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Published on: March 27, 2018
Predicting the mortality of patients with cardiogenic shock after coronary artery bypass grafting
Xiaozheng Zhou1, Wen Tan1, Maomao Liu1
1Center for Cardiac Intensive, Beijing Anzhen Hospital, Capital Medical University, Beijing, China.
Insights
Cardiogenic shock (CS) after coronary artery bypass grafting (CABG) is a major cause of death. A new risk score (ACCS) effectively predicts in-hospital mortality, identifying high-risk patients for better management.
Area of Science:
- Cardiology
- Cardiac Surgery
- Critical Care Medicine
Background:
- Cardiogenic shock (CS) is the primary cause of mortality following coronary artery bypass grafting (CABG).
- Identifying risk factors and developing predictive models for CS post-CABG is critical for improving patient outcomes.
Purpose of the Study:
- To identify independent risk factors for in-hospital mortality in patients undergoing CABG.
- To develop and validate a risk-predictive model for cardiogenic shock after CABG.
Main Methods:
- Retrospective observational study of 496 patients who underwent CABG.
- Logistic regression analysis to identify significant prognostic factors for mortality.
- Development and validation of the Cardiogenic Shock after CABG Score (ACCS) risk model.
Main Results:
- Independent prognostic factors identified: E/A ratio, postoperative brain natriuretic peptide, postoperative arterial lactate, multiple arrhythmias, and carotid artery stenosis.
- The ACCS model demonstrated strong discrimination (AUROC 0.937) and good calibration.
- Cross-validation showed a low mean misdiagnosis rate (5.56%).
Conclusions:
- The ACCS score serves as a validated risk-predictive model for in-hospital mortality in CS patients post-CABG.
- Class III ACCS indicates a potentially worse prognosis, aiding in clinical decision-making.
Introduction:
Cardiogenic shock (CS) is a critical condition and the leading cause of mortality after coronary artery bypass grafting (CABG). To define the risk factors for CS in patients who undergo CABG and create a risk-predictive model is crucial.
Methods:
In this observational study, we retrospectively evaluated consecutive patients who underwent CABG between January 2018 and October 2022 at Beijing Anzhen Hospital. A total of 496 patients were enrolled and categorized into the training (396 cases) and internal test (100 cases) sets. The variables significantly associated with mortality (p < 0.05) were analyzed using logistic regression analyses.
Results:
The E/A ratio at admission, postoperative brain natriuretic peptide, postoperative arterial lactate, two or more arrhythmias at the same time after CABG, and carotid artery stenosis at admission were identified as independent prognostic factors for in-hospital mortality after multivariate logistic regression analysis. The CS after CABG score (ACCS) was established and three classes of ACCS, named classes I (ACCS, <20), II (ACCS, 20-30), and III (ACCS, >30), made up the risk model. The ACCS showed better discrimination with an AUROC of 0.937 (95% confidence interval, 0.982-0.892) and calibration with the Hosmer-Lemeshow test (X2 = 5.854 with 8 df; p = 0.664). In addition, tenfold cross-validation demonstrated that the mean misdiagnosis rate was 5.56% and the lowest misdiagnosis rate was 6.38%.
Conclusion:
The ACCS score represents a risk-predictive model for in-hospital mortality of patients with CS after CABG in acute care settings. Patients identified as class III may have a worse prognosis.
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