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Lin CS, Liu WT, Tsai DJ, Lou YS, Chang CH, Lee CC, Fang WH, Wang CC, Chen YY, Lin WS, Cheng CC, Lee CC, Wang CH, Tsai CS, Lin SH, Lin C. AI-enabled electrocardiography alert intervention and all-cause mortality: a pragmatic randomized clinical trial. Nat Med 2024; 30:1461-1470. [PMID: 38684860 DOI: 10.1038/s41591-024-02961-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/16/2023] [Accepted: 03/29/2024] [Indexed: 05/02/2024]
Abstract
The early identification of vulnerable patients has the potential to improve outcomes but poses a substantial challenge in clinical practice. This study evaluated the ability of an artificial intelligence (AI)-enabled electrocardiogram (ECG) to identify hospitalized patients with a high risk of mortality in a multisite randomized controlled trial involving 39 physicians and 15,965 patients. The AI-ECG alert intervention included an AI report and warning messages delivered to the physicians, flagging patients predicted to be at high risk of mortality. The trial met its primary outcome, finding that implementation of the AI-ECG alert was associated with a significant reduction in all-cause mortality within 90 days: 3.6% patients in the intervention group died within 90 days, compared to 4.3% in the control group (4.3%) (hazard ratio (HR) = 0.83, 95% confidence interval (CI) = 0.70-0.99). A prespecified analysis showed that reduction in all-cause mortality associated with the AI-ECG alert was observed primarily in patients with high-risk ECGs (HR = 0.69, 95% CI = 0.53-0.90). In analyses of secondary outcomes, patients in the intervention group with high-risk ECGs received increased levels of intensive care compared to the control group; for the high-risk ECG group of patients, implementation of the AI-ECG alert was associated with a significant reduction in the risk of cardiac death (0.2% in the intervention arm versus 2.4% in the control arm, HR = 0.07, 95% CI = 0.01-0.56). While the precise means by which implementation of the AI-ECG alert led to decreased mortality are to be fully elucidated, these results indicate that such implementation assists in the detection of high-risk patients, prompting timely clinical care and reducing mortality. ClinicalTrials.gov registration: NCT05118035 .
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Affiliation(s)
- Chin-Sheng Lin
- Division of Cardiology, Department of Internal Medicine, Tri-Service General Hospital, National Defense Medical Center, Taipei, Taiwan, Republic of China
- Medical Technology Education Center, School of Medicine, National Defense Medical Center, Taipei, Taiwan, Republic of China
| | - Wei-Ting Liu
- Division of Cardiology, Department of Internal Medicine, Tri-Service General Hospital, National Defense Medical Center, Taipei, Taiwan, Republic of China
| | - Dung-Jang Tsai
- Medical Technology Education Center, School of Medicine, National Defense Medical Center, Taipei, Taiwan, Republic of China
- Department of Artificial Intelligence and Internet of Things, Tri-Service General Hospital, National Defense Medical Center, Taipei, Taiwan, Republic of China
- Department of Statistics and Information Science, Fu Jen Catholic University, New Taipei City, Taiwan, Republic of China
| | - Yu-Sheng Lou
- Department of Artificial Intelligence and Internet of Things, Tri-Service General Hospital, National Defense Medical Center, Taipei, Taiwan, Republic of China
| | - Chiao-Hsiang Chang
- Division of Cardiology, Department of Internal Medicine, Tri-Service General Hospital, National Defense Medical Center, Taipei, Taiwan, Republic of China
| | - Chiao-Chin Lee
- Division of Cardiology, Department of Internal Medicine, Tri-Service General Hospital, National Defense Medical Center, Taipei, Taiwan, Republic of China
| | - Wen-Hui Fang
- Department of Artificial Intelligence and Internet of Things, Tri-Service General Hospital, National Defense Medical Center, Taipei, Taiwan, Republic of China
- Department of Family and Community Medicine, Tri-Service General Hospital, National Defense Medical Center, Taipei, Taiwan, Republic of China
| | - Chih-Chia Wang
- Department of Family and Community Medicine, Tri-Service General Hospital, National Defense Medical Center, Taipei, Taiwan, Republic of China
| | - Yen-Yuan Chen
- Department and Graduate Institute of Medical Education and Bioethics, National Taiwan University College of Medicine, Taipei, Taiwan, Republic of China
| | - Wei-Shiang Lin
- Division of Cardiology, Department of Internal Medicine, Tri-Service General Hospital, National Defense Medical Center, Taipei, Taiwan, Republic of China
| | - Cheng-Chung Cheng
- Division of Cardiology, Department of Internal Medicine, Tri-Service General Hospital, National Defense Medical Center, Taipei, Taiwan, Republic of China
| | - Chia-Cheng Lee
- Department of Medical Informatics, Tri-Service General Hospital, National Defense Medical Center, Taipei, Taiwan, Republic of China
- Division of Colorectal Surgery, Department of Surgery, Tri-Service General Hospital, National Defense Medical Center, Taipei, Taiwan, Republic of China
| | - Chih-Hung Wang
- Department of Otolaryngology-Head and Neck Surgery, Tri-Service General Hospital, National Defense Medical Center, Taipei, Taiwan, Republic of China
- Graduate Institute of Medical Sciences, National Defense Medical Center, Taipei, Taiwan, Republic of China
| | - Chien-Sung Tsai
- Division of Cardiovascular Surgery, Department of Surgery, Tri-Service General Hospital, National Defense Medical Center, Taipei, Taiwan, Republic of China
| | - Shih-Hua Lin
- Division of Nephrology, Department of Internal Medicine, Tri-Service General Hospital, National Defense Medical Center, Taipei, Taiwan, Republic of China
| | - Chin Lin
- Medical Technology Education Center, School of Medicine, National Defense Medical Center, Taipei, Taiwan, Republic of China.
