Reduction in long-term mortality using remote device monitoring in a large real-world population of patients with implantable defibrillators

AIMS: Remote monitoring (RM) for implantable cardioverter-defibrillators (ICDs) is advocated for the potential of early detection of disease progression and device dysfunction. While studies have examined the effect of RM on clinical outcomes in carefully selected populations of heart failure patients implanted with ICDs from a single vendor, there is a paucity of data in real-world patients. We aimed to assess the long-term effect of RM in a representative ICD population using real-world data. METHODS AND RESULTS: This is an observational retrospective longitudinal study of 1004 patients implanted with an ICD or cardiac resynchronization therapy device (CRT-D) from all device vendors between 2010 and 2021. Patients started on RM (N = 403) within 90 days following de novo device implantation and yearly in-office visits were compared with patients with only bi-yearly in-office follow-up (non-RM, N = 601). In a propensity score matched cohort of 430 patients (mean age 61.4 ± 14.3 years, 26.7% female), all-cause mortality at 4-year was 12.6% in the RM and 27.7% in the non-RM group [hazard ratio (HR) 0.52, 95% confidence interval (CI) 0.32-0.82; P = 0.005]. No difference in inappropriate ICD-therapy (HR 1.90, 95% CI 0.86-4.21; P = 0.122) was observed. The risk of appropriate ICD-therapy (HR 1.71, 95% CI 1.07-2.74; P = 0.026) was higher in the RM group. CONCLUSION: Remote monitoring was associated with a reduction in long-term all-cause and cardiac mortality compared with traditional office visits in a real-world ICD population

Accelerometer-assessed physical behavior and the association with clinical outcomes in implantable cardioverter-defibrillator recipients: A systematic review

Background: Current implantable cardioverter-defibrillator (ICD) devices are equipped with a device-embedded accelerometer capable of capturing physical activity (PA). In contrast, wearable accelerometer-based methods enable the measurement of physical behavior (PB) that encompasses not only PA but also sleep behavior, sedentary time, and rest-activity patterns. Objective: This systematic review evaluates accelerometer-based methods used in patients carrying an ICD or at high risk of sudden cardiac death. Methods: Papers were identified via the OVID MEDLINE and OVID EMBASE databases. PB could be assessed using a wearable accelerometer or an embedded accelerometer in the ICD. Results: A total of 52 papers were deemed appropriate for this review. Out of these studies, 30 examined device-embedded accelerometry (189,811 patients), 19 examined wearable accelerometry (1601 patients), and 3 validated wearable accelerometry against device-embedded accelerometry (106 patients). The main findings were that a low level of PA after implantation of the ICD and a decline in PA were both associated with an increased risk of mortality, heart failure hospitalization, and appropriate ICD shock. Second, PA was affected by cardiac factors (eg, onset of atrial fibrillation, ICD shocks) and noncardiac factors (eg, seasonal differences, societal factors). Conclusion: This review demonstrated the potential of accelerometer-measured PA as a marker of clinical deterioration and ventricular arrhythmias. Notwithstanding that the evidence of PB assessed using wearable accelerometry was limited, there seems to be potential for accelerometers to improve early warning systems and facilitate preventative and proactive strategies

Rationale and design of the SafeHeart study: Development and testing of a mHealth tool for the prediction of arrhythmic events and implantable cardioverter-defibrillator therapy

BACKGROUND: Patients with an implantable cardioverter-defibrillator (ICD) are at a high risk of malignant ventricular arrhythmias. The use of remote ICD monitoring, wearable devices, and patient-reported outcomes generate large volumes of potential valuable data. Artificial intelligence–based methods can be used to develop personalized prediction models and improve early-warning systems. OBJECTIVE: The purpose of this study was to develop an integrated web-based personalized prediction engine for ICD therapy. METHODS: This international, multicenter, prospective, observational study consists of 2 phases: (1) a development study and (2) a feasibility study. We plan to enroll 400 participants with an ICD (with or without cardiac resynchronization therapy) on remote monitoring: 300 participants in the development study and 100 in the feasibility study. During 12-month follow-up, electronic health record data, remote monitoring data, accelerometry-assessed physical behavior data, and patient-reported data are collected. By using machine- and deep-learning approaches, a prediction engine is developed to assess the risk probability of ICD therapy (shock and antitachycardia pacing). The feasibility of the prediction engine as a clinical tool, the SafeHeart Platform, is assessed during the feasibility study. RESULTS: Development study recruitment commenced in 2021. The feasibility study starts in 2022. CONCLUSION: SafeHeart is the first study to prospectively collect a multimodal data set to construct a personalized prediction engine for ICD therapy. Moreover, SafeHeart explores the integration and added value of detailed objective accelerometer data in the prediction of clinical events. The translation of the SafeHeart Platform to clinical practice is examined during the feasibility study

