Independent validation and clinical implications of the risk prediction model for long QT syndrome (1-2-3-LQTS-Risk)

EP Europace ◽  
2021 ◽  
Author(s):  
Andrea Mazzanti ◽  
Alessandro Trancuccio ◽  
Deni Kukavica ◽  
Eleonora Pagan ◽  
Meng Wang ◽  
...  
Keyword(s):  
Risk Score ◽  
Long Qt Syndrome ◽  
Risk Model ◽  
Number Needed To Treat ◽  
Qtc Interval ◽  
Syndrome Type ◽  
Discovery Cohort ◽  
Long Qt ◽  
Qt Syndrome ◽  
Score Model

Abstract Aims Risk stratification of patients with long QT syndrome (LQTS) represents a difficult task. In 2018, we proposed a granular estimate of the baseline 5-year risk of life-threatening arrhythmias (LAE) for patients with LQTS, based on the genotype (long QT syndrome Type 1, long QT syndrome Type 2, and long QT syndrome Type 3) and the duration of the QTc interval. We sought to externally validate a novel risk score model (1-2-3-LQTS-Risk model) in a geographically diverse cohort from the USA and to evaluate its performance and assess potential clinical implication of this novel model. Methods and results The prognostic model (1-2-3-LQTS-Risk model) was derived using data from a prospective, single-centre longitudinal cohort study published in 2018 (discovery cohort) and was validated using an independent cohort of 1689 patients enrolled in the International LQTS Registry (Rochester NY, USA). The validation study revealed a C-index of 0.69 [95% confidence interval (CI): 0.61–0.77] in the validation cohort, when compared with C-index of 0.79 (95% CI: 0.70–0.88) in the discovery cohort. Adopting a 5-year risk ≥5%, as suggested by the ROC curve analysis as the most balanced threshold for implantable cardioverter-defibrillator (ICD) implantation, would result in a number needed to treat (NNT) of nine (NNT = 9; 95% CI: 6.3–13.6). Conclusion The 1-2-3-LQTS-Risk model, the first validated 5-year risk score model for patients with LQTS, can be used to aid clinicians to identify patients at the highest risk of LAE who could benefit most from an ICD implant and avoid unnecessary implants.

Systematic Evaluation of KCNQ1 variant using ACMG/AMP Guidelines and Risk Stratification in Long QT Syndrome Type 1

2020 ◽  
Vol 41 (Supplement_2) ◽  
Author(s):  
A Kashiwa ◽  
T Aiba ◽  
H Makimoto ◽  
N Yagihara ◽  
S Ohno ◽  
...  
Keyword(s):  
Risk Stratification ◽  
Long Qt Syndrome ◽  
Family Members ◽  
Qtc Interval ◽  
Syndrome Type ◽  
Long Qt ◽  
C Terminus ◽  
Qt Syndrome ◽  
Research Grant

Abstract Background Mutation/variant-site specific risk stratification in long-QT syndrome type 1 (LQT1) has been well investigated, but it is still challenging to adopt current enormous genomic information to clinical aspects caused by each mutation/variant. We assessed a novel variant-specific risk stratification in LQT1 patients. Methods We classified a pathogenicity of 142 KCNQ1 variants among 927 LQT1 patients (536 probands and 391 family members) based on the American College of Medical Genetics and Genomics (ACMG) and Association for Molecular Pathology (AMP) guidelines and evaluated whether the ACMG/AMP-based classification was associated with arrhythmic risk in LQT1 patients. Results Among 142 KCNQ1 variants, 60 (42.3%), 58 (40.8%), and 24 (16.9%) variants were classified into pathogenic (P), likely pathogenic (LP), and variant of unknown significance (VUS), respectively. The ACMG/AMP guideline-based classification was significantly associated with syncopal events (particularly those during exercise) and LQT risk score (Schwartz score) in overall population. On the other hand, arrhythmic risk was completely different between probands and families even in the same variants. The baseline QTc interval and variant location could stratify the risk in family members but not in probands, however, the ACMG/AMP-based KCNQ1 variant classification stratified the risk in LQT1 probands as well as family members. Multivariate analysis showed that proband (HR=2.52; 95% CI: 1.93–3.30; p<0.0001), longer QTc interval (≥500ms) (HR=1.41; 95% CI: 1.11–1.79; p<0.0001), variants at membrane spanning (MS) (vs. those at N/C terminus) (HR=1.40; 95% CI: 1.07–1.85; p=0.02), C-loop (vs. N/C terminus) (HR=1.58; 95% CI: 1.11–2.24; p=0.01), and P variants [(vs. LP) (HR=1.71; 95% CI: 1.33–2.23; p<0.0001), (vs. VUS) (HR=1.96; 95% CI: 1.19–3.46; p=0.007)] were significantly associated with syncopal events. A clinical score (0–4) based on the proband, QTc (≥500ms), variant location (MS or C-loop) and P variant by the ACMG/AMP guidelines allowed identification of patients more likely to have arrhythmic events (Figure A and B). Conclusion Comprehensive evaluation of clinical findings and pathogenicity of KCNQ1 variants based on the ACMG/AMP-based evaluation may stratify arrhythmic risk of congenital long-QT syndrome type 1. Figure 1 Funding Acknowledgement Type of funding source: Public grant(s) – National budget only. Main funding source(s): Health Science Research Grant from the Ministry of Health,Labor and Welfare of Japan for Clinical Research on Measures for Intractable Diseases (H24-033, H26-040, H27-032) and a research grant from the Japan Agency for Medical Research and Development (AMED) (15km0305015h0101, 16ek0210073h0001)

