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Liu CW, Wu FH, Hu YL, Pan RH, Lin CH, Chen YF, Tseng GS, Chan YK, Wang CL. Left ventricular hypertrophy detection using electrocardiographic signal. Sci Rep 2023; 13:2556. [PMID: 36781924 PMCID: PMC9924839 DOI: 10.1038/s41598-023-28325-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/16/2022] [Accepted: 01/17/2023] [Indexed: 02/15/2023] Open
Abstract
Left ventricular hypertrophy (LVH) indicates subclinical organ damage, associating with the incidence of cardiovascular diseases. From the medical perspective, electrocardiogram (ECG) is a low-cost, non-invasive, and easily reproducible tool that is often used as a preliminary diagnosis for the detection of heart disease. Nowadays, there are many criteria for assessing LVH by ECG. These criteria usually include that voltage combination of RS peaks in multi-lead ECG must be greater than one or more thresholds for diagnosis. We developed a system for detecting LVH using ECG signals by two steps: firstly, the R-peak and S-valley amplitudes of the 12-lead ECG were extracted to automatically obtain a total of 24 features and ECG beats of each case (LVH or non-LVH) were segmented; secondly, a back propagation neural network (BPN) was trained using a dataset with these features. Echocardiography (ECHO) was used as the gold standard for diagnosing LVH. The number of LVH cases (of a Taiwanese population) identified was 173. As each ECG sequence generally included 8 to 13 cycles (heartbeats) due to differences in heart rate, etc., we identified 1466 ECG cycles of LVH patients after beat segmentation. Results showed that our BPN model for detecting LVH reached the testing accuracy, precision, sensitivity, and specificity of 0.961, 0.958, 0.966 and 0.956, respectively. Detection performances of our BPN model, on the whole, outperform 7 methods using ECG criteria and many ECG-based artificial intelligence (AI) models reported previously for detecting LVH.
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Affiliation(s)
- Cheng-Wei Liu
- Division of Cardiology, Department of Internal Medicine, Tri-Service General Hospital Songshan Branch, National Defense Medical Center, Taipei, Taiwan
| | - Fu-Hsing Wu
- Bachelor Degree Program of Artificial Intelligence, National Taichung University of Science and Technology, Taichung, Taiwan
| | - Yu-Lun Hu
- Department of Management Information Systems, National Chung-Hsing University, Taichung, Taiwan
| | - Ren-Hao Pan
- La Vida Tec. Co. Ltd., Taichung, Taiwan
- Preventive Medicine Center, National Yang Ming Chiao Tung University, Taipei, Taiwan
- Department of Information Management, Tunghai University, Taichung, Taiwan
| | - Chuen-Horng Lin
- Department of Computer Science and Information Engineering, National Taichung University of Science and Technology, Taichung, Taiwan
| | - Yung-Fu Chen
- Department of Dental Technology and Materials Science, Central Taiwan University of Science and Technology, Taichung, Taiwan
| | - Guo-Shiang Tseng
- Division of Cardiology, Department of Internal Medicine, Taoyuan Armed Force General Hospital Hsinchu Branch, Hsinchu, Taiwan
| | - Yung-Kuan Chan
- Department of Management Information Systems, National Chung-Hsing University, Taichung, Taiwan.
| | - Ching-Lin Wang
- Department of Information Management, National Chin-Yi University of Technology, Taichung, Taiwan.
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Lin C, Pan LF, He ZQ, Hsu S. Early prediction of 30- and 14-day all-cause unplanned readmissions. Health Informatics J 2023; 29:14604582231164694. [PMID: 36913624 DOI: 10.1177/14604582231164694] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/15/2023]
Abstract
BACKGROUND An unplanned readmission is a dual metric for both the cost and quality of medical care. METHODS We employed the random forest (RF) method to build a prediction model using a large dataset from patients' electronic health records (EHRs) from a medical center in Taiwan. The discrimination abilities between the RF and regression-based models were compared using the areas under the ROC curves (AUROC). RESULTS When compared with standardized risk prediction tools, the RF constructed using data readily available at admission had a marginally yet significantly better ability to identify high-risk readmissions within 30 and 14 days without compromising sensitivity and specificity. The most important predictor for 30-day readmissions was directly related to the representing factors of index hospitalization, whereas for 14-day readmissions the most important predictor was associated with a higher chronic illness burden. CONCLUSIONS Identifying dominant risk factors based on index admission and different readmission time intervals is crucial for healthcare planning.
