Episode-level and clinical characterization of asymptomatic atrial fibrillation events.

INTRODUCTION: Not all patients experience debilitating symptoms during Atrial Fibrillation (AF), some are asymptomatic. The reasons for this inter- and intrasubject variability is unknown. PURPOSE: The study objective was NOAH characterize episode-level and clinical characteristics associated with symptomatic versus asymptomatic episodes of AF in patients with an implantable cardiac monitor (ICM). METHODS: Patients with an AF episode detected on an ICM between 2007 and 2021 with overlapping clinical data from aggregated Electronic Health Records in the Optum® deidentified data set were included. Symptomatic episodes were labeled in real-time by the patient. Heart rate (HR) at onset, mean HR, AF Evidence Score (a measure of beat-to-beat irregularity), episode duration and Activity Index were evaluated for association with symptom status using multivariable regression modeling. RESULTS: 11 267 patients had AF episodes with clinical data available. The 1776 (15.8%) patients who reported symptomatic AF episodes were younger (67 ± 12 years vs. 71 ± 11 years old, p < .001) and had fewer cardiovascular co-morbidities than patients with asymptomatic AF exclusively. Symptomatic episodes were longer (5.5 [2.4, 14.4] h vs. 3.7 [1.7, 11] h, p < .001), had higher mean HR (103 ± 22 bpm vs. 88 ± 22 bpm, p < .001) and higher AF evidence scores (98 ± 27 vs. 82 ± 24, p < .001). These features were independently associated with symptomatic episodes on multivariable regression analysis and per-subject analysis in patients who had both symptomatic and asymptomatic episodes. DISCUSSION: Episode-level characteristics differed between symptomatic AF episodes versus asymptomatic episodes in patients with ICMs. Symptomatic patients also had less comorbidities. These parameters may be useful in understanding variable symptomatic manifestation and remote stratification of AF episodes.

Stroke Risk as a Function of Atrial Fibrillation Duration and CHA2DS2-VASc Score.

BACKGROUND: Studies of patients with cardiovascular implantable electronic devices show a relationship between atrial fibrillation (AF) duration and stroke risk, although the interaction with CHA2DS2-VASc score is poorly defined. The objective of this study is to evaluate rates of stroke and systemic embolism (SSE) in patients with cardiovascular implantable electronic devices as a function of both CHA2DS2-VASc score and AF duration. METHODS: Data from the Optum electronic health record deidentified database (2007-2017) were linked to the Medtronic CareLink database of cardiovascular implantable electronic devices capable of continuous AF monitoring. An index date was assigned as the later of either 6 months after device implantation or 1 year after electronic health record data availability. CHA2DS2-VASc score was assessed using electronic health record data before the index date. Maximum daily AF burden (no AF, 6 minutes-23.5 hours, and >23.5 hours) was assessed over the 6 months before the index date. SSE rates were computed after the index date. RESULTS: Among 21 768 nonanticoagulated patients with cardiovascular implantable electronic devices (age, 68.6±12.7 years; 63% male), both increasing AF duration (P<0.001) and increasing CHA2DS2-VASc score (P<0.001) were significantly associated with annualized risk of SSE. SSE rates were low in patients with a CHA2DS2-VASc score of 0 to 1 regardless of device-detected AF duration. However, stroke risk crossed an actionable threshold defined as >1%/y in patients with a CHA2DS2-VASc score of 2 with >23.5 hours of AF, those with a CHA2DS2-VASc score of 3 to 4 with >6 minutes of AF, and patients with a CHA2DS2-VASc score ≥5 even with no AF. CONCLUSIONS: There is an interaction between AF duration and CHA2DS2-VASc score that can further risk-stratify patients with AF for SSE and may be useful in guiding anticoagulation therapy.

Characteristics of ventricular tachyarrhythmias and their susceptibility to antitachycardia pacing termination in patients with ischemic and nonischemic cardiomyopathy: A patient-level meta-analysis of three large clinical trials.

