Dynamic prediction of malignant ventricular arrhythmias using neural networks in patients with an implantable cardioverter-defibrillator.

BACKGROUND: Risk stratification for ventricular arrhythmias currently relies on static measurements that fail to adequately capture dynamic interactions between arrhythmic substrate and triggers over time. We trained and internally validated a dynamic machine learning (ML) model and neural network that extracted features from longitudinally collected electrocardiograms (ECG), and used these to predict the risk of malignant ventricular arrhythmias. METHODS: A multicentre study in patients implanted with an implantable cardioverter-defibrillator (ICD) between 2007 and 2021 in two academic hospitals was performed. Variational autoencoders (VAEs), which combine neural networks with variational inference principles, and can learn patterns and structure in data without explicit labelling, were trained to encode the mean ECG waveforms from the limb leads into 16 variables. Supervised dynamic ML models using these latent ECG representations and clinical baseline information were trained to predict malignant ventricular arrhythmias treated by the ICD. Model performance was evaluated on a hold-out set, using time-dependent receiver operating characteristic (ROC) and calibration curves. FINDINGS: 2942 patients (61.7 ± 13.9 years, 25.5% female) were included, with a total of 32,129 ECG recordings during a mean follow-up of 43.9 ± 35.9 months. The mean time-varying area under the ROC curve for the dynamic model was 0.738 ± 0.07, compared to 0.639 ± 0.03 for a static (i.e. baseline-only model). Feature analyses indicated dynamic changes in latent ECG representations, particularly those affecting the T-wave morphology, were of highest importance for model predictions. INTERPRETATION: Dynamic ML models and neural networks effectively leverage routinely collected longitudinal ECG recordings for personalised and updated predictions of malignant ventricular arrhythmias, outperforming static models. 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).

Segmenting computed tomograms for cardiac ablation using machine learning leveraged by domain knowledge encoding.

Background: Segmentation of computed tomography (CT) is important for many clinical procedures including personalized cardiac ablation for the management of cardiac arrhythmias. While segmentation can be automated by machine learning (ML), it is limited by the need for large, labeled training data that may be difficult to obtain. We set out to combine ML of cardiac CT with domain knowledge, which reduces the need for large training datasets by encoding cardiac geometry, which we then tested in independent datasets and in a prospective study of atrial fibrillation (AF) ablation. Methods: We mathematically represented atrial anatomy with simple geometric shapes and derived a model to parse cardiac structures in a small set of N = 6 digital hearts. The model, termed "virtual dissection," was used to train ML to segment cardiac CT in N = 20 patients, then tested in independent datasets and in a prospective study. Results: In independent test cohorts (N = 160) from 2 Institutions with different CT scanners, atrial structures were accurately segmented with Dice scores of 96.7% in internal (IQR: 95.3%-97.7%) and 93.5% in external (IQR: 91.9%-94.7%) test data, with good agreement with experts (r = 0.99; p < 0.0001). In a prospective study of 42 patients at ablation, this approach reduced segmentation time by 85% (2.3 ± 0.8 vs. 15.0 ± 6.9 min, p < 0.0001), yet provided similar Dice scores to experts (93.9% (IQR: 93.0%-94.6%) vs. 94.4% (IQR: 92.8%-95.7%), p = NS). Conclusions: Encoding cardiac geometry using mathematical models greatly accelerated training of ML to segment CT, reducing the need for large training sets while retaining accuracy in independent test data. Combining ML with domain knowledge may have broad applications.

Optimizing patient selection for primary prevention implantable cardioverter-defibrillator implantation: utilizing multimodal machine learning to assess risk of implantable cardioverter-defibrillator non-benefit.

