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European Heart Journal Advance Access originally published online on November 7, 2006
European Heart Journal 2007 28(5):575-580; doi:10.1093/eurheartj/ehl355
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© The European Society of Cardiology 2006. All rights reserved. For Permissions, please e-mail: journals.permissions@oxfordjournals.org

Diagnostic criteria for congenital long QT syndrome in the era of molecular genetics: do we need a scoring system?

Nynke Hofman1, Arthur A.M. Wilde2, Stefan Kääb4, Irene M. van Langen1, Michael W.T. Tanck3, Marcel M.A.M. Mannens1, Martin Hinterseer4, Britt-Maria Beckmann4 and Hanno L. Tan2,*

1 Department of Clinical Genetics, Academic Medical Center, University of Amsterdam, Meibergdreef 9, 1105 AZ Amsterdam, The Netherlands
2 Department of Cardiology, Academic Medical Center, University of Amsterdam, Meibergdreef 9, 1105 AZ Amsterdam, The Netherlands
3 Department of Clinical Epidemiology, Biostatistics, and Bioinformatics, Academic Medical Center, University of Amsterdam, Meibergdreef 9, 1105 AZ Amsterdam, The Netherlands
4 Department of Cardiology, Ludwig-Maximilians University, Klinikum Grosshadern, München, Germany

Received 8 May 2006; revised 4 October 2006; accepted 13 October 2006; online publish-ahead-of-print 7 November 2006.

* Corresponding author. Tel: +31 20 5663264; fax: +31 20 6975458. E-mail address: h.l.tan{at}amc.uva.nl

See page 527 for the editorial comment on this article (doi:10.1093/eurheartj/ehl552)


   Abstract
Top
 Abstract
Introduction
 Methods
 Results
 Discussion
 Conclusion
 Supplementary material
 Acknowledgements
 References
 
Aims Previously published diagnostic systems, based on ECG analysis and clinical parameters (Schwartz criteria and Keating criteria), have been used to estimate the probability of inherited long QT syndrome (LQTS). Nowadays, a certain diagnosis can often be made by DNA testing. We aimed to establish the predictive power of the Schwartz and Keating criteria, using DNA testing as a reference, and to determine the best diagnostic strategy.

Methods and results We studied 513 relatives (aged >10 years) of 77 consecutive LQTS probands with a known disease-causing mutation. The Schwartz criteria identified ‘high probability of LQTS’ (score ≥4) in 41 of 208 mutation carriers, yielding 19% sensitivity and 99% specificity. The Keating criteria had 36% sensitivity and 99% specificity. Alternatively, by analysing QTc duration alone, we found that 430 ms is the optimal cut-off value to distinguish carriers (≥430 ms) from non-carriers (<430 ms), yielding 72% sensitivity and 86% specificity (area under the curve 0.788).

Conclusion The existing clinical criteria have good specificity in identifying mutation carriers. However, their sensitivity is too low for clinical use. Analysis of QTc duration alone is more useful to screen for LQTS carriership (QTc ≥ 430 ms) as its sensitivity is far superior, although its specificity remains acceptable. In genotyped families, genetic testing is the preferred diagnostic test.

Key Words: Long QT syndrome • Ventricular arrhythmias • Molecular genetics • Diagnostic criteria


   Introduction
Top
 Abstract
 Introduction
Methods
 Results
 Discussion
 Conclusion
 Supplementary material
 Acknowledgements
 References
 