BACKGROUND: Implantable cardioverter defibrillators (ICDs) reduce all-cause mortality among cardiomyopathy patients. Whether or not antitachycardia pacing (ATP) is equally effective in ischemic (ICM) and nonischemic (NICM) cardiomyopathy patients remains poorly understood. We describe the distribution of monomorphic (MVT) and non-monomorphic (polymorphic ventricular tachycardia/ventricular fibrillation [PVT/VF]) ventricular tachyarrhythmias among ICM and NICM primary prevention patients. METHODS: This patient-level meta-analysis included primary prevention patients from the Shock-Less (n = 3519), PainFree SST (n = 1917), and PREPARE (n = 690) studies. Distribution of MVT and PVT/VF events were compared with χ2 tests. ATP success was estimated using a generalized estimating equation model to correct for multiple episodes for a patient between cohorts for slow (≥320 ms) and fast (240-310 ms) MVTs. RESULTS: Among 6126 patients, 714 (29% NICM, age 66 ± 13 years, female 18%, EF = 29 ± 12%) had a total of 4444 treated ventricular tachyarrhythmia episodes. The rate of individuals treated for MVT or PVT/VF was comparable between ICM (11.9%) and NICM (11.2%) over 21 ± 10 months. In addition, the distribution of MVT (76% ICM vs. 71% NICM) and PVT/VF (15% ICM vs. 20% NICM) was not significantly different (p = .28). Among MVT episodes, the average tachycardia cycle lengths (332 ± 58 ms ICM vs. 313 ± 40 ms NICM; p = .27) were similar, as was the likelihood of ATP-associated termination (74.6% ICM vs. 76.4% NICM; p = .58). Overall, ATP success was higher for slow (≥320 ms) MVT versus faster (240-310 ms) episodes (84.1% vs. 69%; p < .001). CONCLUSION: In a large cohort of primary prevention ICD patients, ICM and NICM patients have similar rates and proportions of MVT and PVT/VF episodes. ATP-associated termination of MVT was comparable between the two groups.

Evaluation of an Integrated Device Diagnostics Algorithm to Risk Stratify Heart Failure Patients　- Results From the SCAN-HF Study.

BACKGROUND: Integrated device diagnostics, Triage-HF, is useful in risk stratifying patients with heart failure (HF), but its performance for Japanese patients remains unknown. This is a prospective study of Japanese patients treated with a cardiac resynchronization therapy defibrillator (CRT-D), with a Medtronic OptiVol 2.0 feature.Methods and Results:A total of 320 CRT-D patients were enrolled from 2013 to 2017. All received HF treatment in the prior 12 months. Following enrollment, they were followed every 6 months for 48 months (mean, 22 months). Triage-HF-stratified patients at low, medium and high risk statuses at every 30-day period, and HF-related hospitalization occurring for the subsequent 30 days, were evaluated and repeated. The primary endpoint was to assess Triage-HF performance in predicting HF-related hospitalization risk. All device data were available for 279 of 320 patients (NYHA class II or III in 93%; mean left ventricular ejection fraction, 31%). During a total of 5,977 patient-month follow-ups, 89 HF-related hospitalization occurred in 72 patients. The unadjusted event numbers for Low, Medium and High statuses were 19 (0.7%), 42 (1.6%) and 28 (4.1%), respectively. Relative risk of Medium to Low status was 2.18 (95% CI 1.23-3.85) and 5.78 (95% CI 3.34-10.01) for High to Low status. Common contributing factors among the diagnostics included low activity, OptiVol threshold crossing, and elevated night heart rate. CONCLUSIONS: Triage-HF effectively stratified Japanese patients at risk of HF-related hospitalization.

Premature ventricular contraction detection for long-term monitoring in an implantable cardiac monitor.

BACKGROUND: Premature ventricular complexes (PVCs) are an important therapeutic target in symptomatic patients and in the setting of PVC-induced cardiomyopathy; however, measuring burden and therapeutic response is challenging. We developed and validated an algorithm for continuous long-term monitoring of PVC burden in an insertable cardiac monitor (ICM). METHODS: A high-specificity PVC detection algorithm was developed using real-world ICM data and validated using simultaneous Holter data and real-world ICM data. The PVC algorithm uses long-short-long RR interval sequence and morphology characteristics for three consecutive beats to detect the occurrence of single PVC beats. Data are expressed as gross incidence, patient average, and generalized estimating equation estimates, which were used to determine sensitivity, specificity, positive and negative predictive value (PPV, NPV). RESULTS: The PVC detection algorithm was developed on eighty-seven 2-min EGM strips recorded by an ICM to obtain a sensitivity and specificity of 75.9% and 98.8%. The ICM validation data cohort consisted of 787 ICM recorded ECG strips 7-16 min in duration from 134 patients, in which the algorithm detected PVC beats with a sensitivity, specificity, PPV, and NPV of 75.2%, 99.6%, 75.9%, and 99.5%, respectively. In the Holter validation dataset with continuous 2-h snippets from 20 patients, the algorithm sensitivity, specificity, PPV, and NPV were 74.4%, 99.6%, 68.8%, and 99.7%, respectively, for detecting PVC beats. CONCLUSIONS: The PVC detection algorithm was able to achieve a high specificity with only 0.4% of the normal events being incorrectly identified as PVCs, while detecting around three of four PVCs on a continuous long-term basis in ICMs.

