Myocardial infarction (MI) is the most common cause of sudden cardiac death (SCD); over 80% of fatal arrhythmias are caused by structural coronary arterial abnormalities and their consequences (Huikuri et al., 2001). Prediction of SCD currently poses a clinical challenge and identification of high-risk patients needs to be considerably improved (Myerburg, 2001).

Autonomic dysfunction and electrical instability are key factors predisposing MI patients to ventricular tachycardia and ventricular fibrillation and consequent SCD (Schwartz et al., 1992; Liew, 2010; Wellens et al., 2014). Indices of ventricular repolarization lability, such as QT variability (QTV), have attracted considerable interest in the area of risk stratification as they allow for a non-invasive investigation of the autonomic nervous system influence on the ventricular myocardium and the lability of the ventricular repolarization process (Myerburg, 2001; Wellens et al., 2014). Measures of QTV (QTVI, SDQT) are increased post MI and their prognostic value has been demonstrated in several studies (Wellens et al., 2014; Baumert et al., 2016a). QTV was increased in six out of the 12-lead ECG recording of patients with recent MI even after covarying for low T wave amplitude (Hasan et al., 2013) and in post MI patients with reduced LVEF compared to those with preserved LVEF and those with uncomplicated coronary heart disease (Sosnowski et al., 2002). QTV was also increased in coronary disease patients with old MI when compared to those without MI (Yao et al., 2019). QTVI has been shown to be predictive of SCD and all-cause mortality in patients with LVEF between 35% and 40% (Piccirillo et al., 2007). In 24-h Holter recordings of patients with acute MI, QT/RR variability ratio was found to be predictive of all-cause mortality independent of other established risk markers (Jensen et al., 2005).

Beat-to-beat fluctuations in the QT interval (representing the variability of the repolarization process) are the result of a complex process that involves multiple physiological mechanisms. The rate adaptation of the QT interval constitutes a large fraction of QTV in the normal heart (El-Hamad et al., 2015; Baumert et al., 2016b). Respiration also influences QTV indirectly through respiratory sinus arrhythmia (Sutherland et al., 1983; Baumert et al., 2016b). Furthermore, ventricular repolarization is directly influenced by the autonomic nervous system (Merri et al., 1993) via the dense innervation of sympathetic nerves into the ventricular myocardium, contributing to rate-independent QTV (Hall, 2011). Hence, in the compromised heart, excessive sympathetic outflow might initiate lethal ventricular arrhythmias (Ng, 2016).

In the context of MI, few studies report on the rate-independent component of repolarization variability. Zhu et al. (2008) found that repolarization variability independent of heart rate was increased in MI patients compared to age-matched healthy subjects, while the rate-dependent component was not different between the two groups. Others found no difference in the percentage of RR-dependent RT interval variability between MI patients and age-matched healthy subjects (Lombardi et al., 1998). Comparing MI patients with reduced LVEF to those with preserved LVEF and to another group with coronary artery disease but no MI, Sosnowski et al. (2002) reported an increase in overall RT variability and specifically an increase in its HF power, suggesting an important role for respiration. They also observed an uncorrelation between RR and RT interval variability in MI patients with depressed LVEF compared to the other two groups.

There are only few reports on QTV’s individual components in patients post MI, and none on their individual role in risk stratification post MI. In this study, we hypothesize that the component of QTV that cannot be explained by heart rate or respiration, possibly reflecting sympathetic influences on the ventricles, carries important prognostic information. To test this hypothesis, we decomposed QTV into three components (hart rate dependent, respiratory dependent, and independent component) and assessed their individual roles in risk stratification in patients post MI.

Our study demonstrates that heart rate-independent mechanisms play a dominant role in creating excessive QTV observed in MI patients with increased mortality risk. The novel QTV risk score yields improved risk stratification within a subgroup of MI patients who already display traditional risk markers (GRACE score ≥ 120). It can also identify vulnerable patients in patient groups with normal to moderate LVEF (LVEF > 35%).

Repolarization lability can trigger malignant arrhythmias and subsequent SCD in patients post MI (Liew, 2010). QTV was found to be elevated during ischemic episodes compared to non-ischemic episodes (Murabayashi et al., 2002). We found that QTV is elevated in non-surviving MI patients when compared to those who survived.

