(Circulation. 2000;102:2849.)
© 2000 American Heart Association, Inc.
Clinical Investigation and Reports |
Spectrum of ST-T–Wave Patterns and Repolarization Parameters in Congenital Long-QT Syndrome
ECG Findings Identify Genotypes
From LDS Hospital (L.Z., K.W.T., G.M.V., J.F., L.C.G., F.Y.), Salt Lake City, Utah; University of Utah School of Medicine (G.M.V., S.J.C., J.S., I.S, F.Y., M.T.K.), Salt Lake City; University of Michigan (M.H.L.), Ann Arbor; Molecular Cardiology (S.G.P.), Maugeri Foundation, Pavia, Italy; Bikur Cholim Hospital (J.B., A.M.), Jerusalem, Israel; University of Rochester School of Medicine and Dentistry (A.J.M., J.L.R., W.Z.), Rochester, NY; Policlinico S. Matteo IRCCS (P.J.S., C.N.), Milan, Italy; Cleveland Clinic Foundation (Q.W.), Cleveland, Ohio; and Baylor College of Medicine (J.A.T.), Houston, Tex.
Correspondence to G. Michael Vincent, MD, Department of Internal Medicine, LDS Hospital, Physician Office Building, Suite 130, 324 10th Ave, Salt Lake City, UT 84103. E-mail ldgvince{at}ihc.com
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Abstract |
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Background—Congenital long-QT syndrome (LQTS) is caused by mutations of genes encoding the slow component of the delayed rectifier current (LQT1, LQT5), the rapid component of the delayed rectifier current (LQT2, LQT6), or the Na+ current (LQT3), resulting in ST-T–wave abnormalities on the ECG. This study evaluated the spectrum of ST-T–wave patterns and repolarization parameters by genotype and determined whether genotype could be identified by ECG.
Methods and Results—ECGs of 284 gene carriers were studied to determine ST-T–wave patterns, and repolarization parameters were quantified. Genotypes were identified by individual ECG versus family-grouped ECG analysis in separate studies using ECGs of 146 gene carriers from 29 families and 233 members of 127 families undergoing molecular genotyping, respectively. Ten typical ST-T patterns (4 LQT1, 4 LQT2, and 2 LQT3) were present in 88% of LQT1 and LQT2 carriers and in 65% of LQT3 carriers. Repolarization parameters also differed by genotype. A combination of quantified repolarization parameters identified genotype with sensitivity/specificity of 85%/70% for LQT1, 83%/94% for LQT2, and 47%/63% for LQT3. Typical patterns in family-grouped ECGs best identified the genotype, being correct in 56 of 56 (21 LQT1, 33 LQT2, and 2 LQT3) families with mutation results.
Conclusions—Typical ST-T–wave patterns are present in the majority of genotyped LQTS patients and can be used to identify LQT1, LQT2, and possibly LQT3 genotypes. Family-grouped ECG analysis improves genotype identification accuracy. This approach can simplify genetic screening by targeting the gene for initial study. The multiple ST-T patterns in each genotype raise questions regarding the pathophysiology and regulation of repolarization in LQTS.
Key Words: genetics • long-QT syndrome • electrocardiography • waves
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Introduction |
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The characteristic ECG features of the congenital long-QT (LQT) syndrome (LQTS) are QT-interval prolongation and abnormalities of T-wave morphology.1 2 3 4 5 6 7 The syndrome is caused by mutations of genes encoding the slow component of the delayed rectifier current (IKs), the rapid component of the delayed rectifier current (IKr), or the Na+ current (INa), of cardiac ion channels that affect or regulate cardiac repolarization. Five genes with >170 mutations have been identified.8 The genes have been named in sequence of discovery, with initial and new scientific designations in parentheses: LQT1 (KvLQT1 or KCNQ1), LQT2 (HERG or KCNH2), LQT3 (SCN5A), LQT5 (MinK or KCNE1), and LQT6 (MiRP1 or KCNE2). LQT4, with a unique phenotype, has been mapped to chromosome 4 in 1 family, but the gene has not been identified.