AIMS: Left ventricular ejection fraction (LVEF) is suboptimal as a sole marker for predicting sudden cardiac death (SCD). Machine learning (ML) provides new opportunities for personalized predictions using complex, multimodal data. This study aimed to determine if risk stratification for implantable cardioverter-defibrillator (ICD) implantation can be improved by ML models that combine clinical variables with 12-lead electrocardiograms (ECG) time-series features. METHODS AND RESULTS: A multicentre study of 1010 patients (64.9 ± 10.8 years, 26.8% female) with ischaemic, dilated, or non-ischaemic cardiomyopathy, and LVEF ≤ 35% implanted with an ICD between 2007 and 2021 for primary prevention of SCD in two academic hospitals was performed. For each patient, a raw 12-lead, 10-s ECG was obtained within 90 days before ICD implantation, and clinical details were collected. Supervised ML models were trained and validated on a development cohort (n = 550) from Hospital A to predict ICD non-arrhythmic mortality at three-year follow-up (i.e. mortality without prior appropriate ICD-therapy). Model performance was evaluated on an external patient cohort from Hospital B (n = 460). At three-year follow-up, 16.0% of patients had died, with 72.8% meeting criteria for non-arrhythmic mortality. Extreme gradient boosting models identified patients with non-arrhythmic mortality with an area under the receiver operating characteristic curve (AUROC) of 0.90 [95% confidence intervals (CI) 0.80-1.00] during internal validation. In the external cohort, the AUROC was 0.79 (95% CI 0.75-0.84). CONCLUSIONS: ML models combining ECG time-series features and clinical variables were able to predict non-arrhythmic mortality within three years after device implantation in a primary prevention population, with robust performance in an independent cohort.

Quantifying a spectrum of clinical response in atrial tachyarrhythmias using spatiotemporal synchronization of electrograms.

AIMS: There is a clinical spectrum for atrial tachyarrhythmias wherein most patients with atrial tachycardia (AT) and some with atrial fibrillation (AF) respond to ablation, while others do not. It is undefined if this clinical spectrum has pathophysiological signatures. This study aims to test the hypothesis that the size of spatial regions showing repetitive synchronized electrogram (EGM) shapes over time reveals a spectrum from AT, to AF patients who respond acutely to ablation, to AF patients without acute response. METHODS AND RESULTS: We studied n = 160 patients (35% women, 65.0 ± 10.4 years) of whom (i) n = 75 had AF terminated by ablation propensity matched to (ii) n = 75 without AF termination and (iii) n = 10 with AT. All patients had mapping by 64-pole baskets to identify areas of repetitive activity (REACT) to correlate unipolar EGMs in shape over time. Synchronized regions (REACT) were largest in AT, smaller in AF termination, and smallest in non-termination cohorts (0.63 ± 0.15, 0.37 ± 0.22, and 0.22 ± 0.18, P < 0.001). Area under the curve for predicting AF termination in hold-out cohorts was 0.72 ± 0.03. Simulations showed that lower REACT represented greater variability in clinical EGM timing and shape. Unsupervised machine learning of REACT and extensive (50) clinical variables yielded four clusters of increasing risk for AF termination (P < 0.01, χ2), which were more predictive than clinical profiles alone (P < 0.001). CONCLUSION: The area of synchronized EGMs within the atrium reveals a spectrum of clinical response in atrial tachyarrhythmias. These fundamental EGM properties, which do not reflect any predetermined mechanism or mapping technology, predict outcome and offer a platform to compare mapping tools and mechanisms between AF patient groups.

Atrial fibrillation ablation outcome prediction with a machine learning fusion framework incorporating cardiac computed tomography.

BACKGROUND: Structural changes in the left atrium (LA) modestly predict outcomes in patients undergoing catheter ablation for atrial fibrillation (AF). Machine learning (ML) is a promising approach to personalize AF management strategies and improve predictive risk models after catheter ablation by integrating atrial geometry from cardiac computed tomography (CT) scans and patient-specific clinical data. We hypothesized that ML approaches based on a patient's specific data can identify responders to AF ablation. METHODS: Consecutive patients undergoing AF ablation, who had preprocedural CT scans, demographics, and 1-year follow-up data, were included in the study for a retrospective analysis. The inputs of models were CT-derived morphological features from left atrial segmentation (including the shape, volume of the LA, LA appendage, and pulmonary vein ostia) along with deep features learned directly from raw CT images, and clinical data. These were merged intelligently in a framework to learn their individual importance and produce the optimal classification. RESULTS: Three hundred twenty-one patients (64.2 ± 10.6 years, 69% male, 40% paroxysmal AF) were analyzed. Post 10-fold nested cross-validation, the model trained to intelligently merge and learn appropriate weights for clinical, morphological, and imaging data (AUC 0.821) outperformed those trained solely on clinical data (AUC 0.626), morphological (AUC 0.659), or imaging data (AUC 0.764). CONCLUSION: Our ML approach provides an end-to-end automated technique to predict AF ablation outcomes using deep learning from CT images, derived structural properties of LA, augmented by incorporation of clinical data in a merged ML framework. This can help develop personalized strategies for patient selection in invasive management of AF.

