Publications

BACKGROUND: Transcatheter edge-to-edge repair of the mitral valve (mTEER) reduced a hierarchical end point that included death and heart failure hospitalization in COAPT (Cardiovascular Outcomes Assessment of the MitraClip Percutaneous Therapy for Heart Failure Patients With Functional Mitral Regurgitation Trial). However, the magnitude to which mTEER increases the number of days a patient spends at home (DAH) in the first few years after treatment, a patient-centered end point not captured routinely in clinical trials, has not been evaluated. We compared 1- and 2-year DAH among patients with functional mitral regurgitation and heart failure randomized to mTEER plus medical therapy versus medical therapy alone (control) by linking the COAPT trial to comprehensive health care claims data.

METHODS AND RESULTS: We linked data from COAPT trial participants to Medicare fee-for-service claims. DAH was calculated as the number of days alive and spent outside a hospital, skilled nursing facility, inpatient rehabilitation, long-term acute care hospital, emergency department, or observation stay after randomization. Treatment groups were compared using quantile regression to calculate the area under the curve of cumulative distribution functions. We linked 271 patients (mTEER 136/302, control 135/312) for a 2-year follow-up. Mean±SD DAH at 1 year was 312.0±95.6 in mTEER and 298.1±107.5 in controls with similar area under the curve (difference 13.9 days [-10.5 to 38.3], P=0.26). DAH at 2 years was 577.2±235.6 in mTEER and 518.2±253.0 in control with a higher area under the curve in mTEER (difference 59.0 days [0.07 to 117.9], P=0.0497).

CONCLUSIONS: In the COAPT trial linked to Medicare claims, patients randomized to mTEER spent a similar number of DAH at 1 year but more time at home at 2 years compared with medical therapy alone.

BACKGROUND: Lower-limb amputation rates in patients with chronic limb-threatening ischemia vary across the United States, with marked disparities in amputation rates by gender, race, and income status. We evaluated the association of patient, hospital, and geographic characteristics with the intensity of vascular care received the year before a major lower-limb amputation and how intensity of care associates with outcomes after amputation.

METHODS: Using Medicare claims data (2016-2019), beneficiaries diagnosed with chronic limb-threatening ischemia who underwent a major lower-limb amputation were identified. We examined patient, hospital, and geographic characteristics associated with the intensity of vascular care received the year before amputation. Secondary objectives evaluated all-cause mortality and adverse events following amputation.

RESULTS: Of 33 036 total Medicare beneficiaries undergoing major amputation, 7885 (23.9%) were due to chronic limb-threatening ischemia; of these, 4988 (63.3%) received low-intensity and 2897 (36.7%) received high-intensity vascular care. Mean age, 76.6 years; women, 38.9%; Black adults, 24.5%; and of low income, 35.2%. After multivariable adjustment, those of low income (odds ratio, 0.65 [95% CI, 0.58-0.72]; P<0.001), and to a lesser extent, men (odds ratio, 0.89 [95% CI, 0.81-0.98]; P=0.019), and those who received care at a safety-net hospital (odds ratio, 0.87 [95% CI, 0.78-0.97]; P=0.012) were most likely to receive low intensity of care before amputation. High-intensity care was associated with a lower risk of all-cause mortality 2 years following amputation (hazard ratio, 0.79 [95% CI, 0.74-0.85]; P<0.001).

CONCLUSIONS: Patients who were of low-income status, and to a lesser extent, men, or those cared for at safety-net hospitals were most likely to receive low-intensity vascular care. Low-intensity care was associated with worse long-term event-free survival. These data emphasize the continued disparities that exist in contemporary vascular practice.

BACKGROUND: Artificial intelligence (AI)-enabled electrocardiography (ECG) can be used to predict risk of future disease and mortality but has not yet been adopted into clinical practice. Existing model predictions do not have actionability at an individual patient level, explainability, or biological plausibi. We sought to address these limitations of previous AI-ECG approaches by developing the AI-ECG risk estimator (AIRE) platform.

METHODS: The AIRE platform was developed in a secondary care dataset (Beth Israel Deaconess Medical Center [BIDMC]) of 1 163 401 ECGs from 189 539 patients with deep learning and a discrete-time survival model to create a patient-specific survival curve with a single ECG. Therefore, AIRE predicts not only risk of mortality, but also time-to-mortality. AIRE was validated in five diverse, transnational cohorts from the USA, Brazil, and the UK (UK Biobank [UKB]), including volunteers, primary care patients, and secondary care patients.