Once different ion channels were identified, it was surmised that the associated channel dysfunction might produce different ST-T morphologies and repolarization parameters. Initial studies by Moss et al5 and Dausse et al9 reported an association of certain T-wave patterns with LQT1, LQT2, and LQT3. We subsequently recognized other ST-T–wave patterns and hypothesized that patterns might be genotype specific and useful for identifying genotype.
Therefore, the aims of the present study were to determine (1) the spectrum of ST-T–wave patterns and quantified measures of repolarization for each genotype and (2) whether the ST-T–wave patterns and repolarization parameters could be used to identify genotypes.
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Methods |
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Phase I: ECG Characterization of Repolarization by Genotype
The resting 12-lead ECGs of 284 gene carriers from 29 LQTS families were obtained from 5 centers of the International LQTS Registry: Salt Lake City, Utah; Rochester, NY; Detroit, Mich; Pavia, Italy; and Jerusalem, Israel. Many family members had been genotyped irrespective of phenotype, providing a study population that was international in scope and broadly representative of LQTS patients, including those with reduced penetrance and variable expressivity of phenotype.4 There were 131 LQT1 patients (aged 27±24 years, 62 females) from 8 families with 8 different mutations, 93 LQT2 patients (aged 31±21 years, 48 females) from 15 families with 15 different mutations, and 60 LQT3 patients (aged 23±18 years, 27 females) from 6 families with 3 different mutations. All ECGs were taken before or without ß-blocker medication.
The Spectrum of ST-T–Wave Patterns and
Repolarization Parameters
The 284 ECGs were jointly reviewed by Salt Lake City
investigators (L.Z., K.W.T., and L.C.G.). ST-T morphology was evaluated
in all 12 leads, and a representative pattern was determined. If
different patterns were present in different leads, the most prevalent
(present in at least 7 leads) was chosen as the representative pattern
for the ECG. Patterns identified to be characteristic for each genotype
were defined as typical for that genotype, and those not associated
with any genotype were defined as nonspecific. The frequency of typical
patterns in each genotype was determined.
Repolarization parameters were quantified to further define repolarization in each genotype. Measurements were averaged from 2 or 3 consecutive beats in lead II or V5 if the tracing in lead II was technically unsatisfactory. Time variables included the RR interval, ST-segment duration, T-wave duration, and QT interval. The rate-dependent ST and QT parameters were corrected for heart rate by use of Bazett’s formula. Bifid T waves, but not U waves,6 10 were included in the T and QT measurements. T-wave amplitude (isoelectric line to T-wave peak) was measured. In the presence of bifid T waves, the highest T-wave component was used.
Statistical Analysis
Independent t tests were performed
between any 2 genotypes. Because of multiple tests being applied to
each measurement, a Bonferroni correction was used, and a value of
P<0.017 was considered significant. Discriminant
analysis using the stepwise method was performed to determine which (if
any) combination of repolarization parameters could separate the 3
genotypes. The sensitivity and specificity of this classification was
determined.
Phase II: Genotype Identification by
ST-T–Wave Patterns
Genotype Identification by Other
Cardiologists
We evaluated whether typical ST-T–wave
patterns could be used for genotype identification by cardiologists
other than those who identified the characteristic patterns. ECGs from
104 newly identified LQT1 and LQT2 gene carriers from 23 families were
used. Because the LQT3 genotype is uncommon, no new gene carriers were
available. Thus, we randomly selected 42 of 60 LQT3 ECGs used in the
pattern characterization study. The 146 ECGs included 48 LQT1 (aged
29±21 years, 33 females, 5 different mutations), 56 LQT2 (aged 24±17
years, 34 females, 8 different mutations), and 42 LQT3 (aged 27±19
years, 19 females, 2 different mutations) gene carriers. The ECGs
available per family was 6±5.