Previously, simple QTV metrics were combined with conventional risk markers to identify MI patients at high risk of mortality (Jensen et al., 2005; Piccirillo et al., 2007). Here, we sought to decompose QTV into its physiological sources and assessed their individual role in risk stratification. We found that the relative contribution of RR interval variability to QTV was reduced in non-survivors, indicating attenuated rate-adaptation of the QT interval. This attenuation of the QT-RR relationship has also been found in MI patients with reduced LVEF when compared to those with coronary artery disease but no MI (Sosnowski et al., 2002). Others found no difference in the RR dependent component of QTV when comparing MI patients to healthy subjects (Lombardi et al., 1998; Zhu et al., 2008). These aggregate results suggest that the attenuation in QT-RR relationship is only prevalent in high-risk patients rather than in all patients post MI. Possibly, the attenuation is linked to increased sympathetic activation (Zaza et al., 1991; Porta et al., 2010; El-Hamad et al., 2015). Holter recordings showed substantial predictive value of attenuated rate-adaptation of the QT interval for SCD in MI patients (Hintze et al., 2002; Chevalier et al., 2003).

In our study, the respiratory contribution to QTV did not play a significant role in predicting patients at risk in the univariate model (Table 2). Hence, we considered a simplified model without respiration. It’s predictive performance was comparable to the original model (data not shown), suggesting that recording of respiration is not necessary. Contrary to our findings, others have reported an increase in high frequency of RT interval variability in patients with reduced LVEF compared to those with preserved LVEF or with coronary artery disease but no MI (Sosnowski et al., 2002).

The portion of QTV that cannot be explained by rate-adaptation or respiration-related mechanisms was increased in patients who died during the follow-up period (Table 2). In healthy subjects, the unexplained fraction of QTV has been previously shown to correlate with an increase in sympathetic tone during tilt (Porta et al., 2010; El-Hamad et al., 2015) and is augmented in post-MI patients compared to healthy subjects (Zhu et al., 2008).

The pathophysiological mechanisms that increase overall QTV and QTV independent of heart period and respiration and contribute to mortality risk are not fully understood. By design, our study does not facilitate the direct interpretation of the QTV component independent of heart rate and respiration. However, the spectral component of QTV that is independent of RR and respiration exhibits a clear peak in the low-frequency (LF; 0.04-0.15 Hz) region that is elevated in the non-survivors, as shown in Figure 1. It is well-known that sympathetic nerve activity oscillates at the LF rhythm, creating Traube–Hering–Mayer waves in arterial blood pressure that coincide with oscillations in RR variability. Sympathetic nerve activity modulates the relationship between repolarization and cycle length in the LF band (Zaza et al., 1991). It also contributes directly to the magnitude of QTV by affecting L-type channel Ca²⁺ current and the slow delayed rectifier potassium current in ventricular myocytes. Nerve sprouting (Zhou et al., 2004) and sympathetic hyperinnervation following acute MI can create electrical instability, where augmented sympathetic activity can lead to malignant ventricular arrhythmias (Cao et al., 2000). Other studies suggest that systemic inflammation post-MI could contribute at least in part to the increase in QTV (Tseng et al., 2012). Since the severity and prognosis of myocardial infarction are highly dependent on the size and location of the infarcted tissue, affecting the extent of spatial repolarization heterogeneity (Hiromoto et al., 2006), QTV independent of heart rate and respiration may also be affected by the characteristics of the damaged substrate.

Markers of an abnormal substrate or structural heart disease are some of the most common risk factors for SCD, and reduced LVEF is currently considered the most important marker for risk stratification (Wellens et al., 2014). Current international guidelines for implantable cardioverter defibrillator implantation for primary prevention are predominantly based on LVEF (O’Gara et al., 2013; Priori et al., 2015). However, the majority of SCDs occur in patients with preserved LVEF (LVEF > 30%) (Buxton et al., 2007; Wellens et al., 2014). Therefore, the identification of high-risk patients in this cohort is critical.

Several markers of electrical instability and autonomic tone, such as heart rate variability, heart rate turbulence, T wave alternans, and QTV index, have been associated with increased risk of SCD in post MI patients with preserved LVEF (Piccirillo et al., 2007; Gatzoulis et al., 2019). In this group of patients, we found that a higher QTV risk score is associated with a four times higher risk of mortality compared to those with lower values of QTV risk score. Gatzoulis et al. (2019) reported that patients with preserved LVEF who display at least one ECG-derived non-invasive risk factor were at higher risk of SCD. As QTV reflects lability of the ventricular repolarization process, a higher QTV risk score could identify patients more susceptible to ventricular arrhythmia.

