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.

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).

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.

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.

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.

Temporal Association Between Episodes of Atrial Fibrillation and Risk of Ischemic Stroke.

Importance: Understanding the temporal association between atrial fibrillation (AF) and ischemic stroke informs our understanding of the AF-stroke mechanism and treatment of paroxysmal AF. Objective: To define the temporal association between episodes of AF and stroke in patients with cardiac implantable electronic devices (CIEDs). Design, Setting, and Participants: In this case-crossover study, data from a large national electronic health record database were linked with a single-vendor database of heart rhythm records of patients with CIEDs capable of continuous heart rhythm monitoring. Patients with CIEDs who sustained an ischemic stroke who also had 120 days of continuous remote rhythm monitoring prestroke were included. Data were collected from January 2007 to March 2017, and data were analyzed from November 2019 to June 2020. Exposure: AF for 5.5 hours or more on any given day during days 1 to 30 vs days 91 to 120 prestroke. Main Outcomes and Measures: Odds ratio for stroke comparing AF during days 1 to 30 vs 91 to 120 prestroke. This analysis was planned prior to the study. Results: From 466 635 patients included in both the Optum electronic health record and CareLink databases, 891 patients with CIEDs and ischemic stroke with continuous monitoring in the 120 days prestroke were identified. Of 891 included patients, 575 (64.5%) were male, and the median (interquartile range) age was 76 (67-82) years. The vast majority of patients with stroke had either no AF meeting the threshold duration of 5.5 hours or more in both the case and control periods (682 of 891 [76.5%]) or AF of 5.5 hours or more in both periods (143 of 891 [16.0%]). For those not meeting the 5.5-hour AF threshold in either period, there was no or very little AF throughout the 120 days prestroke. A total of 66 patients had informative, discordant arrhythmic states, with 52 having AF of 5.5 hours or more in the case period vs 14 in the control period (odds ratio [OR], 3.71; 95% CI, 2.06-6.70). Stroke risk was increased most in days 1 to 5 following an AF episode (OR, 5.00; 95% CI, 2.62-9.55). AF greater than 23 hours on a given day was associated with the clearest increase in stroke risk (OR, 5.00; 95% CI, 2.08-12.01). Conclusions and Relevance: In this large cohort of patients with CIEDs and continuous rhythm monitoring prior to ischemic stroke, excess stroke risk above baseline was highest within 5 days of an episode of AF of 5.5 hours or more in duration and diminished rapidly thereafter. Our findings are consistent with the traditional view that AF is directly and transiently associated with ischemic stroke. These results provide support for trials of time-delimited anticoagulation for patients with infrequent multihour episodes of AF and rigorous, continuous rhythm monitoring.

Use of Oral Anticoagulation in a Real-World Population With Device Detected Atrial Fibrillation.

Background Guideline recommendations for oral anticoagulation (OAC) in patients with atrial fibrillation (AF) are based on CHA2DS2-VASc score alone. Patients with cardiac implantable electronic devices provide an opportunity to assess how the interaction between AF duration and CHA2DS2-VASc score influences OAC prescription rates. Methods and Results Data from the Optum de-identified Electronic Health Record data set were linked to the Medtronic CareLink database of cardiac implantable electronic devices. An index date was assigned as the later of 6 months after device implant or 1 year after Electronic Health Record data availability. Maximum daily AF duration (no AF, 6 minutes-23.5 hours, and >23.5 hours) was assessed for 6 months before index date. OAC prescription rates were computed as a function of both AF duration and CHA2DS2-VASc score. A total of 35 779 patients with CHA2DS2-VASc scores ≥1 were identified, including 27 198 not prescribed OAC. Overall OAC prescription rate among the 12 938 patients with device-detected AF >6 minutes was 36.7% and significantly higher in those with a maximum daily AF duration >23.5 hours (45.4%) compared with those with 6 minutes to 23.5 hours (28.7%). OAC prescription rates increased monotonically with both increasing AF duration and CHA2DS2-VASc score, reaching a maximum of 67.2% for patients with AF >23.5 hours and a CHA2DS2-VASc score ≥5. Conclusions Real-world prescription of OAC increased with both increasing duration of AF and CHA2DS2-VASc score. This highlights the need for further research into the role of AF duration, stroke risk, and the need for anticoagulation in patients with devices capable of long-term AF monitoring.

Prediction of worsening heart failure events and all-cause mortality using an individualized risk stratification strategy.