ECG tracings were numerically coded, with name, age, sex,
and source of acquisition deleted to avoid any clues as to genotype.
The ECG packet plus instructions and graphical ECG templates
(Figures 1 to 3
) for genotype identification were reviewed by
4 cardiologists (M.H.L., S.G.P., S.J.C., and F.Y.), none of whom was
involved in the pattern characterization study. Each assigned a
genotype for each ECG. If the ECG pattern was not a typical pattern, it
was assigned to the "uncertain" category. After receipt of their
analysis, the ECGs were sent a second time, grouped by family, and the
readers assigned a genotype to each family. Assignment was based on the
presence of a typical pattern(s) in 
1 family member. In the absence
of a typical pattern, the family was labeled as uncertain. Two authors
(G.M.V. and J.F.) compared assignment results with molecular genotype
and calculated sensitivity/specificity of genotype identification by
ECG patterns for both individual and family-grouped ECG
analyses.
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Prospective Genotype Identification in 127
Families Undergoing Genetic Testing
This component examined the accuracy of genotype
identification by investigators experienced in the recognition of
typical ECG patterns. Two investigators (L.Z. and K.W.T.) reviewed
family-grouped ECGs from 233 members of 127 clinically diagnosed LQTS
families, referred from many areas of the world to the Keating
Laboratory at the University of Utah for molecular genetic testing.
QTcs ranged from 410 to 620 ms; 10% had a normal QTc of 
440 ms, and
26% had a normal to borderline QTc of 460 ms.
Genotype identification by ECG used the same written pattern
descriptions and graphic templates used in Genotype Identification by
Other Cardiologists. Eighty families (63%), with 129 clinically
affected members, showed a typical pattern in at least 1 family
member’s ECG, and a genotype was assigned to each family. The
remaining 47 families, with 104 clinically affected members, had only
nonspecific ST-T–wave patterns. There were 3.1±2.3 ECGs per family
with typical patterns versus 1.9±1.2 ECGs per family with nonspecific
patterns (P<0.001). The total time spent reviewing
the 233 ECGs and assigning genotype to 127 families was 
16 hours,
all performed before the molecular genetic results. Single-strand
conformation polymorphism analyses were performed in 1 phenotypically
affected individual from each family and required 1 year for
completion. The accuracy of genotype identification by ECG patterns was
evaluated for the families in which mutations were
identified.
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Results |
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Phase I
Ten typical ST-T–wave patterns were identified: 4 in LQT1, 4 in LQT2, and 2 in LQT3. Representative examples of typical ECG patterns are shown in Figures 1 to 3
.
LQT1 Patterns
LQT1 patterns are shown in
Figure 1. The infantile ST-T–wave pattern
(Figure 1a) is primarily seen in children aged 2 months to 2
years but could be seen in children aged 5 years. It was usually
associated with other infantile ECG features, such as fast heart rate
and right ventricular predominance. A short and ill-defined ST segment
merged immediately with the T-wave upslope, giving the appearance of a
diagonal line to the T-wave upslope. Bifid T waves were common, with
the second component producing the peak of the T wave in most of the
limb and left precordial leads. The T-wave downslope was steep.
Generally, the T wave appeared broad-based, peaked, and asymmetrical.
The QT interval ranged from borderline to obviously prolonged (QTc
470±20 ms).
The broad-based T
wave5
(Figure 1b) is a single, smooth, broad-based T wave that is
present in most leads, particularly evident in the precordial leads.
The absence of a distinct T-wave onset enhanced the broad-based
appearance. The QT interval ranged from normal to obviously prolonged
(QTc 490±40 ms).
For the normal-appearing T wave
(Figure 1c), the T-wave morphology looked normal. The QT
interval ranged from normal to obviously prolonged (QTc 460±40
ms).
In the late-onset normal-appearing T wave
(Figure 1d), the ST segment was prolonged, and the T-wave
morphology was normal. The QTc was 490±40 ms.
