Predictive scores for major bleeding after coronary artery bypass surgery in low operative risk patients

Salsano, Antonio; Dominici, Carmelo; Nenna, Antonio; Olivieri, Guido; Miette, Ambra; Barbato, Raffaele; Sportelli, Elena; Natali, Roberto; Maestri, Francesco; Chello, Massimo; Mariscalco, Giovanni; Santini, Francesco

doi:10.23736/s0021-9509.20.11048-6

Cited by 6 publications

(5 citation statements)

References 26 publications

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“…4 Despite the clinical significance of acute hemorrhage due to its life-threatening characteristics and the need for urgent preparation of massive transfusion to enable optimal management, the development of a prediction model for massive transfusion during surgery is lacking. Several studies have tried to predict massive transfusion during specific types of high-risk procedures, such as liver transplantation, [5][6][7] cardiac surgery, [8][9][10] placenta previa, 11,12 and spine surgery, 13 but the performances of the prediction models were low, with an area under the receiver operating characteristic curve (AUROC) of 0.65 to 0.84. These low predictive powers may originate from the fact that models developed in previous studies were solely based on preoperative factors without incorporating dynamic intraoperative parameters.…”

Section: Introductionmentioning

confidence: 99%

Development and Validation of a Prediction Model for Need for Massive Transfusion During Surgery Using Intraoperative Hemodynamic Monitoring Data

Lee

Kim

et al. 2022

JAMA Netw Open

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ImportanceMassive transfusion is essential to prevent complications during uncontrolled intraoperative hemorrhage. As massive transfusion requires time for blood product preparation and additional medical personnel for a team-based approach, early prediction of massive transfusion is crucial for appropriate management.ObjectiveTo evaluate a real-time prediction model for massive transfusion during surgery based on the incorporation of preoperative data and intraoperative hemodynamic monitoring data.Design, Setting, and ParticipantsThis prognostic study used data sets from patients who underwent surgery with invasive blood pressure monitoring at Seoul National University Hospital (SNUH) from 2016 to 2019 and Boramae Medical Center (BMC) from 2020 to 2021. SNUH represented the development and internal validation data sets (n = 17 986 patients), and BMC represented the external validation data sets (n = 494 patients). Data were analyzed from November 2020 to December 2021.ExposuresA deep learning–based real-time prediction model for massive transfusion.Main Outcomes and MeasuresMassive transfusion was defined as a transfusion of 3 or more units of red blood cells over an hour. A preoperative prediction model for massive transfusion was developed using preoperative variables. Subsequently, a real-time prediction model using preoperative and intraoperative parameters was constructed to predict massive transfusion 10 minutes in advance. A prediction model, the massive transfusion index, calculated the risk of massive transfusion in real time.ResultsAmong 17 986 patients at SNUH (mean [SD] age, 58.65 [14.81] years; 9036 [50.2%] female), 416 patients (2.3%) underwent massive transfusion during the operation (mean [SD] duration of operation, 170.99 [105.03] minutes). The real-time prediction model constructed with the use of preoperative and intraoperative parameters significantly outperformed the preoperative prediction model (area under the receiver characteristic curve [AUROC], 0.972; 95% CI, 0.968-0.976 vs AUROC, 0.824; 95% CI, 0.813-0.834 in the SNUH internal validation data set; P &lt; .001). Patients with the highest massive transfusion index (ie, &gt;90th percentile) had a 47.5-fold increased risk for a massive transfusion compared with those with a lower massive transfusion index (ie, &lt;80th percentile). The real-time prediction model also showed excellent performance in the external validation data set (AUROC of 0.943 [95% CI, 0.919-0.961] in BMC).Conclusions and RelevanceThe findings of this prognostic study suggest that the real-time prediction model for massive transfusion showed high accuracy of prediction performance, enabling early intervention for high-risk patients. It suggests strong confidence in artificial intelligence-assisted clinical decision support systems in the operating field.

