Prediction of current and new development of atrial fibrillation on electrocardiogram with sinus rhythm in patients without structural heart disease

Hirota, Naomi; Suzuki, Shinya; Arita, Takuto; Yagi, Nobuyuki; Otsuka, Takayuki; Kishi, Mikio; Semba, Hiroaki; Kuwabara, Hiroto; Matsuno, Seiki; Kato, Yuko; Uejima, Tokuhisa; Oikawa, Yuji; Matsuhama, Minoru; Inoue, Tatsuya; Yajima, Junji; Yamashita, Takeshi

doi:10.1016/j.ijcard.2020.11.012

Cited by 11 publications

(14 citation statements)

References 19 publications

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“…Such studies were based on the hypothesis that the AF signature due to structural changes in the atria can be identified by 12-lead ECG during SR [5] , [14] because structural changes in the atria predispose to atrial arrhythmia [15] . Moreover, in our previous study using hundreds of ECG parameters analyzed with the random forest algorithm, the importance of ECG parameters in predicting AF was similar in the P wave, QRS complex, and ST-T segment, which suggested that structural changes in the ventricle, presumably due to aging or atherosclerosis, seem to be similarly important [16] .…”

Section: Discussionmentioning

confidence: 90%

Identifying patients with atrial fibrillation during sinus rhythm on ECG: Significance of the labeling in the artificial intelligence algorithm

Suzuki

Motogi

Nakai

et al. 2022

IJC Heart & Vasculature

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Highlights High performance of AI algorithm to detect AF using SR-ECG was confirmed in patients without structural heart disease. The performance of AI-enabled ECG to detect AF was high especially when the algorithm included SR-ECG taken after the index AF-ECG. A similar tendency was observed when the performance was tested in patients with structural heart diseases.

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Section: Discussionmentioning

confidence: 90%

Identifying patients with atrial fibrillation during sinus rhythm on ECG: Significance of the labeling in the artificial intelligence algorithm

Suzuki

Motogi

Nakai

et al. 2022

IJC Heart & Vasculature

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“…After excluding patients with structural heart diseases (n = 4,915); patients aged < 20 years or > 90 years (< 20 or > 90 years; n = 168); and patients with index ECG showing indeterminate axis (R axis > 180°) (n = 76), pacing beats (n = 102), and atrial or ventricular tachyarrhythmia (n = 1,763), 12,837 patients were included in the present study. The structural heart diseases were defined as follows: valvular heart disease, moderate or severe stenosis or regurgitation on echocardiography; coronary artery disease; hypertrophic and dilated cardiomyopathy; and symptomatic heart failure [13].…”

Section: Study Populationmentioning

confidence: 99%

“…The health status and incidences of cardiovascular events and all-cause death were obtained once per year from the medical records or the postal prognosis documents. In the present study, we included the follow-up data until March 2019 and excluded follow-up data from > 3 years after the initial visit to avoid an imbalance in the followup periods as a result of the different registration years (between 2010 and 2018) [13].…”

Section: Patient Follow-upmentioning

confidence: 99%

“…The 12-lead ECG was recorded by a GE ECG machine (GE CardioSoft V6.71 and MAC 5500 HD; GE Healthcare, Chicago, IL), and data were stored using the MUSE data management system [13]. Of the 639 parameters that had been automatically measured, 201 parameters (of which 9 were not lead-specific and 192 [16 × 12 leads] were lead-specific), including the relative coordinate points (i.e., the start point of P-wave), were excluded from the analysis [13]. Accordingly, the remaining 438 parameters (of which 6 were not lead-specific and 432 [36 × 12 leads] were lead-specific) were used in the analysis ( Table 1).…”

Section: Parameters Obtained From Ecgmentioning

confidence: 99%

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Relationship between resting 12-lead electrocardiogram and all-cause death in patients without structural heart disease: Shinken Database analysis