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).

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.

Machine Learning-Enabled Multimodal Fusion of Intra-Atrial and Body Surface Signals in Prediction of Atrial Fibrillation Ablation Outcomes.

BACKGROUND: Machine learning is a promising approach to personalize atrial fibrillation management strategies for patients after catheter ablation. Prior atrial fibrillation ablation outcome prediction studies applied classical machine learning methods to hand-crafted clinical scores, and none have leveraged intracardiac electrograms or 12-lead surface electrocardiograms for outcome prediction. We hypothesized that (1) machine learning models trained on electrograms or electrocardiogram (ECG) signals can perform better at predicting patient outcomes after atrial fibrillation ablation than existing clinical scores and (2) multimodal fusion of electrogram, ECG, and clinical features can further improve the prediction of patient outcomes. METHODS: Consecutive patients who underwent catheter ablation between 2015 and 2017 with panoramic left atrial electrogram before ablation and clinical follow-up for at least 1 year following ablation were included. Convolutional neural network and a novel multimodal fusion framework were developed for predicting 1-year atrial fibrillation recurrence after catheter ablation from electrogram, ECG signals, and clinical features. The models were trained and validated using 10-fold cross-validation on patient-level splits. RESULTS: One hundred fifty-six patients (64.5±10.5 years, 74% male, 42% paroxysmal) were analyzed. Using electrogram signals alone, the convolutional neural network achieved an area under the receiver operating characteristics curve (AUROC) of 0.731, outperforming the existing APPLE scores (AUROC=0.644) and CHA2DS2-VASc scores (AUROC=0.650). Similarly using 12-lead ECG alone, the convolutional neural network achieved an AUROC of 0.767. Combining electrogram, ECG, and clinical features, the fusion model achieved an AUROC of 0.859, outperforming single and dual modality models. CONCLUSIONS: Deep neural networks trained on electrogram or ECG signals improved the prediction of catheter ablation outcome compared with existing clinical scores, and fusion of electrogram, ECG, and clinical features further improved the prediction. This suggests the promise of using machine learning to help treatment planning for patients after catheter ablation.

Machine Learned Cellular Phenotypes in Cardiomyopathy Predict Sudden Death.

RATIONALE: Susceptibility to VT/VF (ventricular tachycardia/fibrillation) is difficult to predict in patients with ischemic cardiomyopathy either by clinical tools or by attempting to translate cellular mechanisms to the bedside. OBJECTIVE: To develop computational phenotypes of patients with ischemic cardiomyopathy, by training then interpreting machine learning of ventricular monophasic action potentials (MAPs) to reveal phenotypes that predict long-term outcomes. METHODS AND RESULTS: We recorded 5706 ventricular MAPs in 42 patients with coronary artery disease and left ventricular ejection fraction ≤40% during steady-state pacing. Patients were randomly allocated to independent training and testing cohorts in a 70:30 ratio, repeated K=10-fold. Support vector machines and convolutional neural networks were trained to 2 end points: (1) sustained VT/VF or (2) mortality at 3 years. Support vector machines provided superior classification. For patient-level predictions, we computed personalized MAP scores as the proportion of MAP beats predicting each end point. Patient-level predictions in independent test cohorts yielded c-statistics of 0.90 for sustained VT/VF (95% CI, 0.76-1.00) and 0.91 for mortality (95% CI, 0.83-1.00) and were the most significant multivariate predictors. Interpreting trained support vector machine revealed MAP morphologies that, using in silico modeling, revealed higher L-type calcium current or sodium-calcium exchanger as predominant phenotypes for VT/VF. CONCLUSIONS: Machine learning of action potential recordings in patients revealed novel phenotypes for long-term outcomes in ischemic cardiomyopathy. Such computational phenotypes provide an approach which may reveal cellular mechanisms for clinical outcomes and could be applied to other conditions.

