Heart最新文献

Background: Current guidelines recommend evaluating patients with ambulatory heart failure (HF) for heart transplantation if their peak oxygen consumption (peak VO₂) is <12 mL/kg/min. However, these recommendations are based on decades-old data.

Methods: We retrospectively analysed 8060 patients with ambulatory HF with cardiopulmonary exercise testing (CPET) data. The primary analysis focused on 1218 patients with left ventricular ejection fraction <40% and peak VO₂ <12 mL/kg/min, enrolled between 2010 and 2022. Survival outcomes (composite of death/left ventricular assist device/heart transplantation) were compared with those of heart transplantation recipients from the International Society for Heart and Lung Transplantation registry. Patients were stratified by ventilatory efficiency (ventilation versus CO₂ production slope (VE/VCO₂) >34 vs ≤34) and presence of exercise oscillatory ventilation. Survival analyses were performed using Kaplan-Meier curves compared with log-rank tests and Cox proportional hazards models, with heart transplantation survival curves reconstructed from aggregate data.

Results: Patients with peak VO₂ <12 mL/kg/min demonstrated better survival than heart transplantation recipients, with survival curves intersecting at approximately 2.7 years. Among those with VE/VCO₂ ≤34, 10-year mortality risk was halved (p<0.01), with survival curves crossing those of heart transplantation recipients around year 4. Absence of exercise oscillatory ventilation was similarly associated with a 50% lower long-term mortality. Combining VE/VCO₂ and exercise oscillatory ventilation identified four distinct risk groups with significantly different 10-year outcomes (p<0.01). Patients with peak VO₂ <12 mL/kg/min, VE/VCO₂ ≤34 and no exercise oscillatory ventilation exhibited survival comparable to heart transplantation recipients at year 5.

Conclusions: In contemporary practice, a peak VO₂ <12 mL/kg/min alone may not reliably identify patients with HF with sufficiently high short-term mortality to warrant heart transplantation referral. VE/VCO₂ and exercise oscillatory ventilation provide important additional risk stratification, supporting re-evaluation of transplant referral criteria to reflect improved HF management and outcomes.

Electrocardiographic imaging (ECGI) is a non-invasive technique that combines patient-specific cardiac anatomy with numerous body-surface potentials to gain insight into the in vivo electrical epicardial activity across the whole heart. This review summarises some of the latest hardware developments for ECGI data acquisition as well as innovative software solutions to address the inverse problem of electrocardiography. It delves into some of the successful clinical applications of ECGI while highlighting the hurdles that still stand in the way of its more widespread roll-out to healthcare. As well as serving as a powerful tool for electrophysiological research, ECGI promises to play an increasingly valuable role in the personalised diagnosis and management of cardiac arrhythmias.

Background: Emerging evidence suggests associations between air pollution, multimorbidity, and atrial fibrillation (AF), but their interplay remains unclear. We evaluated these relationships in a prospective cohort.

Methods: We retrieved a total of 480 344 individuals from the UK Biobank. The quantification of air pollutants (particulate matter with a diameter ≤2.5 μm (PM_2.5), particulate matter with a diameter ≤10 μm (PM₁₀), nitrogen oxides (NO_x) and nitrogen dioxide (NO₂)) was conducted at the geocoded residential locations of each participant. Multimorbidity clusters were determined using latent class analysis using 35 long-term conditions (LTCsc. Three latent classes were identified: non-cardiovascular disease (non-CVD) multimorbidity, mental health multimorbidity and cardiometabolic multimorbidity. These were compared with no LTCs and singular LTCs. Cox models examined the effects of air pollution and multimorbidity status on AF incidence. Counterfactual analyses were performed to calculate the proportion of preventable AF.

