QT Interval and the Risk of Myocardial Infarction and All-Cause Death: A Cohort Study

QT Interval and the Risk of Myocardial Infarction and All‐Cause Death . Introduction: The relationship between QT interval and cardiovascular disease is controversial. Methods: All male residents aged 20–61 years and female residents aged 20–56 years were invited to the Tromsø Study in 1986–1987. A total of 15,558 participants free of heart disease were prospectively followed over 20 years for myocardial infarction and death. QT interval at baseline was measured on lead I of the electrocardiogram. Hazard ratios (HRs) with 95% confidence intervals (CIs) per standard deviation change in QT interval were calculated using a Cox regression model. Results: We identified 756 cases of myocardial infarction and 1,183 all‐cause deaths. Prolonged QT interval was present in 792 (5%) participants. QT interval was not associated with increased risk of myocardial infarction (HR: 0.95, 95% CI: 0.84–1.07, after adjustment for potential confounders). Heart‐rate‐corrected QT interval was a significant predictor for all‐cause death in men (HR: 1.15, 95% CI: 1.03–1.29), but not in women (HR: 1.04, 95% CI: 0.91–1.18), after adjustment for potential confounders. Conclusions: The findings suggest that the previously observed relationship between QT interval and increased risk of cardiovascular death is not mediated by increased risk of myocardial infarction. The clinical utility of the QT interval to identify individuals at high risk for coronary events is limited in a general population without prior heart disease. (J Cardiovasc Electrophysiol, Vol. 23, pp. 846‐852, August 2012)     

Assessment of Microvolt T‐Wave Alternans in High‐Risk Patients with the Congenital Long‐QT Syndrome

Background: Microvolt T‐wave alternans (MTWA) has been used for arrhythmogenic risk stratification in cardiac disease conditions associated with increased risk of sudden cardiac death. Macroscopic T‐wave alternans has been observed in patients with congenital long‐QT syndrome (LQTS). The role of MTWA testing in patients with LQTS has not been established. Objective: To determine the diagnostic value of MTWA testing in high‐risk patients with LQTS. Methods and results: We assessed MTWA in 10 consecutive LQTS index patients who survived cardiac arrest or had documented torsade de pointes tachycardia and 6 first‐degree family members with congenital LQTS which had been genotyped in 13 of 16 subjects (7 index patients, 6 family members). No LQTS‐causing mutation was identified in 3 index patients with overt QT prolongation. MTWA was assessed during standardized bicycle exercise testing using the spectral method and yielded negative (n = 8) or indeterminate (n = 2) results in index patients, respectively. Similarly, all first‐degree family members tested MTWA negative except for one indeterminate result. Two genotype positive family members could not be tested (two children—4 and 9 years of age). Conclusion: In patients with congenital LQTS, free from structural heart disease and with a history of life‐threatening cardiac arrhythmias, assessment of MTWA does not yield diagnostic value. Hence, determination of MTWA in lower risk LQTS patients without spontaneous arrhythmic events is likely not to be useful for arrhythmia risk stratification.     

The Measurement of the QT Interval

The evaluation of every electrocardiogram should also include an effort to interpret the QT interval to assess the risk of malignant arrhythmias and sudden death associated with an aberrant QT interval. The QT interval is measured from the beginning of the QRS complex to the end of the T-wave, and should be corrected for heart rate to enable comparison with reference values. However, the correct determination of the QT interval, and its value, appears to be a daunting task. Although computerized analysis and interpretation of the QT interval are widely available, these might well over- or underestimate the QT interval and may thus either result in unnecessary treatment or preclude appropriate measures to be taken. This is particularly evident with difficult T-wave morphologies and technically suboptimal ECGs. Similarly, also accurate manual assessment of the QT interval appears to be difficult for many physicians worldwide. In this review we delineate the history of the measurement of the QT interval, its underlying pathophysiological mechanisms and the current standards of the measurement of the QT interval, we provide a glimpse into the future and we discuss several issues troubling accurate measurement of the QT interval. These issues include the lead choice, U-waves, determination of the end of the T-wave, different heart rate correction formulas, arrhythmias and the definition of normal and aberrant QT intervals. Furthermore, we provide recommendations that may serve as guidance to address these complexities and which support accurate assessment of the QT interval and its interpretation.     

