Abstract
Background
Short QT syndrome (QTc ≤ 300 ms) is a novel hereditary channelopathy linked to syncope, paroxysmal atrial fibrillation, and sudden cardiac death. However, its epidemiological features remain unsettled.
Objectives
(1) To assess the prevalence of short QT in a large population‐based sample; (2) to evaluate its demographic and clinical correlates and; (3) to determine its prognosis.
Methods
A database of 6.4 million electrocardiograms (ECGs) obtained between 1995 and 2008 among 1.7 million persons was used. An internal, population‐based method for heart rate correction (QTcreg) was used and all ECGs with QTcreg ≤300 ms were manually validated. Linked health plan databases were used for covariate and survival ascertainment.
Results
Of 6,387,070 ECGs, 1086 had an ECG with machine‐read QTcreg ≤300 ms. Only 4% (45/1086) were validated yielding a prevalence of 0.7 per 100,000 or 1 of 141,935 ECGs. At the person level, the overall prevalence of QTcreg ≤300 ms was 2.7 per 100,000 or 1 of 37,335. The factors independently and significantly associated with validated QTcreg ≤300 ms were age over 65 years, Black race, prior history of ventricular dysrhythmias, chronic obstructive pulmonary disease, ST‐T abnormalities, ischemia, bigeminy pattern, and digitalis effect. After 8.3 years of median follow‐up and relative to normal QTcreg, validated QTcreg ≤300 ms was associated after multivariate adjustment with a 2.6‐fold (95% confidence interval [CI] = 1.9–3.7) increased risk of death.
Conclusion
QTcreg ≤300 ms was extraordinarily rare and was associated with significant ECG abnormalities and reduced survival.
Keywords: QT interval, short QT syndrome, epidemiology, prognosis
The short QT syndrome (SQTS) is a hereditary channelopathy characterized by extremely short QT interval (≤300 ms) and by syncope, paroxysmal atrial fibrillation, life‐threatening cardiac arrhythmias, and sudden cardiac death.1, 2, 3, 4 Since first described in 2000,2 only sporadic cases have been reported5, 6, 7 and the European Registry contained only 53 cases in 2011.8 Moreover, prior investigations have been conducted on Caucasian 9, 10, 11 or Japanese populations 12, 13 and therefore the prevalence in other ethnicities is unknown. Short QT may also have a nongenetic etiology as “acquired short QT” has been associated with hypercalcemia, hyperkalemia, acidosis,14 and drugs.15 Short QT may also result from rhythm abnormalities and/or artifacts from automatic QT measurement linked to imprecision in identifying the end of the T wave.16 In addition, scant data exist on the prognosis of short QT.8, 13, 17
The aims of this study among members of a health plan in Northern California were: first, to estimate the prevalence of short QT; second, to examine the independent association between short QT and demographic factors, comorbidities, and electrocardiogram (ECG) abnormalities; and third, to ascertain the prognosis among subjects with short QT.
METHODS
Study Population and Cohort Study Design
Kaiser Permanente of Northern California (KPNC) is a not‐for‐profit integrated health care delivery system serving 3.3 million members. KPNC's population is ethnically and socioeconomically diverse, and is broadly representative of the Northern California population.18
We retrospectively identified 6,547,785 12‐lead ECGs performed on 1.7 million KPNC members as part of routine outpatient or inpatient care between January 1, 1995 and June 30, 2008. ECGs with evidence of pacemakers (n = 105,067) and/or with heart rate out of physiological range (<40 or >180 bpm; n = 51,680) were sequentially excluded, resulting in 6,391,038 ECGs (average of 3.7 ECGs/subject).