Evaluation of a Novel Antiplatelet Therapy Strategy in Patients Undergoing Elective Percutaneous Coronary Intervention.

BACKGROUND: Ticagrelor presents less thrombotic risk compared to clopidogrel in acute coronary syndromes. However, its role in dual antiplatelet therapy (DAPT)-naive patients with stable ischemic heart disease (SIHD) undergoing elective percutaneous intervention (PCI) remains unclear, including uncertainty in the method of conversion to clopidogrel for adequate coverage without increased bleeding risk. OBJECTIVE: Determine the safety and efficacy of ticagrelor loading and transitioning to clopidogrel in patients with SIHD undergoing elective PCI. METHODS: This is a retrospective cohort review of patients with SIHD who underwent elective PCI. The Switch Rx patients were treated with ticagrelor immediately before PCI, converted to clopidogrel 300 mg the day after, and discharged with clopidogrel 75 mg daily. Standard Rx patients, who received a clopidogrel load and received clopidogrel 75 mg daily after the procedure, were analyzed as a matched comparator cohort. The safety outcomes were any bleeding event at 24 hours and 30 days. The efficacy outcomes included major adverse cardiac events (MACE) at 24 hours and 30 days. RESULTS: Five Switch Rx patients (n = 54) experienced bleeding academic research consortium type I bleeding within 24 hours, with no subsequent bleeding observed out to 30 days. When comparing the Switch Rx patients (n = 39) to their matched Standard Rx cohort (n = 39), no MACEs occurred within 30 days and there were no significant differences in safety and efficacy outcomes. CONCLUSION: In DAPT-naive patients undergoing elective PCI for SIHD, a strategy of in-lab ticagrelor transitioning to clopidogrel with a 300-mg load was not associated with increased bleeding or other adverse events.

Safety and Efficacy of Minimal- versus Zero-fluoroscopy Radiofrequency Catheter Ablation for Atrial Fibrillation: A Multicenter, Prospective Study.

Radiofrequency catheter ablation (CA) is an effective treatment for atrial fibrillation (AF) that traditionally requires fluoroscopic imaging to guide catheter movement and positioning. However, advances in electroanatomic mapping (EAM) technology and intracardiac echocardiography (ICE) have reduced procedural reliance on fluoroscopy. We conducted a prospective registry study of 162 patients enrolled at five centers proficient in high-volume, minimal-fluoroscopy CA between March 2016 and March 2018 for the CA of symptomatic, drug-refractory paroxysmal, or persistent AF that sought to assess the safety and efficacy of minimal- versus zero-fluoroscopy AF CA. We evaluated procedural details, acute procedural outcomes and complications, and one-year follow-up data. All operators used an EAM system (CARTO®; Biosense Webster, Irvine, CA, USA) and ICE. Ultimately, two patients did not pursue CA postenrollment. A total of 104 (66%) patients had paroxysmal AF with a mean ejection fraction of 58% ± 9%. Twenty-six (16.3%) patients were scheduled for repeat ablation. A total of 100 (63%) procedures were performed with zero fluoroscopy. The mean fluoroscopy time in the minimal-fluoroscopy group was 1.7 minutes ± 2.8 minutes. Further, the mean procedure duration was 192 minutes ± 37 minutes in the zero-fluoroscopy group and 201 minutes ± 29 minutes in the minimal-fluoroscopy group (p = 0.96). Pulmonary vein isolation was achieved in 153 patients (100%), with an acute procedural complication rate of 1.8%. One-year follow-up data were available for 152 (95%) patients with a mean follow-up time of 11.3 months ± 1.8 months. A total of 118 (76%) patients remained free from arrhythmia for up to 12 months, with no difference between the minimal- and zero-fluoroscopy cohorts (p = 0.18).