The congenital long QT syndrome (LQTS) is a primary inherited cardiac arrhythmia syndrome that may cause syncope and sudden death. A recent estimate of its prevalence is 1:2000.1 Patients are identified by a prolonged QT-interval on the ECG, often after typical complaints such as dizziness, syncope, or cardiac arrest. The usual mode of inheritance is autosomal dominant, with the exception of the autosomal recessive Jervell–Lange–Nielsen type.2,3 The various types of LQTS are associated with mutations in genes encoding distinct cardiac ion channels.4 By far, the most common types are LQT1, LQT2, and LQT3, accounting for 85% of all genotyped congenital LQTS cases.4 Timely (often presymptomatic) identification of disease carriers is highly relevant, because preventive measures and therapies effectively avert sudden death.5 After the first description of LQTS in 19572, at a time when the underlying mechanism was unclear, it became obvious that ECG characteristics within families may be variable, thereby confounding the diagnostic process. In 1985, a first set of diagnostic criteria were formulated by Schwartz.6 These criteria were updated in 1993 by Schwartz et al.,7 implementing experience with sex-related differences in QTc prolongation, the recognition of additional ECG abnormalities, and the first results of linkage analysis, which suggested genetic heterogeneity. Hence, these criteria include various ECG characteristics and the clinical and family history (Table 1) and allow for an estimation of the probability of LQTS. Nowadays, however, with the emergence of molecular genetic testing in clinical practice,8 certain LQTS can be diagnosed when a disease-causing mutation is identified. Although DNA testing reveals mutations and confirms the diagnosis in ~70% of probands, it identifies all carriers among relatives of mutation-positive probands who are tested subsequently. Accordingly, in these relatives, DNA testing can be used to establish the predictive power (sensitivity and specificity) of existing diagnostic criteria, such as the Schwartz and Keating criteria.


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  		Table 1 Diagnostic criteria LQTS—1993 Schwartz6

 
Moreover, the predictive power of new diagnostic criteria may be analysed. Importantly, it is our experience that referring physicians use these diagnostic criteria as a screening method among relatives to decide in whom further analysis and DNA testing should be conducted. Therefore, these criteria should have a high sensitivity (i.e. LQTS should not be missed). Conversely, a high specificity is less crucial; a certain diagnosis is provided by DNA testing, anyway, thus false positives can be ruled out. In this study, using mutation analysis in relatives of mutation-positive probands with the most prevalent LQTS types, we analysed the predictive power of widely used diagnostic criteria of Schwartz et al.7 and Keating et al.9 and explored which diagnostic criteria are most useful in this era of molecular genetics.


   Methods
Top
 Abstract
 Introduction
 Methods
Results
 Discussion
 Conclusion
 Supplementary material
 Acknowledgements
 References
 
Study population
To calculate the predictive power of the diagnostic criteria, we studied relatives of LQTS probands with a known disease-causing mutation. These relatives were included as follows. We studied all genotyped LQT1, LQT2, and LQT3 families, who were counselled at the outpatient Cardiogenetics Clinics of the Academic Medical Center, Amsterdam, The Netherlands or the Department of Cardiology at Ludwig-Maximilians University Clinic Grosshadern, München, Germany in the period 1996–2005. Accordingly, molecular genetic analysis revealed 102 unrelated LQTS probands with genetic aberrance in the associated genes (KCNQ1, KCNH2, and SCN5A, respectively). We first excluded three probands with more than one mutation (compound heterozygotes). The remaining 99 probands (KCNQ1: n = 33, KCNH2: n = 52, and SCN5A: n = 14) had 648 relatives, of whom we excluded children under the age of 10 years (n = 103) because of the age-dependence of QTc in young children and the fact that QTc correction methods used in adults may not be applicable to children.10,11 Moreover, we excluded individuals who used QT-prolonging drugs (n = 2), individuals of whom an ECG was not available (n = 29), and one individual with a left bundle branch block. Thus, the study cohort consisted of 513 relatives (249 males and 264 females), related (mainly in the first and second degrees) to 77 different probands, with a median number of relatives per family of 4 [interquartile range (IQR) 5]. Subsequent molecular genetic analysis among these 513 individuals revealed 208 mutation carriers (Table 2). This study was performed in accordance with the Declaration of Helsinki and with written, informed consent of all patients.


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  		Table 2 Demographic variables, average Schwartz score, and ECG parameters of mutation carriers and non-carriers in LQTS families

 
Schwartz criteria
ECG analysis
QTc duration (Bazett) was first calculated from the average values of three cycles in lead II or lead V5. However, as we found that mean QTc values, measured in the 513 relatives, were similar in leads II and V5 (423.9 ± 43.7 and 423.8 ± 42.5 ms, respectively), we used only mean QTc in V5 for analysis. Specific T wave abnormalities or other relevant electrocardiographic features, including Torsades de pointes, were searched for in all individuals (Table 1).