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Section: Introductionmentioning

confidence: 99%

Development and Validation of a Prediction Model for Need for Massive Transfusion During Surgery Using Intraoperative Hemodynamic Monitoring Data

Lee

Kim

et al. 2022

JAMA Netw Open

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“…TEG values deviated strongest from baseline after CPB. The maximum relative decrease after CPB was 16 [12][13][14][15][16][17][18][19][20][21] % for TEG-MA and 8 [0-16] % for TEG-angle. The correlations between fibrinogen and TEG values are presented in supplementary Table A.…”

Section: Fibrinogen Concentrations and Teg Resultsmentioning

confidence: 99%

“…The association between blood loss and TEG after CPB was explored using linear regression analysis including a multivariable model with a priori selected confounding factors that were based on previously described risk factors for blood loss after cardiac surgery, including gender, body mass index, eGFR-MDRD4 (estimated glomerular filtration rate by modification of diet in renal disease 4 variable equation), baseline hemoglobin and CPB time. [15][16][17][18][19] All analyses were repeated using fibrinogen as the coagulation parameter. No interactions were investigated.…”

Section: Discussionmentioning

confidence: 99%

Association of Plasma Fibrinogen and Thromboelastography With Blood Loss in Complex Cardiac Surgery

Vlot

Dongen

Willemsen

et al. 2021

Clin Appl Thromb Hemost

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Postoperative coagulopathic bleeding is common in cardiac surgery and is associated with increased morbidity and mortality. Ideally, real-time information on in-vivo coagulation should be available. However, up to now it is unclear which perioperative coagulation parameters can be used best to accurately identify patients at increased risk of bleeding. The present study analyzed the associations of perioperative fibrinogen concentrations and whole blood viscoelastic tests with postoperative bleeding in 89 patients undergoing combined cardiac surgery procedures. Postoperative bleeding was recorded until 24 hours after surgery. Regression analyses were performed to establish associations between blood loss and coagulation parameters after cardiopulmonary bypass including a prediction model with known confounding factors for bleeding. Coagulation tests show large changes over the perioperative course with the strongest coagulopathic deviations from baseline after cardiopulmonary bypass. After adjustment for multiple confounders, viscoelastic clot strength instead of fibrinogen concentration showed a similar performance for 24 hour blood loss and a better performance for 6 hour blood loss. This makes intraoperative viscoelastic testing a useful tool to strengthen early clinical decision-making with the potential to reduce perioperative blood transfusions.

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“…24 The WILL-BLEED Score also showed renal dysfunction as a risk despite antiplatelet therapy choice for major bleeding associated with CABG. 25,26 More research is needed in this group to validate their optimal antiplatelet therapy, but for now that group may benefit more from SAPT post CABG due to the risk of bleeding. This study has several limitations.…”

Section: Discussionmentioning

confidence: 99%

Safety Comparison of Monotherapy Aspirin to Dual Antiplatelet Therapy Following Coronary Artery Bypass Surgery

Nei

Wamsley

Mara

et al. 2022

Clin Appl Thromb Hemost

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Background Dual antiplatelet therapy (DAPT) is recommended over single antiplatelet therapy (SAPT) in patients following coronary artery bypass grafting (CABG). The compilation of evidence has focused on the efficacy of DAPT to limit risk of graft occlusion, however the safety, especially in the on-pump CABG population, is less well described. The aim of this study was to assess the safety of DAPT versus SAPT after on-pump CABG. Methods This was a single-center, retrospective cohort analysis of adult patients following isolated on-pump CABG between January 2012 and December 2019 not on oral anticoagulation at discharge. The primary endpoint was occurrence of a composite bleeding event identified by pre-specified ICD codes. Secondary endpoints consisted of 30-day and 1-year mortalities along with individual bleeding components. Results Of the 2341 patients included 1250 patients were in the SAPT arm and 1091 patients in the DAPT arm. The study populations differed by age, prior MI, PAD, and CHF status/stage. Bleeding events occurred in a total of 70 patients (3.0%), with 36 patients (2.9%) in the SAPT arm and 34 patients (3.1%) in the DAPT arm ( P = .74). 30-day (SAPT 0.7% vs DAPT 0.4%) and 1-year (SAPT 3.3% vs DAPT 2.3%) mortality were not significantly different between groups. The most frequent bleed event was in the gastrointestinal tract. Conclusion In this study, DAPT was not associated with an increase in composite bleeding compared to SAPT. This study could reduce the barrier to prescribing of DAPT given previous efficacy data.

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Predictive scores for major bleeding after coronary artery bypass surgery in low operative risk patients

Cited by 6 publications

References 26 publications

Development and Validation of a Prediction Model for Need for Massive Transfusion During Surgery Using Intraoperative Hemodynamic Monitoring Data

Development and Validation of a Prediction Model for Need for Massive Transfusion During Surgery Using Intraoperative Hemodynamic Monitoring Data

Association of Plasma Fibrinogen and Thromboelastography With Blood Loss in Complex Cardiac Surgery

Safety Comparison of Monotherapy Aspirin to Dual Antiplatelet Therapy Following Coronary Artery Bypass Surgery

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