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.

Feasibility of a using chest strap and dry electrode system for longer term cardiac arrhythmia monitoring: Results from a pilot observational study.

BACKGROUND AND AIM: Cardiac arrhythmia diagnostic yield improves with increased duration of monitoring. We investigated patient comfort, diagnostic quality of ECG, and arrhythmia diagnostic yield using a single lead longer term external cardiac monitor (ECM). METHODS: The observational ECM feasibility study enrolled patients with increased risk of cardiac arrhythmia. The ECM investigational prototype was designed using a chest strap with dry electrodes connected to module capable of triggered loop recording of ECG, and automatic detection of arrhythmia. In group-A of study (24-h inpatient), patients wore ECM and Holter that recorded ECG from the ECM and adhesive electrodes. In group-B of study (12-weeks ambulatory), at monthly follow-ups patients filled out a comfort survey and device stored arrhythmia episodes were reviewed. RESULTS: The study enrolled 34 patients (38 % females, average age 57.5 years, 65 % had palpitations, 12 % had syncope). Diagnostic quality ECG was recorded on 76.5 % of the monitoring duration in 12 of 20 patients with reviewable data in group-A, with motion artifacts causing loss in ECG signal for 18.7 % of the time. In 14 patients in group-B, 94.9 % of the survey responses indicated that ECM was comfortable to wear. Cardiac arrhythmia was observed in 4 of 17 patients (24 %) in group-A and 9 of 14 patients (64 %) in group-B in device recorded episodes. All ECM detected pause and tachycardia were inappropriate detections due to motion artifacts and temporary device removal. CONCLUSION: The chest strap-based ECM device was mostly comfortable to wear and recorded diagnostic quality ECG in three-fourth of monitoring period. Cardiac arrhythmia was observed in 64 % of patients over 3-month monitoring along with large number of motion artifact induced inappropriate detections.

The missing link: Unlocking the power of cardiac rhythm monitoring device based QT interval detection.

BACKGROUND: The QT interval is of high clinical value as QT prolongation can lead to Torsades de Pointes (TdP) and sudden cardiac death. Insertable cardiac monitors (ICMs) have the capability of detecting both absolute and relative changes in QT interval. In order to determine feasibility for long-term ICM based QT detection, we developed and validated an algorithm for continuous long-term QT monitoring in patients with ICM. METHODS: The QT detection algorithm, intended for use in ICMs, is designed to detect T-waves and determine the beat-to-beat QT and QTc intervals. The algorithm was developed and validated using real-world ICM data. The performance of the algorithm was evaluated by comparing the algorithm detected QT interval with the manually annotated QT interval using Pearson's correlation coefficient and Bland Altman plot. RESULTS: The QT detection algorithm was developed using 144 ICM ECG episodes from 46 patients and obtained a Pearson's coefficient of 0.89. The validation data set consisted of 136 ICM recorded ECG segments from 76 patients with unexplained syncope and 104 ICM recorded nightly ECG segments from 10 patients with diabetes and Long QT syndrome. The QT estimated by the algorithm was highly correlated with the truth data with a Pearson's coefficient of 0.93 (p < .001), with the mean difference between annotated and algorithm computed QT intervals of -7 ms. CONCLUSIONS: Long-term monitoring of QT intervals using ICM is feasible. Proof of concept development and validation of an ICM QT algorithm reveals a high degree of accuracy between algorithm and manually derived QT intervals.

GIS-based landslide susceptibility zonation mapping using the analytic hierarchy process (AHP) method in parts of Kalimpong Region of Darjeeling Himalaya.

Landslides are one the most destructive and life-endangering hazard in the Darjeeling Himalayan region and keeping in mind the interest of society and their future prospects identification of landslide potential areas is a very pertinent task in this area. Therefore, the present study aimed toward the landslide susceptibility zonation (LSZ) mapping in and around the Kalimpong region by applying Analytic Hierarchy Process (AHP) method integrated with fifteen causative factors including slope, lineament, drainage density, land use land cover, relative relief, soil texture, lithology, elevation, aspect, thrust and faults, plan curvature, profile curvature, road network, topographic wetness index and stream power index. Tolerance and variance inflation factors with Pearson's correlation coefficient are used to assess potential collinearity among the selected factors, and subsequently, the final model has been constructed by enduring an acceptable consistency ratio (<0.10). Thereafter, to classify this region into very low, low, moderate, high and very high susceptible zones quantile, geometric interval, Jenk's natural break and success rate curve (SRC) techniques are adopted to compare and check the optimum LSZ categorization. Considering the identified 647 landslides, Area Under Curve (AUC) of Receiver Operating Characteristic (ROC) curve is used to gauge the best LSZ map. The AUC ROC shows SRC method (m = 0.9) yields the highest result, achieving a prediction accuracy of 79.5% and, therefore, is considered the most promising LSZ form for the present study area. The results obtained from the study highlight the spatial information of areas that may face slope instability and helps government agencies, stakeholders for drafting adequate measures due to absence of proper landslide early warning systems in this region.

