Application of two novel electrical restitution-based ECG markers of ventricular arrhythmia to patients with nonischemic cardiomyopathy.

INTRODUCTION: Sudden cardiac death (SCD) risk assessment is limited, particularly in patients with nonischemic cardiomyopathies. This is the first application, in patients with cardiomyopathies, of two novel risk markers, regional restitution instability index (R2I2) and peak electrocardiogram restitution slope (PERS), which have been shown to be predictive of ventricular arrhythmias (VA) or death in ischemic heart disease patients. METHODS: Blinded retrospective study of 50 patients: 33 dilated cardiomyopathy and 17 other; undergoing electrophysiological study (EPS) for SCD risk stratification, and 29 controls with structurally normal hearts undergoing EPS. R2I2 was calculated from an EPS using electrocardiogram surrogates for action potential duration and diastolic interval. Cut-offs for high and low R2I2/PERS were predefined. RESULTS: R2I2 was significantly higher in study than control patients (0.99 ± 0.05 vs. 0.63 ± 0.04, p < .001). PERS showed a trend to higher values in the study group (1.18[0.63] vs. 1.09[0.54], p = .07). During median follow up of 5.6 years [interquartile range 1.9 years], nine study patients reached the endpoint of VA/death. Patients who experienced VA/death showed trends to higher mean R2I2 (1.14 ± 0.07 vs.0.95 ± 0.05, p = .12) and PERS (1.46[0.49] vs. 1.13[0.62], p = .22). A Cox proportional hazards model using grouped markers: R2I2 < 1.03 + PERS < 1.21/either R2I2 ≥ 1.03 or PERS ≥ 1.21/R2I2 ≥ 1.03 + PERS ≥ 1.21; significantly predicted VA/death (p = .02) with a hazard ratio per positive component of 3.2 (95% confidence interval 1.2-8.8). CONCLUSION: R2I2≥ 1.03 + PERS ≥ 1.21 may predict VA/death in patients with cardiomyopathies. R2I2 ≥ 1.03 + PERS ≥ 1.21 have the potential to play an important role in SCD risk stratification in cardiomyopathies but their validity should be confirmed in a larger study.

Predicting Falls and When to Intervene in Older People: A Multilevel Logistical Regression Model and Cost Analysis.

INTRODUCTION: Falls are the leading cause of injury in older people. Reducing falls could reduce financial pressures on health services. We carried out this research to develop a falls risk model, using routine primary care and hospital data to identify those at risk of falls, and apply a cost analysis to enable commissioners of health services to identify those in whom savings can be made through referral to a falls prevention service. METHODS: Multilevel logistical regression was performed on routinely collected general practice and hospital data from 74751 over 65's, to produce a risk model for falls. Validation measures were carried out. A cost-analysis was performed to identify at which level of risk it would be cost-effective to refer patients to a falls prevention service. 95% confidence intervals were calculated using a Monte Carlo Model (MCM), allowing us to adjust for uncertainty in the estimates of these variables. RESULTS: A risk model for falls was produced with an area under the curve of the receiver operating characteristics curve of 0.87. The risk cut-off with the highest combination of sensitivity and specificity was at p = 0.07 (sensitivity of 81% and specificity of 78%). The risk cut-off at which savings outweigh costs was p = 0.27 and the risk cut-off with the maximum savings was p = 0.53, which would result in referral of 1.8% and 0.45% of the over 65's population respectively. Above a risk cut-off of p = 0.27, costs do not exceed savings. CONCLUSIONS: This model is the best performing falls predictive tool developed to date; it has been developed on a large UK city population; can be readily run from routine data; and can be implemented in a way that optimises the use of health service resources. Commissioners of health services should use this model to flag and refer patients at risk to their falls service and save resources.

Prospective evaluation of two novel ECG-based restitution biomarkers for prediction of sudden cardiac death risk in ischaemic cardiomyopathy.

OBJECTIVE: To improve prediction of sudden cardiac death (SCD) in patients with ischaemic cardiomyopathy (ICM). Electrical heterogeneity is known to contribute to risk of SCD. We have previously developed Regional Restitution Instability Index (R2I2), an ECG-based biomarker, which quantifies cardiac electrical instability by measuring heterogeneity in electrical restitution, and demonstrated its potential utility for risk stratification in a retrospective analysis of patients with ICM. Here, we examined R2I2 in a prospective ICM cohort and also tested the predictive value of another ECG-based biomarker, Peak ECG Restitution Slope (PERS). METHODS: Prospective, blinded, observational study of 60 patients with ICM undergoing implantable cardioverter defibrillator risk stratification. R2I2 was calculated from an electrophysiological study (EPS) using ECG surrogates for action potential duration and diastolic interval. R2I2 quantifies inter-lead electrical restitution heterogeneity. PERS was the peak restitution curve slope taken as a mean across the 12 ECG leads. Endpoints were ventricular arrhythmia (VA)/SCD. RESULTS: Over median follow-up of 22 months, 16 (26.6%) patients achieved endpoint. R2I2 was significantly higher in these patients compared with those without an event (mean ± SEM: 1.11 ± 0.09 vs 0.84 ± 0.04, p=0.003) as was PERS (median(IQR): 1.35(0.60) vs 1.08(0.52), p=0.014). R2I2≥1.03, the cut-off used in our previous study, identified patients with a significantly higher risk of VA/SCD independent of EPS result, LVEF or QRS duration with a relative risk of 6.5 (p=0.008). Patients positive for R2I2 and PERS had a relative risk of VA/SCD 21.6 times that of those negative for R2I2 and PERS (p<0.0001). CONCLUSIONS: R2I2 and PERS each independently and in combination, identify patients with ICM that are at high risk of developing ventricular arrhythmias (VA). R2I2/PERS represent promising risk markers for SCD discrimination. TRIAL REGISTRATION NUMBER: ClinicalTrials.gov Identifier: NCT01944514.