Patient Population

The population comprised patients with prior MIs who underwent programmed electrical stimulation (PES) followed by ICD implantation at the Leiden University Medical Center. All patients underwent transthoracic echocardiography before or <6 months after ICD implantation. For patients undergoing biventricular ICD (CRT-D) implantation, echocardiography was performed after CRT-D placement, with the pacemaker on to ensure representation of current physiology. Exclusion criteria were an uncertain date of MI, the presence of reversible ischemia, pacemaker dependence before ICD implantation, and the occurrence of VT between ICD insertion and echocardiography.

All clinical, electrophysiologic, and echocardiographic data were collected in the departmental cardiology information system (EPD-Vision; Leiden University Medical Center, Leiden, The Netherlands) and retrospectively analyzed.

Echocardiography

Patients were imaged in the left lateral decubitus position using a commercially available system equipped with 3.5-MHz and 5S transducers (Vivid 7 and E9; GE Vingmed Ultrasound AS, Horten, Norway). Two-dimensional grayscale, color, pulsed-wave, and continuous-wave Doppler data were acquired in the parasternal and apical views. Images were recorded digitally in cine-loop format and analyzed offline with EchoPAC version 11.0.0 (GE Vingmed Ultrasound AS). Left atrial volume and LV end-systolic and end-diastolic volumes were measured from the apical two- and four-chamber views and indexed to body surface area. LVEF was calculated using the biplane Simpson’s technique. Mitral regurgitation severity was graded as none/mild or moderate/severe by incorporating a number of echocardiographic indicators, including regurgitant jet vena contracta width, proximal flow convergence, effective regurgitant orifice area measured with the proximal isovelocity surface area method, color jet area, continuous-wave Doppler spectral profile intensity, E-wave velocity, and pulmonary venous flow reversal.

Strain Analysis

Evaluation of LV myocardial deformation was performed with speckle-tracking echocardiography. These measurements were performed by two investigators blinded to patient outcomes using the Q-analysis application on an EchoPAC version 11.0.0 workstation. In each of the three apical views, the LV myocardium was manually contoured, and the contours were adjusted to achieve optimal tracking of all myocardial segments over the cardiac cycle. The software automatically generated curves of strain as a function of time ( Figure 1 ). Using an 18-segment model derived from the three apical views, segmental peak systolic longitudinal myocardial strain and time to peak longitudinal strain were recorded. Longitudinal strain in normally contracting myocardium is by convention negative, but for simplicity, we report absolute strain values, denoted as |strain|. Thus, poorer myocardial contraction is reflected by a lower |strain| value. Myocardial segments were classified as having transmural scar if the longitudinal |strain| value was <4.5%. This threshold has been previously validated against late gadolinium enhancement cardiac magnetic resonance imaging.

Speckle-tracking echocardiographic strain. Representative examples of speckle-tracking strain curves from two patients with LVEFs of 55%. Patient A demonstrated greater heterogeneity in time to peak segmental strain compared with patient B. LVEF , Left ventricular ejection fraction.

LV scar burden was evaluated by the percentage of LV segments exhibiting transmural scar. LV dyssynchrony was defined as the standard deviation of values of time to peak longitudinal systolic strain in this 18-segment model ( Figure 1 ). Global longitudinal strain (GLS) was measured as the mean of peak |strain| values from each myocardial segment.

PES

PES was performed according to current guidelines. In brief, patients were studied in a fasting, nonsedated state and after the discontinuation of antiarrhythmic medications (except for amiodarone) for five half-lives. PES included up to three drive-cycle lengths (600, 500, and 400 msec) with up to three ventricular extrastimuli and burst pacing from the right ventricular apex and right ventricular outflow tract. Positive response to PES was defined as the reproducible induction of sustained monomorphic VT lasting >30 sec or requiring termination because of hemodynamic compromise.

