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Morbidity and Mortality associated with Blood Transfusions in Elective Adult Cardiac Surgery

Open AccessPublished:November 16, 2022DOI:https://doi.org/10.1053/j.jvca.2022.11.012

      Abstract

      Objectives

      Perioperative transfusion thresholds have garnered increasing scrutiny as restrictive strategies have been shown to be noninferior. We utilized data from a statewide academic collaborative to test the association between transfusion and 30-day mortality.

      Design

      All adult patients undergoing coronary artery bypass grafting (CABG) and/or valve operations between 2013 and 2019 in our Academic Cardiac Surgery Consortium were examined. The relationship between the number of overall, packed red blood cell (pRBC) and coagulation product (CP: fresh frozen plasma, cryoprecipitate, platelets) transfusions on 30-day mortality was evaluated. Multivariable regression was used to evaluate predictors of transfusion and study endpoints. Machine learning (ML) models were also developed to predict 30-day mortality and rank transfusion-related features by relative importance.

      Setting

      Academic Cardiac Surgery Consortium of 5 institutions

      Participants

      Patients ≥18 years old undergoing coronary artery bypass grafting and/or valve operations.
      Measurements and Main Results: Of the 7,762 patients (median hematocrit [HCT] 39%, IQR 35-43%) who were included in the final study cohort, over 40% were transfused at least one unit of pRBC or CP. In adjusted analyses, higher preoperative HCT was associated with reduced odds of mortality (aOR 0.95, 95% CI 0.92-0.98), renal failure (aOR 0.95, 95% CI 0.92-0.98), and prolonged mechanical ventilation (aOR 0.97, 95% CI 0.95-0.99). In contrast, perioperative transfusions were associated with increased 30-day mortality after adjustment for preoperative HCT and other baseline features. Machine learning models were able to predict 30-day mortality with an AUC of 0.814-0.850, with perioperative transfusions displaying the highest feature importance.

      Conclusions

      The present analysis found increasing HCT to be associated with lower incidence of mortality. We also found a dose-response association of transfusions with all study endpoints examined.

