Prevalence and prognostic significance of device-detected subclinical atrial fibrillation in patients with heart failure and reduced ejection fraction

Background Cardiac implanted electronic devices (CIEDs) can detect short durations of previously unrecognised atrial fibrillation (AF). The prognostic significance of device-detected subclinical AF, in the context of contemporary heart failure (HF) therapy, is unclear. Methods Amongst patients enrolled in the Remote Monitoring in HF with implanted devices (REM-HF) trial, three categories were defined based on total AF duration in the first year of follow-up: no AF, subclinical AF (≥6 min to ≤24 h), and AF >24 h. All-cause mortality, stroke, and cardiovascular hospitalisation were assessed. Results 1561 patients (94.6%) had rhythm data: 71 (4.6%) had subclinical AF (median of 4 episodes, total duration 3.1 h) and 279 (17.9%) had AF >24 h. During 2.8 ± 0.8 years' follow-up, 39 (2.5%) patients had a stroke. Stroke rate was highest amongst patients with subclinical AF (2.0 per 100-person years) versus no AF or AF >24 h (0.8 and 1.0 per 100-person years, respectively). In the overall cohort, AF >24 h was not an independent predictor of stroke. However, amongst patients with no history of AF (n = 932), new-onset subclinical AF conferred a three-fold higher stroke risk (adjusted HR 3.35, 95%CI 1.15–9.77, p = 0.027). AF >24 h was associated with more frequent emergency cardiovascular hospitalisation (adjusted HR 1.46, 95%CI 1.19–1.79, p < 0.0005). Neither AF classification was associated with mortality. Conclusions In patients with HF and a CIED, subclinical AF was infrequent but, as a new finding, was associated with an increased risk of stroke. Anticoagulation remains an important consideration in this population, particularly when the clinical profile indicates a high stroke risk

Remote management of heart failure using implantable electronic devices

Aims Remote management of heart failure using implantable electronic devices (REM-HF) aimed to assess the clinical and cost-effectiveness of remote monitoring (RM) of heart failure in patients with cardiac implanted electronic devices (CIEDs). Methods and results Between 29 September 2011 and 31 March 2014, we randomly assigned 1650 patients with heart failure and a CIED to active RM or usual care (UC). The active RM pathway included formalized remote follow-up protocols, and UC was standard practice in nine recruiting centres in England. The primary endpoint in the time to event analysis was the 1st event of death from any cause or unplanned hospitalization for cardiovascular reasons. Secondary endpoints included death from any cause, death from cardiovascular reasons, death from cardiovascular reasons and unplanned cardiovascular hospitalization, unplanned cardiovascular hospitalization, and unplanned hospitalization. REM-HF is registered with ISRCTN (96536028). The mean age of the population was 70 years (range 23–98); 86% were male. Patients were followed for a median of 2.8 years (range 0–4.3 years) completing on 31 January 2016. Patient adherence was high with a drop out of 4.3% over the course of the study. The incidence of the primary endpoint did not differ significantly between active RM and UC groups, which occurred in 42.4 and 40.8% of patients, respectively [hazard ratio 1.01; 95% confidence interval (CI) 0.87–1.18; P = 0.87]. There were no significant differences between the two groups with respect to any of the secondary endpoints or the time to the primary endpoint components. Conclusion Among patients with heart failure and a CIED, RM using weekly downloads and a formalized follow up approach does not improve outcomes

Translational animal models for Alzheimer's disease: An Alzheimer's Association Business Consortium Think Tank

Over 5 million Americans and 50 million individuals worldwide are living with Alzheimer\u27s disease (AD). The progressive dementia associated with AD currently has no cure. Although clinical trials in patients are ultimately required to find safe and effective drugs, animal models of AD permit the integration of brain pathologies with learning and memory deficits that are the first step in developing these new drugs. The purpose of the Alzheimer\u27s Association Business Consortium Think Tank meeting was to address the unmet need to improve the discovery and successful development of Alzheimer\u27s therapies. We hypothesize that positive responses to new therapies observed in validated models of AD will provide predictive evidence for positive responses to these same therapies in AD patients. To achieve this goal, we convened a meeting of experts to explore the current state of AD animal models, identify knowledge gaps, and recommend actions for development of next-generation models with better predictability. Among our findings, we all recognize that models reflecting only single aspects of AD pathogenesis do not mimic AD. Models or combinations of new models are needed that incorporate genetics with environmental interactions, timing of disease development, heterogeneous mechanisms and pathways, comorbidities, and other pathologies that lead to AD and related dementias. Selection of the best models requires us to address the following: (1) which animal species, strains, and genetic backgrounds are most appropriate; (2) which models permit efficient use throughout the drug development pipeline; (3) the translatability of behavioral-cognitive assays from animals to patients; and (4) how to match potential AD therapeutics with particular models. Best practice guidelines to improve reproducibility also need to be developed for consistent use of these models in different research settings. To enhance translational predictability, we discuss a multi-model evaluation strategy to de-risk the successful transition of pre-clinical drug assets to the clinic