Comparison of the Four Formulas of Adjusting QT Interval for the Heart Rate in the Middle‐Aged Healthy Turkish Men

Objective: The aim of this study was to evaluate the QT intervals at different rest heart rates in healthy middle‐aged Turkish men and to compare the known four QT adjusting methods for heart rate. Methods and Results: The QT intervals were measured in electrocardiograms of 210 healthy men (mean age = 35–60 years). A curve relating QT intervals and heart rates from 45 to 135 beats/min was constructed for study population. Based on the formula of Bazett, Fridericia, and Framingham, adjusted QT intervals in these range of heart rates were separately estimated. An adjusting nomogram for different heart rates was created using a reference value, which was the measured QT interval at heart rate of 60 beats/min (QT_No= QT + correcting number). These four QT correction methods were compared with each other. The reference value of QT interval at heart rate of 60 beats/min was 382 ms. The relationship between QT and RR interval was linear (r = 0.66, P < 0.001). Nomogram method corrected QT interval most accurately for all the heart rates compared with other three adjusting methods. At heart rates of 60–100 beats/min, the equation of linear regression was QT = 237 + 0.158 × RR (P < 0.001). Bazett's formula gave the poorest results at all the heart rates. The formulas of Fridericia and Framingham were superior to Bazett's formula; however, they overestimated QT interval at heart rate of 60–110 beats/min (P < 0.01). At lower rates (<60 beats/min), all methods except nomogram method, underestimated QT interval (P = 0.03). Conclusion: Among four QT correction formulas, the nomogram method provides the most accurately adjusted values of QT interval for all the heart rates in healthy men. Bazett's formula fails to adjust the QT interval for all the heart rates.     

QT间期与风湿结缔组织疾病

风湿病中QT相关指标的延长是相当常见的,多与疾病本身的免疫和炎症相关,常常预示着复杂的室性心律失常和心源性猝死。因此,对于这些病人,行12导联心电图、24小时心电监测及心脏彩超检查是很有必要的。     

Genotype‐Specific Risk Stratification and Management of Patients with Long QT Syndrome

Long QT syndrome (LQTS) is an inherited disorder associated with life‐threatening ventricular arrhythmias. An understanding of the relationship between the genotype and phenotype characteristics of LQTS can lead to improved risk stratification and management of this hereditary arrhythmogenic disorder. Risk stratification in LQTS relies on combined assessment of clinical, electrocardiographic, and mutations‐specific factors. Studies have shown that there are genotype‐specific risk factors for arrhythmic events including age, gender, resting heart rate, QT corrected for heart rate, prior syncope, the postpartum period, menopause, mutation location, type of mutation, the biophysical function of the mutation, and response to beta‐blockers. Importantly, genotype‐specific therapeutic options have been suggested. Lifestyle changes are recommended according to the prevalent trigger for cardiac events. Beta‐blockers confer greater benefit among patients with LQT1 with the greatest benefit among those with cytoplasmic loops mutations; specific beta‐blocker agents may provide greater protection than other agents in specific LQTS genotypes. Potassium supplementation and sex hormone–based therapy may protect patients with LQT2. Sodium channel blockers such as mexiletine, flecainide, and ranolazine could be treatment options in LQT3.     

