Survival and health economic outcomes in heart failure diagnosed at hospital admission versus community settings: a propensity-matched analysis

BACKGROUND AND AIMS: Most patients with heart failure (HF) are diagnosed following a hospital admission. The clinical and health economic impacts of index HF diagnosis made on admission to hospital versus community settings are not known. METHODS: We used the North West London Discover database to examine 34 208 patients receiving an index diagnosis of HF between January 2015 and December 2020. A propensity score-matched (PSM) cohort was identified to adjust for differences in socioeconomic status, cardiovascular risk and pre-diagnosis health resource utilisation cost. Outcomes were stratified by two pathways to index HF diagnosis: a 'hospital pathway' was defined by diagnosis following hospital admission; and a 'community pathway' by diagnosis via a general practitioner or outpatient services. The primary clinical and health economic endpoints were all-cause mortality and cost-consequence differential, respectively. RESULTS: The diagnosis of HF was via hospital pathway in 68% (23 273) of patients. The PSM cohort included 17 174 patients (8582 per group) and was matched across all selected confounders (p>0.05). The ratio of deaths per person-months at 24 months comparing community versus hospital diagnosis was 0.780 (95% CI 0.722 to 0.841, p<0.0001). By 72 months, the ratio of deaths was 0.960 (0.905 to 1.020, p=0.18). Diagnosis via hospital pathway incurred an overall extra longitudinal cost of £2485 per patient. CONCLUSIONS: Index diagnosis of HF through hospital admission continues to dominate and is associated with a significantly greater short-term risk of mortality and substantially increased long-term costs than if first diagnosed in the community. This study highlights the potential for community diagnosis-early, before symptoms necessitate hospitalisation-to improve both clinical and health economic outcomes

Update on hypertrophic cardiomyopathy and a guide to the guidelines

Hypertrophic cardiomyopathy (HCM) is the most common inherited cardiovascular disorder, affecting 1 in 500 individuals worldwide. Existing epidemiological studies might have underestimated the prevalence of HCM, however, owing to limited inclusion of individuals with early, incomplete phenotypic expression. Clinical manifestations of HCM include diastolic dysfunction, left ventricular outflow tract obstruction, ischaemia, atrial fibrillation, abnormal vascular responses and, in 5% of patients, progression to a 'burnt-out' phase characterized by systolic impairment. Disease-related mortality is most often attributable to sudden cardiac death, heart failure, and embolic stroke. The majority of individuals with HCM, however, have normal or near-normal life expectancy, owing in part to contemporary management strategies including family screening, risk stratification, thromboembolic prophylaxis, and implantation of cardioverter-defibrillators. The clinical guidelines for HCM issued by the ACC Foundation/AHA and the ESC facilitate evaluation and management of the disease. In this Review, we aim to assist clinicians in navigating the guidelines by highlighting important updates, current gaps in knowledge, differences in the recommendations, and challenges in implementing them, including aids and pitfalls in clinical and pathological evaluation. We also discuss the advances in genetics, imaging, and molecular research that will underpin future developments in diagnosis and therapy for HCM

Determinants of shielding behaviour during the COVID-19 pandemic and associations with wellbeing in >7,000 NHS patients: 17-week longitudinal observational study.

BACKGROUND: The UK National Health Service (NHS) classified 2.2 million people as clinically extremely vulnerable (CEV) during the first wave of the 2020 COVID-19 pandemic, advising them to 'shield' - to not leave home for any reason. OBJECTIVE: The aim of this study was to measure the determinants of shielding behaviour and associations with wellbeing in a large NHS patient population, towards informing future health policy. METHODS: Patients contributing to an ongoing longitudinal participatory epidemiology study (LoC-19, n = 42,924) received weekly email invitations to complete questionnaires (17-week shielding period starting 9th April 2020) within their NHS personal electronic health record. Question items focused on wellbeing. Participants were stratified into four groups by self-reported CEV status (qualifying condition) and adoption of shielding behaviour (baselined at week 1 or 2). Distribution of CEV criteria is reported alongside situational variables and uni- and multivariable logistic regression. Longitudinal trends in physical and mental wellbeing were displayed graphically. Free-text responses reporting variables impacting wellbeing were semi-quantified using natural language processing. In the lead up to a second national lockdown (October 23rd, 2020), a follow-up questionnaire evaluated subjective concern if further shielding were advised. RESULTS: 7,240 participants were included. Among the CEV (2,391), 1,133 (47.3%) assumed shielding behaviour at baseline, compared with 633 (15.0%) in the non-CEV group. Those CEV who shielded were more likely to be Asian (Odds Ratio OR 2.02 [1.49-2.76]), female (OR 1.24 [1.05-1.45]), older (OR per year increase 1.01 [1.00-1.02]) and live in a home with outdoor space (OR 1.34 [1.06-1.70]) or 3-4 other inhabitants (3 = OR 1.49 [1.15-1.94], 4 = OR 1.49 [1.10-2.01]); and be solid organ transplant recipients (2.85 [2.18-3.77]) or have severe chronic lung disease (OR 1.63 [1.30-2.04]). Receipt of a government letter advising shielding was reported in 1,115 (46.6%) of CEV and 180 (3.7%) of non-CEV and was associated with adopting shielding behaviour (OR 3.34 [2.82-3.95] and 2.88 [2.04-3.99], respectively). In both groups, shielding was longitudinally associated with worse physical and mental wellbeing (p<.05). Access to food and grocery supplies was a more prevalent concern among those shielding (p<.05). Concern for wellbeing if future shielding was required was most prevalent among the CEV who had originally shielded. CONCLUSIONS: Future health policy must balance the potential protection from COVID-19 against our findings that in this population shielding may have negatively impacted wellbeing and was adopted in many in whom it was not indicated, and variably in whom it was. This therefore also requires clearer public health messaging and support for wellbeing if shielding is to be advised in future pandemic scenarios. CLINICALTRIAL

Point-of-care screening for heart failure with reduced ejection fraction using artificial intelligence during ECG-enabled stethoscope examination in London, UK: a prospective, observational, multicentre study

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-enabled stethoscope can detect LVEF of 40% or lower. These findings highlight the potential for inexpensive, non-invasive, workflow-adapted, point-of-care screening, for earlier diagnosis and prognostically beneficial treatment. FUNDING: NHS Accelerated Access Collaborative, NHSX, and the National Institute for Health Research