Outcomes

VT was identified from examination of ICD electrograms stored after device therapy administration. The appropriateness of this therapy and the nature of the ventricular arrhythmia (to distinguish monomorphic VT from polymorphic VT or ventricular fibrillation) were adjudicated by an electrophysiologist who was blinded to patients’ clinical characteristics. The primary end point was the occurrence of first monomorphic VT requiring device therapy (antitachycardia pacing or shock) after ICD implantation. Patients were censored at the date of most recent outpatient appointment unless the primary end point occurred earlier. In addition, the occurrence and date of occurrence of the following significant competing risks were recorded if they took place after ICD implantation and before VT occurrence: polymorphic VT, ventricular fibrillation, upgrade to CRT-D, VT ablation, revascularization for acute coronary syndrome, or LV reconstructive surgery.

Statistical Analysis

Patients were divided into quartiles according to age of MI (i.e., time from index MI to ICD insertion). Nominally, these groups were described as very old for the quartile with the oldest MI age and recent for the quartile with the youngest MI age. Categorical data are summarized as frequencies and percentages and were compared using χ ² or Fisher exact tests as appropriate. Continuous variables are presented as mean ± SD or median (interquartile range [IQR]) and were compared between groups using analysis of variance or Kruskal-Wallis tests for normally and non-normally distributed data, respectively. Post hoc testing for pairwise comparisons was performed with Bonferroni adjustment. The relationship between scar percentage and MI age was evaluated by generalized linear modeling. Because of the highly skewed distribution and wide variance relative to the mean of scar percentage, a negative binomial probability distribution was assumed.

For time-to-event (monomorphic VT) analysis, a competing-risks strategy was adopted. A competing risk is an event whose occurrence alters or precludes the occurrence of an event of interest (monomorphic VT). When competing risks are present, competing-risks regression is regarded as an appropriate approach to time-to-event analysis. In the present analysis, the following events occurring before the occurrence of the primary end point of monomorphic VT were treated as competing risks: ventricular fibrillation, polymorphic VT, ICD upgrade, coronary revascularization, and LV reconstructive surgery. The covariates that were examined as potential predictors of monomorphic VT were chosen on the basis of clinical relevance or demonstrated prognostic value after MI. These covariates included.

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  Clinical factors (age, gender, hypertension, diabetes mellitus, infarct territory, New York Heart Association functional class III or IV, creatinine clearance, ICD indication [secondary vs primary], revascularization of the infarct-related artery, and age of MI),

  
  
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  QRS duration on electrocardiography,

  
  
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  Conventional echocardiographic parameters (LV end-systolic volume index, left atrial volume index, and LVEF),

  
  
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  Speckle-tracking echocardiographic parameters that formed the focus of the present study (GLS, LV scar burden, and LV dyssynchrony), and

  
  
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  VT inducibility on PES.

Those covariates achieving P values < .20 at the univariate level were evaluated in a multivariate competing-risks analysis that was performed with backward elimination. Differences in the cumulative incidence of monomorphic VT between groups were assessed.

The incremental benefit of novel predictors of monomorphic VT was evaluated first by the Wald test comparing the model with the novel predictor and the model without the novel predictor. Second, the incremental value of novel risk predictors was evaluated using the integrated discrimination improvement index; a value significantly greater than zero is indicative of additive discriminatory value of the new risk predictor of interest.

In 10 randomly selected individuals, echocardiographic strain measurements were repeated by a second investigator to determine interobserver variability. The interrater agreement for classification of scar was evaluated by the κ statistic and the interobserver variability for LV dyssynchrony and GLS by the intraclass correlation coefficient for a two-way random-effects model for consistency of agreement and by the absolute difference between observers divided by the mean of the repeated observations expressed as a percentage.

For all tests, a two-sided α value of 0.05 was adopted to determine statistical significance. Analysis was performed using Stata version 13.1 (StataCorp LP, College Station, TX).