Analysis of clinical parameters
Obtained pedigrees were used for the evaluation of sudden cardiac death in first- or second-degree relatives. The clinical history and molecular diagnosis (carrier/non-carrier) were identified from the medical records. The score obtained using the Schwartz criteria (the Schwartz score) was calculated, blinded to the results of molecular genetic testing.

Keating criteria
The predictive power of the criteria proposed by Keating et al. was also analysed. According to these criteria, individuals are affected if they are asymptomatic with QTc > 470 ms, or if they have typical symptoms with QTc ≥ 450 ms.9

Determination of the predictive power
The predictive power of the diagnostic criteria (Schwartz criteria, Keating criteria, and QTc analysis alone) was expressed as their sensitivity and specificity in predicting mutation carriership.

DNA testing
DNA of probands was extracted from peripheral blood lymphocytes. The polymerase chain reaction technique amplified the genomic DNA, and mutation detection was performed either by direct sequencing or by single-stranded conformational polymorphism or denaturing high-performance liquid chromatography, followed by direct sequencing of fragments with an abnormal elution profile.

Statistical analysis
Schwartz scores are presented as median and IQR; the other continuous data are presented as mean ± standard deviation (SD). Comparisons of categorical or continuous parameters between LQTS carriers and non-carriers were conducted using logistic or linear regression analysis, respectively. For all models, the generalized estimation equations method was used to correct for possible correlations between relatives of the same proband (Table 2). In order to investigate the sensitivity of the cut-off selection point and to obtain 95% confidence intervals (CIs) for all sensitivities, specificities, and areas under the receiver operating curves (ROC) reported, 1000 bootstrap samples were created from the original set. As patients were not independent, bootstrap samples were randomly generated from the original data by drawing with replacement 77 probands each. All relatives of these probands were then included in the bootstrap sample, resulting in a mean ( ± SD) sample size of 513 ± 75 relatives. In a similar fashion, the bootstrap samples for the three LQTS mutations were created. The optimal cut-off value was defined as the QTc value with the maximal sum of sensitivity and specificity. All statistical analyses were carried out using SAS (version 9.1, SAS Institute, Cary, NC, USA) and MATLAB® (version 7.2, The MathWorks, Natick, MA, USA). Throughout, P-values less than 0.05 were considered statistically significant.


   Results
Top
 Abstract
 Introduction
 Methods
 Results
Discussion
 Conclusion
 Supplementary material
 Acknowledgements
 References
 
The demographic variables, average Schwartz score, and ECG characteristics of the LQTS carriers and non-carriers are shown in Table 2. LQTS carriers had, on average, higher Schwartz scores and longer QTc intervals.

Predictive power of the Schwartz criteria
Among the 208 mutation-carrying relatives, the median Schwartz score was 1.25 (IQR: 2.0) (Table 2). A Schwartz score ≥4 (high probability) was observed in 41 relatives, yielding a 19% sensitivity, a 99% specificity, and an area under the ROC of 0.59 (95% CI: 0.57–0.65). The predictive power of the Schwartz criteria was similar between the LQTS types, with sensitivities of a Schwartz score ≥4 of 13, 22, and 15% in LQT1, LQT2, and LQT3, respectively, and specificities ranging from 97 to 100% (Table 3). Given the low sensitivity of the Schwartz criteria among relatives, we also analysed their predictive power among the 99 probands and found the mean Schwartz score to be 3.7 ± 1.8, with a Schwartz score ≥4 in 56 individuals (57%), a Schwartz score 2–3.5 (intermediate probability) in 22, and a Schwartz score 0–1.5 (low probability) in 17. This analysis excluded four probands, in whom the Schwartz score could not be calculated because of their sudden death prior to ECG recording. The diagnosis in these four families was made after screening of first degree relatives.


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  		Table 3 Predictive power of various diagnostic criteria

 
Predictive power of the Keating criteria
The Keating criteria had a similar diagnostic profile as the Schwartz criteria, with a low sensitivity (36%) and a high specificity (99%) (Table 3; Figure 1).


Figure 1
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  		Figure 1 Sensitivity and specificity of QTc duration alone (squares), Schwartz score (circles), and Keating criteria (triangle).