Adapting detection sensitivity based on evidence of irregular sinus arrhythmia to improve atrial fibrillation detection in insertable cardiac monitors.

Aims: Intermittent change in p-wave discernibility during periods of ectopy and sinus arrhythmia is a cause of inappropriate atrial fibrillation (AF) detection in insertable cardiac monitors (ICM). To address this, we developed and validated an enhanced AF detection algorithm. Methods and results: Atrial fibrillation detection in Reveal LINQ ICM uses patterns of incoherence in RR intervals and absence of P-wave evidence over a 2-min period. The enhanced algorithm includes P-wave evidence during RR irregularity as evidence of sinus arrhythmia or ectopy to adaptively optimize sensitivity for AF detection. The algorithm was developed and validated using Holter data from the XPECT and LINQ Usability studies which collected surface electrocardiogram (ECG) and continuous ICM ECG over a 24-48 h period. The algorithm detections were compared with Holter annotations, performed by multiple reviewers, to compute episode and duration detection performance. The validation dataset comprised of 3187 h of valid Holter and LINQ recordings from 138 patients, with true AF in 37 patients yielding 108 true AF episodes ≥2-min and 449 h of AF. The enhanced algorithm reduced inappropriately detected episodes by 49% and duration by 66% with <1% loss in true episodes or duration. The algorithm correctly identified 98.9% of total AF duration and 99.8% of total sinus or non-AF rhythm duration. The algorithm detected 97.2% (99.7% per-patient average) of all AF episodes ≥2-min, and 84.9% (95.3% per-patient average) of detected episodes involved AF. Conclusion: An enhancement that adapts sensitivity for AF detection reduced inappropriately detected episodes and duration with minimal reduction in sensitivity.

How active are young cardiac device patients? Objective assessment of activity in children with cardiac devices.

BACKGROUND: The daily activity of pediatric patients with implantable cardiac devices provides behavioral evidence of functional outcomes. Modern devices provide continuous accelerometer data that are sensitive to movement, but normative values have not been published for pediatric activity rates. This study provides the first normative accelerometer data on activity rates in a large sample of pediatric cardiac device patients. METHODS: Patients were between 3 and 18 years old (N = 1,905) and implanted with a cardiac device from a single device company, and enrolled in remote monitoring. RESULTS: The median age at implant was 14 years (interquartile range = 12-16); 61.3% were male. Data for 4 weeks were extracted from a company database at 53 weeks postimplant and an average of daily activity was calculated. Daily average activity for all patients was 5.4 hours (standard deviation  =  2.0). In a multivariate analysis, increased level of activity was associated with: being male, having a pacemaker versus implantable cardioverter defibrillator (ICD), epicardial device location, rate response turned off, having experienced a shock, and younger age. CONCLUSIONS: These results provide the first baseline data of physical activity in children with implanted cardiac devices and provide a clinical guide to physical activity assessment in this population. Further, our data suggest physical activity in children with implantable cardiac devices may differ based on demographic variables, device type, device location, indication for implantation, and history of ICD shock.

Effects of Spinal Cord Stimulation on Cardiac Sympathetic Nerve Activity in Patients with Heart Failure.

