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Ann Noninvasive Electrocardiol. Author manuscript; available in PMC 2013 October 1.
Published in final edited form as:
PMCID: PMC3481183
NIHMSID: NIHMS384336

QT interval and long-term mortality risk in the Framingham Heart Study

Peter A. Noseworthy, MD,(a)(b) Gina M. Peloso, PhD,(a)(c) Shih-Jen Hwang, PhD,(c) Martin G. Larson, SD,(c)(d)(e) Daniel Levy, MD,(c)(f)(g) Christopher J. O’Donnell, MD, MPH,(c)(g) and Christopher Newton-Cheh, MD, MPH(a)(b)

Abstract

Background

The association between QT interval and mortality has been demonstrated in large, prospective population-based studies, but the strength of the association varies considerably based on the method of heart rate correction. We examined the QT-mortality relationship in the Framingham Heart Study (FHS).

Methods

Participants in the first (original cohort, n = 2,365) and second generation (offspring cohort, n = 4,530) cohorts were included in this study with a mean follow up of 27.5 years. QT interval measurements were obtained manually using a highly reproducible digital caliper technique.

Results

Using Cox proportional hazards regression adjusting for age and sex, a 20 msec increase in QTC (using Bazett’s correction; QT/RR1/2 interval) was associated with a modest increase in risk of all-cause mortality (HR 1.14, 95% CI 1.10–1.18, p<0.0001), coronary heart disease (CHD) mortality (HR 1.15, 95% CI 1.05–1.26, p = 0.003), and sudden cardiac death (SCD, HR 1.19, 95% CI 1.03–1.37, p = 0.02). However, adjustment for heart rate using RR interval in linear regression attenuated this association. The association of QT interval with all-cause mortality persisted after adjustment for cardiovascular risk factors, but associations with CHD mortality and SCD were no longer significant.

Conclusion

In FHS, there is evidence of a graded relation between QTC and all-cause mortality, CHD death, and SCD; however, this association is attenuated by adjustment for RR interval. These data confirm that using Bazett’s heart rate correction, QTC, overestimates the association with mortality. An association with all-cause mortality persists despite a more complete adjustment for heart rate and known cardiovascular risk factors.

Keywords: Heart rate, Mortality, QT interval, Sudden cardiac death

Introduction

Sudden cardiac death (SCD), often due to ventricular arrhythmias, claims approximately 300,000 lives annually in the United States.1 Although the risk of SCD is increased among individuals with clinical risk factors such as history of myocardial infarction, low ejection fraction, or hypertension, most SCD occurs in people with few identifiable predisposing factors.2 A search for additional risk factors for SCD in the general population is a high priority.

One such risk factor is the QT interval. The QT interval is quantitative, easily measured on a surface electrocardiogram and, thus, a readily available tool to potentially stratify risk for SCD. The QT interval is heritable, with 35–45% of its variation attributable to genetic factors.3, 4. Although there is widespread acceptance that the QT interval is a risk factor for adverse outcomes, supporting evidence is mixed. Large, prospective, population-based studies performed in the Netherlands (Amsterdam,5 Rotterdam,6, 7 and Zutphen8), Finland,9 Denmark,10 and the United States (Atherosclerosis Risk in Communities11, 12 and Cardiovascular Health Study13) have shown increased risk of adverse events associated with heart-rate corrected QT interval prolongation. However, in an analysis of the original cohort of Framingham Heart Study (FHS), the QT interval was not associated with all-cause mortality, coronary heart disease (CHD) mortality, or sudden cardiac death.14 We sought to re-examine this relationship in FHS using high-resolution digital electrocardiographic measurements and additional follow up time.

In the current report, we evaluated the QT interval association with all-cause mortality, CHD mortality, and SCD, and we explored the covariate relations of this association with roughly 30 years of follow up.

Methods

Study Sample

The Framingham Heart Study is a prospective community-based study begun in 1948 to evaluate potential risk factors for coronary heart disease.15, 16 The Original cohort included 5,209 men and women who have been examined every 2 years. In 1971, another 5,124 men and women were enrolled in the Framingham Offspring Study (generation 2), including children or spouses of the children of the original cohort.17 Offspring participants underwent examinations roughly every 4 years. Study design and selection criteria have been reported.17 The original and offspring cohorts are predominantly of self-reported European ancestry.