QT Measurement and Adjustment for Heart Rate
All ECGs were obtained using cardiographs manufactured by Philips Medical Systems (Andover, MA, USA). We extracted the raw QT interval and RR measurements that were generated from each 12‐lead waveform by proprietary Philips algorithms.19, 20 In short, the Philips QT measurement technique for finding the T‐wave offset relies on drawing a line from the top of the T wave to a heart‐rate adjusted point forward in time; the vertical distance from each sample point on the waveform to the line is computed and the time point of maximum vertical distance is considered the T‐wave offset.20 Because of previously noted limitations,21 we did not use the Bazett heart rate correction in our analyses. Instead, we performed log‐linear regression of raw QT on RR and then fitted correction equations within 98 strata of gender (two groups), age (seven groups), and race/ethnicity (seven groups) to generate gender‐, age‐ and race/ethnicity‐specific heart rate‐corrected QT values (denoted by QTcreg or “QT corrected by regression” throughout the article). The Pearson correlation between the automatic Bazett‐corrected QT and QTcreg was 0.93, indicating high‐level agreement between the two approaches. We also captured in our database all the automatic ECG diagnostic statements generated by the cardiographs, as approved and edited by the attending physician at the point of care. We further excluded an additional 3968 ECGs with implausible QTcreg values, namely, <200 or >800 ms, respectively, resulting in 6,387,070 ECGs.
Validation of Short QT Tracings by Expert Manual Reading
Among the 6,387,070 ECGs, there were 1086 with a machine‐read QTcreg ≤300 ms (and there were 1235 with a machine‐generated QT corrected by Bazett; 824 were ≤300 ms by both methods, 411 were ≤300 ms by Bazett but not by the creg method, and 262 were ≤300 ms by the creg method but not by Bazett). The 1086 ECGs with a machine‐read QTcreg ≤300 ms were reread by one of four coauthor cardiologists (JAP, JGZ, TJH, AP) using digital calipers (CalECG Version 3, AMPS LLC, New York, NY, USA). Each reader was instructed to perform up to six QT measurements in any chosen lead (leads II, I, and V3 were preferentially chosen), and all the QT measurements were averaged across leads. We then applied the log‐linear heart rate correction to the reread QT values (using the heart rate provided by the cardiographs, so the readers did not reread the heart rate). Of the 1086 tracings, 222 (20%) were deemed technically poor/unreadable, 819 (75%) had a QTcreg >300 ms, and 45 (4%) were confirmed as having a QTcreg ≤300 ms. We also performed a reproducibility study by instructing each of the four readers to remeasure QT intervals in 20 randomly selected ECGs provided as blind duplicates. Measurement consistency was extremely high for duplicate measurements made by the same reader (intraclass correlation coefficients from 0.95 to 0.98), and between different readers (from 0.90 to 0.97). An example of an ECG with a short QTcreg is shown in Figure 1.
Figure 1.
Example of an ECG with short QTcreg from a 42‐year‐old female.
Study Cohort
The study cohort comprised three subgroups: the short QTcreg group (validated QTcreg ≤300 ms; n = 45 subjects), the normal QTcreg group (QTcreg 301–440 ms in men, and 301–460 in women; n = 1,582,797 subjects), and the long QTcreg group (QTcreg >440 ms in men, and >460 ms in women; n = 97,238 subjects). The ECGs in the short QTcreg range that were not validated by the readers (n = 1041) were excluded from further analysis. Persons in the normal QTcreg and long QTcreg groups were selected from the index ECG, created by selecting one ECG per person at random (only one selection is possible if the person had a single ECG).
Baseline Covariates, Cardiovascular Comorbidities and Outcomes, and All‐Cause Mortality
Gender, date of birth, and race/ethnicity were obtained from the patient's Demographics File. Smoking status was obtained from our database of outpatient visits, augmented with data from patient surveys. Diabetes status and prior cancer (except nonmelanoma skin cancer) were obtained by cross‐linkage with our diabetes and cancer registries, respectively, hypertension was ascertained combining outpatient diagnostic codes and outpatient prescription of antihypertensive agents, and hyperlipidemia was ascertained using outpatient prescriptions for cholesterol lowering drugs. Prevalent (i.e., preceding the index date) chronic obstructive pulmonary disease (COPD) and cardiovascular diseases (including acute coronary syndrome, resuscitated cardiac arrest, transient ischemic attack, hemorrhagic stroke, ischemic stroke, heart failure, and ventricular dysrhythmias) were ascertained combining inpatient and outpatient diagnostic and procedure codes (see Appendix for standard ICD‐9 and CPT4 codes). Prior KPNC studies have demonstrated the validity of discharge diagnostic codes for myocardial infarction,22 stroke,23 and heart failure.24 Our mortality data relied on three complimentary sources: official state of California death certificate data, health plan in‐hospital mortality, and Social Security Administration Death Files, and have been validated against the National Death Index.25 The study was approved by the Kaiser Foundation Research Institute Institutional Review Board.