scholarly journals Diagnostic Performance of Various QTc Interval Formulas in a Large Family with Long QT Syndrome Type 3: Bazett's Formula Not So Bad After All …

2003 ◽  
Vol 8 (4) ◽  
pp. 269-274 ◽  
Author(s):  
Jan Brouwer ◽  
Maarten P. Van Den Berg ◽  
Diederick E. Grobbee ◽  
Jaap Haaksma ◽  
Arthur A.M. Wilde
Keyword(s):  
Long Qt Syndrome ◽  
Diagnostic Performance ◽  
Large Family ◽  
Qtc Interval ◽  
Syndrome Type ◽  
Long Qt ◽  
Qt Syndrome ◽  
Type 3

scholarly journals A novel KCNH2 frameshift mutation (c.46delG) associated with high risk of sudden death in a family with congenital long QT syndrome type 2

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Hyun Sok Yoo ◽  
Nancy Medina ◽  
María Alejandra von Wulffen ◽  
Natalia Ciampi ◽  
Analia Paolucci ◽  
...  
Keyword(s):  
Sudden Cardiac Death ◽  
Long Qt Syndrome ◽  
Cardiac Death ◽  
Frameshift Mutation ◽  
Alpha Subunit ◽  
Syndrome Type ◽  
Long Qt ◽  
Qt Syndrome ◽  
Congenital Long Qt Syndrome

Abstract Background The congenital long QT syndrome type 2 is caused by mutations in KCNH2 gene that encodes the alpha subunit of potassium channel Kv11.1. The carriers of the pathogenic variant of KCNH2 gene manifest a phenotype characterized by prolongation of QT interval and increased risk of sudden cardiac death due to life-threatening ventricular tachyarrhythmias. Results A family composed of 17 members with a family history of sudden death and recurrent syncopes was studied. The DNA of proband with clinical manifestations of long QT syndrome was analyzed using a massive DNA sequencer that included the following genes: KCNQ1, KCNH2, SCN5A, KCNE1, KCNE2, ANK2, KCNJ2, CACNA1, CAV3, SCN1B, SCN4B, AKAP9, SNTA1, CALM1, KCNJ5, RYR2 and TRDN. DNA sequencing of proband identified a novel pathogenic variant of KCNH2 gene produced by a heterozygous frameshift mutation c.46delG, pAsp16Thrfs*44 resulting in the synthesis of a truncated alpha subunit of the Kv11.1 ion channel. Eight family members manifested the phenotype of long QT syndrome. The study of family segregation using Sanger sequencing revealed the identical variant in several members of the family with a positive phenotype. Conclusions The clinical and genetic findings of this family demonstrate that the novel frameshift mutation causing haploinsufficiency can result in a congenital long QT syndrome with a severe phenotypic manifestation and an elevated risk of sudden cardiac death.

Diagnosis of epilepsy using an implantable loop recorder in a child with long-QT syndrome type 3

2013 ◽  
Vol 55 (2) ◽  
pp. 251-253 ◽  
Author(s):  
Kazuhiro Takahashi ◽  
Akira Miyake ◽  
Yoshimitsu Otsuka ◽  
Masaharu Ohfu ◽  
Hitoshi Ganaha
Keyword(s):  
Long Qt Syndrome ◽  
Syndrome Type ◽  
Implantable Loop Recorder ◽  
Long Qt ◽  
Loop Recorder ◽  
Qt Syndrome ◽  
Type 3

scholarly journals Association of the hERG mutation with long-QT syndrome type 2, syncope and epilepsy