Participants who attended Original cohort exam 11 (n=2955) or Offspring cohort exam 1 (n=5124) were eligible. Participants were excluded for no ECG available(n = 723), missing demographic information or data on standard cardiac risk factors (n = 228), QRS duration > 120 msec (n = 71), atrial fibrillation or atrial flutter on ECG (n = 1), left or right bundle branch block (n = 125), and use of QT-shortening drugs (digoxin, n = 36). The final sample included 2,365 generation 1 and 4,530 generation 2 participants (n= 6,895). Demographic information and data on standard cardiac risk factors (including total cholesterol, HDL, smoking status, systolic and diastolic blood pressure, treatment with anti-hypertensive medications, body mass index [BMI], and diabetes) were recorded at the time of the index ECG. For subjects with missing demographic and standard cardiac risk factors in the original cohort, the values from the two previous cycles were considered.

QT interval quantification

Standard 12-lead ECGs were obtained at 25 mm/s and 0.1 mV/mm on strips of lined paper (Hewlett Packard), as previously described.18 Digital ECG measurements were made by e Research Technology, Inc., Philadelphia, PA (previously known as Premier Worldwide Diagnostics, Ltd). Using digital calipers, QT, RR, and QRS intervals were measured. The QT interval was defined as the onset of the QRS to the return of the T wave to baseline, taking care to exclude U waves, if present, in leads II, V2, and V5. If a TU complex was present, the T-wave offset was taken to be the nadir of the curve between the T and U waves. The QRS duration was measured in lead II from the beginning of the Q wave to the junction of the S wave and the ST segment. The inter-and intra-observer reproducibility of these measures have been previously reported.3

Trait Definition

For comparability to previously published reports, we examined heart-rate adjusted QT interval (QTC), calculated using Bazett’s correction (mean QT interval in lead II, V2, and V5 divided by the square root of the mean RR interval in lead II, V2, and V5 in milliseconds). We performed additional adjustment for RR interval (in addition to the heart rate correction implicit in the Bazett’s correction) to explore possible residual confounding due to heart rate. We have previously shown that a linear relationship between QT and RR intervals more accurately models the QT-heart rate relationship,18 so we also examined the QTLR variable which was defined as the residual of mean QT interval (from lead II, V2, and V5)regressed on age, sex, and mean RR interval. QTC and QTLR were studied as continuous measures and by quintile. In addition, the previously reported QTC cut-points of 450 msec in men and 470 msec for women were examined in secondary analyses.6

Outcomes

The primary endpoints were all-cause mortality, CHD mortality, and SCD. As previously reported, all suspected cardiovascular events have been reviewed and adjudicated by a panel of three Framingham physician investigators after review of all available Framingham Heart Study examination records, hospitalization records, and physician notes, using previously published criteria.19 CHD death (sudden and non-sudden deaths caused by coronary heart disease) has been established upon review of all available records, if the cause of death was probably CHD and no other cause could be ascribed. As previously described, SCD cases have been identified during follow-up of Framingham Heart Study participants.20 SCD is defined as a CHD death that occurred within 1 hour of symptom onset and without other probable cause of death suggested by the medical record. Each death underwent an internal review by Framingham investigators that attempted to determine the duration of symptoms, if any, before death. Hospital records, primary medical doctor records, and next-of-kin interviews were routinely sought to determine the timing of symptoms before death. Unwitnessed deaths or subjects found dead in bed were excluded from the SCD category when the interval between symptom onset and time found dead could not be determined with certainty to be ≤ 1 hour.

Statistical Analysis

Cox proportional hazards regression using the coxph function in the R Survival package21 with robust variance estimates was performed with QTC or QTLR as a continuous predictor of all-cause mortality, CHD mortality, and SCD. For all analyses, covariates for regression included age, sex, cohort, and a clustering variable on pedigree to account for relatedness (model 1). Secondary models included: model 1a = model 1 + RR interval; model 2 = model 1 + total cholesterol, HDL, smoking status, SBP, DBP, antihypertensive medication use, BMI, DM; model 2a = model 2 + RR interval. Additional models included the independent variables QTLR and QTC as quintiles and clinically defined QTC thresholds of 450 msec in men and 470 msec in women22 in relation to each endpoint. Cut-points for the QTLR variable were not examined since the QTLR measure is not clinically used.

Results

Demographic, clinical, and ECG descriptive data for the study sample are shown by generation (G) in Table 1. In the entire cohort, there were 3,133 deaths from any cause (2,112 in G1 and 1,021 in G2), 469 CHD deaths (325 in G1 and 144 in G2), and 184 SCD events (120 in G1 and 64 in G2).