Statistical Analysis
The prevalence of validated QTcreg ≤300 ms was estimated at the ECG level and then in a sex‐, age‐ and race/ethnicity‐specific manner at the person level using the index ECG. The distribution of demographic, behavioral, and clinical characteristics, as well as of ECG abnormalities was tabulated across the three QTcreg subgroups. We used logistic regression to ascertain the independent correlates of validated QTcreg ≤300 ms.
The date of cohort entry or baseline was the time of the index ECG. We calculated age‐adjusted incidence rates of all‐cause mortality per 10,000 person‐years in the three QTcreg groups. Right censoring was applied at death, discontinuation of health plan membership, or August 31, 2012, whichever took place first. Discontinuation of membership was defined as two consecutive years of disenrollment, even if the subject rejoined the health plan at a later time. We summarized the survival experience of the three QTcreg groups using Kaplan‐Meier curves and the log‐rank test.
To examine the association of short and long QTcreg (relative to QTcreg in the normal range) with all‐cause mortality, we used conditional Cox proportional hazards models with three levels of multivariable adjustment. First, age‐, sex‐ and race/ethnicity‐adjustment (Model 1); then further adjustment for smoking, body mass index, diabetes, hypertension, hyperlipidemia, cancer, and COPD (Model 2); and finally, adjustment for a categorical covariate capturing one or more ECG abnormalities (vs none) (Model 3). To rule out reverse causation by terminal illness, we ran a supplemental model excluding deaths that occurred in the first year of follow‐up. To ascertain the prognosis of isolated short QTcreg, we also considered a model stratifying the cohort by presence of absence of concomitant ECG abnormalities. All statistical analyses were done using SAS version 9.13 (SAS Institute, Inc., Cary, NC, USA).
RESULTS
The mean (SD) QTcreg in the cohort was 411 (31) ms in men and 424 (28) in women. Mean QTcreg was similar in men and women before the age of 9 and after the age of 85 years, respectively, but the trajectories across lifespan differed by gender. Whereas QTcreg decreased markedly in men between ages 19 and 34, it decreased slightly in women between the ages of 10–18 and then rose steadily with increasing age (Fig. 2).
Figure 2.
Mean QTcreg according to age and gender at the index ECG: 791,824 men (solid line) and 886,919 women (dotted line).