LQT2 Patterns
LQT2 patterns are shown in
Figure 2. Bifid T
waves6 7 9
were the hallmark of the LQT2 genotype. They were usually present in
most of the 12 leads. The T-wave amplitude was commonly low, and the QT
interval ranged from normal to markedly prolonged (QTc 470±30 ms).
Four subtypes of bifid T waves were identified: obvious bifid T waves,
subtle bifid T waves of 2 types, and low-amplitude and widely split
bifid T waves.
The obvious bifid T wave is shown in
Figure 2a, with the second component usually occurring early
on the downslope of the first component.
The subtle bifid T waves are of 2 types: (1) with
the second component occurring at the top
(Figure 2b) or (2) on the downslope of the T wave
(Figure 2c). We emphasize the subtle nature of these bifid T
waves because they can be easily missed if they are not carefully
sought.
The low-amplitude and widely split bifid T wave is shown in
Figure 2d). The second component often seems to merge with
the U wave. This pattern tends to mimic the hypokalemic T-wave
configuration.
The bifid T wave can be confused with a TU complex. It is
usually distinguishable as a bifid T wave by careful observation of all
12 leads. For example, in
Figure 2d, the second component of the T wave is merged with
the U wave in leads II, III, aVF, and V2 to
V6, but the end of the T wave can be clarified
in leads I, aVL, and V1, where the U wave is
absent.
LQT3 Patterns
LQT3 patterns are shown in
Figure 3. In late-onset, peaked, and/or biphasic T
waves5
(Figure 3a), a long ST segment was present with a narrow
peaked or biphasic T wave. The T-wave onset and offset were usually
distinct, and the downslope was steep. The QT interval was often
markedly prolonged (QTc 530±40 ms).
In the asymmetrical peaked T wave
(Figure 3b), the T wave was peaked and asymmetrical with a
steep downslope. The QTc was 470±30 ms.
Typical ST-T–wave patterns were seen in 88% of LQT1 and
LQT2 carriers and in 65% of LQT3 carriers
(Table 1). In LQT1, the normal-appearing T wave was the most
common (37%), followed by the broad-based T wave (21%), and
late-onset normal-appearing T wave (15%). Approximately 15% of the
LQT1 gene carriers were young children, and most showed the infantile
pattern. In LQT2, 88% of the gene carriers showed bifid T waves, and
7% showed a nonspecific T wave. There was a 3% overlap of typical
patterns between LQT1 and LQT2. In LQT3, 53% of the carriers exhibited
the late-onset peaked/biphasic T-wave pattern, 12% exhibited the
asymmetrical peaked T-wave pattern, and in 33%, there was overlap with
LQT1 patterns.
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All LQT1 and LQT2 families had different mutations, yet the typical patterns were similar regardless of the mutation. All 4 typical LQT1 patterns were present in 1 LQT1 family, and all 4 typical LQT2 patterns were present in 1 LQT2 family. The majority of LQT1 and LQT2 families demonstrated at least 2 patterns typical for their genotype. Three of 6 LQT3 families displayed both typical LQT3 patterns.
Quantified Repolarization Variables
Results are shown in
Table 2 by genotype and in
Table 3 by ST-T–wave pattern subgroup within each
genotype. QTc ranged from 410 to 620 ms, with 33% (94 of 284) of the
ECGs showing a QTc interval 460 ms. QTc was longest in LQT3 (500±50
ms), with a value of P<0.0001 compared with LQT1
(470±40 ms) and LQT2 (470±30 ms). STc was shortest in LQT2 (130±40
ms, P<0.0001) and longest in LQT3 (240±30 ms,
P<0.0001). T-wave duration was longest in LQT2
(250±40 versus 190±50 ms in LQT1 and 180±50 ms in LQT3,
P<0.0001). T-wave amplitude was lower in LQT2
(0.21±0.25 mV) than in LQT1 (0.34±0.16 mV) and LQT3 (0.30±0.38 mV)
(P<0.0001). However, as shown in
Figure 4, a large overlap of the values between genotypes
prevented genotype discrimination by any individual
parameter.