Results: During a median follow-up of 12.5 years (IQI 11.8 to 13.2), 28 977 incident AF cases occurred. Exposure to PM_2.5 was associated with a 37% higher AF risk per 1 µg/m³ higher PM_2.5 (HR 1.37, 95% CI 1.36 to 1.38), with similar patterns for PM₁₀ (HR 1.18, 95% CI 1.17 to 1.18), NOₓ (HR 1.06, 95% CI 1.06 to 1.06) and NO₂ (HR 1.03, 95% CI 1.03 to 1.03). Compared with no multimorbidity, cardiometabolic multimorbidity showed a 1.84-fold higher AF risk (95% CI 1.76 to 1.93), while non-CVD multimorbidity showed a 2.13-fold higher risk (95% CI 1.97 to 2.30). Additive interactions were observed between air pollution and multimorbidity. For example, participants with high PM_2.5 exposure and non-CVD multimorbidity had a sixfold higher AF risk (95% CI 5.25 to 6.82; relative excess risk due to interaction (RERI) 2.31, 95% CI 1.55 to 3.07), exceeding the sum of individual risks. Counterfactual analysis suggested that reducing PM_2.5 to the 10th percentile could prevent 26-52% of AF cases in multimorbidity subgroups, with the largest reductions in cardiometabolic (52%) clusters.

Conclusion: Air pollution and multimorbidity were independently and synergistically associated with higher AF risk. Mitigation strategies targeting pollution may reduce AF burden, particularly in high-risk populations.

Background: Patients with hypertrophic cardiomyopathy (HCM) often reduce their physical activity due to concerns about sudden cardiac death. However, objective data on activity patterns in HCM, particularly in relation to clinical phenotype and quality of life (QoL), remain limited.

Methods: We assessed physical activity using 7-day accelerometry in 203 patients with HCM and 37 genotype-positive, phenotype-negative (G+/P-) individuals. Outcomes included daily step counts, time spent in moderate-to-vigorous physical activity (MVPA) and sedentariness. QoL was measured using the Kansas City Cardiomyopathy Questionnaire (KCCQ) and the EuroQoL 5-domain 5-level (EQ-5D-5L).

Results: HCM patients took fewer steps/day (5254 vs 6573), engaged in less MVPA (3.4% vs 4.5% of the day) and were more often sedentary (61% vs 35% spending >80% of the day sedentary) compared with G+/P- controls (all p<0.01). Symptomatic and obstructive HCM patients showed the lowest activity levels. Notably, asymptomatic obstructive HCM patients demonstrated reduced activity comparable to symptomatic individuals. Obesity and use of cardiac medications were also associated with lower activity. Step counts were positively associated with QoL scores: a 250 steps/day increment corresponded to a 2.15-point higher KCCQ score and a 1000 steps/day increment to a 0.05-point higher EQ-5D-5L score (both p<0.001), remaining significant after adjustment for age and sex. Most HCM patients (62%) recalled receiving exercise guidance, and many (59%) reported reducing their activity as a result.

Conclusions: Objectively measured physical activity was significantly lower in HCM patients compared with G+/P- individuals, particularly among those with symptoms, obstruction or obesity. Even modestly higher daily step counts were associated with better QoL, highlighting the relevance of individualised, phenotype-informed exercise counselling in HCM.

Background: Artificial intelligence (AI) and machine learning (ML) have shown immense potential in cardiology, leveraging data-driven insights to enhance diagnosis, treatment planning and patient care. This study presents a comprehensive evaluation of US Food and Drug Administration (FDA)-approved AI/ML devices in cardiology, analysing trends in clinical applications, regulatory pathways and evidence transparency.

Methods: FDA clearance summaries from the AI/ML medical device database were reviewed to identify cardiology-specific applications. Devices were categorised using the descriptive, diagnostic, predictive and prescriptive framework. Regulatory pathways, AI technologies and validation data were critically assessed.

Results: Of 1016 FDA-approved AI/ML devices, 277 (27.3%) had cardiology applications, predominantly for imaging (65.3%) and diagnostics (64.3%). Predictive and prescriptive tools constituted only 5.4% and 0.7%, respectively. Most devices (97.1%) were cleared via the 510(k) pathway, with 58.0% at risk of predicate creep. Quality of clinical evidence was limited, with only 3.2% of devices supported by high-quality trials. The type of AI technology was often underreported (58.8%).

Conclusion: While AI/ML technologies are reshaping cardiology, regulatory challenges and reporting transparency impede their optimal use. Strengthened regulatory frameworks, improved trial design and robust post-market surveillance are essential to ensure safety, efficacy and equity in the deployment of AI tools in cardiology.