AIMS: This study aimed to examine the clinical utility of a multisensor, remote, ambulatory diagnostic risk score, TriageHF™, in a real-world, unselected, large patient sample to predict heart failure events (HFEs) and all-cause mortality. METHODS AND RESULTS: TriageHF risk score was calculated in patients in the Optum® database who had Medtronic implantable cardiac defibrillator device from 2007 to 2016. Patients were categorized into three risk groups based on probability for having an HFE within 6 months (low risk <5.4%, medium risk ≥5.4 < 20%, and high risk ≥20%). Data were analysed using three strategies: (i) scheduled monthly data download; (ii) alert-triggered data download; and (iii) daily data download. Study population consisted of 22 901 patients followed for 1.8 ± 1.3 years. Using monthly downloads, HFE risk over 30 days incrementally increased across risk categories (odds ratio: 2.8, 95% confidence interval: 2.5-3.2 for HFE, P < 0.001, low vs. medium risk, and odds ratio: 9.2, 95% confidence interval: 8.1-10.3, P < 0.001, medium vs. high risk). Findings were similar using the other two analytic strategies. Using a receiver operating characteristic curve analysis, sensitivity for predicting HFE over 30 days using high-risk score was 47% (alert triggered) and 51% (daily download) vs. 0.5 per patient year unexplained detection rate. TriageHF risk score also predicted all-cause mortality risk over 4 years. All-cause mortality risk was 14% in low risk, 20% in medium risk, and 38% in high risk. CONCLUSIONS: TriageHF risk score provides a multisensor remote, ambulatory diagnostic method that predicts both HFEs and all-cause mortality.

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.

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.

Real-World Incidence of Pacemaker and Defibrillator Implantation Following Diagnostic Monitoring With an Insertable Cardiac Monitor.

Insertable cardiac monitors (ICM) are used in patients with suspected or known cardiac arrhythmias; the resulting diagnosis can lead to therapeutic interventions such as a pacemaker (PPM) or defibrillator (ICD) implant. We investigated the incidence of these implants in a large, real-world, cohort of ICM patients. The Optum© EHR de-identified database was used to identify patients with cardiovascular diseases, an ICM implant, ≥180 days of follow-up before and after ICM implant, and no previous history of a PPM or ICD. The Kaplan-Meier (KM) incidence estimates for device implants following an ICM implant were determined. A total of 19,173 patients with an ICM implant were identified. During a mean follow-up of 40 months, either a PPM or ICD was implanted in 21% of patients. A device was implanted in 25% of patients with history of syncope compared with 15% in patients with another indication for ICM implant (p <0.001). There was a significantly greater number of PPM implants following an ICM in patients with history of syncope compared with another indication for ICM implant (23% vs 13% p <0.001); in contrast, there was no difference in ICD implants between the 2 groups (3% in both groups, p = 0.84). In conclusion, a PPM or ICD was ultimately implanted in 21% of ICM patients. Pacemaker implant rates varied significantly with indication for ICM implant, whereas ICD implants rates were similar. In particular, patients with history of syncope had the greatest likelihood of needing a PPM during follow-up.

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.

Long-term detection of atrial fibrillation with insertable cardiac monitors in a real-world cryptogenic stroke population.

BACKGROUND: The long-term incidence of atrial fibrillation (AF) in cryptogenic stroke (CS) patients has been explored in carefully controlled clinical trials but real-world data are limited. We investigated the two-year incidence of AF in real-world clinical practice among a large cohort of patients with an insertable cardiac monitor (ICM) placed for AF detection following CS. METHODS: Patients in the de-identified Medtronic Discovery™ Link database who received an ICM (Reveal LINQ™) for the purpose of AF detection following CS were included and monitored for up to 2years. All detected AF episodes (≥2min) were adjudicated. We quantified the AF detection rate using Kaplan-Meier survival estimates, analyzed the median time to initial detection of AF, and simulated the ability of various intermittent monitoring strategies to detect AF. RESULTS: A total of 1247 patients (65.3±13.0years, 53% male) were included and followed for 579±222days. AF episodes (n=4183) were detected in 238 patients, resulting in an AF detection rate of 21.5% at 2years. The median time to AF detection was 112 [IQR 35-293] days. Intermittent monitoring for AF detection was inferior to continuous ICM monitoring with sensitivities ranging from 2.9% (annual 24-hour Holter) to 29.9% (quarterly 7-day Holters), p<0.001. CONCLUSIONS: AF episodes were detected via continuous monitoring with ICMs in approximately 1 of every 5 CS patients within 2years of follow-up. The vast majority of patients with AF would not have been detected with conventional external ambulatory monitors. ICMs should therefore be considered in the evaluation of CS patients.