BACKGROUND: Spinal cord stimulation (SCS) reduces sympathetic activity in animal models of heart failure with reduced ejection fraction (HF) but limited data exist of SCS in patients with HF. The aim of the present study was to test the primary hypothesis that SCS reduces cardiac sympathetic nerve activity in HF patients. Secondary hypotheses were that SCS improves left ventricular function and dimension, exercise capacity, and clinical variables relevant to HF. METHODS: HF patients with a SCS device previously participating in the DEFEAT-HF trial were included in this crossover study with 6-week intervention periods (SCS-ON and SCS-OFF). SCS (50 Hz, 210-µs pulse duration, aiming at T2-T4 segments) was delivered for 12 hours daily. Indices of myocardial sympathetic neuronal function (heart-to-mediastinum ratio, HMR) and activity (washout rate, WR) were assessed using 123 I-metaiodobenzylguanidine (MIBG) scintigraphy. Echocardiography, exercise testing, and clinical data collection were also performed. RESULTS: We included 13 patients (65.3 ± 8.0 years, nine males) and MIBG scintigraphy data were available in 10. HMR was not different comparing SCS-ON (1.37 ± 0.16) and SCS-OFF (1.41 ± 0.21, P = 0.46). WR was also unchanged comparing SCS-ON (41.5 ± 5.3) and SCS-OFF (39.1 ± 5.8, P = 0.30). Similarly, average New York Heart Association class (2.4 ± 0.5 vs 2.3 ± 0.6, P = 0.34), quality of life score (24 ± 16 vs 24 ± 16, P = 0.94), and left ventricular dimension and function as well as exercise capacity were all unchanged comparing SCS-ON and SCS-OFF. CONCLUSION: In patients with HF, SCS (12 hours daily, targeting the T2-T4 segments of the spinal cord) does not appear to influence cardiac sympathetic neuronal activity or function as assessed by MIBG scintigraphy.

The Impact of Duration of Atrial Fibrillation Recurrences on Measures of Health-Related Quality of Life and Symptoms.

BACKGROUND: Little is known about the relationship between daily atrial fibrillation (AF) burden and quality of life (QOL). We sought to determine the influence of atrial tachycardia (AT) or AF burden on measures of QOL and symptoms. METHODS AND RESULTS: We retrospectively analyzed patients with dual-chamber pacemakers from the Atrial Septal Pacing Efficacy Clinical Trial (ASPECT), Atrial Therapy Efficacy and Safety Trial (ATTEST), and aTRial arrhythmias dEtected by implaNted Device diagnostics Study (TRENDS) trials. All patients underwent at least one QOL evaluation. We predefined four AF burden groups: no AT/AF (group 1), ≤30 minutes (group 2), 30 minutes-2 hours (group 3), and >2 hours (group 4) per day. We compared QOL measures using the 12-item Short-Form Health Survey (SF-12; standard 4 week recall) and the AF Symptom Checklist (SC) severity and frequency between groups 2-4 to those in group 1. A total of 798 patients were analyzed (age 72 ± 11 years, 447 male [56%]). SC frequency and severity and SF-12 physical and mental scores worsened significantly when patients in group 4 were compared to patients with no AF. There were no statistically significant differences for any of the measures when comparing group 2 or 3 patients to group 1. By linear regression, only the 2-hour-cutoff had a significant impact on QOL as measured by SC frequency (+3.15, P < 0.001), severity (+3.23, P < 0.001), SF-12 physical score (-2.42, P = 0.013), and SF-12 mental score (-2.11, P = 0.021). CONCLUSION: A daily AT/AF burden of more than 2 hours had significant impact on QOL. This might influence the choice of appropriate cut-off points to determine the success of an AF treatment.

Real-World Experience with Insertable Cardiac Monitors to Find Atrial Fibrillation in Cryptogenic Stroke.

BACKGROUND: The characteristics of atrial fibrillation (AF) episodes in cryptogenic stroke patients have recently been explored in carefully selected patient populations. However, the incidence of AF among a large, real-world population of patients with an insertable cardiac monitor (ICM) placed for the detection of AF following a cryptogenic stroke has not been investigated. METHODS: Patients in the de-identified Medtronic DiscoveryLink™ database who received an ICM (Reveal LINQ™) for the purpose of AF detection following a cryptogenic stroke were included. AF detection rates (episodes ≥2 min) were quantified using Kaplan-Meier survival estimates at 1 and 6 months and compared to the CRYSTAL AF study at 6 months. The time to AF detection and maximum duration of AF episodes were also analyzed. RESULTS: A total of 1,247 patients (age 65.3 ± 13.0 years) were followed for 182 (IQR 182-182) days. A total of 1,521 AF episodes were detected in 147 patients, resulting in AF detection rates of 4.6 and 12.2% at 30 and 182 days, respectively, and representing a 37% relative increase over that reported in the CRYSTAL AF trial at 6 months. The median time to AF detection was 58 (IQR 11-101) days and the median duration of the longest detected AF episode was 3.4 (IQR 0.4-11.8) h. CONCLUSIONS: The real-world incidence of AF among patients being monitored with an ICM after a cryptogenic stroke validates the findings of the CRYSTAL AF trial and suggests that continuous cardiac rhythm monitoring for periods longer than the current guideline recommendation of 30 days may be warranted in the evaluation of patients with cryptogenic stroke.

