Intra-operative red blood cell transfusion and mortality after cardiac surgery

Vlot, Eline A; Verwijmeren, Lisa; Garde, E.M.W. van de; Kloppenburg, Geoffrey T.L.; Dongen, Eric P.A. van; Noordzij, Peter G.

doi:10.1186/s12871-019-0738-2

Cited by 55 publications

(28 citation statements)

References 24 publications

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“…The minimization or elimination of allogenic blood transfusions in the surgical setting is a highly desirable outcome. Receipt of allogenic blood was repeatedly shown to be strongly associated with postoperative mortality, ischemic complications including stroke, myocardial infarction, and renal impairment, prolonged hospital length of stay, and SSI [57][58][59][60][61][62]. Transfusion-related immunomodulation (TRIM) is an umbrella definition describing the phenomena of immune dysfunction attributed to the proinflammatory and immunosuppressive effects of packed red blood cell transfusion.…”

Section: Figurementioning

confidence: 99%

Plasmin, Immunity, and Surgical Site Infection

Hastings

Myles

Medcalf

2021

JCM

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SSI are a universal economic burden and increase individual patient morbidity and mortality. While antibiotic prophylaxis is the primary preventative intervention, these agents are not themselves benign and may be less effective in the context of emerging antibiotic resistant organisms. Exploration of novel therapies as an adjunct to antimicrobials is warranted. Plasmin and the plasminogen activating system has a complex role in immune function. The immunothrombotic role of plasmin is densely interwoven with the coagulation system and has a multitude of effects on the immune system constituents, which may not always be beneficial. Tranexamic acid is an antifibrinolytic agent which inhibits the conversion of plasminogen to plasmin. Clinical trials have demonstrated a reduction in surgical site infection in TXA exposed patients, however the mechanism and magnitude of this benefit is incompletely understood. This effect may be through the reduction of local wound haematoma, decreased allogenic blood transfusion or a direct immunomodulatory effect. Large scale randomised clinical trial are currently being undertaken to better explain this association. Importantly, TXA is a safe and widely available pharmacological agent which may have a role in the reduction of SSI.

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Section: Figurementioning

confidence: 99%

Plasmin, Immunity, and Surgical Site Infection

Hastings

Myles

Medcalf

2021

JCM

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“…Rebro, Zagreb, Croatia. 4 Department for Cardiovascular Diseases, University Hospital Center Zagreb, Zagreb, Croatia. 5 Clinical Department of Transfusion Medicine and Transplantation Biology, University Hospital Centre Zagreb, Zagreb, Croatia.…”

Section: Acknowledgementsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

“…Recently, Vlot et al came out with the study analyzing intra-operative red blood cell transfusion and mortality after cardiac surgery [ 4 ]. Intra-operative transfusion was associated with a more than three-fold increased risk of 30-day mortality [ 4 ]. The association between intra-operative transfusion and mortality persisted after adjustment for known risk factors (adjusted OR 2.6; P = 0.007) [ 4 ].…”

Section: Introductionmentioning

confidence: 99%

“…Intra-operative transfusion was associated with a more than three-fold increased risk of 30-day mortality [ 4 ]. The association between intra-operative transfusion and mortality persisted after adjustment for known risk factors (adjusted OR 2.6; P = 0.007) [ 4 ]. Authors concluded that intra-operative transfusion of red blood cells (RBC) was found to be associated with increased mortality in adults undergoing CABG [ 4 ].…”

Section: Introductionmentioning

confidence: 99%

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Bleeding risk stratification in coronary artery surgery: the should-not-bleed score