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Discriminant analysis, using the stepwise method, on the
other hand, improved the separation of the genotypes. Two canonical
discriminant functions using 3 repolarization variables resulted from
this analysis (see
Figure 5). The sensitivity/specificity for genotype
classification were 85%/70%, 83%/94%, and 47%/63% for LQT1, LQT2,
and LQT3, respectively. Low values for LQT3 and the 70% specificity
value for LQT1 are concordant with the 33% of LQT3 gene carriers that
had T-wave patterns similar to the pattern of LQT1
(Table 1).
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Phase II
Genotype Identification by Other
Cardiologists
By individual ECG analysis, the mean
sensitivity/specificity for LQT1, LQT2, and LQT3 was 61%/71%,
62%/87%, and 33%/98%, respectively. With family-grouped ECG
analysis, the mean sensitivity/specificity increased to 77%/81%,
79%/88%, and 54%/100%, respectively
(Table 4).
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Prospective Genotype Identification in 127
Families Undergoing Genetic Testing
Mutation results were obtained in 63% (80 of 127) of
the families studied. The failure to identify mutations in 37% of the
families may have resulted from incomplete sensitivity of single-strand
conformation polymorphism analysis, the presence of mutations in
regulatory sequences, or the presence of other, currently unidentified,
genes causing LQTS.
The flow chart in
Figure 6 shows that in 80 families with typical ECG
patterns, mutation results were obtained in 56 families. Genotypes were
correctly identified by ST-T–wave patterns in all 56 families,
including 21 LQT1, 33 LQT2, and 2 LQT3. The other 24 families with
typical ECG patterns had no mutations detected. The assigned genotypes
for these families were 3 LQT1, 19 LQT2, and 2 LQT3. Because no
mutations were found, we cannot determine the accuracy of ECG
identification in this group.
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Forty-seven of the 127 families had nonspecific ST-T–wave
patterns. A guess at a genotype was attempted. Twenty-five of these
families obtained mutation results (13 LQT1, 7 LQT2, 1 LQT3, 1 LQT5,
and 3 with 2 mutations). The accuracy of genotype identification was
low, as expected
(Table 5).
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Discussion |
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TopAbstractIntroductionMethodsResultsDiscussionReferences |
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The findings of the present study provide information regarding the pathophysiology of repolarization in LQTS and simple means for identifying the genotype by ECG. We presume that not all LQTS genes have been discovered; thus, other ECG patterns typical for those genotypes may be identified in the future.
Putative Pathophysiology of Typical
ST-T–Wave Patterns
A physiological rationale exists for the different
ST-T–wave morphologies and repolarization parameters, because
different currents are affected. Shimizu and
Antzelevitch11 12
created in vitro canine LQT1, LQT2, and LQT3 models to study the
electrophysiological and ECG manifestations of each genotype. Cellular
electrograms from epicardial, endocardial, and M cells were recorded
along with a transmural ECG.
In the LQT1 model,11 the IKs blocker chromanol 293B homogeneously prolonged action potential durations (APDs) of all cell types, resulting in QT prolongation with little or no change of T-wave morphology, consistent with the normal T wave that we found to be the most common pattern in LQT1. Furthermore, they found little increase in transmural dispersion of APDs, presumably consistent with the low incidence of torsade de pointes at rest in LQT1 patients. ß-Adrenergic stimulation with isoproterenol abbreviated the APDs of the epicardial and endocardial cells but not of the M cells, producing a broad-based T wave, an increase of transmural dispersion of repolarization, and torsade-like arrhythmia. This is consistent with the adrenergic precipitation of symptoms in LQT1. But this LQT1 model does not explain why many LQT1 patients have the broad-based T wave at rest rather than just during adrenergic stimulation, yet they generally have no symptoms at rest.