The overall prevalence of validated QTcreg ≤300 ms at the ECG level was 0.7 per 100,000 (45/6,387,070) or one of 141,935 ECGs. At the person level, the prevalence of validated QTcreg ≤300 ms was 2.7 per 100,000 (45/1,680,080) or one of 37,335 subjects. Of 45 subjects with one ECG with validated short QTcreg, 1 (2%) had one ECG, 4 (9%) had two to five ECGs, and 40 (89%) had more than five ECGs. None of these 45 subjects had a validated QTcreg ≤300 ms on more than one occasion. By contrast, the prevalence of long QTcreg was 12 per 100 at the ECG level (744,683/6,387,070) or one of eight ECGs; at the person level the prevalence of long QTcreg was 6 per 100 (one of 17 subjects). In the group with normal QTcreg, there were 839 (0.05% of normals) subjects with a QTcreg >300 and <321 ms and 7435 (0.47% of normals) subjects with a QTcreg >320 and <341 ms. The short QTcreg group had a mean (SD) QTcreg of 271 (32) ms, and a mean age of 70 years, compared with a mean age of 51 and 65 for the normal QTcreg and long QTcreg groups, respectively. There were no notable differences in prevalence of validated QTcreg ≤300 ms by sex (2.8 and 2.6 per 100,000 persons in men and women, respectively). The prevalence of validated QTcreg ≤300 ms per 100,000 persons was highest in Blacks (5.8), followed by Whites (3.2), Latinos (1.8), and Asian and Pacific Islanders (1.6). By contrast, the prevalence of long QTcreg per 100 persons was highest in Whites (6.7), followed by Blacks (5.6), Asian and Pacific Islanders (4.6), and Latinos (4.5). Compared with the normal QTcreg group, the short QTcreg group had a lower proportion of current smokers, but higher prevalence of obesity and hypertension, along with higher burden of all other comorbidities (see Table 1). The most common ECG abnormalities in the short QTcreg group were ST‐T abnormalities, ischemia, first‐degree AV block, bigeminy pattern, and digitalis effect. Overall, 71% of the short QTcreg group had at least one concomitant ECG abnormality compared with only 32% in the normal QTcreg group.
Table 1.
Characteristics of the Cohort According to QTcreg Subgroups
| QTcreg | |||
|---|---|---|---|
| Short | Normal | Long | |
| Number (%) | 45 (0%) | 1,582,797 (94.2%) | 97,238 (5.8%) |
| Age, years (mean ± SD) | 70.3 ± 12.1 | 51.2 ± 19 | 65.1 ± 18.9 |
| Age Categories, n (%) | |||
| 0–17 | 0 (0%) | 76,093 (4.8%) | 2715 (2.8%) |
| 18–54 | 6 (13.3%) | 815,273 (51.5%) | 20,994 (21.6%) |
| 55–64 | 4 (8.9%) | 298,079 (18.8%) | 16,881 (17.4%) |
| ≥65 | 35 (77.8%) | 393,352 (24.9%) | 56,648 (58.3%) |
| Gender, n (%) | |||
| Male | 22 (48.9%) | 731,167 (46.2%) | 61,390 (63.1%) |
| Female | 23 (51.1%) | 851,630 (53.8%) | 35,848 (36.9%) |
| Race, n (%) | |||
| White | 29 (64.4%) | 834,577 (52.7%) | 60,238 (61.9%) |
| Black | 8 (17.8%) | 129,909 (8.2%) | 7766 (8%) |
| Asian and Pacific Islander | 3 (6.7%) | 173,294 (10.9%) | 8453 (8.7%) |
| Latino/a | 4 (8.9%) | 207,208 (13.1%) | 9775 (10.1%) |
| Native American | 0 (0%) | 8871 (0.6%) | 564 (0.6%) |
| Mixed | 1 (2.2%) | 55,220 (3.5%) | 3684 (3.8%) |
| Missing | 0 (0%) | 173,718 (11%) | 6758 (6.9%) |
| Smoking status, n (%) | |||
| Never | 6 (13.3%) | 503,041 (31.8%) | 20,153 (20.8%) |