Development and validation of a dual sensing scheme to improve accuracy of bradycardia and pause detection in an insertable cardiac monitor.

BACKGROUND: Undersensing of premature ventricular beats and low-amplitude R waves are primary causes for inappropriate bradycardia and pause detections in insertable cardiac monitors (ICMs). OBJECTIVE: The purpose of this study was to develop and validate an enhanced algorithm to reduce inappropriately detected bradycardia and pause episodes. METHODS: Independent data sets to develop and validate the enhanced algorithm were derived from a database of ICM-detected bradycardia and pause episodes in de-identified patients monitored for unexplained syncope. The original algorithm uses an auto-adjusting sensitivity threshold for R-wave sensing to detect tachycardia and avoid T-wave oversensing. In the enhanced algorithm, a second sensing threshold is used with a long blanking and fixed lower sensitivity threshold, looking for evidence of undersensed signals. Data reported includes percent change in appropriate and inappropriate bradycardia and pause detections as well as changes in episode detection sensitivity and positive predictive value with the enhanced algorithm. RESULTS: The validation data set, from 663 consecutive patients, consisted of 4904 (161 patients) bradycardia and 2582 (133 patients) pause episodes, of which 2976 (61%) and 996 (39%) were appropriately detected bradycardia and pause episodes. The enhanced algorithm reduced inappropriate bradycardia and pause episodes by 95% and 47%, respectively, with 1.7% and 0.6% reduction in appropriate episodes, respectively. The average episode positive predictive value improved by 62% (P < .001) for bradycardia detection and by 26% (P < .001) for pause detection, with an average relative sensitivity of 95% (P < .001) and 99% (P = .5), respectively. CONCLUSION: The enhanced dual sense algorithm for bradycardia and pause detection in ICMs substantially reduced inappropriate episode detection with a minimal reduction in true episode detection.

Real-world performance of an enhanced atrial fibrillation detection algorithm in an insertable cardiac monitor.

BACKGROUND: Insertable cardiac monitors (ICMs) are used for long-term ECG monitoring. The Reveal LINQ ICM has an improved atrial fibrillation (AF) detection algorithm. OBJECTIVE: The purpose of this study was to investigate the algorithm's real-world performance in patients with syncope, cryptogenic stroke, and known AF. METHODS: Consecutive patients with implanted ICM and AF detection parameters automatically set and maintained depending on the indication for monitoring were included. A single reviewer annotated all stored episodes after ICM implant. A second reviewer annotated a random sample of 10% of all detected AF episodes. The episode detection positive predictive value as well as true and false detection rates were determined for AF episodes of different durations. RESULTS: The study enrolled 3759 patients (1604 [43%] with syncope, 1049 [28%] with known AF, 1106 [29%] with cryptogenic stroke). Overall, 20,659 AF episodes were detected in 1020 patients. The gross episode detection positive predictive value was 84%, 73%, and 26% for all episodes (≥2 minutes) and improved to 97%, 95%, and 91% for detected AF episodes ≥1 hour in the syncope, known-AF, and cryptogenic stroke patient cohorts, respectively. The true (and false) detection rate was 0.23 (0.05), 3.8 (1.4), and 0.23 (0.65) per patient-month of monitoring for the syncope, known-AF, and cryptogenic stroke patient cohorts, respectively. Limiting ECG storage to the longest detected AF episode significantly reduced the burden of episode adjudication without significantly compromising the identification of patients with true AF. CONCLUSION: The performance of LINQ ICM is dependent on the AF incidence rate in the population being monitored, the programmed sensitivity of AF algorithm, and the duration of detected AF episodes.

Performance of a new atrial fibrillation detection algorithm in a miniaturized insertable cardiac monitor: Results from the Reveal LINQ Usability Study.