Patient Characteristics

A total of 206 patients (median age, 67 years; IQR, 57–73 years; 180 [87%] men) meeting the inclusion criteria were studied. These comprised 171 ICD recipients (83%) and 35 CRT-D recipients (17%). The median MI age was 6.2 years (IQR, 0.66–15 years). Patient characteristics are displayed as a function of age of MI in Table 1 . Individuals with very old MIs (MI age in the highest quartile, >180 months prior) were of more advanced age ( P < .001), had worse renal function ( P = .007), and had longer QRS durations ( P < .001). They also had less frequent revascularization of the infarct-related artery ( P < .001), and the infarct-related artery more often subtended the inferoposterior LV segments ( P = .03) compared with those with more recent MIs. These patients more often received ICDs as secondary prevention ( P = .002).

Echocardiographic and Electrophysiologic Characteristics

Table 2 summarizes the echocardiographic and electrophysiologic characteristics of the patient population. Patients with very old MIs exhibited greater scar burden (14.7 ± 15.8% vs 10.7 ± 11.4%, P = .03) and more left atrial dilatation (36 mL/m ² [IQR, 27–50 mL/m ² ] vs 28 mL/m ² [IQR, 23–35 mL/m ² ], P = .005) compared with patients with recent MIs (MI age in the lowest quartile, <8 months). Patients with very old MIs showed a higher likelihood of VT inducibility compared with those with recent MIs (88% vs 63%, P = .003). To examine the possibility that the increase in scar burden with infarct age was related to the confounding effects of changing practice over time with regard to revascularization of the culprit artery, we also adjusted for revascularization status in the generalized linear model of infarct age. This adjusted analysis did not significantly affect the positive relationship between scar burden and infarct age (coefficient, 0.15; 95% confidence interval [CI], 0.013–0.28; P = .03). We undertook two post hoc sensitivity analyses in which a scarred segment was defined as one with |strain| < 4% and < 5%, respectively. The adjusted coefficients for the relationship between scar burden and infarct age were 0.18 (95% CI, 0.043 to 0.32; P = .01) and 0.11 (95% CI, −0.022 to 0.25; P = .10), respectively.

Table 2

Echocardiographic and electrophysiologic characteristics

Variable	Quartile 1 (<8 mo) ( n = 52)	Quartile 2 (8–75 mo) ( n = 52)	Quartile 3 (75–180 mo) ( n = 51)	Quartile 4 (>180 mo) ( n = 51)	P
LVESVI (mL/m 2 )	41 (33 to 52)	49 (34 to 66)	49 (33 to 64)	47 (35 to 65)	.20
LVEDVI (mL/m 2 )	69 (57 to 85)	80 (65 to 97)	79 (59 to 100)	77 (56 to 97)	.30
LAVI (mL/m 2 )	28 (23 to 35)	27 (21 to 37)	32 (22 to 40)	36 (27 to 50) ∗ , †	.005
Moderate/severe MR	1 (2%)	5 (10%)	4 (9%)	6 (12%)	.20
LVEF (%)	41 ± 9.9	40 ± 13	38 ± 11	36 ± 8.2	.10
GLS (%)	10.9 (9.81 to 14.3)	12.8 (10.4 to 14.9)	−10.1 (8.25 to 13.6)	−11.7 (8.58 to 13.4)	.10
LV percentage scar (%)	10.7 ± 11.4	12.1 ± 15.1	13.1 ± 12.9	14.7 ± 15.8 ∗	.03
Patients with ≥1 dyskinetic myocardial segment	7 (13%)	12 (23%)	11 (25%)	10 (20%)	.60
LV dyssynchrony (msec)	82 ± 29	81 ± 34	97 ± 40	91 ± 43	.09
VT inducible	33 (63%)	30 (58%)	36 (71%)	45 (88%)	.003
VF/PVT inducible	8 (15%)	18 (35%)	8 (16%)	4 (8%)	.006

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