Machine Learning to Classify Intracardiac Electrical Patterns During Atrial Fibrillation: Machine Learning of Atrial Fibrillation.

BACKGROUND: Advances in ablation for atrial fibrillation (AF) continue to be hindered by ambiguities in mapping, even between experts. We hypothesized that convolutional neural networks (CNN) may enable objective analysis of intracardiac activation in AF, which could be applied clinically if CNN classifications could also be explained. METHODS: We performed panoramic recording of bi-atrial electrical signals in AF. We used the Hilbert-transform to produce 175 000 image grids in 35 patients, labeled for rotational activation by experts who showed consistency but with variability (kappa [κ]=0.79). In each patient, ablation terminated AF. A CNN was developed and trained on 100 000 AF image grids, validated on 25 000 grids, then tested on a separate 50 000 grids. RESULTS: In the separate test cohort (50 000 grids), CNN reproducibly classified AF image grids into those with/without rotational sites with 95.0% accuracy (CI, 94.8%-95.2%). This accuracy exceeded that of support vector machines, traditional linear discriminant, and k-nearest neighbor statistical analyses. To probe the CNN, we applied gradient-weighted class activation mapping which revealed that the decision logic closely mimicked rules used by experts (C statistic 0.96). CONCLUSIONS: CNNs improved the classification of intracardiac AF maps compared with other analyses and agreed with expert evaluation. Novel explainability analyses revealed that the CNN operated using a decision logic similar to rules used by experts, even though these rules were not provided in training. We thus describe a scaleable platform for robust comparisons of complex AF data from multiple systems, which may provide immediate clinical utility to guide ablation. Registration: URL: https://www.clinicaltrials.gov; Unique identifier: NCT02997254. Graphic Abstract: A graphic abstract is available for this article.

Termination of persistent atrial fibrillation by ablating sites that control large atrial areas.

AIMS: Persistent atrial fibrillation (AF) has been explained by multiple mechanisms which, while they conflict, all agree that more disorganized AF is more difficult to treat than organized AF. We hypothesized that persistent AF consists of interacting organized areas which may enlarge, shrink or coalesce, and that patients whose AF areas enlarge by ablation are more likely to respond to therapy. METHODS AND RESULTS: We mapped vectorial propagation in persistent AF using wavefront fields (WFF), constructed from raw unipolar electrograms at 64-pole basket catheters, during ablation until termination (Group 1, N = 20 patients) or cardioversion (Group 2, N = 20 patients). Wavefront field mapping of patients (age 61.1 ± 13.2 years, left atrium 47.1 ± 6.9 mm) at baseline showed 4.6 ± 1.0 organized areas, each separated by disorganization. Ablation of sites that led to termination controlled larger organized area than competing sites (44.1 ± 11.1% vs. 22.4 ± 7.0%, P < 0.001). In Group 1, ablation progressively enlarged unablated areas (rising from 32.2 ± 15.7% to 44.1 ± 11.1% of mapped atrium, P < 0.0001). In Group 2, organized areas did not enlarge but contracted during ablation (23.6 ± 6.3% to 15.2 ± 5.6%, P < 0.0001). CONCLUSION: Mapping wavefront vectors in persistent AF revealed competing organized areas. Ablation that progressively enlarged remaining areas was acutely successful, and sites where ablation terminated AF were surrounded by large organized areas. Patients in whom large organized areas did not emerge during ablation did not exhibit AF termination. Further studies should define how fibrillatory activity is organized within such areas and whether this approach can guide ablation.

Online webinar training to analyse complex atrial fibrillation maps: A randomized trial.