Examination of the differential impacts of antitachycardia pacing vs. shock on patient activity in the EMPIRIC study.

AIMS: Implantable cardioverter defibrillators (ICDs) have demonstrated mortality advantages over antiarrhythmic drug therapy, but ICD shock has known detrimental effects on quality of life and psychologic functioning. However, it remains unknown how patient activity level is affected by shock, or by antitachycardia pacing (ATP), which was developed to reduce the treatment burden of shocks. Examine the differential impact of ICD shock and ATP on patient activity level as a novel way to capture the relative behavioural repercussions of these ICD therapies. METHODS AND RESULTS: Accelerometer-derived activity data were analysed for a subset of patients (males = 83%; mean age = 62 years) enrolled in the EMPIRIC trial who received shock (n = 71) or ATP (n = 103). Differences in activity between a week pre-therapy and a week post-therapy were examined to assess the behavioural repercussions of shock vs. ATP when one, few (2-4), or many (5+) therapies were delivered. For patients receiving shock, a significant reduction in activity was observed for few (-26%) and many shocks (-34%) in the first week post-therapy (P < 0.05). In weeks 2-4, activity levels recovered towards baseline levels. In contrast, no level of ATP-only therapy significantly reduced patients' activity levels at any time following therapy. CONCLUSION: This study is the first to evaluate objective, behavioural effects of shock, and whether these effects are comparable with ATP therapy alone. In tandem with existing literature, current results highlight that ICD shocks and ATP have divergent effects on behavioural outcomes, with ATP's effect profile in these domains appearing somewhat favourable.

The prevalence of sinus rhythm in patients with "permanent" atrial fibrillation.

BACKGROUND: The term "permanent" atrial fibrillation (AF) is generally used to describe the rhythm status of patients for whom cardioversion has failed or attempts to restore normal sinus rhythm (NSR) have ceased. However, the rhythm status of such patients is typically assessed by symptoms or intermittent monitoring, and therefore categorization may be imprecise. METHODS: We evaluated the presence of NSR among patients who were identified by their physicians as having permanent AF and who underwent prior insertion of a cardiac rhythm management device in the OMNI study. Patients with a dual- or triple-chamber device (pacemaker, implantable cardiac defibrillator, or cardiac resynchronization therapy) and ≥30 days of device data were studied. We tabulated the percentage of follow-up days spent entirely in NSR, entirely in AF, or in both NSR and AF. RESULTS: A total of 69 patients met inclusion criteria and were followed for 767 ± 479 days. More than 73% of patients experienced ≥1 entire day in NSR. On average, 38.2% of days were spent entirely in NSR, 11.8% of days were spent in a combination of NSR and AF, and only 50.0% of days were spent entirely in AF. The median daily AF burden during follow-up was 14.6 [1.1-23.7] hours/day. CONCLUSIONS: NSR is common in many device patients thought to have permanent AF, suggesting that continuous arrhythmia monitoring could be useful in identifying permanent AF patients who may benefit from renewed rhythm control efforts. Alternatively, some permanent AF patients undergoing atrioventricular nodal ablation may benefit from dual-chamber devices due to likely periods of NSR.

Development and validation of an integrated diagnostic algorithm derived from parameters monitored in implantable devices for identifying patients at risk for heart failure hospitalization in an ambulatory setting.

