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Kwon S, Lee E, Ju H, Ahn HJ, Lee SR, Choi EK, Suh J, Oh S, Rhee W. Machine Learning Prediction for the Recurrence After Electrical Cardioversion of Patients With Persistent Atrial Fibrillation. Korean Circ J 2023; 53:677-689. [PMID: 37653713 PMCID: PMC10625851 DOI: 10.4070/kcj.2023.0012] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/09/2023] [Revised: 05/25/2023] [Accepted: 06/13/2023] [Indexed: 09/02/2023] Open
Abstract
BACKGROUND AND OBJECTIVES There is limited evidence regarding machine-learning prediction for the recurrence of atrial fibrillation (AF) after electrical cardioversion (ECV). This study aimed to predict the recurrence of AF after ECV using machine learning of clinical features and electrocardiograms (ECGs) in persistent AF patients. METHODS We analyzed patients who underwent successful ECV for persistent AF. Machine learning was designed to predict patients with 1-month recurrence. Individual 12-lead ECGs were collected before and after ECV. Various clinical features were collected and trained the extreme gradient boost (XGBoost)-based model. Ten-fold cross-validation was used to evaluate the performance of the model. The performance was compared to the C-statistics of the selected clinical features. RESULTS Among 718 patients (mean age 63.5±9.3 years, men 78.8%), AF recurred in 435 (60.6%) patients after 1 month. With the XGBoost-based model, the areas under the receiver operating characteristic curves (AUROCs) were 0.57, 0.60, and 0.63 if the model was trained by clinical features, ECGs, and both (the final model), respectively. For the final model, the sensitivity, specificity, and F1-score were 84.7%, 28.2%, and 0.73, respectively. Although the AF duration showed the best predictive performance (AUROC, 0.58) among the clinical features, it was significantly lower than that of the final machine-learning model (p<0.001). Additional training of extended monitoring data of 15-minute single-lead ECG and photoplethysmography in available patients (n=261) did not significantly improve the model's performance. CONCLUSIONS Machine learning showed modest performance in predicting AF recurrence after ECV in persistent AF patients, warranting further validation studies.
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Affiliation(s)
- Soonil Kwon
- Department of Internal Medicine, Seoul National University Hospital, Seoul, Korea
| | - Eunjung Lee
- Department of Cardiovascular Medicine, Mayo Clinic, Rochester, MN, USA
| | - Hojin Ju
- Department of Internal Medicine, Seoul National University Hospital, Seoul, Korea
| | - Hyo-Jeong Ahn
- Department of Internal Medicine, Seoul National University Hospital, Seoul, Korea
| | - So-Ryoung Lee
- Department of Internal Medicine, Seoul National University Hospital, Seoul, Korea
| | - Eue-Keun Choi
- Department of Internal Medicine, Seoul National University Hospital, Seoul, Korea
- Department of Internal Medicine, Seoul National University College of Medicine, Seoul, Korea.
| | - Jangwon Suh
- Department of Intelligence and Information, Seoul National University, Seoul, Korea
| | - Seil Oh
- Department of Internal Medicine, Seoul National University Hospital, Seoul, Korea
- Department of Internal Medicine, Seoul National University College of Medicine, Seoul, Korea
| | - Wonjong Rhee
- Department of Intelligence and Information, Seoul National University, Seoul, Korea.
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You SM, Jo HJ, Cho BH, Song JY, Kim DY, Hwang YH, Shon YM, Seo DW, Kim IY. Comparing Ictal Cardiac Autonomic Changes in Patients with Frontal Lobe Epilepsy and Temporal Lobe Epilepsy by Ultra-Short-Term Heart Rate Variability Analysis. Medicina (Kaunas) 2021; 57:666. [PMID: 34203291 PMCID: PMC8304923 DOI: 10.3390/medicina57070666] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/03/2021] [Revised: 06/25/2021] [Accepted: 06/25/2021] [Indexed: 11/16/2022]
Abstract
Background and Objectives: Abnormal epileptic discharges in the brain can affect the central brain regions that regulate autonomic activity and produce cardiac symptoms, either at onset or during propagation of a seizure. These autonomic alterations are related to cardiorespiratory disturbances, such as sudden unexpected death in epilepsy. This study aims to investigate the differences in cardiac autonomic function between patients with temporal lobe epilepsy (TLE) and frontal lobe epilepsy (FLE) using ultra-short-term heart rate variability (HRV) analysis around seizures. Materials and Methods: We analyzed electrocardiogram (ECG) data recorded during 309 seizures in 58 patients with epilepsy. Twelve patients with FLE and 46 patients with TLE were included in this study. We extracted the HRV parameters from the ECG signal before, during and after the ictal interval with ultra-short-term HRV analysis. We statistically compared the HRV parameters using an independent t-test in each interval to compare the differences between groups, and repeated measures analysis of variance was used to test the group differences in longitudinal changes in the HRV parameters. We performed the Tukey-Kramer multiple comparisons procedure as the post hoc test. Results: Among the HRV parameters, the mean interval between heartbeats (RRi), normalized low-frequency band power (LF) and LF/HF ratio were statistically different between the interval and epilepsy types in the t-test. Repeated measures ANOVA showed that the mean RRi and RMSSD were significantly different by epilepsy type, and the normalized LF and LF/HF ratio significantly interacted with the epilepsy type and interval. Conclusions: During the pre-ictal interval, TLE patients showed an elevation in sympathetic activity, while the FLE patients showed an apparent increase and decrease in sympathetic activity when entering and ending the ictal period, respectively. The TLE patients showed a maintained elevation of sympathetic and vagal activity in the pos-ictal interval. These differences in autonomic cardiac characteristics between FLE and TLE might be relevant to the ictal symptoms which eventually result in SUDEP.