In the LQT2 model,12 the IKr blocker D-sotalol produced a greater prolongation of APD in M cells than in epicardial and endocardial cells, resulting in increased transmural dispersion and bifid T waves.
The LQT3 model12 was induced by sea anemone toxin, a Na+ channel inactivation blocker. It prolonged epicardial, M cell, and endocardial APDs by prolonging phase 2, producing a long-ST segment, late-onset T wave, and markedly prolonged QT interval.
Thus, the ECG recording in each model was concordant with one of the typical ST-T–wave patterns for the respective genotype.
Our results raise questions regarding the generation and regulation of repolarizing currents in LQTS. Why are there multiple ST-T–wave patterns in each genotype? What allows the occurrence in single families of all typical patterns for their genotype? Why is there no apparent relationship between ST-T–wave pattern and clinical phenotype severity to specific mutations even when different mutations produce variable degrees of channel dysfunction?13 Clearly, the mechanisms regulating ST-T–wave patterns and QT duration are complex and incompletely understood, perhaps involving other factors such as additional channels, modifier genes, regulatory processes, autonomic activity, serum electrolyte levels, and regional heterogeneity of cardiac ion channel distribution within the myocardium.
Genotype Identification by ECG
Our results indicate that genotype can be identified by
ST-T–wave patterns in the majority of LQTS patients and families. LQT1
and LQT2 account for 90% of genotyped patients, and typical
patterns were present in 88% of patients with these genotypes. The
utility of genotype identification in LQT3 remains somewhat uncertain
given the small number of LQT3 patients in the study and the overlap of
the LQT3 and LQT1 patterns.
Genotype identification by ECG is useful for stratifying molecular genetic studies. With 5 disease genes and >170 mutations already identified, it is very costly and time-consuming to screen all known genes and mutational sites, limiting the application of genetic studies. With a typical ECG pattern, the suspected gene can be the initial target for testing, with a higher likelihood of rapid identification of the mutation. Such a strategy will significantly reduce time and costs, allowing more families to be genotyped and enhancing genotype-phenotype correlation studies. Furthermore, if therapeutic interventions based on specific genotype14 15 are shown to be more effective than empiric therapy, genotype identification by ECG could be helpful for therapeutic decision-making.
Some families had only nonspecific patterns. Fewer ECGs per family were available than those with typical patterns; ECG screening of additional members would probably identify some with typical patterns. Three of the 25 families with genetic results had 2 mutations. Other families may have had mutations in as-yet-unknown genes.
These findings are applicable only to patients and families with an established clinical diagnosis of LQTS. Other forms of heart disease and drugs that alter IKs, IKr, or INa channel function can produce QT prolongation and similar ST-T–wave patterns. These conditions must be excluded before using these ECG patterns for genotype identification.
In summary, 10 typical ST-T–wave patterns exist in the LQT1, LQT2, and LQT3 genotypes. They are present in the majority of genotyped patients. Evaluation of several ECGs from family members increases the likelihood of finding a typical pattern. Typical patterns and quantified repolarization parameters can be used to identify LQT1, LQT2, and possibly LQT3 genotypes in the majority of LQTS patients and families. This approach can direct mutational screening strategies resulting in cost and time savings.
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Acknowledgments |
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We gratefully acknowledge Dr Wataru Shimizu for input regarding the potential physiological mechanisms underlying the typical ST-T patterns in each genotype and Karen J. Chan and Diana L. Handrahan for the statistical analyses.
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Footnotes |
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Guest Editor for this article was Hein J.J. Wellens, MD, University Hospital Maastricht, Maastricht, the Netherlands.
Received March 1, 2000; revision received July 28, 2000; accepted July 28, 2000.
