Temporal Association Between Atrial Fibrillation Burden in Cardiac Implantable Electronic Devices and the Risk of Heart Failure Hospitalization.

BACKGROUND: Atrial fibrillation (AF) events in cardiac implantable electronic devices (CIEDs) are temporally associated with stroke risk. This study explores temporal differences in AF burden associated with HF hospitalization risk in patients with CIEDs. METHODS: Patients with HF events from the Optum de-identified Electronic Health Records from 2007 to 2021 and 120 days of preceding CIED-derived rhythm data from a linked manufacturer's data warehouse were included. AF burden ≥5.5 h/d was defined as an AF event. The AF event burden in the case period (days 1-30 immediately before the HF event) was considered temporally associated with the HF event and compared with the AF event burden in a temporally dissociated control period (days 91-120 before the HF event). The odds ratio for temporally associated HF events and the odds ratio associated with poorly rate-controlled AF (>110 bpm) were calculated. RESULTS: In total, 7257 HF events with prerequisite CIED data were included; 957 (13.2%) patients had AF events recorded only in either their case (763 [10.5%]) or control (194 [2.7%]) periods, but not both. The odds ratio for a temporally associated HF event was 3.93 (95% CI, 3.36-4.60). This was greater for an HF event with a longer stay of >3 days (odds ratio, 4.51 [95% CI, 3.57-5.68]). In patients with AF during both the control and case periods, poor AF rate control during the case period also increased HF event risk (1.78 [95% CI, 1.22-2.61]). In all, 222 of 4759 (5%) patients without AF events before their HF event had an AF event in the 10 days following. CONCLUSIONS: In a large real-world population of patients with CIED devices, AF burden was associated with HF hospitalization risk in the subsequent 30 days. The risk is increased with AF and an uncontrolled ventricular rate. Our findings support AF monitoring in CIED algorithms to prevent HF admissions.

Dynamic risk stratification of worsening heart failure using a deep learning-enabled implanted ambulatory single-lead electrocardiogram.

Aims: Implantable loop recorders (ILRs) provide continuous single-lead ambulatory electrocardiogram (aECG) monitoring. Whether these aECGs could be used to identify worsening heart failure (HF) is unknown. Methods and results: We linked ILR aECG from Medtronic device database to the left ventricular ejection fraction (LVEF) measurements in Optum® de-identified electronic health record dataset. We trained an artificial intelligence (AI) algorithm [aECG-convolutional neural network (CNN)] on a dataset of 35 741 aECGs from 2247 patients to identify LVEF ≤ 40% and assessed its performance using the area under the receiver operating characteristic curve. Ambulatory electrocardiogram-CNN was then used to identify patients with increasing risk of HF hospitalization in a real-world cohort of 909 patients with prior HF diagnosis. This dataset provided 12 467 follow-up monthly evaluations, with 201 HF hospitalizations. For every month, time-series features from these predictions were used to categorize patients into high- and low-risk groups and predict HF hospitalization in the next month. The risk of HF hospitalization in the next 30 days was significantly higher in the cohort that aECG-CNN identified as high risk [hazard ratio (HR) 1.89; 95% confidence interval (CI) 1.28-2.79; P = 0.001] compared with low risk, even after adjusting patient demographics (HR 1.88; 95% CI 1.27-2.79 P = 0.002). Conclusion: An AI algorithm trained to detect LVEF ≤40% using ILR aECGs can also readily identify patients at increased risk of HF hospitalizations by monitoring changes in the probability of HF over 30 days.

Associations Between Changes in Maximum Daily Atrial Fibrillation Duration, Ischemic Stroke, and Mortality.

BACKGROUND: Atrial fibrillation (AF) outcomes are strongly associated with continuous measures of AF burden. OBJECTIVES: This study sought to assess the association between changes in maximum daily AF duration (MDAFD) and stroke or mortality in patients with cardiac implantable electronic devices (CIEDs). METHODS: The Optum deidentified electronic health record data set (2007-2021) was linked with the Medtronic CareLink heart rhythm database. Patients with CIEDs and health care activity recorded in the electronic health record were included, excluding those with oral anticoagulation prescription. MDAFD was assessed 30 days post implant (baseline period) and 30 days before censoring or an event. HRs for the primary analysis were adjusted for components of CHA2DS2-VASc, baseline MDAFD category, and chronic kidney disease. RESULTS: Of 26,400 patients (age 68 ± 13 years; follow-up 2.6 ± 1.6 years) analyzed, 2,544 (9.6%) had AF during baseline. Increased (vs stable or decreased) MDAFD category in follow-up was associated with a higher adjusted rate of stroke and mortality (HR: 1.80; 95% CI: 1.61-2.01). There was no association between decreased MDAFD in follow-up and the combined endpoint (HR: 0.82; 95% CI: 0.68-1.00). Subgroup analysis by baseline MDAFD category demonstrated that increased MDAFD in follow-up was associated with a greater risk of stroke or mortality among patients with no AF at baseline, and decreased MDAFD in follow-up was associated with a lower risk of stroke or mortality among patients with baseline MDAFD of 1 to <5.5 hours and 5.5 to <23.5 hours. CONCLUSIONS: In CIED patients not on oral anticoagulation, increased MDAFD in follow-up was associated with a higher rate of stroke and mortality. These results suggest that AF burden, and associated risk, s not stable over time.