Hirota

Suzuki

Arita

et al. 2021

BMC Cardiovasc Disord

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Background Resting 12-lead electrocardiography is widely used for the detection of cardiac diseases. Electrocardiogram readings have been reported to be affected by aging and, therefore, can predict patient mortality. Methods A total of 12,837 patients without structural heart disease who underwent electrocardiography at baseline were identified in the Shinken Database among those registered between 2010 and 2017 (n = 19,170). Using 438 electrocardiography parameters, predictive models for all-cause death and cardiovascular (CV) death were developed by a support vector machine (SVM) algorithm. Results During the observation period of 320.4 days, 55 all-cause deaths and 23 CV deaths were observed. In the SVM prediction model, the mean c-statistics of 10 cross-validation models with training and testing datasets were 0.881 ± 0.027 and 0.927 ± 0.101, respectively, for all-cause death and 0.862 ± 0.029 and 0.897 ± 0.069, respectively for CV death. For both all-cause and CV death, high values of permutation importance in the ECG parameters were concentrated in the QRS complex and ST-T segment. Conclusions Parameters acquired from 12-lead resting electrocardiography could be applied to predict the all-cause and CV deaths of patients without structural heart disease. The ECG parameters that greatly contributed to the prediction were concentrated in the QRS complex and ST-T segment.

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“…Atrial fibrillation (AF) is a common kind of arrhythmia. Arrhythmia refers to the abnormal speed and uniformity of heart beating and mainly manifests as too fast, too slow, or irregular heart beating [ 1 , 2 ]. When AF occurs, the rapid and irregular electrical signals will cause the fibrillation of the right ventricle and the left atrium so that they cannot contract normally, the blood in the atrium cannot all enter the ventricle, and the atrium and ventricle cannot coordinate normally [ 3 ].…”

Section: Introductionmentioning

confidence: 99%

Intelligent Algorithm-Based Electrocardiography to Predict Atrial Fibrillation after Coronary Artery Bypass Grafting in the Elderly

Feng

Deng

2022

Computational and Mathematical Methods in Medicine

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The objective of this study was to explore the predictive value of electrocardiogram (ECG) based on intelligent analysis algorithm for atrial fibrillation (AF) in elderly patients undergoing coronary artery bypass grafting (CABG). Specifically, 106 elderly patients with coronary heart disease who underwent CABG in the hospital were selected, including 52 patients with postoperative AF (AF group) and 54 patients without arrhythmia (control group). Within 1-3 weeks after operation, the dynamic ECG monitoring system based on Gentle AdaBoost algorithm constructed in this study was adopted. After the measurement of the 12-lead P wave duration, the maximum P wave duration (Pmax) and minimum P wave duration (Pmin) were recorded. As for simulation experiments, the same data was used as the back-propagation algorithm. The results showed that for the detection accuracy of the test samples, the Gentle AdaBoost algorithm showed 93.7% accuracy after the first iteration, and the Gentle AdaBoost algorithm was 16.1% higher than the back-propagation algorithm. Compared with the control group, the detection rate of arrhythmia in patients after CABG was significantly lower ( P < 0.05 ). Bivariate logistic regression analysis on Pmax and Pmin showed as follows: Pmax: 95% confidential interval (CI): 1.024-1.081, P < 0.05 ; Pmin: 95% CI: 1.036-1.117, P < 0.05 . The sensitivity of Pmax and Pmin in predicting paroxysmal AF was 78.2% and 73.4%, respectively; the specificity of them was 80.1% and 85.6%, respectively; the positive predictive value was 81.2% and 83.4%, respectively; and the negative predictive value was 79.5% and 75.3%, respectively. In conclusion, the generalization ability of Gentle AdaBoost algorithm was better than that of back-propagation algorithm, and it can identify arrhythmia better. Pmax and Pmin were important indicators of AF after CABG.

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Prediction of current and new development of atrial fibrillation on electrocardiogram with sinus rhythm in patients without structural heart disease

Cited by 11 publications

References 19 publications

Identifying patients with atrial fibrillation during sinus rhythm on ECG: Significance of the labeling in the artificial intelligence algorithm

Identifying patients with atrial fibrillation during sinus rhythm on ECG: Significance of the labeling in the artificial intelligence algorithm

Relationship between resting 12-lead electrocardiogram and all-cause death in patients without structural heart disease: Shinken Database analysis

Intelligent Algorithm-Based Electrocardiography to Predict Atrial Fibrillation after Coronary Artery Bypass Grafting in the Elderly

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