Heart最新文献

Background: Evidence on the associations of multiple minor ECG abnormalities (EA) with cardiovascular disease (CVD) and mortality in older populations is limited, particularly whether a weighted EA score better predicts CVD risk than a single EA severity.

Methods: We analysed 26 846 Chinese aged 50+ years from Guangzhou Biobank Cohort Study (GBCS), without CVD at baseline. Minor and major EAs were classified based on the Minnesota Code Manual. EA severity was defined as normal, one minor, two or more minor and major abnormalities. Cox regression with backward stepwise selection was conducted to develop EA score. Cox regression was used to examine the associations of EA (severity/score) with incident CVD events, all-cause mortality and CVD mortality. C-index and Net Reclassification Index (NRI) were used to assess the improvement in CVD risk prediction after adding EA (severity/score) to the GBCS model variables.

Results: During an average follow-up of 15.3 (SD=3.5) years, 6232 CVD events and 5960 deaths occurred. Compared with normal ECG, one minor (adjusted HR 1.12, 95% CI 1.05 to 1.19), two or more minor (1.20, 95% CI 1.11 to 1.29) and major abnormalities (1.46, 95% CI 1.31 to 1.63) were associated with a higher risk of incident CVD events. The EA score showed a strong dose-response relationship (0 point as reference): 1-29 points (1.12, 95% CI 1.05 to 1.19), 30-59 points (1.56, 95% CI 1.38 to 1.77), ≥60 points (3.16, 95% CI 2.56 to 3.91) (p value for trend <0.001). Similar findings were observed for all-cause and CVD mortality. Adding EA score improved the C-index for incident CVD events, but the improvement diminished over time (change in C-index: 0.011 (95% CI 0.002 to 0.022) at 3 years to 0.003 (95% CI 0.002 to 0.004) at 15 years). The NRI for 10-year risk was 0.016 (95% CI 0.007 to 0.024), indicating limited utility.

Conclusions: Major EA and multiple minor EAs were associated with higher risks of CVD events and mortality, but the value in improving CVD risk prediction is limited.

Cardiac sarcoidosis (CS) is a rare and complex disease that requires a multidisciplinary approach for diagnosis and treatment. The diagnosis of CS can be confirmed by histological examination of non-caseating granulomas in cardiac or extracardiac tissue, along with supportive clinical and imaging findings. Symptoms are often non-specific, and the yield of endomyocardial biopsy is low due to the patchy nature of cardiac involvement-thus, many cases are not straightforward to diagnose, especially when pulmonary or extracardiac features are absent. Modern non-invasive imaging modalities have unique strengths in assessing the myocardium's structure, function, perfusion, inflammation and fibrosis-abnormalities of all these features exist in CS in varying degrees and can be integrated to assist in the diagnosis. Echocardiography is universally used as the initial imaging test when CS is suspected and provides information on cardiac structure and function, but is limited by inadequate tissue characterisation and differentiation from other infiltrative or restrictive cardiomyopathies. Positron emission tomography (PET) and cardiovascular magnetic resonance (CMR) have good accuracy in diagnosing CS. With current tissue characterisation techniques, such as T1 and T2 parametric mapping, CMR imaging can detect subclinical or early CS. While CMR has better overall prognostic utility for arrhythmic risk and cardiac mortality, fluorodeoxyglucose (FDG)-PET is superior in monitoring disease activity and guiding anti-inflammatory therapy. Hybrid FDG-PET/CMR imaging is a newer, complementary approach that is being increasingly used in centres of excellence. It combines the unique strengths of both modalities, thereby achieving superior sensitivity and specificity.

Background: Pharmacotherapy combinations have been shown to improve survival and reduce hospitalisations in adults with chronic heart failure with reduced ejection fraction (HFrEF); however, their cost-effectiveness when used as first-line treatment remains uncertain.

Methods: A lifetime cohort Markov model was developed from the perspective of the NHS in England to assess the cost-effectiveness of five first-line pharmacotherapy combinations: (i) angiotensin-converting enzyme inhibitor (ACEI) or angiotensin receptor blocker (ARB) and beta-blocker (BB) (NICE-recommended treatment at the time of analysis); (ii) ACEI/ARB, BB and mineralocorticoid receptor antagonists (MRA); (iii) angiotensin receptor-neprilysin inhibitor (ARNI), BB and MRA; (iv) ACEI/ARB, BB, MRA and sodium-glucose cotransporter-2 inhibitor (SGLT2i); and (v) ARNI, BB, MRA and SGLT2i. Baseline hospitalisation and mortality rates were informed by real-world data, while treatment effects (HRs) were derived from a review of randomised controlled trials.