      Introduction

      With over half of cardiac surgical patients receiving allogeneic blood products, perioperative transfusion practices have garnered much scrutiny.[
      • Stover EP
      • Siegel LC
      • Parks R
      • et al.
      Variability in transfusion practice for coronary artery bypass surgery persists despite national consensus guidelines: A 24-institution study.
      ,
      • Snyder-Ramos SA
      • Mhnle P
      • Weng Y-S
      • et al.
      The ongoing variability in blood transfusion practices in cardiac surgery.
      ,
      • Kilic A
      • Whitman GJR.
      Blood Transfusions in Cardiac Surgery: Indications, Risks, and Conservation Strategies.
      ,
      • Robich MP
      • Koch CG
      • Johnston DR
      • et al.
      Trends in blood utilization in United States cardiac surgical patients.
      ] Although critical for augmenting oxygen carrying capacity, stored red blood cell (pRBC) transfusions are associated with a multitude of complications including infections, acute kidney injury, post-cardiotomy syndrome and even mortality.[
      • Schwann TA
      • Habib JR
      • Khalifeh JM
      • et al.
      Effects of Blood Transfusion on Cause-Specific Late Mortality After Coronary Artery Bypass Grafting—Less Is More.
      ,
      • Goel R
      • Patel EU
      • Cushing MM
      • et al.
      Association of Perioperative Red Blood Cell Transfusions With Venous Thromboembolism in a North American Registry.
      ,
      • Horvath KA
      • Acker MA
      • Chang H
      • et al.
      Blood Transfusion and Infection After Cardiac Surgery.
      ] However, several studies have also demonstrated the deleterious effects of anemia and low hematocrit on outcomes following cardiopulmonary bypass and have advocated for pRBC transfusion in select cases.[
      • DeFoe GR
      • Ross CS
      • Olmstead EM
      • et al.
      Lowest hematocrit on bypass and adverse outcomes associated with coronary artery bypass grafting.
      ,
      • Carson JL
      • Duff A
      • Poses RM
      • et al.
      Effect of anaemia and cardiovascular disease on surgical mortality and morbidity.
      ] Prior work examining the independent impact of anemia and transfusions on clinical outcomes has yielded conflicting conclusions. This uncertainty is largely due to the fact that transfusions and preoperative anemia are highly correlated and associated with adverse outcomes. In a study of over 33,411 patients undergoing coronary artery bypass grafting (CABG), LaPar and colleagues found anemia and transfusions to increase the odds of mortality and major complications, although the latter exerted a more profound impact.[
      • LaPar DJ
      • Hawkins RB
      • McMurry TL
      • et al.
      Preoperative anemia versus blood transfusion: Which is the culprit for worse outcomes in cardiac surgery?.
      ]
      To better standardize transfusion practices, threshold hemoglobin levels have been suggested by several groups, with safe levels generally exceeding 7g/dL.[
      • Hajjar LA
      • Vincent J-L
      • Galas FRBG
      • et al.
      Transfusion Requirements After Cardiac Surgery: The TRACS Randomized Controlled Trial.
      ] However, wide variability in center level transfusion rates persist, with 7.8-92.8% of CABG patients receiving stored red cells among national and international reports.[
      • Robich MP
      • Koch CG
      • Johnston DR
      • et al.
      Trends in blood utilization in United States cardiac surgical patients.
      ] A landmark randomized study of restrictive or liberal transfusion strategies in the United Kingdom failed to demonstrate the ability of the former in reducing morbidity and healthcare costs.[
      • Murphy GJ
      • Pike K
      • Rogers CA
      • et al.
      Liberal or Restrictive Transfusion after Cardiac Surgery.
      ] Furthermore, the impact of coagulation products (CP), such as cryoprecipitate, plasma, and platelets, on perioperative outcomes of cardiac operations have not been broadly examined. In the present study, we utilized data from a statewide collaborative of academic hospitals to test whether transfusion of pRBC or CP would be associated with increased risk of 30-day mortality, prolonged mechanical ventilation, acute kidney injury and stroke. We hypothesized increased morbidity and mortality associated with pRBC and CP transfusions in a dose-dependent fashion.

      Methods

      Data for the present study was obtained from our Academic Cardiac Surgery Consortium (UCCSC) repository. Founded in 2013, this consortium is a collaborative encompassing five academic hospitals within our state. Data elements, including those submitted to the Society of Thoracic Surgeons (STS), are collected prospectively and linked to financial data in compliance with policies of individual institutions and approved by a cross-campus system-wide Review Board.
      All adult patients undergoing isolated CABG, isolated valve, CABG and single valve and multi-valve operations between 2013-2019 were identified within the UCCSC. Patients with infective endocarditis, preoperative hematocrit less than 20% (1st percentile) or greater than 45% (90th percentile), as well as those receiving greater than 50 transfusions throughout the index hospitalization were excluded to maintain generalizability of the study findings (Figure 1). Additionally, those requiring extracorporeal life support, left ventricular assist devices or transcatheter valve operations during the index hospitalization were not considered for further analysis (Figure 1). Patients who received at least one unit of either pRBC or CP (intraoperative or postoperative fresh frozen plasma, cryoprecipitate, or platelets) comprised the transfusion group.
      Patient characteristics (age, gender, urgent/emergent status and comorbid conditions) and operative category (isolated CABG, isolated valve, CABG and single valve, and multi-valve operations) were defined according to the Society of Thoracic Surgeons (STS) Adult Cardiac Surgery Database dictionary.[

      STS Adult Cardiac Surgery Database Data Specifications. 2017.

      ] Preoperative hematocrit (HCT) was treated as a continuous variable to reduce bias. In addition, 30-day, all-cause mortality was defined in congruence with the STS definition, as were postoperative prolonged mechanical ventilation (>24 hours of mechanical ventilation following operating room exit), acute renal failure (increase in serum creatinine level 3 times greater than baseline or serum creatinine ≥ 4mg/dL or new requirement in dialysis postoperatively) and stroke.[

      STS Adult Cardiac Surgery Database Data Specifications. 2017.