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.

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.

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.

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.

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.

An ensemble of features based deep learning neural network for reduction of inappropriate atrial fibrillation detection in implantable cardiac monitors.

Background: Multiple studies have reported on classification of raw electrocardiograms (ECGs) using convolutional neural networks (CNNs). Objective: We investigated an application-specific CNN using a custom ensemble of features designed based on characteristics of the ECG during atrial fibrillation (AF) to reduce inappropriate AF detections in implantable cardiac monitors (ICMs). Methods: An ensemble of features was developed and combined to form an input signal for the CNN. The features were based on the morphological characteristics of AF, incoherence of RR intervals, and the fact that AF begets more AF. A custom CNN model and the RESNET18 model were trained using ICM-detected AF episodes that were adjudicated to be true AF or false detections. The trained models were evaluated using a test dataset from independent patients. Results: The training and validation datasets consisted of 31,757 AF episodes (2516 patients) and 28,506 false episodes (2126 patients). The validation set (20% randomly chosen episodes of each type) had an area under the curve of 0.996 for custom CNN (0.993 for RESNET18). Thresholds were chosen to obtain a relative sensitivity and specificity of 99.2% and 92.8%, respectively (99.2% and 87.9% for RESNET18, respectively). The performance in the independent test set (4546 AF episodes from 418 patients; 5384 false episodes from 605 patients) showed an area under the curve of 0.993 (0.991 for RESNET18) and relative sensitivity and specificity of 98.7% and 91.4%, respectively, at chosen thresholds (98.9% and 88.2% for RESNET18, respectively). Conclusion: An ensemble of features-based CNNs was developed that reduced inappropriate AF detection in ICMs by over 90% while preserving sensitivity.

Burden of atrial fibrillation and poor rate control detected by continuous monitoring and the risk for heart failure hospitalization.

INTRODUCTION: Atrial fibrillation (AF) on electrocardiogram has been identified as a risk factor for hospitalizations in patients with heart failure (HF). We investigated whether continuous AF monitoring can identify when patients with HF are at risk for hospitalization. METHODS: In this retrospective analysis of data from 4 studies enrolling patients with HF with cardiac resynchronization therapy defibrillator devices with ≥90 days of follow-up (n = 1561), patients were identified as having AF if they had ≥1 day of >5 minutes of AF and >1 hour of total AF during entire follow-up. In patients with AF, device recorded AF burden (AFb) and ventricular rate during AF (VRAF) over the last 30 days was classified on a monthly basis into 3 evaluation groups: (1) ≥1 day of high burden of paroxysmal AF (≥6 hours) or persistent AF (all 30 days with AFb >23 hours) with poor rate control (VRAF >90 beats/min), (2) ≥1 day of high burden of paroxysmal AF with good rate control (VRAF ≤ 90 beats/min), and (3) no days with high burden of AF (AFb <6 hours) or persistent AF with good rate control. Each group was compared with monthly evaluations in patients without AF using an Anderson-Gill model for occurrence of HF hospitalizations in the next 30 days. RESULTS: Patients with AF (n = 519, 33%) have a greater risk (hazard ratio [HR] 2.0, P < .001) for impending HF hospitalizations during entire follow-up compared with patients with no AF. One day of high burden of paroxysmal AF with good rate control in the last 30 days increases risk for HF hospitalization in the next 30 days (HR 3.4, P < .001). The risk increases further (HR 5.9, P < .001) with 1 day of poor rate control during persistent AF or high burden paroxysmal AF in last 30 days. CONCLUSION: Evaluation of AFb and rate control information on a monthly basis can identify patients at risk for HF hospitalization in the next 30 days.

Uncovering interim clinical events at the time of clinical encounter by reviewing intrathoracic impedance threshold crossings.