Machine learning of electrophysiological signals for the prediction of ventricular arrhythmias: systematic review and examination of heterogeneity between studies

Background: Ventricular arrhythmia (VA) precipitating sudden cardiac arrest (SCD) is among the most frequent causes of death and pose a high burden on public health systems worldwide. The increasing availability of electrophysiological signals collected through conventional methods (e.g. electrocardiography (ECG)) and digital health technologies (e.g. wearable devices) in combination with novel predictive analytics using machine learning (ML) and deep learning (DL) hold potential for personalised predictions of arrhythmic events. Methods: This systematic review and exploratory meta-analysis assesses the state-of-the-art of ML/DL models of electrophysiological signals for personalised prediction of malignant VA or SCD, and studies potential causes of bias (PROSPERO, reference: CRD42021283464). Five electronic databases were searched to identify eligible studies. Pooled estimates of the diagnostic odds ratio (DOR) and summary area under the curve (AUROC) were calculated. Meta-analyses were performed separately for studies using publicly available, ad-hoc datasets, versus targeted clinical data acquisition. Studies were scored on risk of bias by the PROBAST tool. Findings: 2194 studies were identified of which 46 were included in the systematic review and 32 in the meta-analysis. Pooling of individual models demonstrated a summary AUROC of 0.856 (95% CI 0.755–0.909) for short-term (time-to-event up to 72 h) prediction and AUROC of 0.876 (95% CI 0.642–0.980) for long-term prediction (time-to-event up to years). While models developed on ad-hoc sets had higher pooled performance (AUROC 0.919, 95% CI 0.867–0.952), they had a high risk of bias related to the re-use and overlap of small ad-hoc datasets, choices of ML tool and a lack of external model validation. Interpretation: ML and DL models appear to accurately predict malignant VA and SCD. However, wide heterogeneity between studies, in part due to small ad-hoc datasets and choice of ML model, may reduce the ability to generalise and should be addressed in future studies. Funding: This publication is part of the project DEEP RISK ICD (with project number 452019308) of the research programme Rubicon which is (partly) financed by the Dutch Research Council ( NWO). This research is partly funded by the Amsterdam Cardiovascular Sciences (personal grant F.V.Y.T)

Predictors and outcomes of procedural failure of percutaneous coronary intervention of a chronic total occlusion—A subanalysis of the EXPLORE trial

Objective: To evaluate predictors of procedural success of percutaneous coronary intervention (PCI) of chronic total coronary occlusions (CTOs) in a non-infarct-related artery following ST-segment elevation myocardial infarction (STEMI), and demonstrate the effect on left ventricular functionality (LVF), infarct size (IS), and pro-arrhythmic electrocardiogram (ECG) parameters. Background: Predictors of unsuccessful revascularization of a CTO are numerous, although following STEMI, these are lacking. Besides, effects of failed CTO PCI (FPCI) on the myocardium are unknown. Methods: This is a subanalysis of the EXPLORE trial, in which 302 STEMI patients with a concurrent CTO were randomized to CTO PCI (n = 147) or no-CTO PCI (NPCI, n = 154). For the purpose of this subanalysis, we divided patients into successful CTO PCI (SPCI, n = 106), FPCI (n = 41), and NPCI (n = 154) groups. Cardiac magnetic resonance imaging and angiographic data were derived from the EXPLORE database, combined with ECG parameters. To gain more insight, all outcomes were compared with patients that did not undergo CTO PCI. Results: In multivariate regression, only CTO lesion length >20 mm was an independent predictor of procedural failure (OR 3.31 [1.49–7.39]). No significant differences in median left ventricular ejection fraction, left ventricular end-diastolic volume, IS, and the pro-arrhythmic ECG parameters such as QT-dispersion, QTc-time, and TpTe-intervals were seen between the SPCI and FPCI groups at 4 months follow-up. Conclusion: This subanalysis of the EXPLORE trial has demonstrated that a CTO lesion length >20 mm is an independent predictor of CTO PCI failure, whereas procedural failure did not lead to any adverse effects on LVF nor pro-arrhythmic ECG parameters

Deep Learning for Ventricular Arrhythmia Prediction Using Fibrosis Segmentations on Cardiac MRI Data

