European Heart Journal - Quality of Care and Clinical Outcomes最新文献

Aims: The relationship between the level of baseline risk factor control and cardiovascular outcomes in hypertensive patients with blood pressure interventions is not well understood. It is also unclear whether the level of baseline risk factor control is persuasively associated with cardiovascular outcomes in hypertensive patients with a blood pressure lowering strategy.

Method and results: We performed an analysis of the Strategy of Blood Pressure Intervention in the Elderly Hypertensive Patients (STEP) trial. Participants without complete baseline risk factor data were excluded. The primary outcome was a composite of cardiovascular events and all-cause mortality. Cox proportional hazard models were used to calculate the hazard ratio (HR) and estimate the association between risk factor control levels (≥6, 5, 4, and ≤3) and cardiovascular outcomes. A total of 8337 participants were involved in the analysis, and the median follow-up period was 3.19 years. Each additional risk factor uncontrolled was associated with a 24% higher cardiovascular risk (HR 1.24, 95% CI 1.11-1.37). Compared with participants with optimal risk factor control, those with ≤3 factors control exhibited 95% higher cardiovascular risk (HR 1.95, 95% CI 1.37-2.77). The corresponding protective effects of multiple risk factor modification were not influenced by intensive or standard antihypertensive treatment (P for interaction = 0.71).

Conclusion: A stepwise association was observed between cardiovascular risk and the number of risk factor control in hypertensive patients. The more risk factors were modified, the less cardiovascular risk was observed, irrespective of different blood pressure lowering strategies. Comprehensive risk factor control strategies are warranted to reduce cardiovascular disease risk in hypertensive patients.

Background: Use of intravascular ultrasound (IVUS) during percutaneous coronary intervention (PCI) is associated with improved clinical outcomes over angiography alone. Despite this, the adoption of IVUS in clinical practice remains low.

Aims: To examine the cost-effectiveness of IVUS-guided PCI compared to angiography alone in patients with acute coronary syndromes (ACS).

Methods and results: A 1-year decision tree and lifetime Markov model were constructed to compare the cost-effectiveness of IVUS-guided PCI to angiography alone for two hypothetical adult populations consisting of 1000 individuals: ST-elevation myocardial infarction (STEMI) and unstable angina/non-ST-elevation myocardial infarction (UA/NSTEMI) patients undergoing drug-eluting stent (DES) implantation. The United Kingdom (UK) healthcare system perspective was applied using 2019/20 costs. All-cause death, myocardial infarction (MI), repeat PCI, lifetime costs, life expectancy, and quality-adjusted life-years (QALYs) were assessed. Over a lifetime horizon, IVUS-guided PCI was cost-effective compared to angiography alone in both populations, yielding an incremental cost-effectiveness ratio of £3649 and £5706 per-patient in STEMI and UA/NSTEMI patients, respectively.In the 1-year time horizon, the model suggested that IVUS was associated with reductions in mortality, MI, and repeat PCI by 51%, 33%, and 52% in STEMI and by 50%, 29%, and 57% in UA/NSTEMI patients, respectively. Sensitivity analyses demonstrated the robustness of the model with IVUS being 100% cost-effective at a willingness to pay threshold of £20 000 per QALY-gained.

Conclusions: From a UK healthcare perspective, an IVUS-guided PCI strategy was highly cost-effective over angiography alone amongst ACS patients undergoing DES implantation due to the medium- and long-term reduction in repeat PCI, death, and MI.

Dilated cardiomyopathy (DCM) is extensively discussed in numerous expert consensus documents and international guidelines, with differing recommendations. To support clinicians in daily practice and decision-making, we conducted a systematic review of key guidelines and recommendations concerning the diagnosis and clinical management of DCM. Our research encompassed MEDLINE and EMBASE databases for relevant articles published, as well as the websites of relevant scientific societies. We identified two guidelines and one scientific statement that met stringent criteria, thereby qualifying them for detailed systematic analysis. Our review revealed consensus on several key aspects: the definition of DCM, the use of B-type natriuretic peptides and high-sensitivity troponin in laboratory testing, the essential role of multimodality cardiovascular imaging for initial diagnosis, genetic counselling, and the management of advanced disease. Nonetheless, notable areas of variation included the formation of multidisciplinary management teams, the role of cascade genetic testing, pathways for arrhythmic risk stratification, and the criteria for prophylactic defibrillator implantation. Significant evidence gaps persist, particularly regarding the clinical trajectory of genetic, non-genetic and gene-elusive forms of DCM, the use of cardiovascular magnetic resonance in phenotype-negative family members with genotype-positive probands, and the development of potential aetiology-oriented therapies. Addressing these gaps could enhance clinical outcomes and inform future research directions and guideline development.