BACKGROUND: Acute decreases in intrathoracic impedance monitored by implanted devices have been shown to precede heart failure exacerbations, although there is still debate regarding its clinical utility in predicting and preventing future events. However, the usefulness of such information to direct patient encounter and enhance patient recall of relevant preceding clinical events at the point of care has not been carefully examined. METHODS AND RESULTS: In this multicenter study, we interviewed 326 patients with heart failure who received an implanted device with intrathoracic impedance-monitoring capabilities both before and after device information was reviewed. We compared the self-reported clinically relevant events (including heart failure hospitalizations, signs and symptoms of worsening heart failure, changes in diuretic therapy, or other fluid-related events) obtained before and after device interrogation, and then examined the relationship between such events with impedance trends documented by the devices. Over 333 ± 96 days of device monitoring, 215 of 326 patients experienced 590 intrathoracic impedance fluid index threshold-crossing events at the nominal threshold value (60 Ω-d). Review of device-derived information led to the discovery of 221 (37%) previously unreported clinically relevant events in 138 subjects. This included 60 subjects not previously identified as having had clinically relevant events (or 35% of the 171 subjects who did not report events). CONCLUSIONS: Our data demonstrated that reviewing device-derived intrathoracic impedance trends at the time of clinical encounter may help uncover self-reporting of potential clinically relevant events.

Improved algorithm to detect fluid accumulation via intrathoracic impedance monitoring in heart failure patients with implantable devices.

BACKGROUND: Intrathoracic impedance fluid monitoring has been shown to predict worsening congestive heart failure (CHF) in patients with implantable devices. We developed and externally validated a modified algorithm to identify worsening heart failure (HF) by using intrathoracic impedance. METHODS AND RESULTS: The modified algorithm was developed by using published data from 81 CHF subjects averaging 259 days of follow-up. Device-measured daily impedance was input to both the existing and the modified intrathoracic impedance fluid monitoring algorithms to determine a reference impedance and a fluid index (FI). Separate validation sets included 326 cardiac resynchronization therapy device (CRT-D) patients with an average 333 days of follow-up (group 1) and 104 CRT-D/implantable cardioverter/defibrillator (ICD) patients followed for an average of 520 days (group 2). Clinicians and patients in group 2 were blinded to impedance and FI data. HF events included adjudicated HF hospitalizations or emergency room visits. Sensitivity was defined as the percentage of HF events preceded by FI exceeding the predefined threshold (60 Ω-d) within the last 2 weeks. Unexplained detections were FI threshold crossing events not followed by a HF event within 2 weeks. The modified algorithm significantly decreased unexplained detections by 30% (P = .01; GEE) in the development set, 30% (P < .001) in the group 1 validation set, and 43% (P < .001) in group 2. Sensitivity did not change significantly in any group. Simulated monthly review of FI threshold crossings identified subjects at significantly greater risk of worsening HF within the next 30 days. CONCLUSIONS: A modified intrathoracic impedance based fluid detection algorithm lowered the number of unexplained FI threshold crossings and identified patients at significantly increased immediate risk of worsening HF.

A critical link between heart failure self-care and intrathoracic impedance.

BACKGROUND: Effective self-care is regarded as essential to the management of heart failure (HF). The influence of self-care on HF decompensation, however, is not well understood. Accordingly, we examined the relationship between self-care and fluid accumulation accompanying worsening HF as indexed by decreasing intrathoracic impedance (Z). METHODS: Z data were collected from 58 HF patients with OptiVol enabled devices (Medtronic Inc, Minneapolis, Minnesota). Heart failure self-care was measured with the European Heart Failure Self-care Behaviour Scale. Regression modeling was used to describe the influence of HF self-care on the likelihood of a fluid index (FI) threshold crossing, the number of threshold crossings, and number of days spent above threshold. RESULTS: Patients were elderly (74.98 [SD, 8.12] years), with a mean left ventricular ejection fraction of 26.21% (SD, 9.77%), and 63.7% had class New York Heart Association III HF. Patients were followed up for 317 (SD, 96) days; 65.5% had FI threshold crossings (mean 1.45 [SD, 1.56] crossings), spending an average of 33.8 (SD, 42.4) days above FI threshold. Controlling for age, sex, left ventricular ejection fraction, functional class, and duration of follow-up, each additional point on the European Heart Failure Self-care Behaviour Scale was associated with an increase in the odds of having had an FI threshold crossing (adjusted odds ratio, 1.201; 95% confidence interval, 1.013-1.424; P<.05) and more days spent above FI threshold (incidence rate ratio, 1.051; 95% confidence interval, 1.002-1.102; P<.05). CONCLUSION: Intrathoracic impedance measurements obtained from implantable devices provide important information regarding the influence of self-care on fluid accumulation in patients with HF.