BACKGROUND: For clinicians, confidence in atrial fibrillation (AF) episode classification is an important consideration when electing to use insertable cardiac monitors (ICMs). OBJECTIVE: The purpose of this study was to report on the improved AF detection algorithm in the Reveal LINQ ICM. METHODS: The Reveal LINQ Usability Study is a nonrandomized, prospective, multicenter trial. The ICM has been miniaturized, uses wireless telemetry for remote patient monitoring, and its AF algorithm includes a new p-wave filter. At 1 month post-device insertion, Holter monitor data were collected and annotated for true AF episodes ≥2 minutes, and performance metrics were evaluated by comparing Holter annotations with ICM detections. RESULTS: The study enrolled 151 patients (age 56.6 ± 12.1, male 67%). Reasons for monitoring included AF ablation or AF management in 81.5% (n = 123), syncope in 12.6% (n = 19), and other indications in 5.9% (n = 9) of patients. Of the 138 patients with an analyzable Holter recording, a total of 112 true AF episodes were identified in 38 patients (27.5%). The overall accuracy of the ICM to detect durations of AF or non-AF episodes was 99.4%, and the AF burden measured by the ICM was highly correlated with the Holter (Pearson coefficient 0.995). CONCLUSION: The new AF detection algorithm in the Reveal LINQ ICM accurately detects the presence or absence of AF. Additionally, it showed high sensitivity in detecting AF duration in patients with a history of intermittent and symptomatic AF.

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.

P-wave evidence as a method for improving algorithm to detect atrial fibrillation in insertable cardiac monitors.

BACKGROUND: Frequent premature atrial contractions and sick sinus syndrome are primary causes of inappropriate atrial fibrillation (AF) detection in insertable cardiac monitors (ICMs). OBJECTIVE: The study aimed to validate an algorithm designed to reduce inappropriate AF detection on the basis of the identification of a single P wave during the cardiac cycle. METHODS: The original detection algorithm looks for evidence of AF based on differences in the pattern of R-R intervals over a 2-minute period. The improved algorithm reduces evidence for AF detection if P waves are detected. The algorithm was validated by using Holter data, which collected 2 leads of surface electrocardiogram and continuously uplinked ICM electrocardiogram over a 46-hour period. ICM detections were compared with Holter annotations to compute episode and duration detection performance. RESULTS: Valid Holter recordings (8442 hours) were analyzed from 206 patients. True AF was observed in 76 patients, yielding 482 true AF episodes ≥2 minutes in duration and 1191 hours of AF. The algorithm correctly identified 97.8% of the total AF duration and 99.3% of the total sinus or non-AF rhythm duration. The algorithm detected 85% (90% per-patient average) of all AF episodes ≥2 minutes in duration, and 55% (78% per-patient average) of the detected episodes had AF. AF was found in 95% of the detected episodes >1 hour. The improved algorithm reduced inappropriate episodes and duration by 46% and 55%, respectively, while also reducing appropriate episodes and duration by 2% and 0.1%, respectively. CONCLUSION: An improvement in the ICM algorithm for AF detection incorporating P-wave information substantially reduced inappropriately detected episodes and duration, with minimal reduction in sensitivity for detecting AF.

A novel algorithm to assess risk of heart failure exacerbation using ICD diagnostics: validation from RAFT.

BACKGROUND: The integrated diagnostics (ID) algorithm is an implantable device-based tool that collates data pertaining to heart rhythm, heart rate, intrathoracic fluid status, and activity, producing a risk score that correlates with 30-day risk of heart failure (HF) hospitalization. OBJECTIVE: We sought to validate the ID algorithm using the Resynchronization-Defibrillation for Ambulatory Heart Failure Trial. METHODS: Diagnostic measures of the algorithm include OptiVol fluid index, nighttime heart rate, minutes of patient activity, heart rate variability, and combined measure of cardiac rhythm and biventricular pacing. Monthly evaluations of ID parameters were assessed for the development of HF symptoms and hospitalization for HF. RESULTS: A total of 1224 patients were included: 741 (61%) with cardiac resynchronization therapy with defibrillator devices and 483 (39%) with implanted cardioverter-defibrillator only. The mean age was 66 ± 9 years, and 1013 (83%) were men. A total of 37,861 months of follow-up data were available, with 258 HF hospitalizations (event rate 0.68% per month). There were 33 HF hospitalizations during low-risk months (0.21% per month), 123 during medium-risk months (0.66% per month), and 102 during high-risk months (2.61% per month). Compared with low-risk months, and 95% confidence intervals) of HF hospitalizations during medium-risk months was 2.9 (2.0-4.4) and during high-risk months was 10.7 (6.9-16.6). Multivariable analysis demonstrated that each ID variable had independent association with HF hospitalization. CONCLUSION: The risk of HF as determined by the ID algorithm correlated with HF hospitalization and several HF signs and symptoms among patients in the Resynchronization-Defibrillation for Ambulatory Heart Failure Trial. This may present a useful adjunct to detect early signs of HF and adjust therapy to reduce morbidity and costs involved with hospital admission.