| Former | 5 (11.1%) | 142,805 (9%) | 10,581 (10.9%) |
| Current | 4 (8.9%) | 265,024 (16.8%) | 12,843 (13.2%) |
| Missing | 30 (66.7%) | 670,592 (42.4%) | 53,462 (55.1%) |
| Comorbidities, n (%) | |||
| Acute coronary syndrome | 11 (24.4%) | 63,345 (4%) | 12,996 (13.4%) |
| Resuscitated cardiac arrest | 1 (2.2%) | 3466 (0.2%) | 1186 (1.2%) |
| Transient ischemic attack | 6 (13.3%) | 36,108 (2.3%) | 5599 (5.8%) |
| Hemorrhagic stroke | 1 (2.2%) | 4907 (0.3%) | 940 (1%) |
| Ischemic stroke | 6 (13.3%) | 32,376 (2%) | 6122 (6.3%) |
| Heart failure | 16 (35.6%) | 55,293 (3.5%) | 18,347 (18.9%) |
| Ventricular dysrhythmias | 5 (11.1%) | 11,968 (0.8%) | 3617 (3.7%) |
| Obesity | 16 (35.6%) | 336,960 (21.3%) | 22,514 (23.2%) |
| Hypertension | 33 (73.3%) | 541,760 (34.2%) | 56,889 (58.5%) |
| Diabetes | 15 (33.3%) | 73,356 (4.6%) | 12,805 (13.2%) |
| Hyperlipidemia | 24 (53.3%) | 317,978 (20.1%) | 31,670 (32.6%) |
| End‐stage renal disease | 1 (2.2%) | 6201 (0.4%) | 1754 (1.8%) |
| Cancer | 21 (46.7%) | 733,843 (46.4%) | 44,196 (45.5%) |
| COPD | 31 (68.9%) | 619,319 (39.2%) | 41,184 (42.4%) |
| ECG Diagnoses | |||
| Left ventricular hypertrophy | 4 (8.9%) | 171,189 (10.8%) | 16,590 (17.1%) |
| ST‐T abnormalities | 16 (35.6%) | 73,399 (4.6%) | 13,875 (14.3%) |
| ST elevation | 2 (4.4%) | 70,983 (4.5%) | 3387 (3.5%) |
| Left axis deviation | 3 (6.7%) | 66,849 (4.2%) | 9416 (9.7%) |
| First‐degree AV block | 6 (13.3%) | 54,919 (3.5%) | 8069 (8.3%) |
| Abnormal Q waves | 4 (8.9%) | 51,310 (3.2%) | 5635 (5.8%) |
| Early repolarization | 0 (0%) | 42,263 (2.7%) | 710 (0.7%) |
| Ischemia | 11 (24.4%) | 35,783 (2.3%) | 8550 (8.8%) |
| Ventricular premature complexes | 2 (4.4%) | 34,539 (2.2%) | 4681 (4.8%) |
| Left anterior fascicular block | 1 (2.2%) | 33,903 (2.1%) | 3970 (4.1%) |
| Atrial fibrillation | 2 (4.4%) | 27,438 (1.7%) | 7726 (7.9%) |
| Right bundle branch block | 0 (0%) | 23,796 (1.5%) | 9311 (9.6%) |
| Left bundle branch block | 0 (0%) | 10,145 (0.6%) | 11,245 (11.6%) |
| ST depression | 0 (0%) | 16,269 (1%) | 2244 (2.3%) |
| Bifascicular block | 1 (2.2%) | 8767 (0.6%) | 4902 (5%) |
| Chronic pulmonary disease pattern | 1 (2.2%) | 7380 (0.5%) | 420 (0.4%) |
| Right ventricular hypertrophy | 0 (0%) | 7559 (0.5%) | 819 (0.8%) |
| Bigeminy pattern | 5 (11.1%) | 3655 (0.2%) | 1001 (1%) |
| Abnormal T wave | 0 (0%) | 2387 (0.2%) | 691 (0.7%) |
| Incomplete left bundle branch block | 0 (0%) | 2192 (0.1%) | 697 (0.7%) |
| Pericarditis | 0 (0%) | 2163 (0.1%) | 98 (0.1%) |
| Digitalis effect | 5 (11.1%) | 1983 (0.1%) | 126 (0.1%) |
| Trigeminy pattern | 0 (0%) | 1714 (0.1%) | 421 (0.4%) |
| Left posterior fascicular block | 0 (0%) | 1,109 (0.1%) | 110 (0.1%) |
| Wolff‐Parkinson‐White | 0 (0%) | 394 (0%) | 105 (0.1%) |
| Any of the ECG diagnoses above | 32 (71.1%) | 510,495 (32.3%) | 63,900 (65.7%) |
Relative to the normal QTcreg group, the long QTcreg group was older, more likely to be male, White, and exhibited higher prevalence of all comorbidities (except cancer). The most prominent diagnoses in the long QTcreg group were left ventricular hypertrophy, ST‐T abnormalities, and left bundle branch block.
