Dataset Information

Cardiovascular Magnetic Resonance to Predict Appropriate Implantable Cardioverter Defibrillator Therapy in Ischemic and Nonischemic Cardiomyopathy Patients Using Late Gadolinium Enhancement Border Zone: Comparison of Four Analysis Methods.

ABSTRACT: BACKGROUND:Late gadolinium enhancement (LGE) border zone on cardiac magnetic resonance imaging has been proposed as an independent predictor of ventricular arrhythmias. The purpose was to determine whether size and heterogeneity of LGE predict appropriate implantable cardioverter defibrillator (ICD) therapy in ischemic cardiomyopathy (ICM) and nonischemic cardiomyopathy (NICM) patients and to evaluate 4 LGE border-zone algorithms. METHODS AND RESULTS:ICM and NICM patients who underwent LGE cardiac magnetic resonance imaging prior to ICD implantation were retrospectively included. Two semiautomatic algorithms, expectation maximization, weighted intensity, a priori information and a weighted border zone algorithm, were compared with a modified full-width half-maximum and a 2-3SD threshold-based algorithm (2-3SD). Hazard ratios were calculated per 1% increase in LGE. A total of 74 ICM and 34 NICM were followed for 63 months (1-140) and 52 months (0-133), respectively. ICM patients had 27 appropriate ICD events, and NICM patients had 7 ICD events. In ICM patients with primary prophylactic ICD, LGE border zone predicted ICD therapy in univariable and multivariable analysis measured by the expectation maximization, weighted intensity, a priori information, weighted border zone, and modified full-width half-maximum algorithms (hazard ratios 1.23, 1.22, and 1.05, respectively; P<0.05; negative predictive value 92%). For NICM, total LGE by all 4 methods was the strongest predictor (hazard ratios, 1.03-1.04; P<0.05), though the number of events was small. CONCLUSIONS:Appropriate ICD therapy can be predicted in ICM patients with primary prevention ICD by quantifying the LGE border zone. In NICM patients, total LGE but not LGE border zone had predictive value for ICD therapy. However, the algorithms used affects the predictive value of these measures.

SUBMITTER: Jablonowski R

PROVIDER: S-EPMC5580266 | biostudies-literature | 2017 Sep

REPOSITORIES: biostudies-literature

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Cardiovascular Magnetic Resonance to Predict Appropriate Implantable Cardioverter Defibrillator Therapy in Ischemic and Nonischemic Cardiomyopathy Patients Using Late Gadolinium Enhancement Border Zone: Comparison of Four Analysis Methods.

Jablonowski Robert R Chaudhry Uzma U van der Pals Jesper J Engblom Henrik H Arheden Håkan H Heiberg Einar E Wu Katherine C KC Borgquist Rasmus R Carlsson Marcus M

Circulation. Cardiovascular imaging 20170901 9

<h4>Background</h4>Late gadolinium enhancement (LGE) border zone on cardiac magnetic resonance imaging has been proposed as an independent predictor of ventricular arrhythmias. The purpose was to determine whether size and heterogeneity of LGE predict appropriate implantable cardioverter defibrillator (ICD) therapy in ischemic cardiomyopathy (ICM) and nonischemic cardiomyopathy (NICM) patients and to evaluate 4 LGE border-zone algorithms.<h4>Methods and results</h4>ICM and NICM patients who unde ...[more]