ESC Guidelines provide best practice, evidence-based recommendations for diagnosing and treating patients with cardiovascular diseases. It is not always possible for best practices to be followed, however, particularly in low-resource settings. To address this issue, a set of guideline-related documents were created to identify key priorities for users in these settings. The documents highlight the related recommendations and describe key strategies for clinicians to approach implementation of these recommendations or discuss alternatives which are in line with the intention of the recommendations, if not having all of the same advantages. The suggestions cannot be used as exact substitutes for the original recommendations in the guidelines, which have not been altered and continue to reflect best practice. This document on key priorities for low-resource settings was developed by the task force Chairs and other members of the task force which produced the 2023 ESC Guidelines for the management of cardiomyopathies, which are freely available on the ESC website (https://www.escardio.org/Guidelines). This document also underwent external review including international experts from within and beyond Europe and included non-ESC associations. The non-ESC associations were The Interamerican Society of Cardiology (IASC), The Pan-African Society of Cardiology (PASCAR), The Asian Pacific Society of Cardiology (APSC) and The Asean Federation of Cardiology (AFC).

Background and aims: The Academic Research Consortium for High Bleeding Risk Criteria (ARC-HBR) are recommended by guidelines for bleeding risk assessment in ST-elevation myocardial infarction (STEMI). The aim of this study was to identify possible other risk factors and adjust the original ARC-HBR criteria for confounders.

Methods: All consecutive STEMI patients managed in a Finnish tertiary hospital between 2016-2022 were identified using a database search. Data collection was done by reviewing electronic patient records. Bleeding risk was assessed according to the ARC-HBR criteria. The primary endpoint was non-access site bleeding academic research consortium (BARC) type 3 or 5 bleeding during 1-year follow-up.

Results: A total of 1548 STEMI patients were analysed. HBR criteria was fulfilled in 661 (42.7%). Multivariable competing risk analysis identified only 4 individual ARC-HBR criteria as independent risk factors for bleeding. Smoking status was identified as a novel bleeding risk factor. Current and former smokers had increased bleeding risk compared with never smokers (hazard ratio [HR] 3.01, 95% confidence interval [CI] 1.62-5.61 and HR 1.99, CI 1.19-3.34). In those not meeting any ARC-HBR criteria, cumulative BARC 3 or 5 incidence of current smoking was 3.40% and intracranial haemorrhage (ICH) 1.36%. Thus, exceeding ARC-HBR definition for a major criterion. In the non-HBR group the prevalence of current smoking was 40.4% (n = 358).

Conclusions: Current and former smoking predicts major bleeding complications after STEMI. Current smoking is highly prevalent among those classified as non-HBR according to the ARC-HBR criteria.

Background: There is substantial hospital-level variation in 30-day risk-standardized mortality rate (RSMR) and risk-standardized readmission rate (RSRR) after transcatheter aortic valve replacement (TAVR). However, the relationship between hospital RSMRs and RSRRs has not been well characterized.

Methods: We analyzed data on 141,905 Medicare fee-for-service beneficiaries who underwent TAVR across 512 hospitals between October 1, 2015 and December 31, 2020. The primary and secondary outcomes of interest were 30-day all-cause mortality and 30-day all-cause readmissions, respectively. Hierarchical logistic regression models with random hospital-level intercepts were used to estimate RSMRs and RSRRs for each hospital. We used Pearson correlation coefficient (r) and restricted cubic spline regression to determine the relationship between RSMR and RSRR in the overall cohort and within subgroups based on hospital characteristics.

Results: The median (IQR) hospital-level 30-day RSMR was 2.2% (2.1%-2.4%), ranging from 1.3% to 3.5%. Similarly, the median (IQR) hospital-level 30-day RSRR was 13.2% (12.7%-13.8%), ranging from 10.6% to 16.8%. In the overall cohort, there was weak correlation between 30-day RSMR and RSRR after TAVR (r=0.25, 95% CI 0.17-0.33, p<0.001). Subgroup analyses by hospital characteristics demonstrated weakest correlation between RSMR and RSRR for non-JCAHO accredited hospitals (r=0.07), hospitals in the Midwest (r=0.12) and West (r=0.14), and hospitals with low TAVR volume (r=0.15).

Conclusions: Risk-standardized mortality and readmission rates after TAVR are weakly correlated, suggesting that hospital practices and processes of care influencing mortality are likely different from those influencing readmissions after TAVR, thereby necessitating measurement of both outcomes and developing specific interventions to decrease mortality and readmissions.