ArticlePloS one2014
Evaluation of differences in automated QT/QTc measurements between Fukuda Denshi and Nihon Koden systems.
Article in PloS one, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- pedQTNet: A Deep Learning Approach to Estimate Corrected QT Intervals from Multi-Lead Conventional ECG Waveforms in Pediatric Patients.Journal of medical systems · 2026Article
- QT prolongation risk factors and a monitoring strategy in rifampicin-resistant tuberculosis: Findings from the STREAM Stage 2 trial.PLOS global public health · 2026Article
- Effect of dapagliflozin on ventricular repolarization in patients with heart failure with reduced ejection fraction.Heart and vessels · 2023Article
- The Effect of COVID-19 on QTc Prolongation.Cureus · 2022Article
- Determining the clinical significance of computer interpreted electrocardiography conclusions.American journal of cardiovascular disease · 2021Article
- Article
- Incidence and outcomes of long QTc in acute medical admissions.International journal of clinical practice · 2018Observational
- Prevalence and Risk Factors of Drug-Associated Corrected QT Prolongation in Elderly Hospitalized Patients: Results of a Retrospective Analysis of Data Obtained Over 6 Months.Drugs & aging · 2017Article
- Cardiovascular efficacy of sitagliptin in patients with diabetes at high risk of cardiovascular disease: a 12-month follow-up.Cardiovascular diabetology · 2016Observational
- Ventricular repolarization measures for arrhythmic risk stratification.World journal of cardiology · 2016Review
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8 authors.
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Abstract
backgroundAutomatic measurement becomes a preference, and indeed a necessity, when analyzing 1000 s of ECGs in the setting of either drug-inducing QT prolongation screening or genome-wide association studies of QT interval. The problem is that individual manufacturers apply different computerized algorithms to measure QT interval. We conducted a comparative study to assess the outcomes with different automated measurements of QT interval between ECG machine manufacturers and validated the related heart rate correction methods. METHODS AND
resultsHerein, we directly compared these different commercial systems using 10,529 Fukuda Denshi ECGs and 72,754 Nihon Kohden ECGs taken in healthy Japanese volunteers. Log-transformed data revealed an equal optimal heart rate correction formula of QT interval for Fukuda Denshi and Nihon Kohden, in the form of QTc = QT/RR(-0.347). However, with the raw data, the optimal heart rate correction formula of QT interval was in the form of QTc = QT+0.156×(1-RR) for Fukuda Denshi and QTc = QT+0.152×(1-RR) for Nihon Kohden. After optimization of heart rate correction of QT interval by the linear regression model using either log-transformed data or raw data, QTc interval was ∼10 ms longer in Nihon Kohden ECGs than in those recorded on Fukuda Denshi machines. Indeed, regression analysis revealed that differences in the ECG machine used had up to a two-fold larger impact on QT variation than gender difference. Such an impact is likely to be of considerable importance when ECGs for a given individual are recorded on different machines in the setting of multi-institutional joint research.
conclusionsWe recommend that ECG machines of the same manufacturer should be used to measure QT and RR intervals in the setting of multi-institutional joint research. It is desirable to unify the computer algorithm for automatic QT and RR measurements from an ECG.
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