- Department of Artificial Intelligence and Internet of Things, Tri-Service General Hospital, National Defense Medical Center, Taipei, Taiwan, Republic of China.
- School of Public Health, National Defense Medical Center, Taipei, Taiwan, Republic of China.
- Graduate Institute of Aerospace and Undersea Medicine, National Defense Medical Center, Taipei, Taiwan, Republic of China.
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Lin C, Kuo FC, Chau T, Shih JH, Lin CS, Chen CC, Lee CC, Lin SH. Artificial intelligence-enabled electrocardiography contributes to hyperthyroidism detection and outcome prediction. COMMUNICATIONS MEDICINE 2024; 4:42. [PMID: 38472334 DOI: 10.1038/s43856-024-00472-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2023] [Accepted: 03/01/2024] [Indexed: 03/14/2024] Open
Abstract
BACKGROUND Hyperthyroidism is frequently under-recognized and leads to heart failure and mortality. Timely identification of high-risk patients is a prerequisite to effective antithyroid therapy. Since the heart is very sensitive to hyperthyroidism and its electrical signature can be demonstrated by electrocardiography, we developed an artificial intelligence model to detect hyperthyroidism by electrocardiography and examined its potential for outcome prediction. METHODS The deep learning model was trained using a large dataset of 47,245 electrocardiograms from 33,246 patients at an academic medical center. Patients were included if electrocardiograms and measurements of serum thyroid-stimulating hormone were available that had been obtained within a three day period. Serum thyroid-stimulating hormone and free thyroxine were used to define overt and subclinical hyperthyroidism. We tested the model internally using 14,420 patients and externally using two additional test sets comprising 11,498 and 596 patients, respectively. RESULTS The performance of the deep learning model achieves areas under the receiver operating characteristic curves (AUCs) of 0.725-0.761 for hyperthyroidism detection, AUCs of 0.867-0.876 for overt hyperthyroidism, and AUC of 0.631-0.701 for subclinical hyperthyroidism, superior to a traditional features-based machine learning model. Patients identified as hyperthyroidism-positive by the deep learning model have a significantly higher risk (1.97-2.94 fold) of all-cause mortality and new-onset heart failure compared to hyperthyroidism-negative patients. This cardiovascular disease stratification is particularly pronounced in subclinical hyperthyroidism, surpassing that observed in overt hyperthyroidism. CONCLUSIONS An innovative algorithm effectively identifies overt and subclinical hyperthyroidism and contributes to cardiovascular risk assessment.
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Affiliation(s)
- Chin Lin
- School of Medicine, National Defense Medical Center, Taipei, Taiwan ROC
- Graduate Institute of Aerospace and Undersea Medicine, National Defense Medical Center, Taipei, Taiwan ROC
| | - Feng-Chih Kuo
- Division of Endocrinology and Metabolism, Department of Internal Medicine, Tri-Service General Hospital, National Defense Medical Center, Taipei, Taiwan ROC
| | - Tom Chau
- Department of Medicine, Providence St. Vincent Medical Center, Portland, OR, USA
| | - Jui-Hu Shih
- Department of Pharmacy Practice, Tri-Service General Hospital, Taipei, Taiwan ROC
- School of Pharmacy, National Defense Medical Center, Taipei, Taiwan ROC
| | - Chin-Sheng Lin
- Division of Cardiology, Department of Internal Medicine, Tri-Service General Hospital, National Defense Medical Center, Taipei, Taiwan ROC
| | - Chien-Chou Chen
- Division of Nephrology, Department of Medicine, Tri-Service General Hospital, National Defense Medical Center, Taipei, Taiwan ROC
| | - Chia-Cheng Lee
- Department of Medical Informatics, Tri-Service General Hospital, National Defense Medical Center, Taipei, Taiwan ROC
- Division of Colorectal Surgery, Department of Surgery, Tri-Service General Hospital, National Defense Medical Center, Taipei, Taiwan ROC
| | - Shih-Hua Lin
- Division of Nephrology, Department of Medicine, Tri-Service General Hospital, National Defense Medical Center, Taipei, Taiwan ROC.