Beta‐Blocker Efficacy in High‐Risk Patients with the Congenital Long‐QT Syndrome Types 1 and 2: Implications for Patient Management

β‐Blockers for LQTS Types 1 and 2. Background: Beta‐blockers are the mainstay therapy in patients with the congenital long‐QT syndrome (LQTS) types 1 and 2. However, limited data exist regarding the efficacy and limitations of this form of medical management within high‐risk subsets of these populations. Methods and Results: Multivariate analysis was carried out to identify age‐related gender‐ and genotype‐specific risk factors for cardiac events (comprising syncope, aborted cardiac arrest [ACA] or sudden cardiac death [SCD]) from birth through age 40 years among 971 LQT1 (n = 549) and LQT2 (n = 422) patients from the International LQTS Registry. Risk factors for cardiac events included the LQT1 genotype (HR = 1.49, P = 0.003) and male gender (HR = 1.31, P = 0.04) in the 0–14 years age group; and the LQT2 genotype (HR = 1.67, P < 0.001) and female gender (HR = 2.58, P < 0.001) in the 15–40 years age group. Gender–genotype subset analysis showed enhanced risk among LQT1 males (HR = 1.93, P < 0.001) and LQT2 females (HR = 3.28, P < 0.001) in the 2 respective age groups. Beta‐blocker therapy was associated with a significant risk‐reduction in high‐risk patients, including a 67% reduction (P = 0.02) in LQT1 males and a 71% reduction (P < 0.001) in LQT2 females. Life‐threatening events (ACA/SCD) rarely occurred as a presenting symptom among beta‐blocker‐treated patients. However, high‐risk patients who experienced syncope during beta‐blocker therapy had a relatively high rate of subsequent ACA/SCD (>1 event per 100 patient‐years). Conclusions: The present findings suggest that beta‐blocker therapy should be routinely administered to all high‐risk LQT1 and LQT2 patients without contraindications as a first line measure, whereas primary defibrillator therapy should be recommended for those who experience syncope during medical therapy. (J Cardiovasc Electrophysiol, Vol. 21, pp. 893‐901, August 2010)     

Assessment of the Stability of the Individual‐Based Correction of QT Interval for Heart Rate

Background: Modeling the relationship between QT intervals and previous R‐R values remains a challenge of modern quantitative electrocardiography. The technique based on an individual regression model computed from a set of QT–R‐R measurements is presented as a promising alternative. However, a large set of QT–R‐R measurements is not always available in clinical trials and there is no study that has investigated the minimum number of QT–R‐R measurements needed to obtain a reliable individual QT–R‐R model. In this study, we propose guidelines to ensure appropriate use of the regression technique for heart rate correction of QT intervals. Method: Holter recordings from 205 healthy subjects were included in the study. QT–R‐R relationships were modeled using both linear and parabolic regression techniques. Using a bootstrapping technique, we computed the stability of the individual correction models as a function of the number of measurements, the range of heart rate, and the variance of R‐R values. Results: The results show that the stability of QT–R‐R individual models was dependent on three factors: the number of measurements included in its design, the heart‐rate range used to design the model, and the T‐wave amplitude. Practically our results showed that a set of 400 QT–R‐R measurements with R‐R values ranging from 600 to 1000 ms ensure a stable and reliable individual correction model if the amplitude of the T wave is at least 0.3 mV. Reducing the range of heart rate or the number of measurements may significantly impact the correction model. Conclusion: We demonstrated that a large number of QT–R‐R measurements (～400) is required to ensure reliable individual correction of QT intervals for heart rate.     

