
Blood Banks
National Institutes of Health (U.S.)
Observational Studies
Epidemiology
Cohort Studies
Thorax
Retrospective Studies

ASJC Scopus subject areas

  • Hematology
  • Immunology
  • Immunology and Allergy

Cite this

Electronic health record surveillance algorithms facilitate the detection of transfusion-related pulmonary complications. / Clifford, Leanne; Singh, Amandeep; Wilson, Gregory A.; Toy, Pearl; Gajic, Ognjen; Malinchoc, Michael; Herasevich, Vitaly D; Pathak, Jyotishman; Kor, Daryl J.

In: Transfusion, Vol. 53, No. 6, 06.2013, p. 1205-1216.

Research output: Contribution to journalArticle

Clifford, Leanne ; Singh, Amandeep ; Wilson, Gregory A. ; Toy, Pearl ; Gajic, Ognjen ; Malinchoc, Michael ; Herasevich, Vitaly D ; Pathak, Jyotishman ; Kor, Daryl J. / Electronic health record surveillance algorithms facilitate the detection of transfusion-related pulmonary complications. In: Transfusion. 2013 ; Vol. 53, No. 6. pp. 1205-1216.
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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. 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.",
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T1 - Electronic health record surveillance algorithms facilitate the detection of transfusion-related pulmonary complications

AU - Clifford, Leanne

AU - Singh, Amandeep

AU - Wilson, Gregory A.

AU - Toy, Pearl

AU - Gajic, Ognjen

AU - Malinchoc, Michael

AU - Herasevich, Vitaly D

AU - Pathak, Jyotishman

AU - Kor, Daryl J

PY - 2013/6

Y1 - 2013/6

N2 - 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.

AB - 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.

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