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Affiliation(s)
- Sung-Min You
- Department of Biomedical Engineering, Hanyang University, 222, Wangsimni-ro, Seongdong-gu, Seoul 04763, Korea;
| | - Hyun-Jin Jo
- Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, 81, Irwon-ro, Gangnam-gu, Seoul 06351, Korea; (H.-J.J.); (J.-Y.S.); (D.-Y.K.); (Y.-H.H.); (Y.-M.S.)
| | - Baek-Hwan Cho
- Medical AI Research Center, Samsung Medical Center, Sungkyunkwan University School of Medicine, 81, Irwon-ro, Gangnam-gu, Seoul 06351, Korea;
- Department of Medical Device Management and Research, Samsung Advanced Institute for Health Sciences & Technology, Sungkyunkwan University School of Medicine, 81, Irwon-ro, Gangnam-gu, Seoul 06351, Korea
| | - Joo-Yeon Song
- Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, 81, Irwon-ro, Gangnam-gu, Seoul 06351, Korea; (H.-J.J.); (J.-Y.S.); (D.-Y.K.); (Y.-H.H.); (Y.-M.S.)
| | - Dong-Yeop Kim
- Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, 81, Irwon-ro, Gangnam-gu, Seoul 06351, Korea; (H.-J.J.); (J.-Y.S.); (D.-Y.K.); (Y.-H.H.); (Y.-M.S.)
| | - Yoon-Ha Hwang
- Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, 81, Irwon-ro, Gangnam-gu, Seoul 06351, Korea; (H.-J.J.); (J.-Y.S.); (D.-Y.K.); (Y.-H.H.); (Y.-M.S.)
| | - Young-Min Shon
- Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, 81, Irwon-ro, Gangnam-gu, Seoul 06351, Korea; (H.-J.J.); (J.-Y.S.); (D.-Y.K.); (Y.-H.H.); (Y.-M.S.)
| | - Dae-Won Seo
- Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, 81, Irwon-ro, Gangnam-gu, Seoul 06351, Korea; (H.-J.J.); (J.-Y.S.); (D.-Y.K.); (Y.-H.H.); (Y.-M.S.)
| | - In-Young Kim
- Department of Biomedical Engineering, Hanyang University, 222, Wangsimni-ro, Seongdong-gu, Seoul 04763, Korea;
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Park SW, Lee S, Cha WC, Hur KY, Kim JH, Lee MK, Park SM, Jin SM. An Electronic Health Record-Integrated Computerized Intravenous Insulin Infusion Protocol: Clinical Outcomes and in Silico Adjustment. Diabetes Metab J 2020; 44:56-66. [PMID: 31701686 PMCID: PMC7043972 DOI: 10.4093/dmj.2018.0227] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/08/2018] [Accepted: 03/30/2019] [Indexed: 01/04/2023] Open
Abstract
BACKGROUND We aimed to describe the outcome of a computerized intravenous insulin infusion (CII) protocol integrated to the electronic health record (EHR) system and to improve the CII protocol in silico using the EHR-based predictors of the outcome. METHODS Clinical outcomes of the patients who underwent the CII protocol between July 2016 and February 2017 and their matched controls were evaluated. In the CII protocol group (n=91), multivariable binary logistic regression analysis models were used to determine the independent associates with a delayed response (taking ≥6.0 hours for entering a glucose range of 70 to 180 mg/dL). The CII protocol was adjusted in silico according to the EHR-based parameters obtained in the first 3 hours of CII. RESULTS Use of the CII protocol was associated with fewer subjects with hypoglycemia alert values (P=0.003), earlier (P=0.002), and more stable (P=0.017) achievement of a glucose range of 70 to 180 mg/dL. Initial glucose level (P=0.001), change in glucose during the first 2 hours (P=0.026), and change in insulin infusion rate during the first 3 hours (P=0.029) were independently associated with delayed responses. Increasing the insulin infusion rate temporarily according to these parameters in silico significantly reduced delayed responses (P<0.0001) without hypoglycemia, especially in refractory patients. CONCLUSION Our CII protocol enabled faster and more stable glycemic control than conventional care with minimized risk of hypoglycemia. An EHR-based adjustment was simulated to reduce delayed responses without increased incidence of hypoglycemia.
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Affiliation(s)
- Sung Woon Park
- Division of Endocrinology and Metabolism, Department of Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea
| | - Seunghyun Lee
- Department of Creative IT Engineering, Pohang University of Science and Technology (POSTECH), Pohang, Korea
| | - Won Chul Cha
- Department of Emergency Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea
- Department of Digital Health, Samsung Advanced Institute for Health Sciences & Technology, Sungkyunkwan University, Seoul, Korea
| | - Kyu Yeon Hur
- Division of Endocrinology and Metabolism, Department of Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea
| | - Jae Hyeon Kim
- Division of Endocrinology and Metabolism, Department of Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea
| | - Moon Kyu Lee
- Division of Endocrinology and Metabolism, Department of Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea
| | - Sung Min Park
- Department of Creative IT Engineering, Pohang University of Science and Technology (POSTECH), Pohang, Korea.
| | - Sang Man Jin
- Division of Endocrinology and Metabolism, Department of Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea
- Department of Digital Health, Samsung Advanced Institute for Health Sciences & Technology, Sungkyunkwan University, Seoul, Korea.
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