BACKGROUND: We developed and validated a heart failure (HF) risk score combining daily measurements of multiple device-derived parameters. METHODS: Heart failure patients from clinical studies with implantable devices were used to form two separate data sets. Daily HF scores were estimated by combining changes in intra-thoracic impedance, atrial fibrillation (AF) burden, rapid rate during AF, %CRT pacing, ventricular tachycardia, night heart rate, heart rate variability, and activity using a Bayesian model. Simulated monthly follow-ups consisted of looking back at the maximum daily HF risk score in the preceding 30 days, categorizing the evaluation as high, medium, or low risk, and evaluating the occurrence of HF hospitalizations in the next 30 days. We used an Anderson-Gill model to compare survival free from HF events in the next 30 days based on risk groups. RESULTS: The development data set consisted of 921 patients with 9790 patient-months of data and 91 months with HF hospitalizations. The validation data set consisted of 1310 patients with 10 655 patient-months of data and 163 months with HF hospitalizations. In the validation data set, 10% of monthly evaluations in 34% of the patients were in the high-risk group. Monthly diagnostic evaluations in the high-risk group were 10 times (adjusted HR: 10.0; 95% CI: 6.4-15.7, P < 0.001) more likely to have an HF hospitalization (event rate of 6.8%) in the next 30 days compared with monthly evaluations in the low-risk group (event rate of 0.6%). CONCLUSION: An HF score based on implantable device diagnostics can identify increased risk for HF hospitalization in the next 30 days.

Ambulatory Risk Stratification for Worsening Heart Failure in Patients with Reduced and Preserved Ejection Fraction Using Diagnostic Parameters Available in Implantable Cardiac Monitors.

BACKGROUND: Ambulatory risk stratification for worsening heart failure (HF) using diagnostics measured by insertable cardiac monitors (ICM) may depend on the left ventricular ejection fraction (LVEF). We evaluated risk stratification performance in patients with reduced versus preserved LVEF. METHODS: ICM patients with a history of HF events (HFEs) were included from the Optum® de-identified Electronic Health Record dataset merged with ICM device-collected data during 2007-2021. ICM measures nighttime heart rate (NHR), heart rate variability (HRV), atrial fibrillation (AF) burden, rate during AF, and activity duration (ACT) daily. Each diagnostic was categorized into high, medium, or low risk using previously defined features. HFEs were HF-related inpatient, observation unit, or emergency department stays with IV diuresis administration. Patients were divided into two cohorts: LVEF ≤ 40% and LVEF > 40%. A marginal Cox proportional hazards model compared HFEs for different risk groups. RESULTS: A total of 1020 ICM patients with 18,383 follow-up months and 301 months with HFEs (1.6%) were included. Monthly evaluations with a high risk were 2.3, 4.2, 5.0, and 4.5 times (p < 0.001 for all) more likely to have HFEs in the next 30 days compared to those with a low risk for AF, ACT, NHR, and HRV, respectively. HFE rates were higher for patients with LVEF > 40% compared to LVEF ≤ 40% (2.0% vs. 1.3%), and the relative risk between high-risk and low-risk for each diagnostic parameter was higher for patients with LVEF ≤ 40%. CONCLUSIONS: Diagnostics measured by ICM identified patients at risk for impending HFEs. Patients with preserved LVEF showed a higher absolute risk, and the relative risk between risk groups was higher in patients with reduced LVEF.

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.

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.

A Novel Heart Failure Diagnostic Risk Score Using a Minimally Invasive Subcutaneous Insertable Cardiac Monitor.

BACKGROUND: The authors tested the hypothesis that physiological information from sensors within a minimally invasive, subcutaneous, insertable cardiac monitor (ICM) could be used to develop an ambulatory heart failure risk score (HFRS) to accurately identify heart failure (HF) patients, across the ejection fraction spectrum, at high risk of an impending worsening heart failure event (HFE). OBJECTIVES: The purpose of this study was to examine performance of ICM-based, multiparameter, dynamic HFRS to predict HFEs in patients with NYHA functional class II/III HF. METHODS: In 2 observational cohorts, HF patients were implanted with an ICM; subcutaneous impedance, respiratory rate, heart rate and variability, atrial fibrillation burden, ventricular rate during atrial fibrillation, and activity duration were combined into an HFRS to identify the probability of HFE within 30 days. Patients and providers were blinded to the data. HFRS sensitivity and unexplained detection rate were defined in 2 independent patient population data sets. HFEs were defined as hospitalization, observation unit, or emergency department visit with a primary diagnosis of HF, and intravenous diuretic treatment. RESULTS: First data set (development): 42 patients had 19 HFE; second data set (validation): 94 patients had 19 HFE (mean age 66 ± 11 years, 63% men, 50% with LVEF ≥40%, 80% NYHA functional class III). Using a high-risk threshold = 7.5%, development and validation data sets: sensitivity was 73.7% and 68.4%; unexplained detection rate of 1.4 and 1.5 per patient-year; median 47 and 64 days early warning before HFE. CONCLUSIONS: ICM-HFRS provides a multiparameter, integrated diagnostic method with the ability to identify when HF patients are at increased risk of heart failure events. (Reveal LINQ Evaluation of Fluid [REEF]; NCT02275923, Reveal LINQ Heart Failure [LINQ HF]; NCT02758301, Algorithm Using LINQ Sensors for Evaluation and Treatment of Heart Failure [ALLEVIATE-HF]; NCT04452149).