PMID: 28838961

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Project description:Arrhythmogenic right ventricular dysplasia/cardiomyopathy is characterized by ventricular arrhythmias and sudden cardiac death. Once the diagnosis is established, risk stratification to determine whether implantable cardioverter-defibrillator (ICD) placement is warranted is critical. The cohort included 312 patients (163 men, age at presentation 33.6±13.9 years) with definite arrhythmogenic right ventricular dysplasia/cardiomyopathy who received an ICD. Over 8.8±7.33 years, 186 participants (60%) had appropriate ICD therapy and 58 (19%) had an intervention for ventricular fibrillation/flutter. Ventricular tachycardia at presentation (hazard ratio [HR]: 1.86; 95% confidence interval [CI], 1.38-2.49; P<0.001), inducibility on electrophysiology study (HR: 3.14; 95% CI, 1.95-5.05; P<0.001), male sex (HR: 1.62; 95% CI, 1.20-2.19; P=0.001), inverted T waves in ≥3 precordial leads (HR: 1.66; 95% CI, 1.09-2.52; P=0.018), and premature ventricular contraction count ≥1000/24 hours (HR: 2.30; 95% CI, 1.32-4.00; P=0.003) were predictors of any appropriate ICD therapy. Inducibility at electrophysiology study (HR: 2.28; 95% CI, 1.10-4.70; P=0.025) remained as the only predictor after multivariable analysis. The predictors for ventricular fibrillation/flutter were premature ventricular contraction ≥1000/24 hours (HR: 4.39; 95% CI, 1.32-14.61; P=0.016), syncope (HR: 1.85; 95% CI, 1.10-3.11; P=0.021), aged ≤30 years at presentation (HR: 1.76; 95% CI, 1.04-3.00; P<0.036), and male sex (HR: 1.73; 95% CI, 1.01-2.97; P=0.046). Younger age at presentation (HR: 3.14; 95% CI, 1.32-7.48; P=0.010) and high premature ventricular contraction burden (HR: 4.43; 95% CI, 1.35-14.57; P<0.014) remained as independent predictors of ventricular fibrillation/flutter. Complications occurred in 66 participants (21%), and 64 (21%) had inappropriate ICD interventions. Overall mortality was low at 2%, and 4% underwent heart transplantation. These findings represent an important step in identifying predictors of ICD therapy for potentially fatal ventricular fibrillation/flutter and should be considered when developing a risk stratification model for arrhythmogenic right ventricular dysplasia/cardiomyopathy.

Project description:Background Current approaches fail to separate patients at high versus low risk for ventricular arrhythmias owing to overreliance on a snapshot left ventricular ejection fraction measure. We used statistical machine learning to identify important cardiac imaging and time-varying risk predictors. Methods and Results Three hundred eighty-two cardiomyopathy patients (left ventricular ejection fraction ≤35%) underwent cardiac magnetic resonance before primary prevention implantable cardioverter defibrillator insertion. The primary end point was appropriate implantable cardioverter defibrillator discharge or sudden death. Patient characteristics; serum biomarkers of inflammation, neurohormonal status, and injury; and cardiac magnetic resonance-measured left ventricle and left atrial indices and myocardial scar burden were assessed at baseline. Time-varying covariates comprised interval heart failure hospitalizations and left ventricular ejection fractions. A random forest statistical method for survival, longitudinal, and multivariable outcomes incorporating baseline and time-varying variables was compared with (1) Seattle Heart Failure model scores and (2) random forest survival and Cox regression models incorporating baseline characteristics with and without imaging variables. Age averaged 57±13 years with 28% women, 66% white, 51% ischemic, and follow-up time of 5.9±2.3 years. The primary end point (n=75) occurred at 3.3±2.4 years. Random forest statistical method for survival, longitudinal, and multivariable outcomes with baseline and time-varying predictors had the highest area under the receiver operating curve, median 0.88 (95% CI, 0.75-0.96). Top predictors comprised heart failure hospitalization, left ventricle scar, left ventricle and left atrial volumes, left atrial function, and interleukin-6 level; heart failure accounted for 67% of the variation explained by the prediction, imaging 27%, and interleukin-6 2%. Serial left ventricular ejection fraction was not a significant predictor. Conclusions Hospitalization for heart failure and baseline cardiac metrics substantially improve ventricular arrhythmic risk prediction.