BACKGROUND: Specific tools have been recently developed to map atrial fibrillation (AF) and help guide ablation. However, when used in clinical practice, panoramic AF maps generated from multipolar intracardiac electrograms have yielded conflicting results between centers, likely due to their complexity and steep learning curve, thus limiting the proper assessment of its clinical impact. OBJECTIVES: The main purpose of this trial was to assess the impact of online training on the identification of AF driver sites where ablation terminated persistent AF, through a standardized training program. Extending this concept to mobile health was defined as a secondary objective. METHODS: An online database of panoramic AF movies was generated from a multicenter registry of patients in whom targeted ablation terminated non-paroxysmal AF, using a freely available method (Kuklik et al-method A) and a commercial one (RhythmView-method B). Cardiology Fellows naive to AF mapping were enrolled and randomized to training vs no training (control). All participants evaluated an initial set of movies to identify sites of AF termination. Participants randomized to training evaluated a second set of movies in which they received feedback on their answers. Both groups re-evaluated the initial set to assess the impact of training. This concept was then migrated to a smartphone application (App). RESULTS: 12 individuals (median age of 30 years (IQR 28-32), 6 females) read 480 AF maps. Baseline identification of AF termination sites by ablation was poor (40%±12% vs 42%±11%, P = 0.78), but similar for both mapping methods (P = 0.68). Training improved accuracy for both methods A (P = 0.001) and B (p = 0.012); whereas controls showed no change in accuracy (P = NS). The Smartphone App accessed AF maps from multiple systems on the cloud to recreate this training environment. CONCLUSION: Digital online training improved interpretation of panoramic AF maps in previously inexperienced clinicians. Combining online clinical data, smartphone apps and other digital resources provides a powerful, scalable approach for training in novel techniques in electrophysiology.

Utility of Urine Neutrophil Gelatinase-Associated Lipocalin for Worsening Renal Function during Hospitalization for Acute Heart Failure: Primary Findings of the Urine N-gal Acute Kidney Injury N-gal Evaluation of Symptomatic Heart Failure Study (AKINESIS).

BACKGROUND: Worsening renal function (WRF) during acute heart failure (AHF) occurs frequently and has been associated with adverse outcomes, though this association has been questioned. WRF is now evaluated by function and injury. We evaluated whether urine neutrophil gelatinase-associated lipocalin (uNGAL) is superior to creatinine for prediction and prognosis of WRF in patients with AHF. METHODS AND RESULTS: We performed a multicenter, international, prospective cohort of patients with AHF requiring IV diuretics. The primary outcome was whether uNGAL predicted development of WRF, defined as a sustained increase in creatinine of 0.5 mg/dL or ≥50% above first value or initiation of renal replacement therapy, within the first 5 days. The main secondary outcome was a composite of in-hospital adverse events. We enrolled 927 patients (mean 68.5 years of age, 62% men). The primary outcome occurred in 72 patients (7.8%). The first, peak and the ratio of uNGAL to urine creatinine (area under curves (AUC) ≤ 0.613) did not have diagnostic utility over the first creatinine (AUC 0.662). There were 235 adverse events in 144 patients. uNGAL did not predict (AUCs ≤ 0.647) adverse clinical events better than creatinine (AUC 0.695). CONCLUSIONS: uNGAL was not superior to creatinine for predicting WRF or adverse in-hospital outcomes and cannot be recommended for WRF in AHF.

Copeptin to rule out myocardial infarction in Blacks versus Caucasians.

BACKGROUND: Copeptin in combination with troponin has been shown to have incremental value for the early rule-out of myocardial infarction, but its performance in Black patients specifically has never been examined. In light of a potential for wider use, data on copeptin in different relevant cohorts are needed. This is the first study to determine whether copeptin is equally effective at ruling out myocardial infarction in Black and Caucasian races. METHODS: This analysis of the CHOPIN trial included 792 Black and 1075 Caucasian patients who presented to the emergency department with chest pain and had troponin-I and copeptin levels drawn. RESULTS: One hundred and forty-nine patients were diagnosed with myocardial infarction (54 Black and 95 Caucasian). The negative predictive value of copeptin at a cut-off of 14 pmol/l (as in the CHOPIN study) for myocardial infarction was higher in Blacks (98.0%, 95% confidence interval (CI) 96.2-99.1%) than Caucasians (94.1%, 95% CI 92.1-95.7%). The sensitivity at 14 pmol/l was higher in Blacks (83.3%, 95% CI 70.7-92.1%) than Caucasians (53.7%, 95% CI 43.2-64.0%). After controlling for age, hypertension, heart failure, chronic kidney disease and body mass index in a logistic regression model, the interaction term had a P value of 0.03. A cut-off of 6 pmol/l showed similar sensitivity in Caucasians as 14 pmol/l in Blacks. CONCLUSIONS: This is the first study to identify a difference in the performance of copeptin to rule out myocardial infarction between Blacks and Caucasians, with increased negative predictive value and sensitivity in the Black population at a cut-off of 14 pmol/l. This also holds true for non-ST-segment elevation myocardial infarction and, although numbers were small, similar trends exist in the normal troponin population. This may have significant implications for early rule-out strategies using copeptin.