      ]
      The present study had two main objectives: (1) delineate the independent association between preoperative HCT and transfusion status on outcomes of interest (2) develop machine learning (ML) models to predict 30-day mortality and rank transfusion-related covariates based on relative feature importance.
      First, a logistic regression with adjustment for age, gender, urgent/emergent status, operative group, institution and surgical year was developed to evaluate the association of preoperative HCT with postoperative outcomes. Marginal analysis was used to obtain predicted risk of mortality at various HCT levels. Subsequently, LASSO regularization (Least Absolute Shrinkage Operator) was used to select additional relevant variables including race, diabetes, end stage renal disease requiring dialysis, body mass index, prior myocardial infarction, congestive heart failure, cerebrovascular disease, peripheral vascular disease, preoperative albumin, and number of coronary arteries with significant disease.[

      Hastie T, Tibshirani R, Wainwright M. Statistical learning with sparsity: The lasso and generalizations. Boca Raton, F: 2015. doi:10.1201/b18401

      ] Using this model with additional covariates, the association between preoperative HCT and outcomes of interest was reassessed. We then tested for an independent association between preoperative HCT and outcomes of interest, while adjusting for the aforementioned patient and operative factors as well as transfusion status. Marginal analysis was performed to assess the predicted risk of mortality at various HCT levels with and without the inclusion of transfusion as a regression covariate.
      Second, we developed three Random Forest classification models to predict 30-day mortality. Each model adjusted for baseline patient and operative characteristics (described above), in addition to either total transfusions, pRBC transfusions or CP transfusions. Models were trained using a random 70% subsample of data and evaluated with the remaining 30%. We obtained cross-validated performance metrics by repeating the random subsampling, derivation and validation process 10 times. Model performance metrics are reported as means with 95% confidence intervals. In addition, we queried model attributes to rank covariates by their relative feature importance. This concept is often used to identify explanatory variables that are most influential in a ML model's decision making, since feature importance represents the overall improvement in accuracy/predictive power attributable to a certain covariate.
      Continuous variables are reported as medians with interquartile ranges (IQR) and were compared using the Mann-Whitney U test. Categorical variables are reported as proportions and were compared across groups using the Adjusted Wald test. The area under the receiver operating characteristic (AUC or C-statistic) was examined to evaluate logistic regression and Random Forest models. Logistic regression outputs are reported as adjusted odds ratios (aOR) with 95% confidence intervals (95% CI). Statistical analysis was performed using Stata 16.0 (StataCorp, TX) and Python 3.9 (Python Software Foundation, Wilmington, Delaware). It was not appropriate or possible to involve patients or the public in the design, or conduct, or reporting, or dissemination plans of our research. The study was approved by each institutional review board (IRB) as well as the cross-campus IRB agency.

      Results

      Over the study period, 7,762 patients (median HCT 39%, IQR 35-43%) met inclusion criteria, with case distribution reported in Table 1. Overall 30-day mortality was 2.4%, while 9.9% of patients suffered prolonged postoperative mechanical ventilation, 1.5% postoperative stroke, and 3.3% postoperative renal failure. During intra- and postoperative phases of care, 62.0% of patient underwent pRBC or CPT transfusion. Further stratification by category of transfusion revealed that 48.6% were transfused with at least one unit of pRBC and 46.4% with at least one unit of CP. The overall distribution of pRBC and CP utilization are shown in Figures 2a and 2b, respectively. Expectedly, median preoperative HCT was lower for the transfusion group (Table 1).
      Table 1Patient and operative characteristics stratified by transfusion cohort status
      Patient/Operative CharacteristicNo Transfusion

