Characterizing the Epidemiology of Perioperative Transfusion-associated Circulatory Overload

Clifford, Leanne; Jia, Qiong; Yadav, Hemang; Subramanian, Arun; Wilson, Gregory; Murphy, Seán; Pathak, Jyotishman; Schroeder, Darrell R.; Ereth, Mark H.; Kor, Daryl J.

doi:10.1097/aln.0000000000000513

Cited by 103 publications

(94 citation statements)

References 16 publications

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“…Based on passive reporting to the food and drug administration, TACO has been reported as the second leading cause of transfusion-related mortality, surpassed only by TRALI [41]. Similar to TRALI, it can also develop within hours of transfusion, which makes differentiating between TRALI and TACO difficult and often require the use of echocardiograph [42] or a pulmonary artery catheter.…”

Section: Tacomentioning

confidence: 98%

Transfusion in Traumatic Brain Injury

Reddy

Gopinath

Robertson

2015

Curr Treat Options Neurol

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There are three primary blood products that are often transfused in traumatic brain injury: packed red blood cells (pRBCs), platelets, and plasma. For pRBCs, hemoglobin thresholds for transfusion in anemia should be set at 7 g/dl. Higher threshold is warranted only when the patient is clinically symptomatic. For platelets, transfusion thresholds should be at least 50,000/mm(3) for patients without and 100,000/mm(3) for patients with evidence of hemorrhage. Reversal of antiplatelet therapy with platelet transfusion is advisable only in patients with active bleeding. Tests for platelet function are helpful in determining an adequate platelet transfusion in these situations. Fresh frozen plasma transfusion for correction of warfarin-induced coagulopathy is also advisable in patients with active bleeding. If available, activated factor VII can be used in refractory cases. Prothrombin concentrate complexes are also another alternative in refractory situations. Transfusion goals for patients with evidence of hemorrhage should be an international normalization ratio of 1.3 or less. Prophylactic transfusion of plasma in severe traumatic brain injury without intracranial hemorrhage has not been demonstrated to improve outcome. In all situations of product transfusion, patients should be closely observed for signs of volume overload and the development of transfusion-related acute lung injury. The benefit of product transfusion should always be weighed against the risk of a transfusion-related complication.

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Section: Tacomentioning

confidence: 98%

Transfusion in Traumatic Brain Injury

Reddy

Gopinath

Robertson

2015

Curr Treat Options Neurol

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“…This indirect measurement approach is necessary because of the requirement that the system be nonintrusive to the end user -we cannot simply ask the provider if they've made a decision about a transfusion at the time of an alert. It is also necessary because TACO and TRALI are frequently overlooked in clinical practice and are widely considered to have significantly underreported incidences [3,4,10,11]. This obviates comparing incidence rates based on directly charted diagnoses before and after deployment as an appropriate performance metric.…”

Section: Future Work and Conclusionmentioning

confidence: 99%

“…For the retrospective chart review we will follow a procedure similar to that used in [3][4][5]. Using data from the Perioperative Datamart, a natural language processing (NLP) enhanced version of a surveillance algorithm [13] will identify patients who are likely to have suffered transfusionrelated complications.…”

Section: Future Work and Conclusionmentioning

confidence: 99%

“…Two adverse reactions in particular, transfusion-related acute lung injury (TRALI) and transfusion-associated circulatory overload (TACO), described in [1], are especially problematic: Between 2009 and 2013, TRALI accounted for 30-45% of reported transfusion-related fatalities, and TACO accounted for 13-34% [2]. Furthermore, recent studies [3,4] of a non-cardiac surgical patient subpopulation found that those experiencing TACO or TRALI stay in both the ICU and hospital roughly twice as long as those who do not, and have in-hospital mortality rates that are respectively 3.5 and 10 times greater. Consequently, these adverse events not only put the patients at a much higher risk of mortality and poor quality of life outcomes, but also lead to increased utilization of healthcare services potentially leading to an increase in healthcare cost and expenditure.…”

Section: Introductionmentioning

confidence: 98%

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A Clinical Decision Support System for Preventing Adverse Reactions to Blood Transfusion

Murphree

Clifford

Lin

et al. 2015

2015 International Conference on Healthcare Informatics

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During 2011 approximately 21 million blood components were transfused in the United States, with roughly 1 in 414 resulting in complication. For Americans, the two leading causes of transfusion-related death are the respiratory complications Transfusion-related acute lung injury (TRALI) and Transfusion-associated circulatory overload (TACO). Each of these complications results in significantly longer ICU and hospital stays as well as significantly greater rates of mortality. We have developed a set of machine learning models for predicting the likelihood of these adverse reactions in surgical populations. Here we describe deploying these models into a perioperative critical care environment via a continuous monitoring and alerting clinical decision support system. The goal of this system, which directly integrates our suite of machine learning models running in the R statistical environment into a traditional health information system, is to improve transfusion-related outcomes in the perioperative environment. By identifying high-risk patients prior to transfusion, the clinical team may be able to choose a more appropriate therapy or therapeutic course. Identifying highrisk patients for increased observation after transfusion may also allow for a more timely intervention, thereby potentially improving care delivery and resulting patient outcome. An early prototype of this system is currently running in two Mayo Clinic perioperative environments.

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“…Two adverse reactions in particular, transfusion-related acute lung injury (TRALI) and transfusion-associated circulatory overload (TACO), described in [3], are especially problematic: Between 2009 and 2013, TRALI accounted for 30–45% of reported transfusion-related fatalities, and TACO accounted for 13–34% [2]. Furthermore, recent studies [4, 5] of a non-cardiac surgical patient subpopulation found that those experiencing TACO or TRALI stay in both the ICU and hospital roughly twice as long as those who do not, and have in-hospital mortality rates that are respectively 3.5 and 10 times greater. Consequently, these adverse events not only put the patients at a much higher risk of mortality and poor quality of life outcomes, but also lead to increased utilization of healthcare services potentially leading to an increase in healthcare cost and expenditure.…”

Section: Introductionmentioning

confidence: 99%

Ensemble learning approaches to predicting complications of blood transfusion

Murphree

Ngufor

Upadhyaya

et al. 2015

2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

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Of the 21 million blood components transfused in the United States during 2011, approximately 1 in 414 resulted in complication [1]. Two complications in particular, transfusion-related acute lung injury (TRALI) and transfusion-associated circulatory overload (TACO), are especially concerning. These two alone accounted for 62% of reported transfusion-related fatalities in 2013 [2]. We have previously developed a set of machine learning base models for predicting the likelihood of these adverse reactions, with a goal towards better informing the clinician prior to a transfusion decision. Here we describe recent work incorporating ensemble learning approaches to predicting TACO/TRALI. In particular we describe combining base models via majority voting, stacking of model sets with varying diversity, as well as a resampling/boosting combination algorithm called RUSBoost. We find that while the performance of many models is very good, the ensemble models do not yield significantly better performance in terms of AUC.

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Characterizing the Epidemiology of Perioperative Transfusion-associated Circulatory Overload

Cited by 103 publications

References 16 publications

Transfusion in Traumatic Brain Injury

Transfusion in Traumatic Brain Injury

A Clinical Decision Support System for Preventing Adverse Reactions to Blood Transfusion

Ensemble learning approaches to predicting complications of blood transfusion

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