Table 1
Participant characteristics at baseline after exclusions

QTC and QTLR as continuous measures

A 20 msec increase in QTC interval was associated with increased risk of all-cause mortality (HR 1.14, 95% CI 1.10–1.18, p<0.0001), CHD mortality (HR 1.15, 95% CI 1.05–1.26, p = 0.003), and SCD(HR 1.19, 95% CI 1.03–1.37, p = 0.02)in models adjusting for age and sex. 20 msec is approximately the standard deviation of the population mean QTC. After further adjustment for RR interval, in addition to the RR interval correction included in the Bazett’s formula, there was attenuation of the significance of these observations and the association of QTC with SCD became non-significant (Table 2). In analyses adjusting for RR interval using linear regression, a 20 msec increase in QTLR was associated with increased risk of all-cause mortality (HR 1.09, 95% CI 1.04–1.13, p<0.0001), but non-significant associations with CHD mortality or SCD (Table 2).

Table 2
QT interval (QTC and QTLR) and risk of mortality

QTC and QTLR by quintile

Figure 1 shows a graded relation of QTC across the range of QTC quintiles for all-cause mortality and CHD mortality in models adjusting for age and sex. There was a non-significant trend toward significant increase in SCD in the top compared to the bottom quintiles(p = 0.09). Additional adjustment for RR interval, either by additional adjustment of models including QTC or by the use of the QTLR variable further attenuated the association between the QT interval and all-cause mortality, CHD mortality, and SCD. The mean and range QTC for each QTC or QTLR quintile are shown in supplementary table 1

Figure 1
Hazard ratios (HR) for (A) all-cause mortality, (B) CHD mortality, and (C) sudden cardiac death according to QTC interval quintile determined by three methods. In the leftmost figure, QTC was calculated using Bazett’s correction and the relation ...

QTC cut points

A QTC above 450 msec in men and 470 msec in women is associated with an increased risk of all-cause mortality and CHD mortality in models adjusting for age and sex (Table 3; p = <0.0001 and p =0.004). These associations were attenuated with additional adjustment for RR interval (p = 0.005 and p = 0.03, respectively). The association with all-cause or CHD mortality became non-significant after adjustment for cardiovascular risk factors (p = 0.07 and p = 0.06, respectively). A QTC above the clinical cut point was not significantly associated with SCD in any of the models (p > 0.10).

Table 3
QTC prolongation (sex-specific clinical cut-points) and risk of mortality

Discussion

In the Framingham Heart Study, we report findings that are consistent with multiple other reports from other cohorts on the association of the continuous measures of QTC adjusted for heart rate using Bazett’s correction with all-cause mortality, CHD mortality, and SCD in models adjusting for age and sex. However, we demonstrate that additional adjustment for RR interval, either by additional adjustment of QTC for RR interval or by use of QT interval adjusted for RR interval by linear regression, substantially attenuates the association. Additional adjustment for baseline clinical characteristics further attenuates the association of QTC with all-cause or CHD mortality. These data suggest that (1) the Bazett’s formula incompletely adjusts for the heart rate association with mortality, (2) some of the association of QTC with mortality is accounted for by other clinical factors, and (3) that the QT interval itself is a modest contributor to CHD mortality and SCD risk beyond other routinely available clinical data.

Our results differ from some prior reports, although direct comparisons between studies is challenging because results have been reported using different QTC definitions/cut-points, outcomes, and populations. Strauss and colleagues examined subjects in the Rotterdam study (a prospective population-based cohort with nearly 8,000 individuals and 125 adjudicated SCD cases) and showed that a prolonged QTC (>450 msec in men and 470 msec in women, using Bazzet correction23) was associated with more than three-fold risk of SCD in a model adjusting for age alone (HR 3.7, 95% CI 2.0–6.9). This association was somewhat attenuated, but remained significant, after additional adjustment for CHD risk factors and heart rate (HR 2.5, 95%CI 1.3–4.7).6 Our study is of comparable size (in both controls and SCD cases) so the reason for this discrepant finding is not likely to result from power alone. One difference between the studies is that ECG data from participants in the Rotterdam study who had a second ECG during a follow-up visit were included. Thus, if QT prolongation developed after several years of follow up, and reflected a change in the underlying cardiac substrate, it could contribute to the observed association. This approach answers a different question from the prognostic implications of the baseline QT interval for lifetime SCD risk, and, instead, could reflect the dynamic nature of the ECG as cardiac risk evolves though a patient’s lifetime. Indeed, an earlier examination of the association in the Rotterdam study showed a more modest risk increase (HR 1.7, 95% CI 1.0–2.7) for cardiac mortality associated with the top quartile (different QTC cut points were used in these two studies) of QT interval when only the baseline ECG was used.24 The relations of QT interval to cardiovascular mortality has been examined in several other epidemiologic studies, but the results have been inconsistent.514, 25, 26 For instance, the Cardiovascular Health Study, showed a roughly two fold risk of all-cause mortality (attenuated somewhat after adjustment for cardiac risk factors, measures of atherosclerosis, and heart rate) above a QTC of 450 msec,13 the Zutphen study showed a three-fold increased risk of SCD with QTC prolongation above 420 msec (HR, 3.0; 95% CI, 1.0 to 8.9) in men ages 65 to 85 years, but not in younger men,8 and the first report in the Framingham Heart Study showed no association between baseline QTC prolongation and all-cause mortality, sudden death, or coronary mortality.14 More recently, a large study from the Third National Health and Nutrition Examination Survey demonstaated increased risk for mortality due to cardiovascular disease (HR 2.55, 95% CI 1.59–4.09) comparing the 95th percentile of age-, sex-, race-, and RR interval–corrected QT interval with participants in the middle quintile. Additionally, they showed that there appeared to be a U-shaped relationship between QT interval and mortality (i.e. higher mortality at with a shortened or prolonged QT-interval). Our study did not demonstrate this U-shaped relationship.27 Differences in these estimates could be explained by differences in the method of QT measurement or cut-point definition, differences in endpoint definition and adjudication, differences in population characteristics, or duration and method of follow-up.

