Publications

BACKGROUND: The incidence and clinical impact of lead-related venous obstruction (LRVO) among patients with cardiovascular implantable electronic devices (CIEDs) is poorly defined.

OBJECTIVES: The objectives of this study were to determine the incidence of symptomatic LRVO after CIED implant; describe patterns in CIED extraction and revascularization; and quantify LRVO-related health care utilization based on each type of intervention.

METHODS: LRVO status was defined among Medicare beneficiaries after CIED implant from October 1, 2015, to December 31, 2020. Cumulative incidence functions of LRVO were estimated by Fine-Gray methods. LRVO predictors were identified using Cox regression. Incidence rates for LRVO-related health care visits were calculated with Poisson models.

RESULTS: Among 649,524 patients who underwent CIED implant, 28,214 developed LRVO, with 5.0% cumulative incidence at maximum follow-up of 5.2 years. Independent predictors of LRVO included CIEDs with >1 lead (HR: 1.09; 95% CI: 1.07-1.15), chronic kidney disease (HR: 1.17; 95% CI: 1.14-1.20), and malignancies (HR: 1.23; 95% CI: 1.20-1.27). Most patients with LRVO (85.2%) were managed conservatively. Among 4,186 (14.8%) patients undergoing intervention, 74.0% underwent CIED extraction and 26.0% percutaneous revascularization. Notably, 90% of the patients did not receive another CIED after extraction, with low use (2.2%) of leadless pacemakers. In adjusted models, extraction was associated with significant reductions in LRVO-related health care utilization (adjusted rate ratio: 0.58; 95% CI: 0.52-0.66) compared with conservative management.

CONCLUSIONS: In a large nationwide sample, the incidence of LRVO was substantial, affecting 1 of every 20 patients with CIEDs. Device extraction was the most common intervention and was associated with long-term reduction in recurrent health care utilization.

AIMS: Frailty is associated with increased morbidity and mortality in patients undergoing left atrial appendage closure (LAAC). This study aimed to compare the performance of two claims-based frailty measures in predicting adverse outcomes following LAAC.

METHODS: We identified patients 66 years and older who underwent LAAC between October 1, 2016, and December 31, 2019, in Medicare fee-for-service claims. Frailty was assessed using the previously validated Hospital Frailty Risk Score (HFRS) and Kim Claims-based Frailty Index (CFI). Patients were identified as frail based on HFRS ≥5 and CFI ≥0.25.

RESULTS: Of the 21,787 patients who underwent LAAC, frailty was identified in 45.6% by HFRS and 15.4% by CFI. There was modest agreement between the two frailty measures (kappa 0.25, Pearson's correlation 0.62). After adjusting for age, sex, and comorbidities, frailty was associated with higher risk of 30-day mortality, 1-year mortality, 30-day readmission, long hospital stay, and reduced days at home (p < .01 for all) regardless of the frailty measure used. The addition of frailty to standard comorbidities significantly improved model performance to predict 1-year mortality, long hospital stay, and reduced days at home (Delong p-value < .001).

CONCLUSION: Despite significant variation in frailty detection and modest agreement between the two frailty measures, frailty status remained highly predictive of mortality, readmissions, long hospital stay, and reduced days at home among patients undergoing LAAC. Measuring frailty in clinical practice, regardless of the method used, may provide prognostic information useful for patients being considered for LAAC, and may inform shared decision-making in this population.

There is increasing focus on applying deep learning methods to electrocardiograms (ECGs), with recent studies showing that neural networks (NNs) can predict future heart failure or atrial fibrillation from the ECG alone. However, large numbers of ECGs are needed to train NNs, and many ECGs are currently only in paper format, which are not suitable for NN training. We developed a fully-automated online ECG digitisation tool to convert scanned paper ECGs into digital signals. Using automated horizontal and vertical anchor point detection, the algorithm automatically segments the ECG image into separate images for the 12 leads and a dynamical morphological algorithm is then applied to extract the signal of interest. We then validated the performance of the algorithm on 515 digital ECGs, of which 45 were printed, scanned and redigitised. The automated digitisation tool achieved 99.0% correlation between the digitised signals and the ground truth ECG (n = 515 standard 3-by-4 ECGs) after excluding ECGs with overlap of lead signals. Without exclusion, the performance of average correlation was from 90 to 97% across the leads on all 3-by-4 ECGs. There was a 97% correlation for 12-by-1 and 3-by-1 ECG formats after excluding ECGs with overlap of lead signals. Without exclusion, the average correlation of some leads in 12-by-1 ECGs was 60-70% and the average correlation of 3-by-1 ECGs achieved 80-90%. ECGs that were printed, scanned, and redigitised, our tool achieved 96% correlation with the original signals. We have developed and validated a fully-automated, user-friendly, online ECG digitisation tool. Unlike other available tools, this does not require any manual segmentation of ECG signals. Our tool can facilitate the rapid and automated digitisation of large repositories of paper ECGs to allow them to be used for deep learning projects.