 
Predictive power of QTc analysis alone
When calculating the Schwartz score, we found that it was mostly determined by the QTc duration. T wave alternans were not found in any relative, whereas specific T wave abnormalities (e.g. notched T waves in three leads)6,12 were only present in 14. Torsades de pointes was documented in 33 of the 99 probands, but in none of their 513 relatives. Of note, a history of syncope, although being an important criterion in existing scoring systems, was not specific for unmasking mutation carriers among relatives, as 50 of 87 who had experienced syncope at least once, in the presence or absence of stress, were non-carriers.

Given these observations, we analysed the predictive power of QTc analysis alone by constructing ROC curves. We found that a QTc duration ≥430 ms (95% CI: 428–434) was the optimal cut-off value to predict mutation carriership, with an area under the curve of 0.788 (95% CI: 0.749–0.825). A QTc duration ≥430 ms had a 72% sensitivity and an 86% specificity among relatives. The predictive power was similar among the LQTS types, with a sensitivity of 60, 79, and 67% and a specificity of 88, 85, and 81% in LQT1, LQT2, and LQT3, respectively (Table 3).

Effects of sex on predictive power of QTc analysis alone
Given the reported sex-related differences in normal QTc duration,7,11 we analysed whether sex had an effect on the diagnostic power of QTc analysis alone (Table 4). We found that the sensitivity/specificity of a cut-off value of 430 ms was 71/91% in males and 73/81% in females. The optimal cut-off value to distinguish carriers from non-carriers was 430 ms (95% CI: 426–440) in males and 437 ms (95% CI: 434–438) in females. In females, a cut-off value of 437 ms had a sensitivity of 70% and a specificity of 90%. Of note, using the widely accepted upper level of normal QTc intervals of 440 ms for males and 460 ms for females, 47% (males 39% and females 54%) of all mutation carriers had QTc durations within their respective sex-specific normal limits. To account for sex-specific changes in QTc interval during puberty, we also conducted these analyses by excluding children under the age of 15. This analysis did not yield fundamentally different results (see Supplementary material online, Table S1).


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  		Table 4 Effect of sex on predictive power of QTc analysis alone (relatives only)

 

   Discussion
Top
 Abstract
 Introduction
 Methods
 Results
 Discussion
Conclusion
 Supplementary material
 Acknowledgements
 References
 
We found that in LQT1, LQT2, and LQT3 families with known causal gene mutations, the widely accepted Schwartz and Keating criteria, although having high specificity, severely under-diagnose disease carriers (sensitivity of ‘high probability’, according to Schwartz criteria, 19%). Significant under-diagnosis was also found among probands, despite the fact that probands are generally more seriously affected than their relatives. A high probability of LQTS (Schwartz criteria) was found only in 57% of probands with a confirmed molecular diagnosis. If it is accepted that preventive measures and treatments are highly effective in preventing sudden death5 and identification of disease carriers is therefore crucial, it is clear that the sensitivity, i.e. the ability to identify carriers of the familial mutation, among both probands and relatives, of these diagnostic criteria is unacceptably low. It should be noted that these criteria were defined at a time when no test to make a certain diagnosis of LQTS was available. Instead, these criteria were used as the most reliable method to estimate the probability of LQTS. This circumstance is probably reflected by the excellent specificity of these criteria, as also found here. However, with the present availability of DNA testing for a certain diagnosis of disease carriership, a high specificity is less important. Instead, a high sensitivity is more relevant. We found that a simple diagnostic strategy, consisting only of measurement of QTc duration, is more useful when DNA testing is available to confirm disease carriership. In particular, using QTc = 430 ms as a cut-off value has more useful diagnostic power than the existing clinical criteria to distinguish disease carriers (QTc ≥ 430 ms) from non-carriers (QTc < 430 ms), because the sensitivity is far superior (72%). Similarly, we consider the specificity of 86% of this test reasonable, given that definitive proof/disproof of carriership is provided by subsequent DNA testing of the known mutation, which is usually completed fast (less than 4 weeks in our institutions).