      N=2,943
      Transfusion

      N=4,819
      Overall

      N=7,762


      P value
      Age, y, median (IQR)63 (55-70)66 (58-73)65 (57-72)<0.001
      Female sex, n (%)675 (22.9%)1,566 (32.5%)2,241 (28.9%)<0.001
      Race Category (n=7,075)<0.001
      White1,678 (65.3%)2,431 (58.3%)4,109 (61.0%)
      Black120 (4.7%)258 (6.2%)378 (5.6%)
      Hispanic566 (22.0%)815 (19.6%)1,381 (20.5%)
      Asian207 (8.1%)663 (15.9%)870 (12.9%)
      Body mass index, kg/m2, median (IQR) (N=7,740)28.1 (25.1-31.9)26.7 (23.6-30.4)27.2 (24.1 - 30.9)<0.001
      Cerebrovascular disease, n (%)396 (13.5%)818 (16.7%)1,214 (15.6%)<0.001
      Chronic lung disease, n(%)483 (16.4%)950 (19.7%)1,433 (18.5%)<0.001
      Hypertension, n(%)2,212 (75.2%)3,803 (78.9%)6,015(77.5%)<0.001
      Diabetes, n(%)999 (33.9%)1,813 (37.6%)2,812 (36.2%)0.001
      Peripheral arterial disease, n(%)193 (6.6%)543 (11.3%)736 (9.5%)<0.001
      Renal Failure (hemodialysis), n(%)63 (2.1%)379 (7.9%)442 (5.7%)<0.001
      Liver Disease140 (4.8%)355 (7.4%)495 (6.4%)<0.001
      Total Albumin, median (IQR) (N=6249)4.1 (3.8-4.4)3.9 (3.5-4.3)4.0 (3.6-4.3)<0.001
      Previous MI, n(%)809 (27.5%)1,569 (32.6%)2,378 (30.6%)<0.001
      CHF, n(%)2,639(49.8%)2,824 (36.4%)5,463(41.8%)<0.001
      NYHA Class, n(%)<0.001
      None2,205 (74.9%)3,035 (63.0%)5,240(32.8%)
      I111 (15.0%)212 (11.9%)323 (4.1%)
      II285 (38.6%)558 (31.3%)843 (10.9%)
      III289 (39.2%)734 (41.1%)1,023 (13.2%)
      IV53 (7.2%)280 (15.7%)335 (4.3%)<0.001
      Ejection Fraction, %, median (IQR) (N=7,361)60 (52-65)58 (48-63)60 (50-65)0.001
      Left main disease >50%, n(%)2,345 (79.7%)3,979(82.6%)6,324 (81.5%)0.47
      Vessel Disease, n(%) (N=6,926)0.27
      Single814 (30.4%)1,202 (28.3%)2,016 (29.1%)
      Double243 (9.1%)387 (9.1%)630 (9.1%)
      3 or more1618 (23.4%)2,662 (38.4%)4,280 (61.7%)
      Last preoperative HCT, %, median (IQR)41 (38-44)38 (33-42)39 (35-43)<0.001
      Lowest HCT, %, median (IQR)27 (24-31)23 (20-26)25 (21-28)<0.001
      Predicted risk of mortality, %, median (IQR)0.4 (0.2-0.7)1.0 (0.5-2.3)0.7 (0.3-1.6)<0.001
      Operative Characteristics (N=7,760)
      Bypass Support, n(%)2,361 (80.3%)4,605 (95.6%)6,966 (89.8%)<0.001
      None, n(%)581 (19.8%)213 (4.4%)794 (10.2%)
      Operative Status, n(%) (N=7,728)<0.001
      Elective1,944 (66.4%)2,550 (53.1%)4,494 (58.2%)
      Urgent963 (32.3%)2,024 (42.2%)2,987 (38.7%)
      Emergent21 (0.7%)220 (4.6%)241 (3.1%)
      Emergent salvage06 (0.1%)6 (0.08%)
      Cardiopulmonary bypass time, min, median (IQR) (N=6,907)131 (103-169)152 (117-204)144 (111-191)
      Aortic cross-clamp time, min, median (IQR) (N=6,657)94 (75-121)108 (81-143)103 (78-135)
      Lowest intraoperative HCT, %, median (IQR) (N=6,036)27 (24- 30)23 (20-26)25(21-28)
      Postoperative HCT (N=2,910)30 (27-33)28 (25-30)28 (26-31)
      IMA use, (N=4,694)<0.001
      LIMA1,495 (83.4%)2,454 (84.0%)3,940 (83.9%)
      RIMA29 (1.6%)27 (0.9%)56 (1.2%)
      BIMA158 (8.9%)149 (5.1%)307 (6.5%)
      None100 (5.6%)291 (10.0%)391 (8.4%)
      IABP, n(%)65 (2.2%)374 (7.8%)439 (5.7%)<0.001
      Total All Blood Product Use04 (2-8)2 (0-5)<0.001
      Total Number of pRBC02 (1-4)0 (0-2)<0.001
      Total Number of Coagulation Products02 (0-5)0 (0-3)<0.001
      Preop Anemia, n (%)67 (2.3%)665 (13.8%)743 (9.5%)
      Operative Group<0.001
      Isolated CABG1,578 (53.6%)2,094 (43.5%)3,672 (47.3%)
      Isolated Valve1,077 (36.6%)1,520 (31.5%)2,597 (33.5%)
      CABG/Valve207 (7.0%)837 (17.4%)1,493 (19.2%)
      Multivalve81 (2.8%)368 (7.6%)449 (5.8%)
      CABG- Coronary artery bypass graft, HCT- preoperative hematocrit, IMA-internal mammary use, NYHA-New York Heart Association Classification, pRBC-packed red blood cell
      Figure 2
      Figure 2Frequency of pRBC (a) and CP (b) transfusions for overall study cohort Legend: Histogram of pRBC and CP transfusion for the final study cohort. pRBC- packed red blood cell,CP-coagulation product
      Patients requiring transfusions were older, more commonly female and of Asian race. In addition, compared to others, the transfused group had a lower median body mass index (Table 1). The STS predicted morbidity (8.7 vs 14.3%, P<0.001) and mortality (0.8 vs 1.7%, P<0.001) scores were significantly higher for patients receiving a transfusion. Several chronic medical conditions such as peripheral vascular disease, dialysis dependence, and liver dysfunction were also more prevalent in the transfusion cohort (Table 1). Moreover, intra-aortic balloon pump was more commonly employed among patients who received a transfusion (7.8 vs 2.2%, P<0.001). Relative to others, the transfusion cohort had longer median cross clamp time (94 vs 108 min, P<0.001) and greater operative complexity, with a more frequent performance of CABG and single valve (7.0 vs 17.4%, P<0.001) and multi-valve (2.8 vs 7.6%, P<0.001) operations. Significant center level variation was also noted, with a transfusion rate ranging from 8.3% to 35.7% of patients at each center (P<0.001).