AIMS: Accurately determining atrial arrhythmia mechanisms from a 12-lead electrocardiogram (ECG) can be challenging. Given the high success rate of cavotricuspid isthmus (CTI) ablation, identification of CTI-dependent typical atrial flutter (AFL) is important for treatment decisions and procedure planning. We sought to train a convolutional neural network (CNN) to classify CTI-dependent AFL vs. non-CTI dependent atrial tachycardia (AT), using data from the invasive electrophysiology (EP) study as the gold standard.

METHODS AND RESULTS: We trained a CNN on data from 231 patients undergoing EP studies for atrial tachyarrhythmia. A total of 13 500 five-second 12-lead ECG segments were used for training. Each case was labelled CTI-dependent AFL or non-CTI-dependent AT based on the findings of the EP study. The model performance was evaluated against a test set of 57 patients. A survey of electrophysiologists in Europe was undertaken on the same 57 ECGs. The model had an accuracy of 86% (95% CI 0.77-0.95) compared to median expert electrophysiologist accuracy of 79% (range 70-84%). In the two thirds of test set cases (38/57) where both the model and electrophysiologist consensus were in agreement, the prediction accuracy was 100%. Saliency mapping demonstrated atrial activation was the most important segment of the ECG for determining model output.

CONCLUSION: We describe the first CNN trained to differentiate CTI-dependent AFL from other AT using the ECG. Our model matched and complemented expert electrophysiologist performance. Automated artificial intelligence-enhanced ECG analysis could help guide treatment decisions and plan ablation procedures for patients with organized atrial arrhythmias.

In 2019, Germany passed the Digital Healthcare Act, which, among other things, created a "Fast-Track" regulatory and reimbursement pathway for digital health applications in the German market. The pathway explicitly provides for flexibility in how researchers can present evidence for new digital products, including the use of real-world data and real-world evidence. Against this backdrop, the Digital Medicine Society and the Health Innovation Hub of the German Federal Ministry of Health convened a set of roundtable discussions to bring together international experts in evidence generation for digital medicine products. This Viewpoint highlights findings from these discussions with the aims of (1) accelerating and stimulating innovative approaches to digital medical product evaluation, and (2) promoting international harmonisation of best evidentiary practices. Advancing these topics and fostering international agreement on evaluation approaches will be vital to the safe, effective, and evidence-based deployment and acceptance of digital health applications globally.

BACKGROUND: Conventional superior access for cardiac implantable electronic devices (CIEDs) is not always possible and femoral CIEDs (F-CIED) are an alternative option when leadless systems are not suitable. The long-term outcomes and extraction experiences with F-CIEDs, in particular complex F-CIED (ICD/CRT devices), remain poorly understood.

METHODS: Patients referred for F-CIEDs implantation between 2002 and 2019 at two tertiary centers were included. Early complications were defined as ≤30 days following implant and late complications >30 days.

RESULTS: Thirty-one patients (66% male; age 56 ± 20 years; 35% [11] patients with congenital heart disease) were implanted with F-CIEDs (10 ICD/CRT and 21 pacemakers). Early complications were observed in 6.5% of patients: two lead displacements. Late complications at 6.8 ± 4.4 years occurred in 29.0% of patients. This was higher with complex F-CIED compared to simple F-CIED (60.0% vs. 14.3%, p = .02). Late complications were predominantly generator site related (n = 8, 25.8%) including seven infections/erosions and one generator migration. Eight femoral generators and 14 leads (median duration in situ seven [range 6-11] years) were extracted without complication.

CONCLUSIONS: Procedural success with F-CIEDs is high with clinically acceptable early complication rates. There is a notable risk of late complications, particularly involving the generator site of complex devices following repeat femoral procedures. Extraction of chronic F-CIED in experienced centers is feasible and safe.

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.

The "right to repair" movement highlights opportunities to reduce health care costs and promote public health resilience through increased competition in the way in which medical devices are serviced and updated over their lifespan. We review legislative and legal facets of third-party repair of medical devices, and conclude with specific recommendations to help this market function more efficiently to the benefit of patients and health care systems.