2016 ◽  
Vol 13 (3) ◽  
pp. 2467-2475 ◽  
Author(s):  
GUOLIANG LI ◽  
RUI SHI ◽  
JINE WU ◽  
WENQI HAN ◽  
AIFENG ZHANG ◽  
...  
Keyword(s):  
Long Qt Syndrome ◽  
Syndrome Type ◽  
Long Qt ◽  
Qt Syndrome

scholarly journals Paediatric/young versus adult patients with long QT syndrome

Open Heart ◽  
2021 ◽  
Vol 8 (2) ◽  
pp. e001671
Author(s):  
Sharen Lee ◽  
Jiandong Zhou ◽  
Kamalan Jeevaratnam ◽  
Wing Tak Wong ◽  
Ian Chi Kei Wong ◽  
...  
Keyword(s):  
Long Qt Syndrome ◽  
Primary Outcome ◽  
Population Based ◽  
Public Hospitals ◽  
Qtc Interval ◽  
Onset Age ◽  
Long Qt ◽  
History Of ◽  
Qt Syndrome ◽  
Random Survival Forest

IntroductionLong QT syndrome (LQTS) is a less prevalent cardiac ion channelopathy than Brugada syndrome in Asia. The present study compared the outcomes between paediatric/young and adult LQTS patients.MethodsThis was a population-based retrospective cohort study of consecutive patients diagnosed with LQTS attending public hospitals in Hong Kong. The primary outcome was spontaneous ventricular tachycardia/ventricular fibrillation (VT/VF).ResultsA total of 142 LQTS (mean onset age=27±23 years old) were included. Arrhythmias other than VT/VF (HR 4.67, 95% CI (1.53 to 14.3), p=0.007), initial VT/VF (HR=3.25 (95% CI 1.29 to 8.16), p=0.012) and Schwartz score (HR=1.90 (95% CI 1.11 to 3.26), p=0.020) were predictive of the primary outcome for the overall cohort, while arrhythmias other than VT/VF (HR=5.41 (95% CI 1.36 to 21.4), p=0.016) and Schwartz score (HR=4.67 (95% CI 1.48 to 14.7), p=0.009) were predictive for the adult subgroup (>25 years old; n=58). A random survival forest model identified initial VT/VF, Schwartz score, initial QTc interval, family history of LQTS, initially asymptomatic and arrhythmias other than VT/VF as the most important variables for risk prediction.ConclusionClinical and ECG presentation varies between the paediatric/young and adult LQTS population. Machine learning models achieved more accurate VT/VF prediction.

scholarly journals Paediatric/young versus adult patients with long QT syndrome or catecholaminergic polymorphic ventricular tachycardia

2021 ◽  
Author(s):  
Sharen Lee ◽  
Jiandong Zhou ◽  
Kamalan Jeevaratnam ◽  
Wing Tak Wong ◽  
Ian Chi Kei Wong ◽  
...  
Keyword(s):  
Ventricular Tachycardia ◽  
Long Qt Syndrome ◽  
Primary Outcome ◽  
Public Hospitals ◽  
Adult Patients ◽  
Polymorphic Ventricular Tachycardia ◽  
Qtc Interval ◽  
Long Qt ◽  
Old Patients ◽  
Qt Syndrome

AbstractIntroductionLong QT syndrome (LQTS) and catecholaminergic ventricular tachycardia (CPVT) are less prevalent cardiac ion channelopathies than Brugada syndrome in Asia. The present study compared paediatric/young and adult patients with these conditions.MethodsThis was a territory-wide retrospective cohort study of consecutive patients diagnosed with LQTS and CPVT attending public hospitals in Hong Kong. The primary outcome was spontaneous ventricular tachycardia/ventricular fibrillation (VT/VF).ResultsA total of 142 LQTS (mean onset age= 27±23 years old) and 16 CPVT (mean presentation age=11±4 years old) patients were included. For LQTS, arrhythmias other than VT/VF (HR=4.67, 95% confidence interval=[1.53-14.3], p=0.007), initial VT/VF (HR=3.25 [1.29-8.16], p=0.012) and Schwartz score (HR=1.90 [1.11-3.26], p=0.020) were predictive of the primary outcome for the overall cohort, whilst arrhythmias other than VT/VF (HR=5.41 [1.36-21.4], p=0.016) and Schwartz score (HR=4.67 [1.48-14.7], p=0.009) were predictive for the adult subgroup (>25 years old; n=58). All CPVT patients presented before the age of 25 but no significant predictors of VT/VF were identified. A random survival forest model identified initial VT/VF, Schwartz score, initial QTc interval, family history of LQTS, initially asymptomatic, and arrhythmias other than VT/VF as the most important variables for risk prediction in LQTS, and initial VT/VF/sudden cardiac death, palpitations, QTc, initially symptomatic and heart rate in CPVT.ConclusionClinical and ECG presentation vary between the pediatric/young and adult LQTS population. All CPVT patients presented before the age of 25. Machine learning models achieved more accurate VT/VF prediction.

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