The independent correlates of validated QTcreg ≤300 ms were age over 65, ventricular dysrhythmias, COPD, ST‐T abnormalities, ischemia, bigeminy pattern, and digitalis effect (see Table 2). The strongest associations were with bigeminy pattern followed by digitalis effect.
Table 2.
Independent Correlates of Validated QTcreg ≤300 ms
| Independent Correlates | Odds Ratio (95% CI) |
|---|---|
| Age 65+ versus age 55–64 | 3.8 (1.3–11.0) |
| Black versus White | 2.3 (1.0–5.1) |
| Ventricular dysrhythmias | 2.9 (1.1–8.1) |
| COPD | 2.4 (1.3–4.5) |
| ST‐T abnormalities | 3.2 (1.5–6.6) |
| Myocardial ischemia/infarction | 2.5 (1.1–5.6) |
| Bigeminy pattern | 18.9 (7.2–49.4) |
| Digitalis effect | 16.2 (5.5–47.8) |
After 8.5 years of median follow‐up, we observed 33, 209,580, and 37,478 deaths in the short, normal, and long QTcreg groups (Table 3). The 33 deaths that occurred in the short QTcreg group had the following underlying causes: 18 (55%) cardiovascular, 7 (21%) respiratory, 1 (3%) cancer, 4 (12%) other, and 3 (9%) missing. There was a highly statistically significant (P < 0.0001) separation of survival across QTcreg groups, with worse survival among those with validated short QTcreg, subjects with long QTcreg in the middle, and best survival among those with normal QTcreg (Fig. 3, Panel A). Subjects with validated short QTcreg who had associated ECG abnormalities showed a worse prognosis than subjects with validated short QTcreg without associated ECG abnormalities (Fig. 3, Panel B), but their difference in survival did not attain statistical significance (P = 0.07). Both short and long QTcreg were associated with increased hazard of all‐cause mortality (Table 3). In minimally adjusted analyses (Model 1) and relative to normal QTcreg, short QTcreg was associated with 3.3‐fold, whereas long QTcreg was associated with 1.9‐fold increased hazard of death, respectively. There was only a modest attenuation of the increased hazards after multivariate adjustments in Models 2 and 3. Furthermore, the hazard ratio was only marginally attenuated after exclusion of early mortality, ruling out the possibility that short QTcreg is merely a marker of impending death. When the cohort was stratified by presence of ECG abnormalities, we found that the associations with short QTcreg tended to be more pronounced in the subcohort with concomitant ECG abnormalities.
Table 3.
Association between Short and Long QTcreg with All‐Cause Mortality in the Entire Cohort and Stratifying by Presence of ECG Abnormalities
| Short | Normal | Long | |
|---|---|---|---|
| Entire cohort | |||
| Number of events | 33 | 209,580 | 37,478 |
| Number of subjects | 45 | 1,582,797 | 97,238 |
| Age‐adjusted rate per 10,000 person‐years | 319.5 | 75.7 | 137.0 |
| Model 1 hazard ratio (95% CI) | 3.34 (2.37–4.69) | 1.00 (ref) | 1.90 (1.88–1.92) |
| Model 2 hazard ratio (95% CI) | 2.75 (1.95–3.86) | 1.00 (ref) | 1.76 (1.74–1.78) |
| Model 3 hazard ratio (95% CI) | 2.62 (1.86‐3.68) | 1.00 (ref) | 1.61 (1.59‐1.62) |
| Entire cohort after excluding deaths in the first year of follow‐up | |||
| Number of events | 20 | 134,049 | 20,466 |
| Number of subjects | 32 | 1,507,266 | 80,226 |
| Age‐adjusted rate per 10,000 person‐years | 213.0 | 51.3 | 82.1 |
| Model 1 hazard ratio (95% CI) | 3.92 (2.53–6.08) | 1.00 (ref) | 1.82 (1.79–1.85) |
| Model 2 hazard ratio (95% CI) | 3.49 (2.26–5.37) | 1.00 (ref) | 1.71 (1.68–1.73) |
| Model 3 hazard ratio (95% CI) | 3.45 (2.24–5.33) | 1.00 (ref) | 1.56 (1.54–1.59) |
| Subcohort with no ECG abnormalities | |||
| Number of events | 7 | 97,339 | 8941 |
| Number of subjects | 13 | 1,072,302 | 33,338 |