Interaction of Localized Drivers and Disorganized Activation in Persistent Atrial Fibrillation: Reconciling Putative Mechanisms Using Multiple Mapping Techniques.

BACKGROUND: Mechanisms for persistent atrial fibrillation (AF) are unclear. We hypothesized that putative AF drivers and disorganized zones may interact dynamically over short time scales. We studied this interaction over prolonged durations, focusing on regions where ablation terminates persistent AF using 2 mapping methods. METHODS: We recruited 55 patients with persistent AF in whom ablation terminated AF prior to pulmonary vein isolation from a multicenter registry. AF was mapped globally using electrograms for 360±45 cycles using (1) a published phase method and (2) a commercial activation/phase method. RESULTS: Patients were 62.2±9.7 years, 76% male. Sites of AF termination showed rotational/focal patterns by methods 1 and 2 (51/55 vs 55/55; P=0.13) in spatially conserved regions, yet fluctuated over time. Time points with no AF driver showed competing drivers elsewhere or disordered waves. Organized regions were detected for 61.6±23.9% and 70.6±20.6% of 1 minute per method (P=nonsignificant), confirmed by automatic phase tracking (P<0.05). To detect AF drivers with >90% sensitivity, 8 to 32 s of AF recordings were required depending on driver definition. CONCLUSIONS: Sites at which persistent AF terminated by ablation show organized activation that fluctuate over time, because of collision from concurrent organized zones or fibrillatory waves, yet recur in conserved spatial regions. Results were similar by 2 mapping methods. This network of competing mechanisms should be reconciled with existing disorganized or driver mechanisms for AF, to improve clinical mapping and ablation of persistent AF. CLINICAL TRIAL REGISTRATION: URL: http://www.clinicaltrials.gov. Unique identifier: NCT02997254.

Usefulness of Proneurotensin to Predict Cardiovascular and All-Cause Mortality in a United States Population (from the Reasons for Geographic and Racial Differences in Stroke Study).

Cardiovascular disease is a leading cause of death. Proneurotensin is a biomarker associated with the development of cardiovascular disease, cardiovascular mortality, and all-cause mortality. We assessed the association of fasting proneurotensin with mortal events by gender and race (black-white) in a US population. Using a case-cohort subpopulation of the Reasons for Geographic and Racial Differences in Stroke study, fasting proneurotensin was measured on a 1,046-person subcohort and in 651 participants with incident coronary heart disease. Higher proneurotensin was associated with all-cause mortality (hazard ratio [HR] 1.6 per interquartile range, 95% confidence interval [CI] 1.3 to 1.9) and cardiovascular mortality (HR 1.8, 95% CI 1.2 to 2.6). For all-cause and cardiovascular mortality, association was stronger in women (HR 1.9, 95% CI 1.4 to 2.6 and HR 2.5, 95% CI 1.4 to 4.7, respectively) than men (HR 1.4, 95% CI 1.0 to 1.8 and HR 1.4, 95% CI 0.9 to 2.3, respectively), although this difference was not significant. Proneurotensin predicted all-cause mortality in both races and was not predictive of cardiovascular mortality in whites but was in blacks. Proneurotensin was not associated with incident coronary heart disease events. Elevated proneurotensin levels predicted all-cause and cardiovascular mortality in both genders, with a trend toward stronger association in women. Associations were similar in blacks and whites. In conclusion, proneurotensin may be a useful biomarker for all-cause and cardiovascular mortality regardless of race, and it is potentially specific in women.