Manual vs. automatic assessment of the QT-interval and corrected QT.

AIMS: Sudden cardiac death (SCD) is challenging to predict. Electrocardiogram (ECG)-derived heart rate-corrected QT-interval (QTc) is used for SCD-risk assessment. QTc is preferably determined manually, but vendor-provided automatic results from ECG recorders are convenient. Agreement between manual and automatic assessments is unclear for populations with aberrant QTc. We aimed to systematically assess pairwise agreement of automatic and manual QT-intervals and QTc. METHODS AND RESULTS: A multi-centre cohort enriching aberrant QTc comprised ECGs of healthy controls and long-QT syndrome (LQTS) patients. Manual QT-intervals and QTc were determined by the tangent and threshold methods and compared to automatically generated, vendor-provided values. We assessed agreement globally by intra-class correlation coefficients and pairwise by Bland-Altman analyses and 95% limits of agreement (LoA). Further, manual results were compared to a novel automatic QT-interval algorithm. ECGs of 1263 participants (720 LQTS patients; 543 controls) were available [median age 34 (inter-quartile range 35) years, 55% women]. Comparing cohort means, automatic and manual QT-intervals and QTc were similar. However, pairwise Bland-Altman-based agreement was highly discrepant. For QT-interval, LoAs spanned 95 (tangent) and 92 ms (threshold), respectively. For QTc, the spread was 108 and 105 ms, respectively. LQTS patients exhibited more pronounced differences. For automatic QTc results from 440-540 ms (tangent) and 430-530 ms (threshold), misassessment risk was highest. Novel automatic QT-interval algorithms may narrow this range. CONCLUSION: Pairwise vendor-provided automatic and manual QT-interval and QTc results can be highly discrepant. Novel automatic algorithms may improve agreement. Within the above ranges, automatic QT-interval and QTc results require manual confirmation, particularly if T-wave morphology is challenging.

Determination and Interpretation of the QT Interval.

BACKGROUND: Long QT syndrome (LQTS) is associated with potentially fatal arrhythmias. Treatment is very effective, but its diagnosis may be challenging. Importantly, different methods are used to assess the QT interval, which makes its recognition difficult. QT experts advocate manual measurements with the tangent or threshold method. However, differences between these methods and their performance in LQTS diagnosis have not been established. We aimed to assess similarities and differences between these 2 methods for QT interval analysis to aid in accurate QT assessment for LQTS. METHODS: Patients with a confirmed pathogenic variant in KCNQ1(LQT1), KCNH2(LQT2), or SCN5A(LQT3) genes and their family members were included. Genotype-positive patients were identified as LQTS cases and genotype-negative family members as controls. ECGs were analyzed with both methods, providing inter- and intrareader validity and diagnostic accuracy. Cutoff values based on control population's 95th and 99th percentiles, and LQTS-patients' 1st and 5th percentiles were established based on the method to correct for heart rate, age, and sex. RESULTS: We included 1484 individuals from 265 families, aged 33±21 years and 55% females. In the total cohort, QTTangent was 10.4 ms shorter compared with QTThreshold (95% limits of agreement±20.5 ms, P<0.0001). For all genotypes, QTTangent was shorter than QTThreshold ( P<0.0001), but this was less pronounced in LQT2. Both methods yielded a high inter- and intrareader validity (intraclass correlation coefficient >0.96), and a high diagnostic accuracy (area under the curve >0.84). Using the current guideline cutoff (QTc interval 480 ms), both methods had similar specificity but yielded a different sensitivity. QTc interval cutoff values of QTTangent were lower compared with QTThreshold and different depending on the correction for heart rate, age, and sex. CONCLUSION: The QT interval varies depending on the method used for its assessment, yet both methods have a high validity and can both be used in diagnosing LQTS. However, for diagnostic purposes current guideline cutoff values yield different results for these 2 methods and could result in inappropriate reassurance or treatment. Adjusted cutoff values are therefore specified for method, correction formula, age, and sex. In addition, a freely accessible online probability calculator for LQTS ( www.QTcalculator.org ) has been made available as an aid in the interpretation of the QT interval.

CHA2DS2-VASc score and blood biomarkers to identify patients with atrial high-rate episodes and paroxysmal atrial fibrillation.