Many patients at high risk of life-threatening ventricular arrhythmias (VA) and sudden cardiac death (SCD) who received an implantable cardioverter defibrillator (ICD), never receive appropriate device therapy. The presence of fibrosis on LGE CMR imaging is shown to be associated with increased risk of VA. Therefore, there is a strong need for both automatic segmentation and quantification of cardiac fibrosis as well as better risk stratification for SCD. This study first presents a novel two-stage deep learning network for the segmentation of left ventricle myocardium and fibrosis on LGE CMR images. Secondly it aims to effectively predict device therapy in ICD patients by using a graph neural network approach which incorporates both myocardium and fibrosis features as well as the left ventricle geometry. Our segmentation network outperforms previous state-of-the-art methods on 2D CMR data, reaching a Dice score of 0.82 and 0.77 on myocardium and fibrosis segmentation, respectively. The ICD therapy prediction network reaches an AUC of 0.60 while using only CMR data and outperforms baseline methods based on current guideline markers for ICD implantation. This work lays a strong basis for future research on improved risk stratification for VA and SCD

The effect of revascularization of a chronic total coronary occlusion on electrocardiographic variables. A sub-study of the EXPLORE trial

Introduction: Chronic total coronary occlusions (CTOs) have been associated with a higher prevalence of ventricular arrhythmias compared to patients without a CTO. We evaluated the effect of CTO revascularization on electrocardiographic (ECG) variables. Methods: We studied a selection of ST-elevation myocardial infarction patients with a concomitant CTO enrolled in the EXPLORE trial. ECG variables and cardiac function were analysed at baseline and at 4 months follow-up. Results: Patients were randomized to percutaneous coronary intervention (PCI) of their CTO (n = 77) or to no-CTO PCI (n = 81). At follow-up, median QT dispersion was significantly lower in the CTO PCI group compared to the no-CTO PCI group (46 ms [33–58] vs. 54 ms [37–68], P = 0.043). No independent association was observed between ECG variables and cardiac function. Conclusion: Revascularization of a CTO after STEMI significantly shortened QT dispersion at 4 months follow-up. These findings support the hypothesis that CTO revascularization reduces the pro-arrhythmic substrate in CTO patients

Test of lepton flavour universality using B0→D∗−τ+ντB^0 \to D^{*-}\tau^+\nu_{\tau} decays with hadronic τ\tau channels

The branching fraction $\mathcal{B}(B^0 \to D^{*-}\tau^+\nu_\tau)$ is measured relative to that of the normalisation mode $B^0 \to D^{*-}\pi^+\pi^-\pi^+$ using hadronic $\tau^+ \to \pi^+\pi^-\pi^+(\pi^0)\bar{\nu}_\tau$ decays in proton-proton collision data at a centre-of-mass energy of 13 TeV collected by the LHCb experiment, corresponding to an integrated luminosity of 2 fb$^{-1}$. The measured ratio is $\mathcal{B}(B^0 \to D^{*-}\tau^+\nu_\tau)/\mathcal{B}(B^0 \to D^{*-}\pi^+\pi^-\pi^+)= 1.70 \pm 0.10^{+0.11}_{-0.10}$, where the first uncertainty is statistical and the second is related to systematic effects. Using established branching fractions for the $B^0 \to D^{*-}\pi^+\pi^-\pi^+$ and $B^0 \to D^{*-} \mu^+\nu_\mu$ modes, the lepton universality test, $\mathcal{R}(D^{*-}) \equiv \mathcal{B}(B^0 \to D^{*-}\tau^+\nu_\tau)/\mathcal{B}(B^0 \to D^{*-} \mu^+\nu_\mu)$ is calculated, $$\mathcal{R}(D^{*-}) = 0.247 \pm 0.015 \pm 0.015 \pm 0.012\, ,$$ where the third uncertainty is due to the uncertainties on the external branching fractions. This result is consistent with the Standard Model prediction and with previous measurements.The branching fraction B(B0→D*-τ+ντ) is measured relative to that of the normalization mode B0→D*-π+π-π+ using hadronic τ+→π+π-π+(π0)ν¯τ decays in proton-proton collision data at a center-of-mass energy of 13 TeV collected by the LHCb experiment, corresponding to an integrated luminosity of 2  fb-1. The measured ratio is B(B0→D*-τ+ντ)/B(B0→D*-π+π-π+)=1.70±0.10-0.10+0.11, where the first uncertainty is statistical and the second is related to systematic effects. Using established branching fractions for the B0→D*-π+π-π+ and B0→D*-μ+νμ modes, the lepton universality test R(D*-)≡B(B0→D*-τ+ντ)/B(B0→D*-μ+νμ) is calculated, R(D*-)=0.247±0.015±0.015±0.012, where the third uncertainty is due to the uncertainties on the external branching fractions. This result is consistent with the Standard Model prediction and with previous measurements.The branching fraction $\mathcal{B}(B^0 \to D^{*-}\tau^+\nu_{\tau})$ is measured relative to that of the normalisation mode $B^0 \to D^{*-}\pi^+\pi^-\pi^+$ using hadronic $\tau^+ \to \pi^+\pi^-\pi^+(\pi^0)\bar{\nu}_{\tau}$ decays in proton-proton collision data at a centre-of-mass energy of 13 TeV collected by the LHCb experiment, corresponding to an integrated luminosity of 2 fb$^{-1}$. The measured ratio is $\mathcal{B}(B^0 \to D^{*-}\tau^+\nu_{\tau})/\mathcal{B}(B^0 \to D^{*-}\pi^+\pi^-\pi^+)= 1.70 \pm 0.10^{+0.11}_{-0.10}$, where the first uncertainty is statistical and the second is related to systematic effects. Using established branching fractions for the $B^0 \to D^{*-}\pi^+\pi^-\pi^+$ and $B^0 \to D^{*-} \mu^+\nu_\mu$ modes, the lepton universality test, $\mathcal{R}(D^{*-}) \equiv \mathcal{B}(B^0 \to D^{*-}\tau^+\nu_{\tau})/\mathcal{B}(B^0 \to D^{*-} \mu^+\nu_\mu)$ is calculated, $$\mathcal{R}(D^{*-}) = 0.247 \pm 0.015 \pm 0.015 \pm 0.012\, ,$$ where the third uncertainty is due to the uncertainties on the external branching fractions. This result is consistent with the Standard Model prediction and with previous measurements

