Dataset Information

Machine learning algorithms to predict major bleeding after isolated coronary artery bypass grafting.

ABSTRACT:

Objectives

Postoperative major bleeding is a common problem in patients undergoing cardiac surgery and is associated with poor outcomes. We evaluated the performance of machine learning (ML) methods to predict postoperative major bleeding.

Methods

A total of 1,045 patients who underwent isolated coronary artery bypass graft surgery (CABG) were enrolled. Their datasets were assigned randomly to training (70%) or a testing set (30%). The primary outcome was major bleeding defined as the universal definition of perioperative bleeding (UDPB) classes 3-4. We constructed a reference logistic regression (LR) model using known predictors. We also developed several modern ML algorithms. In the test set, we compared the area under the receiver operating characteristic curves (AUCs) of these ML algorithms with the reference LR model results, and the TRUST and WILL-BLEED risk score. Calibration analysis was undertaken using the calibration belt method.

Results

The prevalence of postoperative major bleeding was 7.1% (74/1,045). For major bleeds, the conditional inference random forest (CIRF) model showed the highest AUC [0.831 (0.732-0.930)], and the stochastic gradient boosting (SGBT) and random forest models demonstrated the next best results [0.820 (0.742-0.899) and 0.810 (0.719-0.902)]. The AUCs of all ML models were higher than [0.629 (0.517-0.641) and 0.557 (0.449-0.665)], as achieved by TRUST and WILL-BLEED, respectively.

Conclusion

ML methods successfully predicted major bleeding after cardiac surgery, with greater performance compared with previous scoring models. Modern ML models may enhance the identification of high-risk major bleeding subpopulations.

SUBMITTER: Gao Y

PROVIDER: S-EPMC9366116 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature

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Publications

Machine learning algorithms to predict major bleeding after isolated coronary artery bypass grafting.

Gao Yuchen Y Liu Xiaojie X Wang Lijuan L Wang Sudena S Yu Yang Y Ding Yao Y Wang Jingcan J Ao Hushan H

Frontiers in cardiovascular medicine 20220728

<h4>Objectives</h4>Postoperative major bleeding is a common problem in patients undergoing cardiac surgery and is associated with poor outcomes. We evaluated the performance of machine learning (ML) methods to predict postoperative major bleeding.<h4>Methods</h4>A total of 1,045 patients who underwent isolated coronary artery bypass graft surgery (CABG) were enrolled. Their datasets were assigned randomly to training (70%) or a testing set (30%). The primary outcome was major bleeding defined as ...[more]

PMID: 35966564

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Project description:BackgroundProlonged or excessive bleeding after cardiac surgery can lead to a broad spectrum of secondary complications. One of the underlying causes is incomplete wound drainage, with subsequent accumulation of blood and clots in the pericardium. We developed the continuous postoperative pericardial flushing (CPPF) therapy to improve wound drainage and reduce postoperative blood loss and bleeding-related complications after cardiac surgery. This study compared CPPF to standard care in patients after coronary artery bypass grafting (CABG).MethodsThis is a single center, open label, randomized trial that enrolled patients at the Amsterdam UMC, location AMC, Amsterdam, the Netherlands. The study was registered at the 'Netherlands Trial Register', study identifier NTR5200 [1]. Adults undergoing CABG were randomly assigned to receive CPPF therapy or standard care, participants and investigators were not masked to group assignment. The primary end point was postoperative blood loss in the first 12-hours after surgery.FindingsBetween the January 15, 2014 and the March 13, 2017, 169 patients were enrolled and assigned to CPPF therapy (study group; n = 83) or standard care (control group; n = 86). CPPF reduced postoperative blood loss when compared to standard care (median differences -385 ml, reduction 76% p=≤0.001), with the remark that these results are overestimated due to a measurement error in part of the study group. None of patients in the study group required reoperation for non-surgical bleeding versus 3 (4%, 95% CI -0.4% to 7.0%) in the control group. None of the patients in the study group suffered from cardiac tamponade, versus 3 (4%, 95% CI -0,4% to 7.0%) in the control group. The incremental cost-effectiveness ratio was €116.513 (95% bootstrap CI €-882.068 to €+897.278).InterpretationThe use of CPPF therapy after CABG seems to reduce bleeding and bleeding related complications. With comparable costs and no improvement in Qualty of Life (QoL), cost consideration for the implementation of CPPF is not relevant. None of the patients in the study group required re-interventions for non-surgical bleeding or acute cardiac tamponade, which underlines the proof of concept of this novel therapy.FundingThis study was funded by ZonMw, the Netherlands organization for health research and development (project 837001405).

Project description:BACKGROUND:To date, studies evaluating outcome improvements associated with participation in physician-led collaboratives have been limited by the absence of a contemporaneous control group. We examined post cardiac surgery pneumonia rates associated with participation in a statewide, quality improvement collaborative relative to a national physician reporting program. METHODS AND RESULTS:We evaluated 911 754 coronary artery bypass operations (July 1, 2011, to June 30, 2017) performed across 1198 hospitals participating in a voluntary national physician reporting program (Society of Thoracic Surgeons [STS]), including 33 that participated in a Michigan-based collaborative (MI-Collaborative). Unlike STS hospitals not participating in the MI-Collaborative (i.e., STSnonMI) that solely received blinded reports, MI-Collaborative hospitals received a multi-faceted intervention starting November 2012 (quarterly in-person meetings showcasing unblinded data, webinars, site visits). Eighteen of the MI-Collaborative hospitals received additional support to implement recommended pneumonia prevention practices ("MI-CollaborativePlus"), whereas 15 did not ("MI-CollaborativeOnly"). We evaluated rates of postoperative pneumonia, adjusting for patient mix and hospital effects. Baseline patient characteristics were qualitatively similar between groups and time. During the preintervention period (Q3/2011 through Q3/2012), there was no statistically significant difference in the adjusted odds of pneumonia for STS hospitals participating in the MI-Collaborative compared to the STS non-MI hospitals. However, during the intervention period (Q4/2012 through Q2/2017), there was a significant 2% reduction per quarter in the adjusted odds of pneumonia for MI-Collaborative hospitals (n=33) relative to the STS-nonMI hospitals. There was a significant 3% per quarter reduction in the adjusted odds of pneumonia for the MI-CollaborativeOnly (n=15) hospitals relative to the STS-nonMI hospitals. Over the course of the overall study period, the STS-nonMI hospitals had a 1.96% reduction in risk-adjusted pneumonia (pre- vs. intervention periods), which was less than the MI-Collaborative (3.23%, P=0.011). Over the same time period, the MI-CollaborativePlus (n=18) reduced adjusted pneumonia rates by 10.29%, P=0.001. CONCLUSIONS:Participation in a physician-led collaborative was associated with significant reductions in pneumonia relative to a national quality reporting program. Interventions including collaborative learning may yield superior outcomes relative to solely using physician feedback reporting. CLINICAL TRIAL REGISTRATION:URL: https://www.clinicaltrials.gov . Unique identifier: NCT02068716.

Dataset Information

Machine learning algorithms to predict major bleeding after isolated coronary artery bypass grafting.

Objectives

Methods

Results

Conclusion

Publications

Machine learning algorithms to predict major bleeding after isolated coronary artery bypass grafting.

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