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M. Viitasalo, L. Oikarinen, H. Swan, K. A. Glatter, H. Vaananen, H. Fodstad, N. Chiamvimonvat, K. Kontula, L. Toivonen, and M. M. Scheinman Ratio of Late to Early T-Wave Peak Amplitude in 24-h Electrocardiographic Recordings as Indicator of Symptom History in Patients With Long-QT Syndrome Types 1 and 2 J. Am. Coll. Cardiol., January 3, 2006; 47(1): 112 - 120. [Abstract] [Full Text] [PDF]
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M. Viitasalo, L. Oikarinen, H. Swan, K. A. Glatter, H. Vaananen, H. Fodstad, N. Chiamvimonvat, K. Kontula, L. Toivonen, and M. M. Scheinman Ratio of Late to Early T-Wave Peak Amplitude in 24-h Electrocardiographic Recordings as Indicator of Symptom History in Patients With Long-QT Syndrome Types 1 and 2 J. Am. Coll. Cardiol., December 13, 2005; (2005) j.jacc.2005.07.068v1. [Abstract] [Full Text] [PDF]
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L. Zhang, D. W. Benson, M. Tristani-Firouzi, L. J. Ptacek, R. Tawil, P. J. Schwartz, A. L. George, M. Horie, G. Andelfinger, G. L. Snow, et al. Electrocardiographic Features in Andersen-Tawil Syndrome Patients With KCNJ2 Mutations: Characteristic T-U-Wave Patterns Predict the KCNJ2 Genotype Circulation, May 31, 2005; 111(21): 2720 - 2726. [Abstract] [Full Text] [PDF]
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T. Rossenbacker, K. Mubagwa, R. J. Jongbloed, J. Vereecke, K. Devriendt, M. Gewillig, E. Carmeliet, D. Collen, H. Heidbuchel, and P. Carmeliet Novel Mutation in the Per-Arnt-Sim Domain of KCNH2 Causes a Malignant Form of Long-QT Syndrome Circulation, March 1, 2005; 111(8): 961 - 968. [Abstract] [Full Text] [PDF]
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 C. E. Clancy and R. S. Kass Inherited and Acquired Vulnerability to Ventricular Arrhythmias: Cardiac Na+ and K+ Channels Physiol Rev, January 1, 2005; 85(1): 33 - 47. [Abstract] [Full Text] [PDF]
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L. Zhang, G. M. Vincent, M. Baralle, F. E. Baralle, B. D. Anson, D. W. Benson, B. Whiting, K. W. Timothy, J. Carlquist, C. T. January, et al. An intronic mutation causes long QT syndrome J. Am. Coll. Cardiol., September 15, 2004; 44(6): 1283 - 1291. [Abstract] [Full Text] [PDF]
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S. P. Etheridge, S. J. Compton, M. Tristani-Firouzi, and J. W. Mason A new oral therapy for long QT syndrome: Long-term oral potassium improves repolarization in patients with HERG mutations J. Am. Coll. Cardiol., November 19, 2003; 42(10): 1777 - 1782. [Abstract] [Full Text] [PDF]
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T. A. Beery, M. Dyment, K. Shooner, T. K. Knilans, and D. W. Benson A Candidate Locus Approach Identifies a Long QT Syndrome Gene Mutation Biol Res Nurs, October 1, 2003; 5(2): 97 - 104. [Abstract] [PDF]
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W. Zareba, A. J. Moss, E. H. Locati, M. H. Lehmann, D. R. Peterson, W. J. Hall, P. J. Schwartz, G. M. Vincent, S. G. Priori, J. Benhorin, et al. Modulating effects of age and gender on the clinical course of long QT syndrome by genotype J. Am. Coll. Cardiol., July 2, 2003; 42(1): 103 - 109. [Abstract] [Full Text] [PDF]
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S. M. Al-Khatib, N. M. A. LaPointe, J. M. Kramer, and R. M. Califf What Clinicians Should Know About the QT Interval JAMA, April 23, 2003; 289(16): 2120 - 2127. [Abstract] [Full Text] [PDF]