Project description:Background Outcomes data in patients with cardiac amyloidosis after implantable cardioverter-defibrillator (ICD) implantation are limited. We compared outcomes of patients with ICDs implanted for cardiac amyloidosis versus nonischemic cardiomyopathies (NICMs) and evaluated factors associated with mortality among patients with cardiac amyloidosis. Methods and Results Using National Cardiovascular Data Registry's ICD Registry data between April 1, 2010 and December 31, 2015, we created a 1:5 propensity-matched cohort of patients implanted with ICDs with cardiac amyloidosis and NICM. We compared mortality between those with cardiac amyloidosis and matched patients with NICM using Kaplan-Meier survival curves and Cox proportional hazards models. We also evaluated risk factors associated with 1-year mortality in patients with cardiac amyloidosis using multivariable Cox proportional hazards regression models. Among 472 patients with cardiac amyloidosis and 2360 patients with propensity-matched NICMs, 1-year mortality was significantly higher in patients with cardiac amyloidosis compared with patients with NICMs (26.9% versus 11.3%, P<0.001). After adjustment for covariates, cardiac amyloidosis was associated with a significantly higher risk of all-cause mortality (hazard ratio [HR], 1.80; 95% CI, 1.56-2.08). In a multivariable analysis of patients with cardiac amyloidosis, several factors were significantly associated with mortality: syncope (HR, 1.78; 95% CI, 1.22-2.59), ventricular tachycardia (HR, 1.65; 95% CI, 1.15-2.38), cerebrovascular disease (HR, 2.03; 95% CI, 1.28-3.23), diabetes mellitus (HR, 1.55; 95% CI, 1.05-2.27), creatinine = 1.6 to 2.5 g/dL (HR, 1.99; 95% CI, 1.32-3.02), and creatinine >2.5 (HR, 4.34; 95% CI, 2.72-6.93). Conclusions Mortality after ICD implantation is significantly higher in patients with cardiac amyloidosis than in patients with propensity-matched NICMs. Factors associated with death among patients with cardiac amyloidosis include prior syncope, ventricular tachycardia, cerebrovascular disease, diabetes mellitus, and impaired renal function.

Project description:Background One third of primary prevention implantable cardioverter-defibrillator patients receive appropriate therapy, but all remain at risk of defibrillator complications. Information on these complications in contemporary cohorts is limited. This study assessed complications and their risk factors after defibrillator implantation in a Dutch nationwide prospective registry cohort and forecasts the potential reduction in complications under distinct scenarios of updated indication criteria. Methods and Results Complications in a prospective multicenter registry cohort of 1442 primary implantable cardioverter-defibrillator implant patients were classified as major or minor. The potential for reducing complications was derived from a newly developed prediction model of appropriate therapy to identify patients with a low probability of benefitting from the implantable cardioverter-defibrillator. During a follow-up of 2.2 years (interquartile range, 2.0-2.6 years), 228 complications occurred in 195 patients (13.6%), with 113 patients (7.8%) experiencing at least one major complication. Most common ones were lead related (n=93) and infection (n=18). Minor complications occurred in 6.8% of patients, with lead-related (n=47) and pocket-related (n=40) complications as the most prevailing ones. A surgical reintervention or additional hospitalization was required in 53% or 61% of complications, respectively. Complications were strongly associated with device type. Application of stricter implant indication results in a comparable proportional reduction of (major) complications. Conclusions One in 13 patients experiences at least one major implantable cardioverter-defibrillator-related complication, and many patients undergo a surgical reintervention. Complications are related to defibrillator implantations, and these should be discussed with the patient. Stricter implant indication criteria and careful selection of device type implanted may have significant clinical and financial benefits.

Dataset Information

Cardiovascular Magnetic Resonance to Predict Appropriate Implantable Cardioverter Defibrillator Therapy in Ischemic and Nonischemic Cardiomyopathy Patients Using Late Gadolinium Enhancement Border Zone: Comparison of Four Analysis Methods.

Publications

Cardiovascular Magnetic Resonance to Predict Appropriate Implantable Cardioverter Defibrillator Therapy in Ischemic and Nonischemic Cardiomyopathy Patients Using Late Gadolinium Enhancement Border Zone: Comparison of Four Analysis Methods.

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