Results: Among individuals able to tolerate an ACEI, the combination of ACEI, BB, MRA and SGLT2i (cost, £12 124; quality-adjusted life years (QALYs), 5.72) was found to be the most cost-effective first-line treatment option with an incremental cost-effectiveness ratio (ICER) of £7699.Among individuals unable to tolerate an ACEI, the combination of ARNI, BB, MRA and SGLT2i (cost, £18 950; QALYs, 6.04) was found to be the most cost-effective first-line treatment option with an ICER of £15 821. The next most cost-effective first-line treatment option was the combination of ARB, BB, MRA and SGLT2i (cost, £11 842; QALYs, 5.59). These findings were primarily driven by the greater relative QALY gain of ARNI compared with ARB.

Conclusions: This study demonstrates that a first-line quadruple pharmacotherapy combination is cost-effective compared with a stepwise approach for treating people with HFrEF, suggesting that wider adoption of early initiation of quadruple pharmacotherapy may improve health outcomes and optimise healthcare resource use.

Background: Optimisation of medical therapy is recommended for patients with newly diagnosed non-ischaemic cardiomyopathies (NICM) before consideration of a primary preventive implantable cardioverter-defibrillator (ICD). During this optimisation period, patients face a potentially elevated risk for sudden cardiac death (SCD) that can be countered with a wearable cardioverter-defibrillator (WCD). This systematic review aims to assess the risk for SCD in patients with newly diagnosed NICM.

Methods: A systematic review was performed in Medline, Embase and Cochrane Library last updated on March 2025. Studies with patients aged ≥18 years with newly diagnosed NICM (≤90 days) who were recipients of WCD were included. Study selection, study quality assessment and data extraction were performed by two reviewers independently. Data on percentage of patients with appropriate WCD shocks (as proxy for sustained ventricular arrhythmia, potentially leading to SCD), inappropriate WCD shocks and device implantation were pooled by random-effects model.

Results: 50 non-controlled observational studies were included, comprising a total of 10 066 patients with NICM. The percentage of appropriate shocks was 1% (87/7708; 95% CI 1% to 2%) in patients with NICM, 2% (16/1049; 95% CI 1% to 2%) in patients with myocarditis, 3% (7/183; 95% CI 0% to 20%) in peripartum cardiomyopathy, 2% (2/102; 95% CI 0% to 7%) in Takotsubo syndrome and 1% (8/594; 95% CI 1% to 3%) for congenital/inherited or genetic cardiomyopathy. Inappropriate shocks ranged from 0% to 1%. At the end of follow-up, between 6% (Takotsubo syndrome) and 43% (congenital/inherited or genetic cardiomyopathy) of patients received an ICD.

Conclusion: Patients with NICM face a significant risk of SCD during the drug optimisation period before deciding if they qualify for ICD implantation. Results of this meta-analysis are based on non-comparative studies; however, the assessment of an appropriate shock delivered and recorded by the WCD is highly reliable.

Prospero registration number: CRD42024555879.

Background: Raised cardiac troponin-I is a common finding in patients hospitalised with acute viral infections, including but not limited to COVID-19. This often occurs in the absence of overt myocardial injury presenting a challenge for interpretation. The mechanisms underlying troponin elevation are uncertain.

Methods: The CISCO-19 (Cardiovascular Imaging in SARS-CoV-19) study (NCT04403607) is a prospective, multicentre cohort study, in which hospitalised PCR-confirmed COVID-19 participants (N=267) underwent multisystem evaluation at enrolment and at 28-60 days. The study incorporated plasma proteomics (SOMAscan V.4.1), cardiovascular MRI and clinical biomarkers. Of these, 211 had baseline plasma proteomic data and 185 completed follow-up sampling. Matched proteomic and imaging data were available for 155 participants (mean age: 55 years (SD 12); 43% female).

Results: A high likelihood of myocarditis was identified in 13.2% (N=21/159) of participants. High-sensitivity troponin-I was modestly elevated at enrolment (median 3 ng/L; IQR 2-6; n=159). Among males (n=90), 9.3% had a high-sensitivity troponin that exceeded 34 ng/L. Among females (n=69), 4.5% exceeded 16 ng/L. Smooth muscle myosin light chain proteins were downregulated at follow-up (log2 fold change -0.12 to -0.6; all adjusted p<0.02) and positively correlated with high-sensitivity troponin-I, but not N-terminal brain natriuretic peptide or cardiac MRI indices (n=155).

Conclusions: Troponin elevation, exemplified here by COVID-19, could reflect systemic vascular injury. Recognising this mechanism may refine interpretation of cardiac biomarkers in viral illness and supports the investigation of vascular injury in future therapeutic strategies and biomedical studies.