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Affiliation(s)
- Chaohsin Lin
- Department of Risk Management and Insurance, 517768National Kaohsiung University of Science and Technology, Kaohsiung, Taiwan
| | - Li-Fei Pan
- Department of General Affairs Administration, 38024Kaohsiung Veterans General Hospital, Kaohsiung, Taiwan
| | - Zuo-Quan He
- Department of Risk Management and Insurance, 517768National Kaohsiung University of Science and Technology, Kaohsiung, Taiwan
| | - Shuofen Hsu
- Department of Risk Management and Insurance, 517768National Kaohsiung University of Science and Technology, Kaohsiung, Taiwan
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Sun CH, Chou YY, Lee YS, Weng SC, Lin CF, Kuo FH, Hsu PS, Lin SY. Prediction of 30-Day Readmission in Hospitalized Older Adults Using Comprehensive Geriatric Assessment and LACE Index and HOSPITAL Score. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2022; 20:348. [PMID: 36612671 PMCID: PMC9819393 DOI: 10.3390/ijerph20010348] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/19/2022] [Revised: 12/07/2022] [Accepted: 12/21/2022] [Indexed: 06/17/2023]
Abstract
(1) Background: Elders have higher rates of rehospitalization, especially those with functional decline. We aimed to investigate potential predictors of 30-day readmission risk by comprehensive geriatric assessment (CGA) in hospitalized patients aged 65 years or older and to examine the predictive ability of the LACE index and HOSPITAL score in older patients with a combination of malnutrition and physical dysfunction. (2) Methods: We included patients admitted to a geriatric ward in a tertiary hospital from July 2012 to August 2018. CGA components including cognitive, functional, nutritional, and social parameters were assessed at admission and recorded, as well as clinical information. The association factors with 30-day hospital readmission were analyzed by multivariate logistic regression analysis. The predictive ability of the LACE and HOSPITAL score was assessed using receiver operator characteristic curve analysis. (3) Results: During the study period, 1509 patients admitted to a ward were recorded. Of these patients, 233 (15.4%) were readmitted within 30 days. Those who were readmitted presented with higher comorbidity numbers and poorer performance of CGA, including gait ability, activities of daily living (ADL), and nutritional status. Multivariate regression analysis showed that male gender and moderately impaired gait ability were independently correlated with 30-day hospital readmissions, while other components such as functional impairment (as ADL) and nutritional status were not associated with 30-day rehospitalization. The receiver operating characteristics for the LACE index and HOSPITAL score showed that both predicting scores performed poorly at predicting 30-day hospital readmission (C-statistic = 0.59) and did not perform better in any of the subgroups. (4) Conclusions: Our study showed that only some components of CGA, mobile disability, and gender were independently associated with increased risk of readmission. However, the LACE index and HOSPITAL score had a poor discriminating ability for predicting 30-day hospitalization in all and subgroup patients. Further identifiers are required to better estimate the 30-day readmission rates in this patient population.
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Affiliation(s)
- Chia-Hui Sun
- Department of Family Medicine, Taichung Veterans General Hospital, Taichung 40705, Taiwan
| | - Yin-Yi Chou
- Center for Geriatrics & Gerontology, Taichung Veterans General Hospital, Taichung 40705, Taiwan
- Division of Allergy, Immunology and Rheumatology, Department of Internal Medicine, Taichung Veterans General Hospital, Taichung 40705, Taiwan
| | - Yu-Shan Lee
- Center for Geriatrics & Gerontology, Taichung Veterans General Hospital, Taichung 40705, Taiwan
- Department of Neurology, Neurological Institute, Taichung Veterans General Hospital, Taichung 40705, Taiwan
| | - Shuo-Chun Weng
- Center for Geriatrics & Gerontology, Taichung Veterans General Hospital, Taichung 40705, Taiwan
- Division of Nephrology, Department of Internal Medicine, Taichung Veterans General Hospital, Taichung 40705, Taiwan
| | - Cheng-Fu Lin
- Center for Geriatrics & Gerontology, Taichung Veterans General Hospital, Taichung 40705, Taiwan
- Division of Occupational Medicine, Department of Emergency, Taichung Veterans General Hospital, Taichung 40705, Taiwan
| | - Fu-Hsuan Kuo
- Center for Geriatrics & Gerontology, Taichung Veterans General Hospital, Taichung 40705, Taiwan
- Department of Neurology, Neurological Institute, Taichung Veterans General Hospital, Taichung 40705, Taiwan
| | - Pi-Shan Hsu
- Department of Family Medicine, Taichung Veterans General Hospital, Taichung 40705, Taiwan
| | - Shih-Yi Lin
- Center for Geriatrics & Gerontology, Taichung Veterans General Hospital, Taichung 40705, Taiwan
- Division of Endocrinology and Metabolism, Department of Internal Medicine, Taichung Veterans General Hospital, Taichung 40705, Taiwan
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