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. 2024 Oct 8;84(15):1407-1420.
doi: 10.1016/j.jacc.2024.06.046. Epub 2024 Aug 30.

Arrhythmic Risk Stratification by Cardiovascular Magnetic Resonance Imaging in Patients With Nonischemic Cardiomyopathy

Daniel J Hammersley  1 , Abbasin Zegard  2 , Emmanuel Androulakis  3 , Richard E Jones  4 , Osita Okafor  2 , Suzan Hatipoglu  3 , Lukas Mach  5 , Amrit S Lota  5 , Zohya Khalique  5 , Antonio de Marvao  6 , Ankur Gulati  7 , Resham Baruah  3 , Kaushik Guha  8 , James S Ware  9 , Upasana Tayal  5 , Dudley J Pennell  5 , Brian P Halliday  5 , Tian Qiu  10 , Sanjay K Prasad  5 , Francisco Leyva  11
Affiliations

Arrhythmic Risk Stratification by Cardiovascular Magnetic Resonance Imaging in Patients With Nonischemic Cardiomyopathy

Daniel J Hammersley et al. J Am Coll Cardiol. .

Abstract

Background: Myocardial fibrosis (MF) forms part of the arrhythmic substrate for ventricular arrhythmias (VAs).

Objectives: This study sought to determine whether total myocardial fibrosis (TF) and gray zone fibrosis (GZF), assessed using cardiovascular magnetic resonance, are better than left ventricular ejection fraction (LVEF) in predicting ventricular arrhythmias in patients with nonischemic cardiomyopathy (NICM).

Methods: Patients with NICM in a derivation cohort (n = 866) and a validation cohort (n = 848) underwent quantification of TF and GZF. The primary composite endpoint was sudden cardiac death or VAs (ventricular fibrillation or ventricular tachycardia).

Results: The primary endpoint was met by 52 of 866 (6.0%) patients in the derivation cohort (median follow-up: 7.5 years; Q1-Q3: 5.2-9.3 years). In competing-risks analyses, MF on visual assessment (MFVA) predicted the primary endpoint (HR: 5.83; 95% CI: 3.15-10.8). Quantified MF measures permitted categorization into 3 risk groups: a TF of >0 g and ≤10 g was associated with an intermediate risk (HR: 4.03; 95% CI: 1.99-8.16), and a TF of >10 g was associated with the highest risk (HR: 9.17; 95% CI: 4.64-18.1) compared to patients with no MFVA (lowest risk). Similar trends were observed in the validation cohort. Categorization into these 3 risk groups was achievable using TF or GZF in combination or in isolation. In contrast, LVEF of <35% was a poor predictor of the primary endpoint (validation cohort HR: 1.99; 95% CI: 0.99-4.01).

Conclusions: MFVA is a strong predictor of sudden cardiac death and VAs in NICM. TF and GZF mass added incremental value to MFVA. In contrast, LVEF was a poor discriminator of arrhythmic risk.

Keywords: arrythmia; fibrosis; nonischemic cardiomyopathy; risk stratification; sudden cardiac death.

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Conflict of interest statement

Funding Support and Author Disclosures This work was supported by a National Heart and Lung Institute Foundation grant awarded to Drs Prasad, Hammersley, Jones, Tayal, and Halliday as well as a British Society for Heart Failure Research Fellowship and a British Heart Foundation Clinical Research Training Fellowship (FS/CRTF/23/24444) awarded to Dr Mach. Additionally, the study was supported by Rosetrees Trust, the Alexander Jansons Myocarditis UK Foundation, a BHF Intermediate Clinical Research Fellowship awarded to Dr Halliday (FS/ICRF/21/26019), and an MRC Fellowship awarded to Dr Tayal (MRC MR/W023830/1). This work was additionally supported by The British Heart Foundation (RE/18/4/34215; SP/17/11/32885), Royston Centre for Cardiomyopathy Research, Sir Jules Thorn Charitable Trust (21JTA), Medical Research Council (UK), National Institute for Health Research, Royal Brompton Cardiovascular Biomedical Research Unit, and National Institute for Health Research Imperial College Biomedical Research Centre. Medtronic Plc provided funding for the salary as a research fellow for Dr Zegard. Boston Scientific provided funding for Dr Qiu (statistician). These companies had no participation whatsoever in the study. The views expressed in this work are those of the authors and not necessarily those of the funders. Dr Hammersley has received research funding from Siemens. Dr Baruah is an employee of AstraZeneca. Dr Guha has received honoraria from Bayer, Pfizer, Novartis, AstraZeneca, and Servier Laboratories; has received an unrestricted educational grant from Biotronik; and has received travel assistance from Abbott Laboratories, Medtronic, Biotronik, and Boston Scientific. Dr Ware has acted as a consultant for MyoKardia, Foresite Labs, Pfizer, and Health Lumen. Dr Halliday has received honoraria from AstraZeneca. All other authors have reported that they have no relationships relevant to the contents of this paper to disclose.

