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Background: There have been limited data examining the temporal relationship between device-derived daily activity and ventricular arrhythmias (VAs). Objective: We aimed to assess whether activity predicted VAs or VAs predicted changes in activity. Methods: The CERTITUDE registry includes over 55,000 implanted devices active on Home Monitoring. Daily data on activity are captured by a 1-axis accelerometer. Temporal changes in activity during treated VAs were analyzed using the first event and 7-day activity windows (baseline, pre-event, and postevent). Baseline period was defined as 31 to 38 days prior to VA. VAs were categorized by heart rate (≤200 beats/min, >200 beats/min) and treatment (shock or antitachycardia pacing). Differences were assessed using the binomial proportion test and case-crossover analysis. Results: A total of 5631 devices (3688 implantable cardioverter-defibrillators, 1943 cardiac resynchronization therapy defibrillators) were analyzed with a cumulative follow-up duration of 18,354 years (5.6 million days). Patients with VA events >200 beats/min with shock (n = 593) had a significant decline in activity post-VA with a median –8.7% reduction (interquartile range –24.6% to 7.3%, P < 0.001). However, there was no reduction in activity before VA events >200 beats/min (P = .690) or before or after VA events >200 beats/min with antitachycardia pacing. However, VA events ≤200 beats/min with shock had reductions in activity following the event (–5.8%, interquartile range –29.5% to 12.3%, P = .003). Case-crossover analyses confirmed lower activity rates following for VA events >200 beats/min with shock. Conclusion: In the CERTITUDE registry, we have shown a temporal decline in device-derived activity following VA events >200 beats/min and for VA events

Background: Despite clear associations between arrhythmia burden and cardiovascular risk, clinical risk scores that predict cardiovascular events do not incorporate individual-level arrhythmia characteristics from long-term continuous monitoring (LTCM). Objectives: This study evaluated the performance of risk models that use data from LTCM and patient claims for prediction of heart failure (HF) and ischemic stroke. Methods: We retrospectively analyzed features extracted from up to 14 days of LTCM electrocardiogram (ECG) data linked to patient-level claims data for 320,974 Medicare beneficiaries who underwent ZioXT ambulatory monitoring. We created predictive models for HF hospitalization, stroke hospitalization, and new-onset HF within 1 year using LASSO Cox regression for variable selection among ambulatory ECG variables and components of the CHA2DS2-VASc score. Results: A model that included components of the CHA2DS2-VASc and all ambulatory ECG variables had greater discrimination for HF hospitalization (C-statistic 0.85, 95% CI: 0.84-0.86) than the CHA2DS2-VASc (C-statistic 0.73, 95% CI: 0.72-0.74), but performed similarly to the CHA2DS2-VASc for prediction of stroke hospitalization (C-statistic 0.75 [95% CI: 0.74-0.77] vs 0.71 [95% CI: 0.70-0.72], respectively). Atrial fibrillation was associated with greater risk in the most predictive models (HF hospitalization, HR: 1.53 [95% CI: 1.35-1.72]; stroke hospitalization, HR: 1.58 [95% CI: 1.30-1.93]), and premature ventricular couplets were associated with greater risk of HF hospitalization (HR: 1.54, 95% CI: 1.43-1.65). Conclusions: The CHA2DS2-VASc performed modestly for prediction of stroke and HF events; predictive ability improved significantly with addition of LTCM ECG covariates. The presence of atrial fibrillation and ventricular ectopy on 14-day LTCM were strongly associated with HF events.

Background: We performed a trial to evaluate the efficacy of a blended intervention with personalized health coaching and virtual cardiac rehabilitation to improve medication adherence and risk factors. The trial was terminated early. Here, we describe findings from a root cause analysis and lessons learned. Methods: SmartGUIDE was an open-label, single-center trial that randomized participants with coronary artery disease who were prescribed a statin and/or P2Y12 inhibitor 1:1 to either usual care or the added use of a mobile app with components of cardiac rehabilitation paired with personal virtual coaching. The primary outcome was medication adherence: proportion of days covered (PDC). The planned sample size was 132. We performed a root cause analysis to evaluate processes from study development to closure. Results: During trial conduct, the technology start-up withdrew the intervention. The study was terminated early with 63 participants randomized and data from 26 available for analysis. The median PDC was high in both groups (intervention group 94%, interquartile range [IQR] 88%–96%; control group: 99%, IQR 95%–100%). Root cause analysis identified factors for not achieving trial objectives: key factors that limited enrollment (inclusion criteria, low penetration of compatible smartphones), participant retention or engagement (poor app product, insufficient technology support), and suboptimal choice of a technology partner (technology start-up’s inexperience in health care, poor product design, inadequate fundraising). Conclusion: We identified important and preventable factors leading to trial failure. These factors may be common across digital health trials and may explain prior observations that many such trials are never completed. Careful vetting of technology partners and more pragmatic study designs may prevent these missteps.

The evidence of direct oral anticoagulants (DOACs) usage for venous thromboembolism (VTE) in patients at extremes of body weight or mass index is limited. In such situations, warfarin may be more frequently used. We investigated warfarin time in the therapeutic international normalized ratio range (TTR) and DOAC adherence based on the calculated proportion of days covered (PDC) by pill coverage from a DOAC prescription in patients with VTE across all body sizes. Using data from the Veterans Health Administration (VA), we identified first-time patients with VTE between 2013 and 2018 treated with warfarin or DOACs. We analyzed 28,245 patients with warfarin TTR (N = 10,167) or DOAC PDC(N = 18,078). For warfarin-treated patients after index VTE, mean TTR was lower over shorter treatment durations (TTR 30 vs TTR 180 [mean ± SD]: 43.8% ± 33.5% vs 58.8% ± 23.5%). Mean TTR over 180 days after VTE was lowest for patients