      Risk-adjusted predictors of perioperative transfusion included increasing age, non-White race, history of prior cardiac surgery, preoperative congestive heart failure, dialysis dependence, cerebrovascular disease, peripheral vascular disease, number of diseased coronary vessels and intra-aortic balloon pump utilization (Figure 3). Furthermore, preoperative hematocrit was inversely associated with risk-adjusted predicted rate of blood product use (Figure 4). Risk-adjusted analysis also demonstrated center-level variation in odds of transfusion, as two centers had significantly different odds of blood receipt, compared to the reference institution (aOR 0.34, 95% CI 0.23- 0.40; aOR 1.49, 95% CI 1.22-1.82).
      Figure 3
      Figure 3Risk-adjusted patient and operative factors associated with perioperative transfusion. Legend: Red vertical bar as reference. Horizontal blue error bars 95% confidence interval. Prior MI-myocardial infarction. IsoCABG-Isolated CABG, IsoValve-Isolated valve, CABG/Valve- Combined coronary artery bypass graft/Valve, Multi-valve- multiple cardiac valve operations without coronary artery bypass.
      Figure 4
      Figure 4Predicted probability of rate of transfusion by preoperative hematocrit Legend: Risk-adjusted predicted probability of transfusion across range of preoperative hematocrit. Model not inclusive of transfusions received.
      The parsimonious risk-adjusted model of mortality, with age, gender, operative type, baseline hematocrit, elective/urgent or emergent operation, and operative year as covariates, revealed a 5% reduction of odds of 30-day mortality with every 1-point increase in preoperative HCT (aOR 0.95, 95% CI 0.92-0.98, Figure 5a). However, with additional adjustment for body mass index, dialysis dependence, race, preoperative serum albumin levels, preoperative HCT was no longer associated with the odds of 30-day mortality (aOR 1.03, 95% CI 0.99-1.08, Figure 5b). In contrast, the total number of transfusions exhibited a dose-dependent relationship with mortality, despite adjustment for preoperative hematocrit (Figure 6a). Stratification by pRBC and CP transfusions demonstrated similar results, as shown in Figure 6b and 6c, respectively.
      Figure 5
      Figure 5Risk-adjusted predicted 30-day mortality by preoperative hematocrit with (a) and without (b) adjustment for transfusion status.Legend: Risk-adjusted predicted probability of mortality across range of preoperative hematocrit.
      Figure 6
      Figure 6Risk-adjusted predicted 30-day mortality by number of overall (a), pRBC (b) and CPT (c) transfusions. Legend: Incremental increase in perioperative mortality when considering number of perioperative transfusions overall and when stratifying by category of transfusion product
      Increasing preoperative HCT was associated with a reduction in the odds of prolonged ventilation (aOR 0.97, 95% CI 0.95-0.99) and acute renal failure (aOR 0.95, 95%CI 0.92-0.98). In contrast, blood transfusions were not associated with increased odds of prolonged ventilation (aOR 1.02, 95% CI 0.99-1.04) and renal failure (aOR 0.99, 95% CI 0.95-1.02). Regardless, an incremental increase in odds of prolonged ventilator support, postoperative renal failure, and stroke was noted for every additional unit of pRBC or CP transfused (eFigure 2-4).
      In a secondary analysis, we created three unique models for the prediction of 30-day mortality, each adjusted for baseline patient and surgical characteristics, in addition to either total transfusions, RBC transfusions, or CP transfusions. The AUCs for these 3 models were 0.850, 0.830, and 0.814, respectively. Feature importance of included variables for each model are represented visually in Figure 7, with transfusion variables carrying most importance for predictive performance and accuracy.
      Figure 7
      Figure 7Feature importance for individual Random-Forest risk models for overall transfusions(a), pRBC(b), and CPT(c). Legend: Model coefficients ranked by absolute value as defined by the multivariable logistic regression analysis. Feature impact on model output is ranked in descending order. Positive values signify that as the variable increases, the risk of mortality increases. Negative values signify that as the variable increases, the risk of mortality decreases. Dots are colored according to the values of features for the respective patient and accumulate vertically to depict density. Red represents higher feature values. Blue represents lower feature values.