Atrial fibrillation (AF) is a common arrhythmia associated with increased risk of stroke, heart failure and mortality. Advances in genomic research have revealed a complex genetic architecture underlying AF. Genome-wide association studies have identified hundreds of loci of common variants, while sequencing efforts have linked rare variants to cardiomyopathy-related pathways. Polygenic risk scores (PGS) offer a promising tool for AF risk stratification, demonstrating predictive value for disease onset, complications and perioperative outcomes. Multiancestry studies have improved the performance and generalisability of AF-PGS across populations. However, challenges remain in clinical translation, including still limited trial data, unbalanced ancestry representation, phenotype heterogeneity and variability in score calibration. Integration of PGS with other molecular scores may enhance predictive accuracy. Standardised evaluation metrics and prospective validation are essential to establish clinical utility. This review summarises recent advances in AF genetics and polygenic prediction, highlighting opportunities to refine risk assessment and guide personalised prevention strategies in cardiovascular care.

Background: Severe aortic stenosis (AS) is commonly associated with advanced cardiac damage, including right ventricular dysfunction (RVD), pulmonary hypertension (PH) and tricuspid regurgitation (TR), which may worsen prognosis after transcatheter aortic valve implantation (TAVI). This systematic review and meta-analysis aimed to assess the effect of these conditions on short-term and mid-term mortality and rehospitalisation following TAVI.

Methods: We conducted a systematic search of PubMed, Scopus and Web of Science for studies published up to June 2025. Eligible studies included adults with AS undergoing TAVI and reported outcomes at 1 month, 6 months or 12 months stratified by the presence of RVD, PH or TR. Studies had to report either HRs, risk ratios (RRs) or sufficient raw event data for mortality or rehospitalisation. Data were synthesised using a random-effects meta-analysis. Subgroup analyses were conducted by cardiac damage severity according to the Généreux staging system and stratified by valve type and diagnostic modality. Risk of bias in included studies was assessed using the Joanna Briggs Institute's checklist for cohort studies. Meta-regression was performed to explore sources of between-study heterogeneity.

Results: A total of 34 studies including 26 076 patients met inclusion criteria. Twelve-month HRs for all-cause mortality increased with advancing cardiac damage: borderline stage HR 1.61 (1.22-2.12), stage 3 HR 2.06 (1.63-2.60) and stage 4 HR 2.77 (2.11-3.64). RRs followed a similar trend. Cardiovascular mortality was highest in stage 4 (HR 3.13 (1.20-8.17); RR 2.63 (1.54-4.47)). Rehospitalisation data were limited but suggested elevated risk in stage 3 (RR 1.33 (1.12-1.58)). Meta-regression indicated that age, sex and comorbidities contributed to between-study heterogeneity, particularly in stage 3 analyses.

Conclusion: Extravalvular cardiac damage, especially RVD (stage 4), is strongly associated with increased short-term and mid-term mortality and rehospitalisation after TAVI. Even borderline-stage patients face elevated risk, underscoring the continuous nature of AS-related cardiac injury. Incorporating cardiac damage staging into preprocedural assessment can enhance risk stratification and guide management to improve patient outcomes.

Prospero registration number: CRD420250638838.

Artificial intelligence (AI) is reshaping cardiovascular imaging, transforming it from a set of diagnostic tests into powerful tools of precision medicine. This review traces this evolution through the lens of Thomas Lewis's legacy of clinical science, which championed the integration of physiology, experimentation and patient care. Modern AI fulfils that perspective by extracting biological information from routine imaging and linking it with molecular data to reveal mechanisms of disease, forecast outcomes and personalise therapy. An illustrative example of this translational pathway is the Fat Attenuation Index (FAI) score and AI-Risk model. The FAI Score is a new image analysis method that measures coronary inflammation from routine coronary CT angiograms (CCTA) by analysing the CT attenuation gradients within pericoronary adipose tissue in a standardised way, with strong value in predicting future cardiovascular events. The AI Risk model is a prognostic algorithm that integrates FAI Score, metrics of the extent of coronary plaque (by incorporating the Duke score) and clinical risk factors, to generate an accurate prediction of an individual's risk for a cardiovascular event, which is used clinically for risk stratification and decision making. Emerging big data-driven fields such as radiotranscriptomics, merging imaging data with multidimensional biological profiles, enable non-invasive 'molecular biopsies' that accelerate precision cardiology. Alongside these advances, the review addresses the ethical, regulatory and environmental challenges of AI deployment. Ultimately, AI is the current version of Lewis's perspective of translation: physiology shown in pixels, algorithms turning biology into care and discovery reaching its highest goal, which is to improve patient outcomes.

Heart failure with preserved ejection fraction (HFpEF) is a syndrome characterised by cardiac and non-cardiac physiologic disturbances, commonly underpinned by cardiometabolic abnormalities, which culminate in elevated left ventricular filling pressures and progressive symptoms of exercise intolerance. Arrhythmias, particularly atrial fibrillation (AF), are common in HFpEF and have important clinical implications. AF complicates the diagnosis and management of HFpEF. In this review, we synthesise the impact of AF on disease detection, symptom burden and prognosis and HFpEF treatment. Furthermore, we review pharmacological and interventional therapies that may mitigate the risk of AF and improve how patients with HFpEF feel, function and survive.