First observation and branching fraction measurement of the Λb0→Ds−p {\Lambda}_b^0\to {D}_s^{-}p decay

International audienceThe first observation of the ${\Lambda}_b^0\to {D}_s^{-}p$ decay is presented using proton-proton collision data collected by the LHCb experiment at a centre-of-mass energy of $\sqrt{s}$ = 13 TeV, corresponding to a total integrated luminosity of 6 fb$^{−1}$. Using the ${\Lambda}_b^0\to {\Lambda}_c^{+}{\pi}^{-}$ decay as the normalisation mode, the branching fraction of the ${\Lambda}_b^0\to {D}_s^{-}p$ decay is measured to be $\mathcal{B}\left({\Lambda}_b^0\to {D}_s^{-}p\right)=\left(12.6\pm 0.5\pm 0.3\pm 1.2\right)\times {10}^{-6}$, where the first uncertainty is statistical, the second systematic and the third due to uncertainties in the branching fractions of the ${\Lambda}_b^0\to {\Lambda}_c^{+}{\pi}^{-}$, ${D}_s^{-}\to {K}^{-}{K}^{+}{\pi}^{-}$ and ${\Lambda}_c^{+}\to p{K}^{-}{\pi}^{+}$ decays.[graphic not available: see fulltext

Transverse polarisation measurement of Λ\Lambda hyperons in ppNe collisions at sNN\sqrt{s_{NN}}=68.4 GeV with the LHCb detector

A measurement of the transverse polarization of the $\Lambda$ and $\bar{\Lambda}$hyperons in $p$Ne fixed-target collisions at $\sqrt{s_{NN}}$=68.4 GeV is presented using data collected by the LHCb detector. The polarization is studied using the decay $\Lambda \rightarrow p \pi^-$ together with its charge conjugated process, the integrated values measured are $$P_{\Lambda} = 0.029 \pm 0.019 \, (\rm{stat}) \pm 0.012 \, (\rm{syst}) \, ,$$ $$P_{\bar{\Lambda}} = 0.003 \pm 0.023 \, (\rm{stat}) \pm 0.014 \,(\rm{syst}) \,$$ Furthermore, the results are shown as a function of the Feynman $x$ variable, transverse momentum, pseudorapidity and rapidity of the hyperons, and are compared with previous measurements.A measurement of the transverse polarization of the $\Lambda$ and $\bar{\Lambda}$ hyperons in $p$Ne fixed-target collisions at $\sqrt{s_{NN}}$ = 68.4 GeV is presented using data collected by the LHCb detector. The polarization is studied using the decay $\Lambda \rightarrow p \pi^-$ together with its charge conjugated process, the integrated values measured are $$P_{\Lambda} = 0.029 \pm 0.019 \, (\rm{stat}) \pm 0.012 \, (\rm{syst}) \, ,$$ $$P_{\bar{\Lambda}} = 0.003 \pm 0.023 \, (\rm{stat}) \pm 0.014 \,(\rm{syst}) \,.$$ Furthermore, the results are shown as a function of the Feynman~$x$~variable, transverse momentum, pseudorapidity and rapidity of the hyperons, and are compared with previous measurements