      Comment

      Blood component therapy remains a critical facet of perioperative management in cardiac surgical patients. Given the association of transfusions with worse clinical outcomes reported, concerted efforts have been made to re-examine transfusion thresholds and practices. The present study provides a contemporary perspective of the impact of blood component therapy, including pRBC and coagulation products, on perioperative outcomes following elective adult cardiac operations. Transfusion of blood products was common in our academic statewide collaborative, with pRBC and CP transfusions demonstrating a dose-dependent association with outcomes of interest, such as 30-day all-cause mortality, perioperative renal failure and prolonged mechanical ventilation. Secondarily, we created machine learning models with moderately high predictive accuracy for 30-day mortality, which was driven in large part by perioperative transfusion exposures.
      Consistent with the literature, our study demonstrated the association of preoperative hematocrit with 30-day all-cause mortality, postoperative prolonged mechanical ventilation and renal failure. Not surprisingly, the impact of preoperative anemia was diminished with the addition of patient and operative characteristics as well as total number of pRBC and CP transfusions. The present analysis adds to a growing body of literature that has demonstrated that while preoperative anemia is a significant contributor to perioperative morbidity and mortality[10], pRBC and CP transfusions may have a profound effect on outcomes regardless of baseline hematocrit level. Consistent with another institutional study from the Maryland Cardiac Surgery Quality Initiative[16], we found a dose response association between the number of transfusions and risk of mortality, prolonged ventilatory support, postoperative renal failure, and stroke. While the study by Ad et al shares a number of similarities in design, the present analysis excluded patient outliers in regards to preoperative hematocrit and number of transfusions utilized in order to improve generalizability.[