Although the optimal QTc cut-off value was slightly different for males (430) and females (437), reflecting the observation that QTc values in females were generally longer than those in males,7 we recommend to use a QTc cut-off value of 430 for both groups, for the sake of practicability and because, in females, sensitivity at cut-off 437 was lower than that at cut-off 430. Our finding that the optimal QTc cut-off value to distinguish carriers from non-carriers is as low as 430 ms, i.e. well within the normal range, may shift existing paradigms in LQTS and spawn revisions in clinical decision-making. Thus, finding a QTc duration in the upper range of normal in a relative of an LQTS proband should not provide the false reassurance that this individual will not carry LQTS (Figure 2). Previous studies also indicated reduced penetrance in LQTS (i.e. normal QTc values in mutation carriers). However, these studies included significantly fewer patients (n = 19913 and n = 4614) and/or did not systematically analyse the diagnostic power of existing diagnostic criteria. For instance, analysis of specific ECG abnormalities, according to the Schwartz criteria, was not performed.13–15 These observations clearly impart added importance to molecular genetic investigation, as DNA testing should be ordered in relatives of an LQTS proband, even if their QTc lie within the normal range. At the same time, these observations provide strong justification for the current design of specialized Cardiogenetics Clinics, where ECG analysis is provided along with DNA testing and genetic counselling.16 Clearly, results of DNA testing must be quickly available, particularly in patients with a QTc duration in the upper range of normal, to allow for decision-making regarding treatment and to minimize the time spent in suspense and potential psychological distress by the patients.


Figure 2
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  		Figure 2 QTc durations in non-carriers and carriers.

 
Study limitations
Our analyses of the predictive power of analysis of ECGs with or without clinical parameters to identify LQTS carriership require that causal mutations are identified in the investigated families, because they are used as gold standards. We cannot be sure that our analyses also apply to those families where causal mutations have not been detected. However, there is no reason to assume that these analyses should turn out substantially different in these families. The general applicability of our findings is further supported by the fact that causal mutations are found in the majority of the LQTS families.17,18 Similarly, although, in accordance with our study design, our analyses were restricted to LQT1, LQT2, and LQT3, we believe that this is not a severe restriction in clinical practice, as these LQT types constitute the vast majority of all genotyped LQTS cases (85%).4 Conversely, we chose to exclude children below the age of 10 years, because it is not resolved which method of QT correction (for heart rate) is most accurate in this age group.10,11 Therefore, it is unknown whether our findings are applicable to children.


   Conclusion
Top
 Abstract
 Introduction
 Methods
 Results
 Discussion
 Conclusion
Supplementary material
 Acknowledgements
 References
 
Existing clinical diagnostic criteria for LQTS, including the broadly accepted Schwartz and Keating criteria, have excellent specificity, but very low sensitivity in identifying disease carriers, particularly in relatives of genotyped individuals. Measurement of QTc duration alone is superior to the Schwartz and Keating criteria when DNA testing is available for the confirmation of disease carriership, as it has far better sensitivity while retaining reasonable specificity. Using this method, the optimal cut-off value to distinguish carriers from non-carriers lies within the normal range at QTc = 430 ms.


   Supplementary material
Top
 Abstract
 Introduction
 Methods
 Results
 Discussion
 Conclusion
 Supplementary material
Acknowledgements
 References
 
Supplementary material is available at European Heart Journal online.


   Acknowledgements
Top
 Abstract
 Introduction
 Methods
 Results
 Discussion
 Conclusion
 Supplementary material
 Acknowledgements
References
 
H.L.T. was supported by the Royal Netherlands Academy of Arts and Sciences (KNAW), the Netherlands Heart Foundation (NHS 2002B191), and the Bekales Foundation. A.A.M.W. was supported by the Netherlands Heart Foundation (Grant NHS 2003T302) and the Foundation Leducq (Grant 05 CVD ‘Alliance Against Sudden Cardiac Death’). S.K., M.H., and B.-M.B. were supported by the Bundesministerium für Bildung und Forschung (BMBF) in the context of the National Genome Research Network (NGFN) (Grant 01GS0499).

Conflict of interest: none declared.