AIMS: Paroxysmal atrial fibrillation (PAF) is often asymptomatic but nonetheless harmful. We evaluated the performance of disease-related blood biomarkers and CHA2DS2-VASc score to discriminate for PAF in patients with continuous rhythm monitoring. METHODS AND RESULTS: Clinical data and blood samples were obtained from patients with dual-chamber pacemakers selected according to the absence (no_AHRE) or presence of Atrial High-Rate Episodes (AHRE) >6 min in recent device history (case-control approach). We included 93 patients (n = 49 AHRE, n = 44 no_AHRE). In a subgroup with high AHRE burden and confirmed PAF 15 biomarkers were evaluated (n = 19 AHRE-AF vs. n = 20 no_AHRE). Significantly regulated biomarkers were then tested in all patients to distinguish no_AHRE from AHRE (receiver operating characteristics analysis). Hsp27, TGFß1, cystatin C, matrix metalloproteinases MMP-2,-3,-9, albumin, and serum uric acid were not altered in the subgroup. Tissue inhibitors of metalloproteinases (TIMP) -1,-2,-4; NT-proANP, NT-proBNP, IL-6 and serum amyloid protein A were significantly different in AHRE vs. no_AHRE (subgroup and whole cohort), with best discriminatory performance for TIMP-4. Biomarkers performed better than CHADS2-VASc for AHRE discrimination. Intracardial electrograms and medical history from seven AHRE patients suggested atrial tachycardia and not AF (AHRE-AT). Four of the most relevant regulated biomarkers (TIMP-4, TIMP-2, SAA, NT-proBNP) behaved similarly in AHRE-AT and AHRE-AF. NT-proBNP >150 pg/mL indicated an odds ratio of 12.9 for AHRE. Combining two biomarkers significantly improved discrimination of AHRE. CONCLUSION: TIMP-4, NT-proANP, NT-proBNP were strongest associated with PAF and AHRE. The discriminatory performance of CHADS2-VASc for PAF was increased by addition of selected biomarkers.

Genetic Susceptibility for Atrial Fibrillation in Patients Undergoing Atrial Fibrillation Ablation.

BACKGROUND: Ablation is a widely used therapy for atrial fibrillation (AF); however, arrhythmia recurrence and repeat procedures are common. Studies examining surrogate markers of genetic susceptibility to AF, such as family history and individual AF susceptibility alleles, suggest these may be associated with recurrence outcomes. Accordingly, the aim of this study was to test the association between AF genetic susceptibility and recurrence after ablation using a comprehensive polygenic risk score for AF. METHODS: Ten centers from the AF Genetics Consortium identified patients who had undergone de novo AF ablation. AF genetic susceptibility was measured using a previously described polygenic risk score (N=929 single-nucleotide polymorphisms) and tested for an association with clinical characteristics and time-to-recurrence with a 3 month blanking period. Recurrence was defined as >30 seconds of AF, atrial flutter, or atrial tachycardia. Multivariable analysis adjusted for age, sex, height, body mass index, persistent AF, hypertension, coronary disease, left atrial size, left ventricular ejection fraction, and year of ablation. RESULTS: Four thousand two hundred seventy-six patients were eligible for analysis of baseline characteristics and 3259 for recurrence outcomes. The overall arrhythmia recurrence rate between 3 and 12 months was 44% (1443/3259). Patients with higher AF genetic susceptibility were younger (P<0.001) and had fewer clinical risk factors for AF (P=0.001). Persistent AF (hazard ratio [HR], 1.39 [95% CI, 1.22-1.58]; P<0.001), left atrial size (per cm: HR, 1.32 [95% CI, 1.19-1.46]; P<0.001), and left ventricular ejection fraction (per 10%: HR, 0.88 [95% CI, 0.80-0.97]; P=0.008) were associated with increased risk of recurrence. In univariate analysis, higher AF genetic susceptibility trended towards a higher risk of recurrence (HR, 1.08 [95% CI, 0.99-1.18]; P=0.07), which became less significant in multivariable analysis (HR, 1.06 [95% CI, 0.98-1.15]; P=0.13). CONCLUSIONS: Higher AF genetic susceptibility was associated with younger age and fewer clinical risk factors but not recurrence. Arrhythmia recurrence after AF ablation may represent a genetically different phenotype compared to AF susceptibility.

Identification of transcripts from a subtraction library which might be responsible for the mild phenotype in an intrafamilially variable course of Duchenne muscular dystrophy.

While frame-shift mutations are usually found in Duchenne muscular dystrophy (DMD), in-frame mutations are associated with the less severe phenotype of Becker's muscular dystrophy. Exceptions have been reported in both directions suggesting the existence of modifying genes, which might be helpful for innovation of new therapeutic strategies. We report on the very rare case of an intrafamilially different course of DMD, with the younger brother being far less affected than the older one when compared at the same age. In this context, we constructed a subtraction library enriched for transcripts over-expressed in the patient with the milder phenotype. Twelve random clones were sequenced, followed by database analysis. Six of them, casein kinase 1 alpha 1, RAP2B, dynactin 3 light chain, core binding factor beta, myosin light polypeptide 2 and one hypothetical gene, were further analysed by real-time RT-PCR. All these genes were over-expressed 3-20 times in the less affected patient compared with the more severely affected one. Casein kinase 1 and the hypothetical gene showed even a slightly higher expression than the control. Up-regulation of myosin light polypeptide 2, one of the most sensitive markers of muscle fibre regeneration, obviously reflects the milder phenotype. Casein kinase 1, dynactin and core binding factor are supposed to be involved in cell cycle pathways. RAP is a component of the signalling network which controls fundamental cellular processes such as proliferation and differentiation. All four might be interesting candidates for a therapeutic approach to diminish progression of dystrophy in DMD.