      Ad N, Massimiano PS, Rongione AJ, et al. Number and Type of Blood Products are Negatively Associated With Outcomes Following Cardiac Surgery. Ann Thorac Surg Published Online First: 28 July 2021. doi:10.1016/J.ATHORACSUR.2021.06.061

      ] Finally, our analysis spans a shorter period of time during which evolution of blood transfusion practice is inevitable and we employed machine learning algorithms to validate the prior hierarchical modeling. The addition of feature importance and machine learning analysis further amplifies previous analyses that have attempted to perform hierarchical modeling of the relationship between anemia, transfusions, and mortality. Furthermore, we acknowledge the potential for preoperative hematocrit which thereby mediate the effect of transfusions on outcomes. While the transfusion decision straddles concern regarding tissue-hypoxia in patients with potentially limited cardiopulmonary reserve, our findings add further support to restrictive transfusion policies when clinically feasible.
      Similar studies examining coagulation products are sparser than the literature examining the outcome of pRBC on perioperative outcomes of cardiac surgery, with a general lack of prospective studies examining platelet and plasma transfusion. One such meta-analysis, performed by Yanagawa et al., aimed to address the apparent surgical equipoise regarding the impact of platelet transfusions in patients undergoing cardiac surgery, with no significant increase in postoperative death, stroke, myocardial infarction, reoperation for bleeding, infection or dialysis associated with platelet transfusion.[
      • Yanagawa B
      • Ribeiro R
      • Lee J
      • et al.
      Platelet Transfusion in Cardiac Surgery: A Systematic Review and Meta-Analysis.
      ] However, this analysis was under-powered and suffered from study heterogeneity due to pooling of observational studies and variability in quantity and type of platelet transfusion. Another retrospective analysis of over 32,000 isolated CABG patients found no increase in morbidity with empiric platelet transfusion for microvascular bleeding.[
      • McGrath T
      • Koch CG
      • Xu M
      • et al.
      Platelet Transfusion in Cardiac Surgery Does Not Confer Increased Risk for Adverse Morbid Outcomes.
      ] In the present work, we performed a composite analysis of coagulation products, considering plasma, platelet, and cryoprecipitate transfusions in one group, given the difficulty in identifying the distinct effects of transfusion on study outcomes. Our findings of increased morbidity and mortality with coagulation product transfusion are consistent with a separate pooled analysis of two randomized controlled studies that demonstrated increased all-cause mortality with plasma transfusions and infectious-related mortality with platelet transfusions.[
      • Bilgin YM
      • van de Watering LMG
      • Versteegh MIM
      • et al.
      Postoperative complications associated with transfusion of platelets and plasma in cardiac surgery.
      ] Given the potential for adversely impacting perioperative outcomes, the need for goal-directed treatment of coagulopathy is warranted and remains a relevant concern for cardiac surgeons.
      The degree of variability amongst 5 academic institutions with a statewide health-system underscores the numerous factors that contribute to transfusion decision-making. Even within a relatively homogenous system with a unified overlying advisory board, the extent of variation was notable. These findings are consistent with numerous observational studies that demonstrated significant variation in transfusion practices.[
      • Jin R
      • Zelinka ES
      • McDonald J
      • et al.
      Effect of hospital culture on blood transfusion in cardiac procedures.
      ,
      • Bennett-Guerrero E
      • Zhao Y
      • O'Brien SM
      • et al.
      Variation in use of blood transfusion in coronary artery bypass graft surgery.
      ] Whether this is related to the documented risk profile and limitations in capturing patient acuity and severity within an administrative database versus inherent biases in transfusion practices is unknown. Across all institutions, viscoelastic coagulation testing is employed, but we are unable to obtain assay results in order standardize transfusion indications in a retrospective manner. Various groups have shown that programmatic focus on blood conservation has significantly improved transfusion rates over time.[
      • DeAnda A
      • Baker KM
      • Roseff SD
      • et al.
      Developing a blood conservation program in cardiac surgery.
      ] Nonetheless, perioperative transfusions remain an elusive opportunity for quality improvement that is intertwined with the value of care delivered and standardized thresholds and definitions are needed.[
      • Shander A
      • Hofmann A
      • Ozawa S
      • et al.
      Activity-based costs of blood transfusions in surgical patients at four hospitals.
      ]
      Regardless of transfusion thresholds, the present analysis builds upon a vast literature demonstrating an association between blood transfusions and adverse events following cardiac surgery. [
      • Schwann TA
      • Habib JR
      • Khalifeh JM
      • et al.
      Effects of Blood Transfusion on Cause-Specific Late Mortality After Coronary Artery Bypass Grafting—Less Is More.
      ,
      • Goel R
      • Patel EU
      • Cushing MM
      • et al.
      Association of Perioperative Red Blood Cell Transfusions With Venous Thromboembolism in a North American Registry.
      ,
      • Horvath KA
      • Acker MA
      • Chang H
      • et al.
      Blood Transfusion and Infection After Cardiac Surgery.
      ,
      • Koch C
      • Li L
      • Figueroa P
      • et al.
      Transfusion and Pulmonary Morbidity After Cardiac Surgery.
      ,
      • Koch CG
      • Li L
      • Duncan AI
      • et al.
      Morbidity and mortality risk associated with red blood cell and blood-component transfusion in isolated coronary artery bypass grafting.
      ] Although interpretation of this association warrants consideration that transfusions serve as a surrogate marker of patient severity and adverse outcomes may not be an inherent consequence of transfusion itself, a number of theories are postulated on how transfusions may be harmful: triggering systemic inflammatory response and adverse immunomodulatory effects through reduction of circulating lymphocytes, to mention a few. Recent results from a randomized controlled study of short-term combination treatment of IV iron, subcutaneous erythropoietin alpha, vitamin b12, and folic acid was associated with reduced RBC and total allogeneic transfusions in patients with preoperative anemia undergoing elective cardiac surgery and may reflect the evolving trends in management beyond modification of transfusion thresholds.[
      • Spahn DR
      • Schoenrath F
      • Spahn GH
      • et al.
      Effect of ultra-short-term treatment of patients with iron deficiency or anaemia undergoing cardiac surgery: a prospective randomised trial.
      ] Not unexpectedly, female patients, those of Asian self-declared race, and those with lower body mass index, received higher rates of perioperative transfusion. Thus, inclusion of these factors in machine-learning models, able to account for interactions between various confounders, may allow for optimized transfusion triggers.