Petricevic

Petričević

Pašalić

et al. 2021

J Cardiothorac Surg

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Background An estimated 20% of allogeneic blood transfusions in the United States are associated with cardiac surgery. It is estimated that 11% of red cell resources were used for transfusion support of patients undergoing coronary artery bypass grafting (CABG) with a documented wide variability in transfusion rate (7.8 to 92.8%). To address the issue of unnecessary transfusions within the CABG population, we developed a model to predict which patients are at low risk of bleeding for whom transfusion treatment might be considered unnecessary. Herein we present our “SHOULD-NOT-BLEED-SCORE” application developed for the Windows® software platform which is based on our previous research. Methods This study is aimed to develop a user-friendly application that stratifies patients with respect to bleeding risk. The statistical model we used in our previous research was focused on detection of CABG patients at low risk of bleeding. The rationale behind such an approach was to identify a CABG patient subgroup at low risk of bleeding. By identifying patients at low risk of bleeding we can define a subgroup of patients for whom transfusion treatment might be considered unnecessary. We developed a Windows platform application based on risk modelling which we previously calculated for 1426 patients undergoing elective CABG from January 2010 to January 2018. Results The SHOULD-NOT-BLEED-SCORE risk score is developed for the Windows software platform. A mathematical model that is based on multivariate analysis was used for app development. The variables that entered the scoring system were: Age; Body Mass Index; Chronic Renal Failure; Preoperative Clopidogrel Exposure; Preoperative Red Blood Cells Count; Preoperative Fibrinogen Level; Preoperative Multiplate ASPI test area under the curve (AUC) units. The SHOULD-NOT-BLEED-SCORE identifies/predicts patients without a risk for excessive bleeding with strong discriminatory performance (Receiver Operating Curve (ROC) analysis AUC 72.3%, p < 0.001). Conclusion The SHOULD-NOT-BLEED risk scoring application may be useful in the preoperative risk screening process. The clinical and economic burden associated with unnecessary transfusions may be adequately addressed by a preoperative scoring system detecting patients at low risk of bleeding for whom transfusion treatment might be considered unnecessary.

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Comparison of Machine Learning Models Including Preoperative, Intraoperative, and Postoperative Data and Mortality After Cardiac Surgery

et al. 2022

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ImportanceA variety of perioperative risk factors are associated with postoperative mortality risk. However, the relative contribution of routinely collected intraoperative clinical parameters to short-term and long-term mortality remains understudied.ObjectiveTo examine the performance of multiple machine learning models with data from different perioperative periods to predict 30-day, 1-year, and 5-year mortality and investigate factors that contribute to these predictions.Design, Setting, and ParticipantsIn this prognostic study using prospectively collected data, risk prediction models were developed for short-term and long-term mortality after cardiac surgery. Included participants were adult patients undergoing a first-time valve operation, coronary artery bypass grafting, or a combination of both between 1997 and 2017 in a single center, the University Medical Centre Groningen in the Netherlands. Mortality data were obtained in November 2017. Data analysis took place between February 2020 and August 2021.ExposureCardiac surgery.Main Outcomes and MeasuresPostoperative mortality rates at 30 days, 1 year, and 5 years were the primary outcomes. The area under the receiver operating characteristic curve (AUROC) was used to assess discrimination. The contribution of all preoperative, intraoperative hemodynamic and temperature, and postoperative factors to mortality was investigated using Shapley additive explanations (SHAP) values.ResultsData from 9415 patients who underwent cardiac surgery (median [IQR] age, 68 [60-74] years; 2554 [27.1%] women) were included. Overall mortality rates at 30 days, 1 year, and 5 years were 268 patients (2.8%), 420 patients (4.5%), and 612 patients (6.5%), respectively. Models including preoperative, intraoperative, and postoperative data achieved AUROC values of 0.82 (95% CI, 0.78-0.86), 0.81 (95% CI, 0.77-0.85), and 0.80 (95% CI, 0.75-0.84) for 30-day, 1-year, and 5-year mortality, respectively. Models including only postoperative data performed similarly (30 days: 0.78 [95% CI, 0.73-0.82]; 1 year: 0.79 [95% CI, 0.74-0.83]; 5 years: 0.77 [95% CI, 0.73-0.82]). However, models based on all perioperative data provided less clinically usable predictions, with lower detection rates; for example, postoperative models identified a high-risk group with a 2.8-fold increase in risk for 5-year mortality (4.1 [95% CI, 3.3-5.1]) vs an increase of 11.3 (95% CI, 6.8-18.7) for the high-risk group identified by the full perioperative model. Postoperative markers associated with metabolic dysfunction and decreased kidney function were the main factors contributing to mortality risk.Conclusions and RelevanceThis study found that the addition of continuous intraoperative hemodynamic and temperature data to postoperative data was not associated with improved machine learning–based identification of patients at increased risk of short-term and long-term mortality after cardiac operations.

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Intra-operative red blood cell transfusion and mortality after cardiac surgery

Cited by 55 publications

References 24 publications

Plasmin, Immunity, and Surgical Site Infection

Plasmin, Immunity, and Surgical Site Infection

Bleeding risk stratification in coronary artery surgery: the should-not-bleed score

Comparison of Machine Learning Models Including Preoperative, Intraoperative, and Postoperative Data and Mortality After Cardiac Surgery

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