Coronary Revascularization in Patients With Advanced Chronic Kidney Disease

Coronary revascularisation in patients with chronic kidney disease and end-stage renal disease: A meta-analysis

OBJECTIVES

To compare coronary artery bypass grafting (CABG) and percutaneous coronary intervention (PCI) for revascularising coronary arteries in patients with chronic kidney disease (CKD) and end-stage renal disease (ESRD). CKD is described as a continuous decrease in the glomerular filtration rate or abnormalities in kidney structure or function.

METHODS

PubMed, Cochrane Library and Embase databases were searched for studies on the revascularisation of coronary arteries in patients with CKD and ESRD.

RESULTS

Since no randomised controlled trials (RCTs) have addressed this issue so far, 31 observational studies involving 74 805 patients were included in this meta-analysis. Compared with PCI, patients undergoing CABG have significantly higher early mortality (CKD: RR = 1.62, 95% CI: 1.17-2.25, pheterogeneity = 0.476, I²  = 0; ESRD: RR = 1.99, 95% CI: 1.46-2.71, pheterogeneity = 0.001, I²  = 66.9%). Patients with ESRD undergoing CABG have significantly lower all-cause mortality (RR = 0.95, 95% CI: 0.93-0.96, pheterogeneity < 0.001, I²  = 82.9%) and cardiac mortality (RR = 0.73, 95% CI: 0.58-0.92, pheterogeneity = 0.908, I²  = 0). The long-term risk of repeat revascularisation (CKD: RR = 0.24, 95% CI: 0.19-0.30, pheterogeneity = 0.489, I²  = 0; ESRD: RR = 0.23, 95% CI: 0.15-0.34, pheterogeneity = 0.012, I²  = 54.4%) and myocardial infarction (CKD: RR = .57, 95% CI: 0.38-0.85, pheterogeneity = 0.025, I²  = 49.9%; ESRD: RR = 0.42, 95% CI: 0.40-0.44, pheterogeneity = 0.49, I²  = 0) remained significantly higher in the PCI group.

CONCLUSIONS

Patients with ESRD, but not CKD, who underwent CABG had significantly lower all-cause mortality and cardiac mortality. However, CABG was associated with an increased risk of early mortality in patients with CKD or ESRD. Adequately powered, contemporary, prospective RCTs are needed to define the optimal revascularisation strategy for patients with CKD and ESRD.

Development of a machine learning model to predict the risk of late cardiogenic shock in patients with ST-segment elevation myocardial infarction

Background

The in-hospital mortality of patients with ST-segment elevation myocardial infarction (STEMI) increases to more than 50% following a cardiogenic shock (CS) event. This study highlights the need to consider the risk of delayed calculation in developing in-hospital CS risk models. This report compared the performances of multiple machine learning models and established a late-CS risk nomogram for STEMI patients.

Methods

This study used logistic regression (LR) models, least absolute shrinkage and selection operator (LASSO), support vector regression (SVM), and tree-based ensemble machine learning models [light gradient boosting machine (LightGBM) and extreme gradient boosting (XGBoost)] to predict CS risk in STEMI patients. The models were developed based on 1,598 and 684 STEMI patients in the training and test datasets, respectively. The models were compared based on accuracy, the area under the curve (AUC), recall, precision, and Gini score, and the optimal model was used to develop a late CS risk nomogram. Discrimination, calibration, and the clinical usefulness of the predictive model were assessed using C-index, calibration plotd, and decision curve analyses.

Results

A total of 2282 STEMI patients recruited between January 1, 2016 and May 31, 2020, were included in the complete dataset. The linear models built using LASSO and LR showed the highest overall predictive power, with an average accuracy over 0.93 and an AUC above 0.82. With a C-index of 0.811 [95% confidence interval (CI): 0.769-0.853], the LASSO nomogram showed good differentiation and proper calibration. In internal validation tests, a high C-index value of 0.821 was achieved. Decision curve analysis (DCA) and clinical impact curve (CIC) examination showed that compared with the previous score-based models, the LASSO model showed superior clinical relevance.

Conclusions

In this study, five machine learning methods were developed for in-hospital CS prediction. The LASSO model showed the best predictive performance. This nomogram could provide an accurate prognostic prediction for CS risk in patients with STEMI.

Comparative Analysis of the Effect of Renal Function on the Spectrum of Coronary Artery Disease

BACKGROUND

We aim to assess the differential effect of renal impairment across the spectrum of patients with ischaemic heart disease and to study if any established risk factors may modify this risk.

METHODS

A total of 2013 patients who underwent revascularization for ST-segment elevation myocardial infarction or invasive physiology assessment were included. Renal impairment was defined as glomerular filtration rate less than 60 ml/min/1.73m². Clinical endpoints were prospectively collected, and the primary endpoint was defined as the composite endpoints of death, myocardial infarction, and unplanned revascularization.

RESULTS

593 (30%) presented with ST-segment elevation myocardial infarction, and 1362 (70%) stable patients had invasive ischaemia assessment which resulted in 37% receiving revascularization and 63% being deferred. Renal impairment was associated with increased adverse events in myocardial infarction [HR 1.77 (95% CI 1.15 to 2.74)], but not in the revascularized stable group [(HR 1.14 (95% CI 0.62 to 2.08)] or the deferred group [HR 1.31 (95% CI 0.84 to 2.03)]. There was an exponential increase in the future risk in ST-segment elevation myocardial infarction patients with severe renal dysfunction (glomerular filtration rate < 30) compared to a linear relationship in stable patients. Age and hypertension were the only two factors that had a differential impact across three groups (P<0.05 for interactions), with inconsistent directional effect of hypertension between medically managed and revascularized groups.

CONCLUSIONS

The magnitude of risk of renal impairment varies according to the clinical presentation of coronary artery disease with more weighted risk in myocardial infarction compared to stable patients.