Supplementary Material Sinnecker et al.
Supplementary Material for the article Sinnecker et al. “Expiration-triggered sinus
arrhythmia predicts outcome in survivors of acute myocardial infarction”
Contents:
1. Supplementary Methods
2. Supplementary Results
3. Supplementary Figures
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Supplementary Material Sinnecker et al.
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Supplementary Methods
Optimization of thresholds 1 and 1 for ETA determination
For the PRSA procedure which was used to calculate ETA, it is necessary to define the
expiration phase, i.e. the numerical values of the thresholds 1 and 2 in the analytical
respiration signal (Figure 1 of the paper). The 1 and 2 values resulting in optimum
separation between survivors and non-survivors were determined by a systematic
optimization, in which areas under the ROC curve for 5-year all-cause mortality were
calculated for every combination of 1 and 2 (Supplementary Figure 2). Based on this
analysis, the threshold values 0.6 and -0.1 were chosen for 1 and 2, respectively. It should
be noted that these settings were not crucial, as selection of any thresholds near to these
values produced similarly-high AUC values (see Supplementary Figure 2).
Supplementary Results
Relation of ETA with -blocker use
-blockers, which modulate the sympatho-vagal balance, are an established therapy after
myocardial infarction proven to reduce mortality. Therefore, it could be speculated that
favorable ETA is a surrogate for adequate autonomic control due to -blocker therapy.
Several lines of evidence indicate that this is not the case:
The vast majority of patients included in the study were treated with -blockers (see Table 1).
However, five-year all-cause mortality did not significantly differ between patients with and
without b-blocker therapy (7.6% vs. 9.1%, p=0.71). ETA values in patients with and without
-blocker therapy were not significantly different (mean ± standard error 0.75 ± 0.11 vs. 0.25
± 0.71, p=0.27). In a multivariable Cox model incorporating ETA and -blocker use, -blocker
use was not a significant mortality predictor (p=0.71), while ETA performed very similar to a
univariable model.
Supplementary Material Sinnecker et al.
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We conclude that, in our study, the predictive power of ETA was not due to being a surrogate
for -blocker use.
Relation of ETA with infarct localization
Vagal tone may be influenced by the localization of MI. We therefore investigated whether
the predictive power of ETA is influenced by infarct localization (i.e. anterior vs. inferior wall
MI).
Of the 941 patients, 391 and 435 had anterior and inferior wall infarction, respectively. ETA
values were not significantly different in these groups (p=0.47). Similarly, mortality rates were
not significantly different in these groups (p=0.39). Expectedly, in a univariable Cox model,
infarct localization was not a significant mortality predictor. In a multivariable Cox model
considering infarct localization and ETA, ETA ≤0.19 ms remained a strong mortality predictor
(hazard ratio 5.3, 95% CI 3.0–9.6, p<0.001). We conclude that infarct localization is not a
significant confounder in our data.
Relation of ETA with heart failure
Heart failure may result in a shift of the sympatho-vagal balance towards more sympathetic
activation. It could be therefore speculated that reduced ETA is a surrogate for clinical heart
failure. Of the 941 patients, 114 had clinical signs of heart failure (defined here as NYHA
functional class >2 or Killipp class >1). Indeed, ETA values were significantly smaller in
patients with clinical heart failure than in the remaining patients (mean (95% CI) -0.014 (0,80–0.78) vs. 0.83 (0.60–1.06), p=0.005). However, when presence of clinical heart failure
was entered in a Cox model together with ETA, ETA ≤0.19 ms remained a strong mortality
predictor (hazard ratio 5.0, 95% CI 2.9–8.5, p<0.001). Additional inclusion of respiratory rate,
which may represent a subclinical sign of heart failure, into the model only marginally
reduced the association of ETA with mortality (hazard ratio 4.3, 95% CI 2.5–7.4, p<0.001).
Thus, although ETA values may be influenced by the presence of heart failure, heart failure
is not a significant confounder in our data.
Supplementary Material Sinnecker et al.
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ETA and cardiac /sudden cardiac mortality
The primary endpoint of the prospective ART trial was all-cause mortality, which amounted to
72 deaths. Of these deaths, 33 were classified as cardiac and, of these, 11 as sudden
cardiac deaths. In univariable Cox analysis, ETA ≤0.19 ms was also significantly associated
with cardiac death and with sudden cardiac death, with hazard ratios (95% CIs) of 5.3 (2.4–
11.8, p<0.001) and 4.6 (1.3 – 17.4, p=0.024), respectively (Supplementary Figure 3).
The limited number of events limits the validity of multivariable models considering many risk
predictors with respect to the more specific endpoints cardiac and sudden cardiac death.
Therefore, we performed multivariable Cox analyses in which ETA was entered together with
additional risk factors in a pairwise fashion (Supplementary Figure 3). These risk predictors
comprised LVEF, the clinical GRACE score, ECG time intervals (PR, QRS, QTc), the
frequency of occurrence of VPCs, the presence of late potentials in a signal-averaged ECG
(SAECG), heart rate turbulence (turbulence onset and turbulence slope), deceleration
capacity of heart rate, spontaneous baroreflex sensitivity, respiratory rate, post-extrasystolic
blood pressure potentiation (PESP), and periodic repolarization dynamics (PRD).
When any of these risk predictors was added to ETA in a multivariable model, ETA remained
significantly associated with mortality with respect to all investigated endpoints (see
Suppelementary Figure 3). Moreover, the hazard ratios for ETA were similar for all three
endpoints. We conclude that ETA is also an independent predictor of cardiac death and
sudden cardiac death.
Supplementary Material Sinnecker et al.
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Supplementary Figures
Supplementary Figure 1. ETA improves the area under the ROC curve of risk prediction models.
ROC curves with respect to 5-year all-cause mortality for models consisting of (A) left-ventricular
ejection fraction (LVEF), GRACE score, presence of diabetes mellitus, and respiratory rate, (B) PR
interval, QRS duration, heart rate-corrected QT interval (QTc), heart rate turbulence (HRT), and
presence of late potentials in the signal-averaged electrocardiogram (SAECG), and (C) heart rate
turbulence (HRT), deceleration capacity of heart rate (DC), and periodic repolarization dynamics
(PRD) are shown in black (Model1). ROC curves of the same models extended by ETA are shown in
red (Model 2). For each model, the AUC value and 95% confidence interval are given. The p value for
the AUC comparison Model1 vs. Model2 is also given.
Supplementary Material Sinnecker et al.
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Supplementary Figure 2. A color-coded map showing AUC values for the prediction of 5-year allcause mortality by ETA calculated using different combinations of threshold values 1 and 2 in the
PRSA analysis.
Supplementary Material Sinnecker et al.
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Supplementary Figure 3. For the three endpoints all-cause mortality (A), cardiac mortality (B), and
sudden cardiac mortality (C), hazard ratios and 95% confidence intervals for ETA ≤0.19 ms are shown
that were obtained by univariable Cox analysis (top row) or by multivariable Cox analysis considering
ETA and one additional risk predictor as indicated (below). P values for the hazard ratios are shown.
LVEF: left-ventricular ejection fraction. PR: PR interval. QRS: QRS width. QTc: frequency-corrected
QT interval. VPC: ventricular premature complexes. LP in SAECG: late potentials in signal-averaged
ECG. HRT: heart rate turbulence. TO: turbulence onset. TS: turbulence slope. DC: deceleration
capacity of heart rate. BRSPRSA: spontaneous baroreflex sensitivity, assessed by phase-rectified signal
averaging. PESP: post-extrasystolic blood pressure potentiation. PRD: periodic repolarization
dynamics.