   References
Top
 Abstract
 Introduction
 Methods
 Results
 Discussion
 Conclusion
 Supplementary material
 Acknowledgements
 References
 

  1. Crotti L, Stramba-Badiali M, Ferrandi C. (2005) Prevalence of the long QT syndrome. Circulation 112:Suppl. II, II-660.
  2. Jervell A and Lange-Nielsen F. (1957) Congenital deafmutism, functional heart disease with prolongation of the QT interval, and sudden death. Am Heart J 54:59–68.[CrossRef][Web of Science][Medline]
  3. Schwartz PJ, Spazzolini C, Crotti L, Bathen J, Amlie JP, Timothy K, Shkolnikova M, Berul CI, Bitner-Glindzicz M, Toivonen L, Horie M, Schulze-Bahr E, Denjoy I. (2006) The Jervell and Lange–Nielsen syndrome: natural history, molecular basis, and clinical outcome. Circulation 113:783–790.
  4. Wilde AAM and Bezzina CR. (2005) Genetics in cardiac arrhythmias. Heart 91:1352–1358.[Free Full Text]
  5. Shimizu W. (2005) The long QT syndrome: therapeutic implications of a genetic diagnosis. Cardiovasc Res 67:347–356.[Abstract/Free Full Text]
  6. Schwartz PJ. (1985) Idiopathic long QT syndrome: progress and questions. Am Heart J 2:399–411.
  7. Schwartz PJ, Moss AJ, Michael Vincent GM, Crampton RS. (1993) Diagnostic criteria for the long QT syndrome—an update. Circulation 88:782–784.
  8. Zareba W, Moss AJ, Schwartz PJ, Vincent GM, Robinson JL, Priori SG, Benhorin J, Locati EH, Towbin JA, Keating MT, Lehmann MH, Hall WJ. (1998) Influence of genotype on the clinical course of the long-QT syndrome. International Long-QT Syndrome Registry Research Group. N Engl J Med 339:960–965.[Abstract/Free Full Text]
  9. Keating M, Atkinson D, Dunn C, Timothy K, Vincent GM, Leppert M. (1991) Linkage of a cardiac arrhythmia, the long QT syndrome, and the Harvey ras-1 gene. Science 252:704–706.[Abstract/Free Full Text]
  10. Wernicke JF, Faries D, Breitung R, Girod D. (2005) QT correction methods in children and adolescents. J Cardiovasc Electrophysiol 16:76–81.[CrossRef][Web of Science][Medline]
  11. Rautaharju PM, Zhou SH, Wong S, Calhoun HP, Berenson GS, Prineas R, Davignon A. (1992) Sex differences in the evolution of the electrocardiographic QT interval with age. Can J Cardiol 8:690–695.[Web of Science][Medline]
  12. Malfatto G, Beria G, Sala S, Bonazzi O, Schwartz PJ. (1994) Quantitative analysis of T wave abnormalities and their prognostic implications in the idiopathic long QT syndrome. J Am Coll Cardiol 23:296–301.[Abstract]
  13. Vincent GM, Timothy KW, Leppert M, Keating M. (1992) The spectrum of symptoms and QT intervals in carriers of the gene for the long-QT syndrome. N Engl J Med 327:846–852.[Abstract]
  14. Priori SG, Napolitano C, Schwartz PJ. (1999) Low penetrance in the long-QT syndrome; clinical impact. Circulation 99:529–533.
  15. Napolitano C, Priori SG, Schwartz PJ, Bloise R, Ronchetti E, Nastoli J, Bottelli G, Cerrone M, Leonardi S. (2005) Genetic testing in the long QT syndrome; development and validation of an efficient approach to genotyping in clinical practice. JAMA 294:2975–2980.[Abstract/Free Full Text]
  16. van Langen IM, Hofman N, Tan HL, Wilde AA. (2004) Family and population strategies for screening and counselling of inherited cardiac arrhythmias. Ann Med 36:Suppl. 1, 116–124.
  17. Hofman N, van Langen IM, Tan HL, Mannens MM, Wilde AA. (2004) The yield of molecular genetic testing in primary electrical heart disease. Circulation 110:Suppl. III, III-693.
  18. van Langen IM, Birnie E, Alders M, Jongbloed RJ, Le Marec H, Wilde AA. (2003) The use of genotype–phenotype correlations in mutation analysis for the long QT syndrome. J Med Genet 40:141–145.[Free Full Text]

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