The QTV risk score was only marginally predictive in the group with reduced LVEF. This could be explained by the fact that patients with reduced LVEF have a higher competing risk of death from heart failure than other modes of death when compared to patients with preserved LVEF (Hall et al., 2018). The GRACE score is a hospital discharge risk score shown to be predictive of mortality in patients with acute coronary syndrome. It combines multiple prognostic factors such as age, history of heart failure and MI, and heart rate (Tang et al., 2007). Patients with a GRACE score ≥ 120 are considered to be at high risk. In our study, the QTV risk score was able to identify a subgroup of patients at higher risk within the subgroup of high-risk patients identified by a GRACE score ≥ 120.

Our study has several limitations. Current guidelines recommend using 12-lead ECG screening for specific high-risk patients (Priori et al., 2015); we used single-lead ECG in favor of lower complexity and cost. By choosing the lead with the largest T wave and the lowest noise, we obtained a reasonably good approximation of the global repolarization duration. However, potentially valuable information on the repolarization dispersion across leads could not be assessed. Although we used high-resolution ECG, conventional ECG sampled at > 300 Hz is sufficient for QTV analysis (Baumert et al., 2016a). Patients were studied at rest, and while autonomous nervous system provocation tests tend to increase the predictive power of ECG markers, their practical use in patients is limited due to the complexity of tests (Wellens et al., 2014). Stationary segments of heart period, QT interval and respiratory time series were present in all recordings as judged by visual assessment. Fully automated methods should be considered in the future for a more rigorous assessment. The 5-year mortality rate in our cohort was low, and hence, the identified groups with increased mortality may be small. This may have impacted our analysis by possibly reducing its statistical power and broadening the confidence intervals of the Kaplan–Meier curves, as seen in Figures 3–5. Accurate identification of specific patients at risk based on QTV alone might be challenging. Patients who were not in sinus rhythm were excluded from the analysis, which introduced a mortality bias. We did not systematically measure serum creatine kinase-muscle/brain and troponin levels. Since the study enrollment, improved treatments for MI have become clinically available (e.g., improved stents and antithrombotic drugs) that may have affected our findings. Our follow-up protocol did not include data collection on change in patient characteristics and therapy adherence. The primary study endpoint was all-cause mortality. Future studies should validate our findings on data from a different cohort stratifying for cardiovascular mortality, in particular, SCD.

In conclusion, the QTV risk score might help stratify high-risk patients that already display traditional risk markers and identify patients at higher risk of mortality, albeit their preserved LVEF. The implementation of risk stratification strategies to predict risk in patients with preserved LVEF requires the use of a combination of several risk markers. New stratification strategies which can be translated into daily clinical practice are necessary to stratify patients with moderate and normal LVEF yet are at higher risk of SCD. The QTV risk score is a non-invasive risk marker that can be easily incorporated into daily clinical practice. However, the prognostic value of the QTV risk score needs to be prospectively validated on other cohorts, and further investigation into its utility for specifically predicting SCD is required.

The data presented in this article are not readily available. Data access requests should be emailed to author Georg Schmidt directly.

The studies involving human participants were reviewed and approved by the ethics committees at the hospital of the Technische Universität München, the German Heart Centre, and the Klinikum Rechts der Isar, both in Munich, Germany. The patients/participants provided their written informed consent to participate in this study.

FE-H was responsible for the conception, modeling, analysis, and interpretation of data; for drafting of the manuscript and revising it critically for important intellectual content; and for the final approval of the manuscript submitted. SB was responsible for the processing and analysis of data; for revising the manuscript critically for important intellectual content; and for the final approval of the manuscript submitted. AM was responsible for the analysis and interpretation of data; for drafting of the manuscript; and for the final approval of the manuscript submitted. AS was responsible for the conception, design, and analysis of the data; for revising the manuscript critically for important intellectual content; and for the final approval of the manuscript submitted. GS was responsible for the conception, design, analysis, and interpretation of data; for revising the manuscript critically for important intellectual content; and for the final approval of the manuscript submitted. MB was responsible for the conception, design, and interpretation of data; for drafting of the manuscript and revising it critically for important intellectual content; and for the final approval of the manuscript submitted. All authors contributed to the article and approved the submitted version.

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.