Risk stratifier for sudden cardiac death beyond the left ventricular ejection fraction in Chagas cardiomyopathy.

BACKGROUND: Sudden cardiac death (SCD) risk markers are needed in Chagas cardiomyopathy (CC). Action potential duration restitution (APDR) dynamics is capable of extracting information on cardiac regional heterogeneity. This study intends to develop a patient-specific variables-based algorithm to predict SCD in the low-intermediate subgroups of the Rassi risk score. METHODS: Cross-sectional study of patients who underwent 24-h Holter for research purposes between January 1992 and February 2017. From 4-h ECG segment, RR series were generated and APDR dynamics metrics were calculated. Classification tree and sensitivity analysis were applied. As outcomes, SCD, SCD-free and non-cardiovascular death and 34 variables were included. RESULTS: Two hundred twenty-one (129 in the group SCD-free, 80 in the SCD group and 12 non-cardiovascular death group) were analyzed. In the groups with and without SCD (209 patients), the median age was 66 years, 52% were female, the cardiac involvement was mild to moderate in 72% with a Rassi point median of 8 (IQ: 3 to 11). The SCD group had more ventricular remodeling and more ventricular electrical instability. The occurrence of a %beats QTend/TendQ ratio > 1 (AUC, 0.96 (95% CI 0.89-0.98) present in more than 56.7% of the 4-h ECG segments was sufficient to identify patients of the SCD subgroup. Variables representing different stages of CC were also relevant in the model. CONCLUSION: It is possible to use APDR dynamics as an adjuvant in the SCD risk assessment in a subgroup of patients with a high risk of SCD and a very low risk of non-CV death with high power of discrimination.

Rationale and study design of the MINERVA study: Multicentre Investigation of Novel Electrocardiogram Risk markers in Ventricular Arrhythmia prediction-UK multicentre collaboration.

INTRODUCTION: The purpose of this study is to assess the ability of two new ECG markers (Regional Repolarisation Instability Index (R2I2) and Peak Electrical Restitution Slope) to predict sudden cardiac death (SCD) or ventricular arrhythmia (VA) events in patients with ischaemic cardiomyopathy undergoing implantation of an implantable cardioverter defibrillator for primary prevention indication. METHODS AND ANALYSIS: Multicentre Investigation of Novel Electrocardiogram Risk markers in Ventricular Arrhythmia prediction is a prospective, open label, single blinded, multicentre observational study to establish the efficacy of two ECG biomarkers in predicting VA risk. 440 participants with ischaemic cardiomyopathy undergoing routine first time implantable cardioverter-defibrillator (ICD) implantation for primary prevention indication are currently being recruited. An electrophysiological (EP) study is performed using a non-invasive programmed electrical stimulation protocol via the implanted device. All participants will undergo the EP study hence no randomisation is required. Participants will be followed up over a minimum of 18 months and up to 3 years. The first patient was recruited in August 2016 and the study will be completed at the final participant follow-up visit. The primary endpoint is ventricular fibrillation or sustained ventricular tachycardia >200 beats/min as recorded by the ICD. The secondary endpoint is SCD. Analysis of the ECG data obtained during the EP study will be performed by the core lab where blinding of patient health status and endpoints will be maintained. ETHICS AND DISSEMINATION: Ethical approval has been granted by Research Ethics Committees Northern Ireland (reference no. 16/NI/0069). The results will inform the design of a definitive Randomised Controlled Trial (RCT). Dissemination will include peer reviewed journal articles reporting the qualitative and quantitative results, as well as presentations at conferences and lay summaries. TRIAL REGISTRATION NUMBER: NCT03022487.

The temporal stability of recurrence quantification analysis attributes from chronic atrial fibrillation electrograms

Abstract Introduction The temporal behavior of atrial electrograms (AEGs) collected during persistent atrial fibrillation (persAF) directly affects ablative treatment outcomes. We investigated different durations of AEGs collected during persAF using recurrence quantification analysis (RQA). Methods 797 bipolar AEGs with different durations (from 0.5 s to 8 s) from 18 patients were investigated. Four RQA-based attributes were evaluated based on AEG durations: determinism (DET); recurrence rate (RR); laminarity (LAM); and diagonal lines' entropy (ENTR). The Spearman correlation (ρ) between each duration versus 8 s was calculated. AEG classification was performed following the CARTO criteria (Biosense Webster) and receiving operating characteristic (ROC) curves were created for the RQA variables. Results The RQA variables successfully discriminated the AEGs: the area under the ROC curves were as high as 0.70 for AEGs with 3.5 s or greater. Three types of AEGs were found using these variables: normal, fractionated and temporally unstable. The number of unstable AEGs decreased with longer AEG segments. Different AEG durations significantly affected the RQA variables (P<0.0001), with no statistical difference between the durations 6 s, 7 s and 8 s for DET, LAM and ENTR, and no difference between 7 s and 8 s for RR (P<0.0001). AEGs with 3 s or longer have shown ρ ≥ 80% for all variables. Conclusion The RQA variables have been shown effective in the characterization of AEGs collected during persAF with a shorter duration than current recommendations, which motivates their use for the characterization of atrial substrate during persAF ablation.

A study on the optimum order of autoregressive models for heart rate variability.

Heart rate variability (HRV) has been used as a non-invasive marker of the activity of the autonomic nervous system and its spectrum analysis gives a measure of the sympatho-vagal balance. If short segments are used in an attempt to improve temporal resolution, autoregressive spectral estimation, where the mode] order must be estimated, is preferred. In this paper we compare four criteria for the estimation of the 'optimum' model order for an autoregressive (AR) process applied to short segments of tachograms used for HRV analysis. The criteria used were Akaike's final prediction error, Akaike's information criterion, Parzen's criterion of autoregressive transfer function and Rissanen's minimum description length method, and they were first applied to tachograms to verify (i) the range and distribution of model orders obtained and (ii) if the different techniques suggest the same model order for the same frames. The four techniques were then tested using a true AR process of known order p = 6; this verified the ability of the criteria to estimate the correct order of a true AR process and the effect, on the spectrum, of choosing a wrong model order was also investigated. It was found that all the four criteria underestimate the true AR order; specifying a fixed model order was then looked at and it is recommended that an AR order not less than p = 16, should be used for spectral analysis of short segments of tachograms.