      Limitations

      The present study has several limitations that warrant further discussion. First, although patient preoperative, intra- and postoperative hematocrit is available, the decision for transfusion administration is not documented and clinical context prompting transfusion is not prospectively collected. Furthermore, the period of surveillance is limited to the first thirty days following hospitalization. Given the composite analysis of intra- and postoperative transfusions, we are further unable to provide a cause-effect analysis of transfusions and perioperative complications such as renal failure, prolonged intubation, and stroke. These events may also be associated with the need for additional transfusions, thus, confounding the interpretation of the present analysis. Hemodynamic parameters and vasopressor support, which may often dictate the indication for transfusion to optimize oxygen carrying capacity or address clinically significant coagulopathy, are also not characterized in a continuous fashion. Furthermore, post-transfusion response is not noted.

      Conclusion

      In conclusion, blood product transfusion remains prevalent and highly variable within our consortium of academic health centers. Higher preoperative hematocrit was associated with lower mortality and adverse events. The relationship between preoperative hematocrit and mortality was attenuated with the inclusion of perioperative transfusions, suggesting that at least some of the effect of hematocrit on mortality is mediated by perioperative transfusions. Finally, we developed machine learning models for the prediction of 30-day mortality, for which perioperative transfusions displayed greatest feature importance.

      Uncited References

      [
      • Preacher KJ
      • Hayes AF.
      Asymptotic and resampling strategies for assessing and comparing indirect effects in multiple mediator models.
      ]

      Declaration of interests

      The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

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