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...

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.

Funding Acknowledgements Type of funding sources: Public grant(s) – National budget only. Main funding source(s): UKRI CDT in AI for Healthcare http://ai4health.io and British Heart Foundation Background  Data curation is an important process that structures and organises data, supporting research and the development of artificial intelligence models. However, manually curating a large volume of medical data is a time-consuming, repetitive and costly process that puts additional strain on clinical experts. The curation becomes more complex and demanding as more data sources are used. This leads to an introduction of disparity in the data structure and protocols.  Purpose  Here, we propose an automatic framework to curate large volumes of heterogenous cardiac MRI scans acquired across different sites and scanner vendors. Our framework requires minimal expert involvement throughout and works directly on DICOM images from the scanner or PACS.  The resulting structured standardised data allow for straightforward image analysis, hypothesis testing and the training and application of artificial intelligence models.  Methods  It is broken down into three main components anonymisation, cataloguing and outlier detection (see Figure 1). Anonymisation automatically removes any identifiable patient information from the DICOM image attributes. These data are replaced with anonymised labels, whilst maintaining relevant longitudinal information from each patient. DICOM attributes are also used to automatically group the different images according to imaging sequence (e.g. CINE, Delayed-Enhancement, T1 maps), acquisition geometry (e.g. short-axis, 2-chamber, 4-chamber) and imaging attributes (e.g. slice thickness, TE, TR), for easier querying. The sorting characteristics are flexible and can easily be defined by the user. Finally, we detect and flag, for subsequent manual inspection, any outliers within those groups, based on the similarity levels of chosen DICOM attributes. This framework additionally offers interactive image visualisation to allow users to assess its performance in real time.  Results  We tested the performance of ACUR CMRI on 26,668 CMR image series (723,531 images) from 858 patient examinations, which took place across two sites in four different scanners. With an average execution time per patient of 100 seconds, ACUR was able to sort imaging data with 1191 different sequence names into 43 categories. The framework can be freely downloaded from https://bitbucket.org/cmr-ai-working-group/acur/.  Conclusions  We present ACUR, an automatic framework to curate large volumes of heterogeneous cardiac MRI data. We show how it can quickly and automatically curate data, grouping it according to desired imaging characteristics defined in DICOM attributes. The proposed framework is flexible and ideally suited as a pre-processing tool for large biomedical imaging data studies.

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