Figures

Figure 1
Figure 1
Cardiac Magnetic Resonance in Nonischemic Cardiomyopathy (A) Late gadolinium enhancement cardiovascular magnetic resonance images were visually assessed to determine whether myocardial fibrosis (MF) was present or absent. If MF was present (appears white on late gadolinium enhancement), quantification was undertaken. (B) To this end, epicardial and endocardial contours (green and red, respectively) were semiautomatically delineated. Total fibrosis and gray zone mass were quantified using various signal thresholding methods. In this example, the basal segments showed extensive, heterogeneous MF (yellow arrows) in a noncoronary distribution over the left ventricular free wall, with a distinct epicardial and midmyocardial distribution toward the midventricular and apical segments. (C) The polar maps show the distribution of MF according to the American Heart Association 16-segment model and to smaller segments (100 segments over 8 short-axis slices, starting from the junction of the right ventricular wall and the interventricular septum [white line]). The scale range is from 0 (green, no MF) to 100% (black, entire segment is 100% MF).
Figure 2
Figure 2
Study Flow Chart Flow chart illustrating the assembly of the derivation and validation cohorts. CMR = cardiovascular magnetic resonance; ICM = ischemic cardiomyopathy; LGE = late gadolinium enhancement; NICM = nonischemic cardiomyopathy.
Figure 3
Figure 3
MFVA and LVEF in Relation to the Primary Endpoint Cumulative hazard estimates for the primary endpoint in the derivation sample, stratified by (A) MFVA or (B) LVEF (<35% or ≥35%). LVEF = left ventricular ejection fraction; MFVA = myocardial fibrosis on visual assessment.
Figure 4
Figure 4
Quantified TF and GZF in Relation to the Primary Endpoint Cumulative hazard estimates of the primary arrhythmic endpoint in the derivation sample, stratified according to (A) total fibrosis mass according to the 2-SD method (low: >0 and ≤10 g; high: >10 g) and (B) gray zone fibrosis mass according to the 3-SD method (low: >0 and ≤3 g; high: >3 g). GZF = gray zone fibrosis; MFVA = myocardial fibrosis on visual assessment; TF = total fibrosis.
Figure 5
Figure 5
Decision Curve Analysis The graphs show decision curves in the derivation cohort, comparing the net benefit of MF (y-axis) across different threshold probabilities of the primary endpoint (x-axis). The decision curve reflects the tradeoff between true positive predictions and false positive predictions for a given strategy. The area under the decision curve quantifies the overall clinical utility of the predictive model. The dotted horizontal gray line indicates the net benefit of not testing any patient (“test none”), whereas the solid diagonal line shows the net benefit of testing all patients (“test all”). The dashed colored decision curves indicate the net benefit of using LVEF or MF measures in prediction models. See Supplemental Figure 1 for analysis of the validation cohort. GZF = gray zone fibrosis; GZF3SD = gray zone fibrosis according to the 3-SD method; LVEF = left ventricular ejection fraction; MFVA = myocardial fibrosis on visual assessment; TF = total fibrosis; TF2SD = total fibrosis according to the 2-SD method.
Central Illustration
Central Illustration
Risk Stratification in Nonischemic Cardiomyopathy Using Cardiovascular Magnetic Resonance A total of 1,714 patients with (NICM) were enrolled independently in 2 centers: a derivation cohort and a validation cohort. Late gadolinium enhancement cardiovascular magnetic resonance was used to determine the presence of MFVA. If MFVA was present, TF and gray zone fibrosis was quantified. To this end, areas of MF, which appear white on late gadolinium enhancement, were semiautomatically delineated on short-axis images, using signal thresholding techniques. As shown at the upper right, LVEF of <35% was associated with a higher risk of the primary endpoint on competing-risks analyses (HR: 1.91; 95% CI: 1.11-3.29) of the derivation cohort. MFVA was a powerful predictor of the primary endpoint (HR: 5.83; 95% CI: 3.15-10.8) (middle right). Quantification of TF permitted categorization into low-, intermediate- (HR: 4.03; 95% CI: 1.99-8.16), and high-risk (HR: 9.17; 95% CI: 4.64-18.1) groups (bottom right). TF mass was quantified according to the 2-SD method and expressed as low (>0 to ≤10 g) or high (>10 g). LGE = late gadolinium enhancement; LVEF = left ventricular ejection fraction; MF = myocardial fibrosis; MFVA = myocardial fibrosis on visual assessment; NICM = nonischemic cardiomyopathy; SCD = sudden cardiac death; TF = total fibrosis; VF = ventricular fibrillation; VT = ventricular tachycardia.
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References

    1. Arbelo E., Protonotarios A., Gimeno J.R., et al. 2023 ESC guidelines for the management of cardiomyopathies. Eur Heart J. 2023;44:3503–3626. - PubMed
    1. Chrispin J., Merchant F.M., Lakdawala N.K., et al. Risk of arrhythmic death in patients with nonischemic cardiomyopathy: JACC review topic of the week. J Am Coll Cardiol. 2023;82:735–747. - PubMed
    1. Heidenreich P.A., Bozkurt B., Aguilar D., et al. 2022 AHA/ACC/HFSA guideline for the management of heart failure. J Am Coll Cardiol. 2022;79:e263–e421. - PubMed
    1. McDonagh T.A., Metra M., Adamo M., et al. 2021 ESC guidelines for the diagnosis and treatment of acute and chronic heart failure: developed by the Task Force for the Diagnosis and Treatment of Acute and Chronic Heart Failure of the European Society of Cardiology (ESC). With the special contribution of the Heart Failure Association (HFA) of the ESC. Eur Heart J. 2021;42:3599–3726. - PubMed
    1. Zeppenfeld K., Tfelt-Hansen J., de Riva M., et al. 2022 ESC guidelines for the management of patients with ventricular arrhythmias and the prevention of sudden cardiac death: developed by the Task Force for the Management of Patients With Ventricular Arrhythmias and the Prevention of Sudden Cardiac Death of the European Society of Cardiology (ESC). Endorsed by the Association for European Paediatric and Congenital Cardiology (AEPC) Eur Heart J. 2022;43:3997–4126. - PubMed