Hospital costs and prognosis in end-stage renal disease patients receiving coronary artery bypass grafting

Coronary Artery Bypass Grafting in Dialysis-Dependent Patients - Key Peri-Operative Considerations

Cardiovascular disease represents the leading cause of mortality in dialysis-dependent (DD) patients, with the great majority of these patients afflicted by severe coronary artery disease. As rates of end-stage renal disease increase worldwide, DD patients represent a growing proportion of the coronary artery bypass grafting (CABG) cohort. Yet, these patients are complex, with crucial changes in their haemodynamic and physiologic profiles that complicate revascularisation surgery. First, this comprehensive literature review explores the outcomes and prognostic factors for DD patients undergoing CABG. We then summarise the intricacies relating to important peri-operative decisions such as use of cardio-pulmonary bypass and choice of conduit.

The Effect of Chronic and End Stage Renal Disease on Long-term Outcomes after Infrainguinal Bypass

OBJECTIVES

Patients undergoing infrainguinal bypass for chronic limb threatening ischemia (CLTI) with renal dysfunction are at an increased risk for perioperative and long-term morbidity and mortality. Our goal was to examine perioperative and 3-year outcomes after lower extremity bypass for CLTI stratified by kidney function.

METHODS

A retrospective, single-center analysis of lower extremity bypass for CLTI was performed between 2008 and 2019. Kidney function was categorized as normal (estimated glomerular filtration rate (eGFR) ≥60ml/min/1.73m²), chronic kidney disease (CKD) (eGFR15-59ml/min/1.73m²), and end stage renal disease (ESRD) (eGFR<15ml/min/1.73m²). Kaplan-Meier and multivariable analysis were performed.

RESULTS

There were 221 infrainguinal bypasses performed for CLTI. Patients were classified by renal function as normal (59.7%), CKD (24.4%), and ESRD (15.8%). Average age was 66 years and 65% were male. Overall, 77% had tissue loss with 9%, 45%, 24%, and 22% being Wound, Ischemia, and foot Infection (WIfI) stages 1-4, respectively. The majority (58%) of bypass targets were infrapopliteal and 58% used ipsilateral greater saphenous vein. The 90-day mortality and readmission rates were 2.7% and 49.8%, respectively. ESRD, compared to CKD and normal renal function, respectively, had the highest 90-day mortality (11.4% vs. 1.9% vs. .8%, P=.002) and 90-day readmission (69% vs. 55% vs. 43%, P=.017). On multivariable analysis, ESRD, but not CKD, was associated with higher 90-day mortality (OR 16.9, 95% CI 1.83-156.6, P=.013) and 90-day readmission (OR 3.02, 95% CI 1.2-7.58, P=.019). Kaplan Meier 3-year analysis showed no difference between groups for primary patency or major amputation, however ESRD, compared to CKD and normal renal function, respectively, had worse primary-assisted patency (60% vs. 76% vs. 84%, P=.03) and survival (72% vs. 96% vs. 94%, P=.001). On multivariable analysis, ESRD and CKD were not associated with 3-year primary patency loss/death, but ESRD was associated with higher primary-assisted patency loss (HR 2.61, 95% CI 1.23-5.53, P=.012). ESRD and CKD were not associated with 3-year major amputation/death. ESRD was associated with higher 3-year mortality (HR 4.95, 95% CI 1.52-16.2, P=.008) while CKD was not.

CONCLUSION

ESRD, but not CKD, was associated with higher perioperative and long-term mortality after lower extremity bypass for CLTI. Although ESRD was associated with lower long-term primary-assisted patency, there were no differences in loss of primary patency or major amputation.

Clinical outcomes in end stage renal disease on dialysis and severe coronary artery disease: A real-world study

BACKGROUND

The optimal management of patients with end-stage renal disease (ESRD) on dialysis with severe coronary artery disease (CAD) has not been determined.

METHODS

Between 2013 and 2017, all patients with ESRD on dialysis who had left main (LM) disease, triple vessel disease (TVD) and/or severe CAD for consideration of coronary artery bypass graft (CABG) were included. Patients were divided into 3 groups based on final treatment modality: CABG, percutaneous coronary intervention (PCI), optimal medical therapy (OMT). Outcome measures include in-hospital, 180-day, 1-year and overall mortality and major adverse cardiac events (MACE).

RESULTS

In total, 418 patients were included (CABG 11.0%, PCI 65.6%, OMT 23.4%). Overall, 1-year mortality and MACE rates were 27.5% and 55.0% respectively. Patients who underwent CABG were significantly younger, more likely to have LM disease and have no prior heart failure. In this non-randomized setting, treatment modality did not impact on 1-year mortality, although the CABG group had significantly lower 1-year MACE rates (CABG 32.6%, PCI 57.3%, OMT 59.2%; CABG vs. OMT p < 0.01, CABG vs. PCI p < 0.001). Independent predictors of overall mortality include STEMI presentation (HR 2.31, 95% CI 1.38-3.86), prior heart failure (HR 1.84, 95% CI 1.22-2.75), LM disease (HR 1.71, 95% CI 1.26-2.31), NSTE-ACS presentation (HR 1.40, 95% CI 1.03-1.91) and increased age (HR 1.02, 95% CI 1.01-1.04).

CONCLUSION

Treatment decisions for patients with severe CAD with ESRD on dialysis are complex. Understanding independent predictors of mortality and MACE in specific treatment subgroups may provide valuable insights into the selection of optimal treatment options.

The Prediction Model of Medical Expenditure Appling Machine Learning Algorithm in CABG Patients

Most patients face expensive healthcare management after coronary artery bypass grafting (CABG) surgery, which brings a substantial financial burden to the government. The National Health Insurance Research Database (NHIRD) is a complete database containing over 99% of individuals' medical information in Taiwan. Our research used the latest data that selected patients who accepted their first CABG surgery between January 2014 and December 2017 (n = 12,945) to predict which factors will affect medical expenses, and built the prediction model using different machine learning algorithms. After analysis, our result showed that the surgical expenditure (X4) and 1-year medical expenditure before the CABG operation (X14), and the number of hemodialysis (X15), were the key factors affecting the 1-year medical expenses of CABG patients after discharge. Furthermore, the XGBoost and SVR methods are both the best predictive models. Thus, our research suggests enhancing the healthcare management for patients with kidney-related diseases to avoid costly complications. We provide helpful information for medical management, which may decrease health insurance burdens in the future.

Machine-Learning Techniques for Feature Selection and Prediction of Mortality in Elderly CABG Patients

Coronary artery bypass surgery grafting (CABG) is a commonly efficient treatment for coronary artery disease patients. Even if we know the underlying disease, and advancing age is related to survival, there is no research using the one year before surgery and operation-associated factors as predicting elements. This research used different machine-learning methods to select the features and predict older adults' survival (more than 65 years old). This nationwide population-based cohort study used the National Health Insurance Research Database (NHIRD), the largest and most complete dataset in Taiwan. We extracted the data of older patients who had received their first CABG surgery criteria between January 2008 and December 2009 (n = 3728), and we used five different machine-learning methods to select the features and predict survival rates. The results show that, without variable selection, XGBoost had the best predictive ability. Upon selecting XGBoost and adding the CHA2DS score, acute pancreatitis, and acute kidney failure for further predictive analysis, MARS had the best prediction performance, and it only needed 10 variables. This study's advantages are that it is innovative and useful for clinical decision making, and machine learning could achieve better prediction with fewer variables. If we could predict patients' survival risk before a CABG operation, early prevention and disease management would be possible.