FINDINGS: AIRE accurately predicts risk of all-cause mortality (BIDMC C-index 0·775, 95% CI 0·773-0·776; C-indices on external validation datasets 0·638-0·773), future ventricular arrhythmia (BIDMC C-index 0·760, 95% CI 0·756-0·763; UKB C-index 0·719, 95% CI 0·635-0·803), future atherosclerotic cardiovascular disease (0·696, 0·694-0·698; 0·643, 0·624-0·662), and future heart failure (0·787, 0·785-0·789; 0·768, 0·733-0·802). Through phenome-wide and genome-wide association studies, we identified candidate biological pathways for the prediction of increased risk, including changes in cardiac structure and function, and genes associated with cardiac structure, biological ageing, and metabolic syndrome.

INTERPRETATION: AIRE is an actionable, explainable, and biologically plausible AI-ECG risk estimation platform that has the potential for use worldwide across a wide range of clinical contexts for short-term and long-term risk estimation.

FUNDING: British Heart Foundation, National Institute for Health and Care Research, and Medical Research Council.

BACKGROUND: Late career physicians (LCPs; physicians working beyond age 65 to 75 years) may be at higher risk for delivering unsafe care. To oversee LCPs, some health care organizations (HCOs) have adopted LCP policies requiring cognitive, physical, and practice performance screening assessments. Despite recent controversies, little is known about the content and implementation of such policies.

OBJECTIVE: To characterize key features of LCP policies and the perspectives of medical leaders responsible for policy development and implementation.

DESIGN: Mixed-methods study using content analysis and key informant interviews.

SETTING: 29 U.S. HCOs with LCP policies active in 2020.

PARTICIPANTS: 21 purposively sampled interviewees in physician leadership roles at 18 HCOs.

MEASUREMENTS: Descriptive statistics of policy features and content analysis of interviews.

RESULTS: Although policies had many commonalities-mandatory universal screening at a trigger age around 70 years, a strategy of screening followed by in-depth assessment of positive results, and commitment to patient safety as the key motive-they varied substantially in the testing required, funding, processes after a positive screening result, and decision making around concerning results. Policies prioritized institutional discretion in interpreting and responding to test results; many lacked clear language about appeals or other procedural protections for physicians. Leaders were generally satisfied with policies but reported preemptive retirements as physicians approached the screening age and cautioned that substantial investment in cultivating physicians' buy-in was required for successful rollout.

LIMITATIONS: Sampled policies and interviews may not be representative of all HCOs. The analysis excluded the experiences of HCOs that tried and failed to implement LCP screening.

CONCLUSION: Policies about LCPs are considered successful by institutional leaders. Policy variations and early adopters' implementation experiences highlight opportunities to improve physician acceptance and program rigor.

PRIMARY FUNDING SOURCE: The Greenwall Foundation.

BACKGROUND: Subtle, prognostically important ECG features may not be apparent to physicians. In the course of supervised machine learning, thousands of ECG features are identified. These are not limited to conventional ECG parameters and morphology. We aimed to investigate whether neural network-derived ECG features could be used to predict future cardiovascular disease and mortality and have phenotypic and genotypic associations.

METHODS: We extracted 5120 neural network-derived ECG features from an artificial intelligence-enabled ECG model trained for 6 simple diagnoses and applied unsupervised machine learning to identify 3 phenogroups. Using the identified phenogroups, we externally validated our findings in 5 diverse cohorts from the United States, Brazil, and the United Kingdom. Data were collected between 2000 and 2023.

RESULTS: In total, 1 808 584 patients were included in this study. In the derivation cohort, the 3 phenogroups had significantly different mortality profiles. After adjusting for known covariates, phenogroup B had a 20% increase in long-term mortality compared with phenogroup A (hazard ratio, 1.20 [95% CI, 1.17-1.23]; P<0.0001; phenogroup A mortality, 2.2%; phenogroup B mortality, 6.1%). In univariate analyses, we found phenogroup B had a significantly greater risk of mortality in all cohorts (log-rank P<0.01 in all 5 cohorts). Phenome-wide association study showed phenogroup B had a higher rate of future atrial fibrillation (odds ratio, 2.89; P<0.00001), ventricular tachycardia (odds ratio, 2.00; P<0.00001), ischemic heart disease (odds ratio, 1.44; P<0.00001), and cardiomyopathy (odds ratio, 2.04; P<0.00001). A single-trait genome-wide association study yielded 4 loci. SCN10A, SCN5A, and CAV1 have roles in cardiac conduction and arrhythmia. ARHGAP24 does not have a clear cardiac role and may be a novel target.