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W. Shimizu, T. Noda, H. Takaki, T. Kurita, N. Nagaya, K. Satomi, K. Suyama, N. Aihara, S. Kamakura, K. Sunagawa, et al. Epinephrine unmasks latent mutation carriers with LQT1 form of congenital long-QT syndrome J. Am. Coll. Cardiol., February 19, 2003; 41(4): 633 - 642. [Abstract] [Full Text] [PDF]
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K. Takenaka, T. Ai, W. Shimizu, A. Kobori, T. Ninomiya, H. Otani, T. Kubota, H. Takaki, S. Kamakura, and M. Horie Exercise Stress Test Amplifies Genotype-Phenotype Correlation in the LQT1 and LQT2 Forms of the Long-QT Syndrome Circulation, February 18, 2003; 107(6): 838 - 844. [Abstract] [Full Text] [PDF]
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 I M Van Langen, E Birnie, M Alders, R J Jongbloed, H Le Marec, and A A M Wilde The use of genotype-phenotype correlations in mutation analysis for the long QT syndrome J. Med. Genet., February 1, 2003; 40(2): 141 - 145. [Full Text] [PDF]
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 N. Goldschlager, A. E. Epstein, B. P. Grubb, B. Olshansky, E. Prystowsky, W. C. Roberts, M. M. Scheinman, and for the Practice Guidelines Subcommittee, North Am Etiologic Considerations in the Patient With Syncope and an Apparently Normal Heart Arch Intern Med, January 27, 2003; 163(2): 151 - 162. [Full Text] [PDF]
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E. Moric, E. Herbert, M. Trusz-Gluza, A. Filipecki, U. Mazurek, and T. Wilczok The implications of genetic mutations in the sodium channel gene (SCN5A) Europace, January 1, 2003; 5(4): 325 - 334. [Abstract] [Full Text] [PDF]
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 X. H.T. Wehrens, M. A. Vos, P. A. Doevendans, and H. J.J. Wellens Novel Insights in the Congenital Long QT Syndrome Ann Intern Med, December 17, 2002; 137(12): 981 - 992. [Abstract] [Full Text] [PDF]
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M. Viitasalo, L. Oikarinen, H. Swan, H. Vaananen, K. Glatter, P. J. Laitinen, K. Kontula, H. V. Barron, L. Toivonen, and M. M. Scheinman Ambulatory Electrocardiographic Evidence of Transmural Dispersion of Repolarization in Patients With Long-QT Syndrome Type 1 and 2 Circulation, November 5, 2002; 106(19): 2473 - 2478. [Abstract] [Full Text] [PDF]
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D. di Bernardo, A. Murray, A. A.M. Wilde, and D. M. Roden T-Wave Shape in Clinical Research Response Circulation, October 9, 2001; 104 (15): e80 - e80. [Full Text] [PDF]
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A. A.M. Wilde and D. Escande LQT genotype-phenotype relationships: patients and patches Cardiovasc Res, September 1, 2001; 51(4): 627 - 629. [Full Text] [PDF]
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A. A. M. Wilde and D. M. Roden Predicting the Long-QT Genotype From Clinical Data : From Sense to Science Circulation, December 5, 2000; 102(23): 2796 - 2798. [Full Text] [PDF]
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 I. Rivolta, H. Abriel, M. Tateyama, H. Liu, M. Memmi, P. Vardas, C. Napolitano, S. G. Priori, and R. S. Kass Inherited Brugada and Long QT-3 Syndrome Mutations of a Single Residue of the Cardiac Sodium Channel Confer Distinct Channel and Clinical Phenotypes J. Biol. Chem., August 10, 2001; 276(33): 30623 - 30630. [Abstract] [Full Text] [PDF]
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K. Gima and Y. Rudy Ionic Current Basis of Electrocardiographic Waveforms: A Model Study Circ. Res., May 3, 2002; 90(8): 889 - 896. [Abstract] [Full Text] [PDF]
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