| Age‐adjusted rate per 10,000 person‐years | 257.3 | 63.5 | 124.1 |
| Model 1 hazard ratio (95% CI) | 2.52 (1.21–5.26) | 1.00 (ref) | 2.09 (2.04–2.14) |
| Model 2 hazard ratio (95% CI) | 2.02 (0.97–4.21) | 1.00 (ref) | 1.97 (1.93–2.01) |
| Subcohort with one or more ECG abnormalities | |||
| Number of events | 26 | 112,241 | 28,537 |
| Number of subjects | 32 | 510,495 | 63,900 |
| Age‐adjusted rate per 10,000 person‐years | 383.9 | 103.8 | 159.7 |
| Model 1 hazard ratio (95% CI) | 3.24 (2.21–4.75) | 1.00 (ref) | 1.58 (1.56–1.60) |
| Model 2 hazard ratio (95% CI) | 3.04 (2.07–4.45) | 1.00 (ref) | 1.52 (1.50–1.54) |
Figure 3.
(A) Kaplan‐Meier survival curves in the three QTcreg subgroups; (B) Kaplan‐Meier survival curves in the short QTcreg subgroup stratifying by presence of ECG abnormalities. The numeric tables contain number of noncensored patients at each time point.
DISCUSSION
In an unprecedented database of more than 6.3 million ECGs in 1.7 million people, we have found that, after expert manual review of the 1086 ECGs that had an automated QTcreg ≤300 ms, only 45 were satisfactorily validated (0.003% of subjects). Remarkably, we did not find any individual with a validated QTcreg ≤300 ms more than once. Thus, in our population, QTcreg ≤300 ms appeared to be episodic rather than a stable trait. By contrast, 12% of all ECGs and about 6% of subjects in the index ECG cohort had long QTcreg.
There are some prior studies on the prevalence of short QT. Reinig and Engel examined 479,120 ECGs in 106,432 patients and reported 215 tracings from 138 patients with SQTS (0.13% or one of 771).26 However, none were validated after manual checking.26 Gallagher et al. examined 12,012 Italian subjects who underwent routine medical examinations for occupational reasons.10 In their study, the shortest QTc encountered was 335 ms.10 The Kobza et al. study among 41,767 Swiss male conscripts reported that none of the subjects presented a QTc <300 ms and the prevalence of QTc <320 ms was 0.02%.11 Anttonen et al. studied 10,822 middle‐aged Finnish men and reported a prevalence of QTc <320 ms of 0.1%.9 Finally, a Japanese study included 10,984 consecutive patients and only three subjects (0.03%) exhibited QTc <300 ms.12
Unlike Miyamoto et al.,13 who reported a biphasic age distribution of short QT, peaking at young and old age, we found a monotonic increase of the prevalence of short QT with advancing age. Our results pertaining to the gender distribution also differ from theirs: whereas they found a male predominance,13 we noted very similar prevalence of short QT in men and women. To the best of our knowledge, ours is the first report of prevalence of short QT in a U.S. population that includes the main four ethnic groups, and provides evidence that is highest in Blacks, lowest in Asians and Pacific Islanders, and intermediate in Whites and Latinos.
The etiology of short QT has not been fully elucidated. To date, gain‐of‐function mutations in KCNH2, KCNQ1, KCNJ2 (encoding potassium channels) as well as loss‐of‐function mutations in CACNA1C and CACNB2b (encoding L‐type calcium channel subunits), have been reported.27, 28, 29 Furthermore, a number of medications including digitalis,30 and conditions including hypercalcemia,31 dilated cardiomyopathy,32 epilepsy,33 and chronic fatigue syndrome34 have been associated with QT shortening. However, it was beyond the scope of this investigation to perform genotyping and to examine these drug exposures (besides digitalis effect, which was provided as an automatic diagnostic statement) and these additional comorbidities.