Circadian Variation and Waking Hour Dynamics of the QT Interval

Background: The incidence of sudden cardiac death is maximal in the morning hours. Although ventricular arrhythmias have been implicated as a potential mechanism, and several neurohumoral factors affecting myocardial excitability have been shown to be raised in the early morning hours, it is not known if there is any circadian variation in the dynamics of ventricular repolarization when studied on a beat-to-beat basis. The objective of this study was to examine the range, diurnal variations, and circadian distribution of the variability of the QT interval in healthy subjects. Method: We developed and validated a new method for continuous measurement of QT intervals from 24-hour Holter recordings. The QT intervals measured semi-automatically were corrected by a linear regression formula derived independently for each patient from his own QT and RR values in 32 healthy males (20 ± 0.4 years). QT variability was assessed by the mean standard deviation of the average of consecutive uncorrected QT intervals (SDA-QT Index) and corrected QT intervals (SDA-QTc index) over 5-minute segments. The rate-dependent changes of the QT interval were studied as a function of the slope of the regression line between the QT and RR values. Results: The average QTc range was mean (SD) 79 (± 28) ms; the average maximal QTc interval was 481 (± 24) ms. The 95% upper confidence limit for the mean 24-hour QTc interval was 443 ms. The RR, QT, and QTc intervals were longer, while the SDA-QT and SDA-QTc indices were shorter during sleep. Hourly averages of the SDA-QT and SDA- QTc index revealed a sudden increase in QT variability in the first hour of waking (P < 0.0001 and P = 0.006). Conclusion: The dynamic behavior of the QT interval shows significant diurnal variations. The maximal QTc interval over 24 hours is longer than previously assumed. The period shortly following awakening is characterized by a peak in the variability of the QT interval. These changes may be indicative of autonomic instability during the early waking hours and correspond with the peak incidence of sudden arrhythmic death.     

美托洛尔对充血性心力衰竭患者QT离散度的影响及临床意义

目的探讨美托洛尔对充血性心力衰竭（CHF）患者QT离散度的影响及临床意义。方法将56例CHF患者随机分为治疗组（28例）和对照组（28例）,对照组采用常规治疗,治疗组在常规治疗的基础上加用美托洛尔,并作治疗前后的QTd测量及比较。结果QTd与心功能受损的程度呈正相关;CHF伴室性心律失常者QTd大于不伴室性心律失常者（P〈0.05）;给予美托洛尔治疗后CHF患者QTd明显缩短（P〈0.05）。结论CHF患者QTd明显增大。美托洛尔可使QTd缩小,对防治室性心律失常和猝死有重要意义。     

QT dynamicity and sudden death after myocardial infarction: results of a long-term follow-up study

INTRODUCTION: The aim of this study was to determine whether impaired adaptation of the QT interval to changes in heart rate predicts sudden death after an acute myocardial infarction. METHODS AND RESULTS: The Groupe d'Etude du Pronostic de l'Infarctus du Myocarde (GREPI) trial was a prospective multicenter study designed to evaluate the long-term outcome of myocardial infarction. QT dynamicity was evaluated in 265 patients by analyzing 24-hour Holter recordings obtained 9 to 14 days after myocardial infarction. The linear regression slope of QT intervals measured to the apex and to the end of the T wave (QTe) plotted against RR intervals was calculated using a dedicated Holter algorithm. The value of QT/RR in predicting sudden death and total mortality was compared with those of ejection fraction, heart rate variability, and late potentials. Mean follow-up was 81 +/- 27 months. There were 73 deaths, of which 23 were sudden. Of all the parameters, an increased diurnal QTe/RR slope (>0.18) was the strongest independent predictor of sudden death (relative risk 6.07, confidence interval 1.48-24.95, P = 0.01). CONCLUSION: Increased diurnal QTe dynamicity is independently predictive of sudden death among patients with myocardial infarction. This simple parameter may help to stratify risk and select patients who may benefit from antiarrhythmic prophylaxis.     

Beat‐to‐Beat Heart Rate and QT Variability in Patients with Congestive Cardiac Failure: Blunted Response to Orthostatic Challenge

