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

The role of gender in resting full-cycle ratio (RFR) guided coronary revascularization

BACKGROUND

Gender-based differences in clinical outcomes of patients undergoing fractional flow reserved (FFR) guided coronary revascularization is well documented. This study aimed to compare resting full-cycle ratio (RFR) values between men and women and whether this translated into difference in clinical outcomes in patients who underwent RFR-guided coronary revascularization.

METHODS

This was a retrospective single-centre study of consecutive patients who underwent RFR-guided revascularization for coronary lesions with intermediate degree of stenosis. The primary endpoint was a composite of all-cause mortality, myocardial infarction (MI), unplanned revascularization, and unstable angina requiring hospital admission at one year.

RESULTS

In 373 consecutive patients (510 lesions, 26% women) there was no statistically significant difference in RFR value between men and women (0.90 ± 10 versus 0.90 ± 11, P = 0.95). There was no statistically significant difference between men and women in the primary endpoint, even after adjustment to the imbalance between the two groups [3.7% vs. 3.0%; HR 1.43, 95% CI (0.46 to 4.43), P = 0.54]; or its individual components of death (1.1% vs 0.8%, P = 0.76), MI (1.9% vs 0.8%, P = 0.38) or unplanned revascularization, including unstable angina admissions (2.6% vs 2.3%, P = 0.82). The comparable clinical outcomes were consistent across all different subgroups, including clinical presentation, diabetes status, left ventricle systolic function, kidney function, and the interrogated coronary artery.

CONCLUSION

Our study suggests no significant gender-based difference in the value of RFR or 1-year clinical outcomes in patients undergoing resting physiology guided coronary revascularization.

1-Year Mortality Prediction through Artificial Intelligence Using Hemodynamic Trace Analysis among Patients with ST Elevation Myocardial Infarction

Background and Objectives: Patients presenting with ST Elevation Myocardial Infarction (STEMI) due to occlusive coronary arteries remain at a higher risk of excess morbidity and mortality despite being treated with primary percutaneous coronary intervention (PPCI). Identifying high-risk patients is prudent so that close monitoring and timely interventions can improve outcomes. Materials and Methods: A cohort of 605 STEMI patients [64.2 ± 13.2 years, 432 (71.41%) males] treated with PPCI were recruited. Their arterial pressure (AP) wave recorded throughout the PPCI procedure was analyzed to extract features to predict 1-year mortality. After denoising and extracting features, we developed two distinct feature selection strategies. The first strategy uses linear discriminant analysis (LDA), and the second employs principal component analysis (PCA), with each method selecting the top five features. Then, three machine learning algorithms were employed: LDA, K-nearest neighbor (KNN), and support vector machine (SVM). Results: The performance of these algorithms, measured by the area under the curve (AUC), ranged from 0.73 to 0.77, with accuracy, specificity, and sensitivity ranging between 68% and 73%. Moreover, we extended the analysis by incorporating demographics, risk factors, and catheterization information. This significantly improved the overall accuracy and specificity to more than 76% while maintaining the same level of sensitivity. This resulted in an AUC greater than 0.80 for most models. Conclusions: Machine learning algorithms analyzing hemodynamic traces in STEMI patients identify high-risk patients at risk of mortality.

Antiplatelet Therapy in End-stage Renal Disease Patients on Maintenance Dialysis: a State-of-the-art Review

Patients with end-stage renal disease (ESRD) on maintenance dialysis have an increased risk of ischaemic events, such as recurrent myocardial infarction (MI) and stroke. Potent antiplatelet therapy may help mitigate this risk. Nonetheless, ERSD patients are also at increased risk of bleeding due to their complex vascular milieu, which limits the routine use of potent P2Y12 inhibitors. Moreover, these patients are often underrepresented or excluded from major clinical trials leaving a significant gap in existing knowledge. Understanding the mechanisms of this paradox may serve as a benchmark for the development of ESRD trials. The present review aims to provide an overview of the pathophysiological nature of increased bleeding and ischaemic risks in ERSD patients as well as summarize available evidence of antiplatelet use and propose new concepts to guide physicians in selecting appropriate drug regimes for this high-risk cohort.

The Role of Coronary Physiology in Contemporary Percutaneous Coronary Interventions

Invasive assessment of coronary physiology has radically changed the paradigm of myocardial revascularization in patients with coronary artery disease. Despite the prognostic improvement associated with ischemia-driven revascularization strategy, functional assessment of angiographic intermediate epicardial stenosis remains largely underused in clinical practice. Multiple tools have been developed or are under development in order to reduce the invasiveness, cost, and extra procedural time associated with the invasive assessment of coronary physiology. Besides epicardial stenosis, a growing body of evidence highlights the role of coronary microcirculation in regulating coronary flow with consequent pathophysiological and clinical and prognostic implications. Adequate assessment of coronary microcirculation function and integrity has then become another component of the decision-making algorithm for optimal diagnosis and treatment of coronary syndromes. This review aims at providing a comprehensive description of tools and techniques currently available in the catheterization laboratory to obtain a thorough and complete functional assessment of the entire coronary tree (both for the epicardial and microvascular compartments).

Sex-based difference in fractional flow reserve and its impact on clinical outcomes

BACKGROUND

Obesity is a real public health problem and is of growing concern. People are resorting to surgical or endoscopic means to fight against overweight and obesity. In recent years, there has been a marked increase in the use of these means and in particular the insertion of a gastric balloon which seems to present less risk than surgical methods. Renal complications from intragastric balloon placement are extremely rare. We report here the case of compression of the left renal vein revealed by lumbar pain and hematuria in an overweight 39-year-old woman who benefited from the balloon gastric placement one month before symptoms. The scanner made the diagnosis and showed a good evolution after the withdrawal of the balloon.

METHODS

This was a prespecified and retrospective analysis of all consecutive patients who underwent FFR assessment for intermediate coronary lesions between January 2014 and December 2015. The primary endpoint was defined as the 1-year composite of cardiac death, vessel-related myocardial infarction, and clinically-driven target vessel revascularization.

RESULTS

In 1554 lesions (23% in women), FFR was lower in men [0.83 ±0.09 vs 0.85 ±0.08, P = .004] driven by LAD values (for LAD P < .001, LCx or RCA P> .40). In proximal lesions (PLs), FFR was lower in men [0.83 ±0.10 vs 0.85 ±0.08, P = .004] with comparable values in non-PLs [0.84 ±0.09 vs 0.85 ±0.08, P = .36]. In PLs, the primary endpoint was higher in women [HR_(adjusted) 3.18 (1.08-9.37), P = .035] with comparable outcomes in non-PLs (P = .032 for interaction). In deferred lesions, the primary endpoint was higher in women [HR_(adjusted) 2.73 (1.10-6.74), P = .03] with no differences in revascularized lesions across sex (P = .02 for interaction). Results were consistent when using propensity score matching analysis.

CONCLUSIONS

There is a sex-based difference in FFR, particularly in stenoses subtending large myocardium, and more evident in deferred lesions.

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.