CONCLUSIONS: Neural network-derived ECG features can be used to predict all-cause mortality and future cardiovascular diseases. We have identified biologically plausible and novel phenotypic and genotypic associations that describe mechanisms for the increased risk identified.

BACKGROUND: Pacing-induced cardiomyopathy (PICM) is a frequent complication of right ventricular pacing that often requires reoperation for biventricular or conduction system pacing. Better methods for predicting PICM may inform initial pacing strategy and follow-up monitoring.

OBJECTIVE: The purpose of this study was to determine whether the spatial ventricular gradient (SVG), a vectorcardiographic marker of ventricular electrical and mechanical heterogeneity, is associated with the subsequent development of PICM.

METHODS: This was a retrospective study of patients with pacemakers implanted between 2003 and 2012 at the Hospital of the University of Pennsylvania. Baseline demographic, echocardiographic, and electrocardiographic parameters, including SVG magnitude, elevation, and azimuth, were measured from standard 12-lead electrocardiograms. Adjusted Cox proportional hazards modeling was used to assess the associations between the SVG and the risk of PICM over follow-up.

RESULTS: Of the 203 patients with a median age of 74 years (p25-p75 64-79), 110 (54%) male, and median baseline left ventricular ejection fraction 65% (p25-p75 57-70), 44 (22%) developed PICM during follow-up. In unadjusted Cox regression, male sex, native QRS duration in patients without bundle branch block, and both native and paced mean adjusted SVG azimuth predicted future PICM. After multivariable adjustment, higher tertile (tertile 3 vs tertiles 1-2) of the mean adjusted SVG azimuth before (adjusted hazard ratio 1.95; P = .047) and immediately after (adjusted hazard ratio 2.55; P=.003) pacemaker implantation remained significant predictors of PICM.

CONCLUSION: Assessment of the SVG both before and immediately after pacemaker implantation can help identify patients at elevated risk of PICM and may identify a cohort of patients who would be better served with initial biventricular or conduction system pacing.

Degenerative mitral valve disease is common. Up to a quarter of patients with degenerative mitral valve disease may be asymptomatic despite having severe valve regurgitation. Current guideline indications for intervention in asymptomatic patient are centred on left ventricular dimensions and ejection fraction and may include consideration in atrial fibrillation, pulmonary hypertension and those with left atrial dilatation. However, despite intervention according to these recommendations, patients remain at risk of post-operative heart failure and mortality. Newer risk markers have been developed including left ventricular and atrial strain, myocardial fibrosis demonstrated using late gadolinium enhancement, mitral annular disjunction and ventricular arrhythmia burden. Translating newer markers into clinical practice will require integrating and identifying high-risk phenotypes that benefit from early intervention using machine learning techniques and artificial intelligence. Valve repair is the recommended intervention. However, repair rate and durability are dependent on both operator and centre volumes as well as valve characteristics. Recent advancements, including robotic surgery, may enhance repair rates; however, larger datasets are necessary to confirm these improvements. Efforts should focus on establishing high-volume regional centres of excellence for mitral valve repair.

BACKGROUND: In 2019, the US Food and Drug Administration issued a warning that symptomatic relief from claudication using paclitaxel-coated devices might be associated with an increase in mortality over 5 years. We designed a discrete-choice experiment (DCE) to quantify tradeoffs that patients would accept between a decreased risk of clinically driven target-vessel revascularization (CDTVR) and increased mortality risk.

METHODS: Patients with claudication symptoms were recruited from seven medical centers to complete a web-based survey including eight DCE questions that presented pairs of hypothetical device profiles defined by varying risks of CDTVR and overall mortality at 2 and 5 years. Random-parameters logit models were used to estimate relative preference weights, from which the maximum-acceptable increase in 5-year mortality risk was derived.

RESULTS: A total of 272 patients completed the survey. On average, patients would accept a device offering reductions in CDTVR risks from 30% to 10% at 2 years and from 40% to 30% at 5 years if the 5-year mortality risk was less than 12.6% (95% CI: 11.8-13.4%), representing a cut-point of 4.6 percentage points above a baseline risk of 8%. However, approximately 40% chose the device alternative with the lower 5-year mortality risk in seven (20.6%) or eight (18.0%) of the eight DCE questions regardless of the benefit offered.

CONCLUSIONS: Most patients in the study would accept some incremental increase in 5-year mortality risk to reduce the 2-year and 5-year risks of CDTVR by 20 and 10 percentage points, respectively. However, significant patient-level variability in risk tolerance underscores the need for systematic approaches to support benefit-risk decision making.