Background: Congestive cardiac failure is associated with increased sympathetic activity and impaired baroreflex function. We sought to test the hypothesis that these patients also have blunted response of beat‐to‐beat QT interval variability during orthostatic challenge. Methods: We compared beat‐to‐beat heart rate and QT interval data in 17 patients with congestive cardiac failure and 17 age‐matched normal controls in supine normal breathing, supine controlled breathing, and standing controlled breathing conditions. The ECG data were acquired in lead II configuration at a sampling rate of 1000 Hz. Results: Supine controlled breathing was associated with an increase in spectral HF power (0.15–0.5 Hz) of HR and QT interval time series compared to spontaneous breathing condition only in controls. While there were significant changes in HR, HR LF power, HR LF/HF ratios, and QT variability measures in standing posture in controls, there were no such changes in patients. Conclusions: This impairment of postural changes of HR variability is most likely due to an impaired baroreceptor function in patients with congestive heart failure. The etiology of this is likely due to an increased cardiac sympathetic and a decreased vagal function. However, the relationship of postural changes in beat‐to‐beat QT interval variability and baroreflex need further investigation.     

Ion Channel Mechanisms Related to Sudden Cardiac Death in Phenotype‐Negative Long‐QT Syndrome Genotype–Phenotype Correlations of the KCNQ1(S349W) Mutation

Phenotype‐Negative LQTS. Background: Data regarding possible ion channel mechanisms that predispose to ventricular tachyarrhythmias in patients with phenotype‐negative long‐QT syndrome (LQTS) are limited. Methods and Results: We carried out cellular expression studies for the S349W mutation in the KCNQ1 channel, which was identified in 15 patients from the International LQTS Registry who experienced a high rate of cardiac events despite lack of significant QTc prolongation. The clinical outcome of S349W mutation carriers was compared with that of QTc‐matched carriers of haploinsufficient missense (n = 30) and nonsense (n = 45) KCNQ1 mutations. The channels containing the mutant S349W subunit showed a mild reduction in current (<50%), in the haploinsuficient range, with an increase in maximal conductance compared with wild‐type channels. In contrast, expression of the S349W mutant subunit produced a pronounced effect on both the voltage dependence of activation and the time constant of activation, while haploinsuficient channels showed no effect on either parameter. The cumulative probability of cardiac events from birth through age 20 years was significantly higher among S349W mutation carriers (58%) as compared with carriers of QTc‐matched haploinsufficent missense (21%, P = 0.004) and nonsense (25%, P = 0.01) mutations. Conclusions: The S349W mutation in the KCNQ1 potassium channel exerts a relatively mild effect on the ion channel current, whereas an increase in conductance compensates for impaired voltage activation of the channel. The changes observed in voltage activation of the channel may underlie the mechanisms predisposing to arrhythmic risk among LQTS patients with a normal‐range QTc. (J Cardiovasc Electrophysiol, Vol. 22, pp. 193‐200, February 2011)     

Predictors of Long‐Term Risk for Heart Failure Hospitalization after Acute Myocardial Infarction

Background: Data on the value of baseline brain natriuretic peptide (BNP) and autonomic markers in predicting heart failure (HF) hospitalization after an acute myocardial infarction (AMI) are limited. Methods: A consecutive series of patients with AMI without a previous history of HF (n = 569) were followed up for 8 years. At baseline, the patients had a blood sample for determination of BNP, a 24‐hour Holter recording for evaluating heart rate variability (HRV) and heart rate turbulence (HRT), and an assessment of baroreflex sensitivity (BRS) using phenylephrine test. Results: During the follow‐up, 79 (14%) patients were hospitalized due to HF. Increased baseline BNP, decreased HRV, HRT, and BRS had a significant association with HF hospitalization in univariate comparisons (P < 0.001 for all). After adjusting with all the relevant clinical parameters, BNP, HRV, and HRT still significantly predicted HF hospitalization (P < 0.001 for BNP and for the short‐term scaling exponent α₁, P < 0.01 for turbulence slope). In the receiver operator characteristics curve analysis, the area under the curve for BNP was 0.77, for the short‐term scaling exponent α₁ 0.69, for turbulence slope 0.71, and for BNP/standard deviation of all N‐N intervals ratio 0.80. Conclusion: Baseline increased BNP and impaired autonomic function after AMI yield significant information on the long‐term risk for HF hospitalization. Ann Noninvasive Electrocardiol 2010;15(3):250–258