We found the unadjusted risk of all-cause mortality increased in a graded fashion across the range of QTC quintiles. Although non-significant, this trend persisted after adjustment for additional risk factors and was observed for CHD mortality and SCD. This observation supports the concept that the QT interval may reflect incremental risk across the range of “normal” values, rather than only above a particular threshold of an extreme QT. Furthermore, it provides some motivation for the study of the recently discovered QT-modifying genetic variants in relation to SCD,28, 29 but since the QT-SCD association is modest, the effect of these variants on mortality may be small individually.

The observation of residual confounding by heart rate, even after Bazett’s correction, is not surprising. Heart rate and QT interval are inextricably linked. Heart rate is the principal determinant of the QT interval,30 and is, itself, a predictor of SCD and all-cause mortality.31, 32 Furthermore, separating the determinants of heart rate, QT interval, and SCD risk is challenging because heart rate is heritable,33 and by some estimates, 40% of the heritability of the QT interval could be through genes that also contribute to heart rate.34

The major cardiovascular societies as well as the Food and Drug Administration have made formal recommendation against the use of the Bazett formula in research, drug development and testing, and clinical practice. Indeed, the AHA/ACCF/HRS Recommendations for the Standardization and Interpretation of the Electrocardiogram advise against the use of Bazetts formula and expressly encourage use of linear regression functions.35 However, the Bazett formula remains the most commonly used correction in routine clinical practice, likely due habit, ease of use, and the relative difficulty of applying alternate correction methods (especially population-specific regression-based methods).

We show that additional correction for clinical factors known to increase CHD and SCD risk (namely total cholesterol, HDL, smoking status, SBP, DBP, antihypertensive medication use, BMI, and DM) attenuates the association between QT interval and mortality. Thus, clinical factors explain a portion of the QT-associated mortality risk and could be considered with QT in clinical SCD risk assessment. Although there is great interest in refining SCD risk stratification, for indications for implantable cardioverter defibrillator (ICD) implantation for example, it is unlikely that considering a single baseline QT interval will add substantially to current strategies.

The strengths of the current study include its large sample of prospectively followed individuals and community-based cohort ascertainment, detailed clinical information, rigorous outcome adjudication, reproducible ECG measurements, and long duration and minimal loss to follow up. The major limitation is the study’s low power to show association with SCD given relatively few cases.

Conclusion

In conclusion, we confirm an association between QTC using Bazett’s correction and mortality, but show that more complete adjustment for heart rate and CHD risk factors substantially attenuates the association. The QT interval appears to be a modest risk marker for sudden cardiac death.

Supplementary Material

Acknowledgments

Funding:

This work was supported by the Max Schaldach Fellowship in Cardiac Pacing and Electrophysiology (P.A.N.), the NIH/NHLBI (HL080025, HL098283C.N.-C.), the Doris Duke Charitable Foundation (C.N.-C.), and the Burroughs Wellcome Fund (C.N.-C.). The FHS was supported by the National Heart Lung and Blood Institute of the National Institutes of Health and Boston University School of Medicine (Contract No. N01-HC-25195).

Footnotes

Disclosures:

The authors report no relationships with industry relevant to this work. Dr. Newton-Cheh is on a Merck scientific advisory board.

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