Background: Composite outcomes in cardiovascular trials often group events of unequal clinical importance, and conventional analyses may obscure treatment trade-offs. Generalised pairwise comparisons (GPC), expressed as a win ratio (WR), allow for hierarchical ranking of events and incorporation of recurrent outcomes, providing a potentially more intuitive assessment of benefit-risk.

Methods: In a prespecified exploratory analysis of the 2×2 factorial, randomised CLEAR (Colchicine and Spironolactone in Patients with Myocardial Infarction) trial (7062 patients within 72 hours of acute myocardial infarction (MI) and percutaneous coronary intervention), we applied both time-to-first and recurrent-event GPC to reassess low-dose colchicine (0.5 mg daily) and spironolactone (25 mg daily) versus placebo. For the colchicine comparison, the hierarchical benefit-risk outcome included all-cause death, stroke, recurrent MI, unplanned ischaemia-driven revascularisation, serious infection or diarrhoea. For the spironolactone comparison, the outcome included all-cause death, stroke, MI, new or worsening heart failure, significant ventricular arrhythmia, hyperkalaemia or gynaecomastia/gynaecodynia. GPC results were compared with Cox, logistic and Andersen-Gill models.

Results: For colchicine, the time-to-first event GPC showed a 12% lower proportional win rate compared with placebo (WR 0.88, 95% CI 0.79 to 0.98; win difference -2.10%, 95% CI -3.84 to -0.37), driven largely by excess diarrhoea. For spironolactone, patients experienced a 14% lower win rate (WR 0.86, 95% CI 0.75 to 0.99; win difference -1.46%, 95% CI -2.84% to -0.08%), largely attributable to gynaecomastia and hyperkalaemia. Conventional statistical approaches yielded concordant results. Across both interventions, higher-order efficacy outcomes (death, MI, stroke, heart failure) showed no benefit.

Conclusions: In patients with post-MI, both low-dose colchicine and spironolactone demonstrated disadvantageous benefit-risk profiles, reinforcing that neither agent should be used routinely. This prespecified application of GPC provided results consistent with traditional methods but offered a clinically intuitive framework for interpreting composite outcomes.

Background: Patients with marginalised characteristics experience delayed ST-elevation myocardial infarction (STEMI) diagnosis despite fast-track protocols. We aimed to determine whether patients with phonetically uncommon surnames in our community experience delays from first medical contact (FMC) to STEMI diagnosis compared with patients with common surnames within an established fast-track network.

Methods: The Fast-Track Protocol for ST-Elevation Myocardial Infarction prospective registry enrolled consecutive STEMI patients from June 2008 to November 2024. Patient surnames were classified as phonetically common or uncommon using standardised phonetic matching against Canton Fribourg population data. Generalised linear models examined FMC-to-diagnosis time, FMC-to-balloon time and infarct size markers. Cox regression assessed major adverse cardiac and cerebrovascular events (MACE) at 30 days, 1 year and 5 years.

Results: Among 1208 patients, 284 (23.5%) had phonetically uncommon surnames. Patients with uncommon names experienced prolonged FMC-to-diagnosis time (59.4±87.6 vs 40.6±37.6 min; mean difference +16.8 min; p=0.009) and FMC-to-balloon time (116.8±90.5 vs 97.5±45.7 min; mean difference +17.5 min; p=0.016). Patients with uncommon names were significantly more likely to exceed the 90 min FMC-to-balloon threshold (39.2% vs 48.4%; p=0.010) and the 120 min threshold (16.4% vs 23.5%; p=0.018). Diagnosis-to-balloon time remained unaffected (p>0.80). Peak creatine kinase muscle-brain showed non-significant elevation (mean difference +52.0 U/L; p=0.077). No differences were observed in MACE at 30 days and 5 years between patients with common and uncommon names.

Conclusions: Patients with phonetically uncommon surnames experienced significant STEMI diagnostic delays within an efficient fast-track network. Protocol-driven care following diagnosis operated equitably, leading to no difference in long-term MACE.