Besides older age and Black race, QTcreg ≤300 ms was significantly associated with prior history of ventricular dysrhythmias and COPD. In our series of 46 subjects with QTcreg ≤300 ms, we documented only one patient (2%) with prior resuscitated cardiac arrest. This finding is in contrast with the experience of the European SQTS Registry, where 23% of the cases (12 of 53) had a history of cardiac arrest.8 The COPD finding is paradoxical, since β2‐agonists (adrenergic compounds used to treat airway obstruction), have been shown to prolong the QT interval.35
Validated QTcreg ≤300 ms was also strongly related to other ECG abnormalities, namely, ST‐T abnormalities, ischemia, bigeminy pattern, and digitalis effect. Unlike Miyamoto et al.,13 we did not find an excess of early repolarization among subjects with validated QTcreg ≤300 ms.
The strengths of our study include the very large sample size, the age, sex and ethnic diversity of the population, the availability of longitudinal follow‐up, the manual expert validation of all ECG tracings with QTcreg ≤300 ms, and the fact that the data come from a real‐world health care setting. Our findings also have notable limitations. First, the ECGs used for this analysis were obtained in the course of clinical care as part of diagnostic work‐ups or preoperative protocols. Thus, these ECGs were obtained “for cause” and probably reflect a different population than those attending routine screening and/or annual physical examinations. Second, we did not have measures of the J point‐T peak interval, which have been shown to discriminate benign from pathological forms of SQTS.4 Third, because we did not have data on genotypes, family history, or clinical states that might be associated with electrolyte disturbances or acute disease processes, we were unable to establish a diagnosis of SQTS among subjects found to have a validated QTcreg ≤300 ms.4 This remains subject of future investigation. Fourth, for logistic reasons (prohibitive cost), long QT measures or QT tracings in the 300–320 ms range were not validated by expert reading. In addition, the ECG statement of digitalis effect was not validated against prescription records or inpatient charts.
In conclusion, validated QTcreg ≤300 ms was extremely rare and appeared to be episodic rather than stable. Validated QTcreg ≤300 ms was associated with older age, Black race, and significant ECG abnormalities, and was a significant independent predictor of all‐cause mortality.
Ascertainment of Cardiovascular Endpoints using International Classification of Diseases Ninth Revision (ICD‐9) Codes
Acute coronary syndrome (ACS): primary inpatient or outpatient 410.x or primary inpatient 411.1 or primary inpatient 414.x + 411.1 as secondary
Resuscitated cardiac arrest: primary inpatient code 427.5
Transient ischemic attack: primary inpatient or outpatient 435.x
Ischemic stroke: primary inpatient codes 433.01, 433.11, 433.21, 433.31, 433.81, 433.91, 434.01, 434.11, 434.91, 436.x
Hemorrhagic stroke: primary inpatient 430.x, 431.x
Heart failure: primary inpatient or outpatient 428.x, 402.01, 402.11, 402.91, 398.91, 404.01, 404.03, 404.11, 404.91, 404.93
Ventricular dysrhythmias: primary or secondary paroxysmal ventricular tachycardia (427.1), paroxysmal tachycardia, unspecified (427.2), ventricular fibrillation and flutter (427.41, 427.42)
This work was funded by a grant from Johnson & Johnson Pharmaceutical Research & Development, LLC.
Conflicts of Interest: Carlos Iribarren: Research Grant >$50K from Johnson & Johnson; Alfred D. Round, Jonathan A. Peng, Meng Lu, Arthur L. Klatsky, Jonathan G. Zaroff, Taylor J. Holve, and Amit Prasad: No Conflicts of Interest; Paul Stang: Janssen Research & Development, LLC employee.
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