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.

Ineffective cardiac resynchronization pacing is associated with poor outcomes in a nationwide cohort analysis.

BACKGROUND: Delivery of cardiac resynchronization therapy (CRT) requires left ventricular myocardial capture to achieve clinical benefits. OBJECTIVE: We sought to determine whether ineffective pacing affects survival. METHODS: Ineffective ventricular pacing (VP) was defined as the difference between the percentage of delivered CRT (%VP) and the percentage of EffectivCRT in CRT devices. Using the Optum de-identified electronic health record data set and Medtronic CareLink data warehouse, we identified patients implanted with applicable devices with at least 30 days of follow-up. Kaplan-Meier and Cox proportional hazards models assessed the effect of %VP and % ineffective VP on survival. RESULTS: Among 7987 patients with 2.1 ± 1.0 years of follow-up, increasing ineffective VP was associated with decreasing survival: the highest observed survival was in the quartile with <0.08% ineffective VP and the lowest survival was in the quartile with >1.47% ineffective VP (85.1% vs 75.7% at 3 years; P < .001). As expected, patients with more than the median %VP of 97.7% had better survival than did patients with <97.7% VP (84.2% vs 77.8%; P < .001). However, patients who had >97.7% VP but >2% ineffective VP had similar survival to patients with <97.7% VP but ≤2% ineffective VP (81.6% vs 79.4%; P = .54). A multivariable Cox proportional hazards model demonstrated that <97.7% VP (adjusted hazard ratio 1.29; 95% confidence interval 1.14-1.46; P < .001) and >2% ineffective VP (hazard ratio 1.35; 95% confidence interval 1.18-1.54; P < .001) were both significantly associated with decreased survival. CONCLUSION: Ineffective VP is associated with decreased survival. In addition to maximizing the percentage of delivered CRT pacing, every effort should be made to minimize ineffective VP.

Temporal Characteristics of Device-Based Individual and Integrated Risk Metrics in Patients With Chronic Heart Failure.

BACKGROUND: Temporal characteristics of a multimetric risk score and its individual parameters before, during, and after a heart failure (HF) event have not been defined. OBJECTIVES: A large real-world patient cohort with implantable cardioverter-defibrillators and cardiac resynchronization therapy (CRT) defibrillators was used to define these temporal characteristics. METHODS: Deidentified health records were linked to manufacturer's device database in 17,886 patients. Multimetric risk score combined daily measures of impedance, heart rate, activity, heart rate variability, and atrial fibrillation (AF) burden, AF ventricular rate, CRT pacing, and ventricular tachycardia episodes and shocks. HF event was defined as an inpatient, emergency department, or observation unit stay with primary diagnosis of HF and intravenous diuretic agents administration. Changes in risk parameters during 60 days before, during, and after an HF event were compared in patients with no HF readmissions vs patients with HF readmission. RESULTS: A total of 1,174 patients had HF events with no HF readmission, and 282 patients had HF events with HF readmission. Diagnostic risk score was higher on all 60 days before and after a HF event in patients with HF readmission compared with patients with no readmission (P < 0.001). Change in risk score from admission to discharge was similar in patients with and without HF readmission, but the risk score fell more significantly 7 after discharge and 30 days after admission in patients without HF readmission (P < 0.001). CONCLUSIONS: Temporal characteristics of risk metrics were significantly different in patients with no HF readmissions vs patients with HF readmission; patients without HF recurrence had larger recovery of risk metrics values toward normal.