Aims This meta-analysis aims to quantify the association of reduced coronary flow with all-cause mortality and major adverse cardiovascular events (MACE) across a broad range of patient groups and pathologies. Methods and results We systematically identified all studies between 1 January 2000 and 1 August 2020, where coronary flow was measured and clinical outcomes were reported. The endpoints were all-cause mortality and MACE. Estimates of effect were calculated from published hazard ratios (HRs) using a random-effects model. Seventy-nine studies with a total of 59 740 subjects were included. Abnormal coronary flow reserve (CFR) was associated with a higher incidence of all-cause mortality [HR: 3.78, 95% confidence interval (CI): 2.39–5.97] and a higher incidence of MACE (HR 3.42, 95% CI: 2.92–3.99). Each 0.1 unit reduction in CFR was associated with a proportional increase in mortality (per 0.1 CFR unit HR: 1.16, 95% CI: 1.04–1.29) and MACE (per 0.1 CFR unit HR: 1.08, 95% CI: 1.04–1.11). In patients with isolated coronary microvascular dysfunction, an abnormal CFR was associated with a higher incidence of mortality (HR: 5.44, 95% CI: 3.78–7.83) and MACE (HR: 3.56, 95% CI: 2.14–5.90). Abnormal CFR was also associated with a higher incidence of MACE in patients with acute coronary syndromes (HR: 3.76, 95% CI: 2.35–6.00), heart failure (HR: 6.38, 95% CI: 1.95–20.90), heart transplant (HR: 3.32, 95% CI: 2.34–4.71), and diabetes mellitus (HR: 7.47, 95% CI: 3.37–16.55). Conclusion Reduced coronary flow is strongly associated with increased risk of all-cause mortality and MACE across a wide range of pathological processes. This finding supports recent recommendations that coronary flow should be measured more routinely in clinical practice, to target aggressive vascular risk modification for individuals at higher risk.

Summary Background Most patients who have heart failure with a reduced ejection fraction, when left ventricular ejection fraction (LVEF) is 40% or lower, are diagnosed in hospital. This is despite previous presentations to primary care with symptoms. We aimed to test an artificial intelligence (AI) algorithm applied to a single-lead ECG, recorded during ECG-enabled stethoscope examination, to validate a potential point-of-care screening tool for LVEF of 40% or lower. Methods We conducted an observational, prospective, multicentre study of a convolutional neural network (known as AI-ECG) that was previously validated for the detection of reduced LVEF using 12-lead ECG as input. We used AI-ECG retrained to interpret single-lead ECG input alone. Patients (aged ≥18 years) attending for transthoracic echocardiogram in London (UK) were recruited. All participants had 15 s of supine, single-lead ECG recorded at the four standard anatomical positions for cardiac auscultation, plus one handheld position, using an ECG-enabled stethoscope. Transthoracic echocardiogram-derived percentage LVEF was used as ground truth. The primary outcome was performance of AI-ECG at classifying reduced LVEF (LVEF ≤40%), measured using metrics including the area under the receiver operating characteristic curve (AUROC), sensitivity, and specificity, with two-sided 95% CIs. The primary outcome was reported for each position individually and with an optimal combination of AI-ECG outputs (interval range 0–1) from two positions using a rule-based approach and several classification models. This study is registered with ClinicalTrials.gov , NCT04601415 . Findings Between Feb 6 and May 27, 2021, we recruited 1050 patients (mean age 62 years [SD 17·4], 535 [51%] male, 432 [41%] non-White). 945 (90%) had an ejection fraction of at least 40%, and 105 (10%) had an ejection fraction of 40% or lower. Across all positions, ECGs were most frequently of adequate quality for AI-ECG interpretation at the pulmonary position (979 [93·3%] of 1050). Quality was lowest for the aortic position (846 [80·6%]). AI-ECG performed best at the pulmonary valve position (p=0·02), with an AUROC of 0·85 (95% CI 0·81–0·89), sensitivity of 84·8% (76·2–91·3), and specificity of 69·5% (66·4–72·6). Diagnostic odds ratios did not differ by age, sex, or non-White ethnicity. Taking the optimal combination of two positions (pulmonary and handheld positions), the rule-based approach resulted in an AUROC of 0·85 (0·81–0·89), sensitivity of 82·7% (72·7–90·2), and specificity of 79·9% (77·0–82·6). Using AI-ECG outputs from these two positions, a weighted logistic regression with l2 regularisation resulted in an AUROC of 0·91 (0·88–0·95), sensitivity of 91·9% (78·1–98·3), and specificity of 80·2% (75·5–84·3). Interpretation A deep learning system applied to single-lead ECGs acquired during a routine examination with an ECG...

A significant clinical problem is patients presenting with exercise-limiting dyspnoea, sometimes with associated chest pain, in the absence of detectable left ventricular (LV) systolic dysfunction, coronary artery disease, or lung disease. Often the patients are older, female, and have isolated basal septal hypertrophy (BSH), frequently on a background of mild hypertension. The topic of breathlessness in patients with clinical heart failure, but who have a normal ejection fraction (HFNEF) has attracted significant controversy over the past few years. This review aims to analyse the literature on BSH, identify the possible associations between BSH and HFNEF, and consequently explore possible pathophysiological mechanisms whereby clinical symptoms are experienced.

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