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Chen YHJ, Lin CS, Lin C, Tsai DJ, Fang WH, Lee CC, Wang CH, Chen SJ. An AI-Enabled Dynamic Risk Stratification for Emergency Department Patients with ECG and CXR Integration. J Med Syst 2023; 47:81. [PMID: 37523102 DOI: 10.1007/s10916-023-01980-x] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2023] [Accepted: 07/21/2023] [Indexed: 08/01/2023]
Abstract
Emergency department (ED) triage scale determines the priority of patient care and foretells the prognosis. However, the information retrieved from the initial assessment is limited, hindering the risk identification accuracy of triage. Therefore, we sought to develop a 'dynamic' triage system as secondary screening, using artificial intelligence (AI) techniques to integrate information from initial assessment data and subsequent examinations. This retrospective cohort study included 134,112 ED visits with at least one electrocardiography (ECG) and chest X-ray (CXR) in a medical center from 2012 to 2022. Additionally, an independent community hospital provided 45,614 ED visits as an external validation set. We trained an eXtreme gradient boosting (XGB) model using initial assessment data to predict all-cause mortality in 7 days. Two deep learning models (DLMs) using ECG and CXR were trained to stratify mortality risks. The dynamic triage levels were based on output from the XGB-triage and DLMs from ECG and CXR. During the internal and external validation, the area under the receiver operating characteristic curve (AUC) of the XGB-triage model was >0.866; furthermore, the AUCs of DLMs using ECG and CXR were >0.862 and >0.886, respectively. The dynamic triage scale provided a higher C-index (0.914-0.920 vs. 0.827-0.843) than the original one and demonstrated better predictive ability for 5-year mortality, 30-day ED revisit, and 30-day discharge. The AI-based risk scale provides a more accurate and dynamic stratification of mortality risk in ED patients, particularly in identifying patients who tend to be overlooked due to atypical symptoms.
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Affiliation(s)
| | - Chin-Sheng Lin
- Division of Cardiology, Department of Internal Medicine, Tri-Service General Hospital, National Defense Medical Center Taipei, Taipei, Taiwan
- Medical Technology Education Center, School of Medicine, National Defense Medical Center, Taipei, Taiwan
| | - Chin Lin
- School of Public Health, National Defense Medical Center, Taipei, Taiwan
- Medical Technology Education Center, School of Medicine, National Defense Medical Center, Taipei, Taiwan
- Graduate Institutes of Life Sciences, National Defense Medical Center, Taipei, Taiwan
| | - Dung-Jang Tsai
- Center for Artificial Intelligence and Internet of Things, Tri-Service General Hospital, National Defense Medical Center, Taipei, Taiwan
- Department of Statistics and Information Science, Fu Jen Catholic University, New Taipei City, Taiwan
| | - Wen-Hui Fang
- Center for Artificial Intelligence and Internet of Things, Tri-Service General Hospital, National Defense Medical Center, Taipei, Taiwan
- Department of Family and Community Medicine, Tri-Service General Hospital, National Defense Medical Center, Taipei, Taiwan
| | - Chia-Cheng Lee
- Medical Informatics Office, Tri-Service General Hospital, National Defense Medical Center, Taipei, Taiwan
- Division of Colorectal Surgery, Department of Surgery, Tri-Service General Hospital, National Defense Medical Center, Taipei, Taiwan
| | - Chih-Hung Wang
- Graduate Institutes of Life Sciences, National Defense Medical Center, Taipei, Taiwan
- Department of Otolaryngology-Head and Neck Surgery, Tri-Service General Hospital, National Defense Medical Center, Taipei, Taiwan
| | - Sy-Jou Chen
- Department of Emergency Medicine, Tri-Service General Hospital, National Defense Medical Center, No.161, Sec. 6, Minquan E. Rd., Neihu Dist., Taipei City, 11490, Taiwan.
- Graduate Institute of Injury Prevention and Control, College of Public Health and Nutrition, Taipei Medical University, Taipei, Taiwan.
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Lin C, Chau T, Lin CS, Shang HS, Fang WH, Lee DJ, Lee CC, Tsai SH, Wang CH, Lin SH. Point-of-care artificial intelligence-enabled ECG for dyskalemia: a retrospective cohort analysis for accuracy and outcome prediction. NPJ Digit Med 2022; 5:8. [PMID: 35046489 PMCID: PMC8770475 DOI: 10.1038/s41746-021-00550-0] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/13/2021] [Accepted: 12/13/2021] [Indexed: 11/09/2022] Open
Abstract
Dyskalemias are common electrolyte disorders associated with high cardiovascular risk. Artificial intelligence (AI)-assisted electrocardiography (ECG) has been evaluated as an early-detection approach for dyskalemia. The aims of this study were to determine the clinical accuracy of AI-assisted ECG for dyskalemia and prognostic ability on clinical outcomes such as all-cause mortality, hospitalizations, and ED revisits. This retrospective cohort study was done at two hospitals within a health system from May 2019 to December 2020. In total, 26,499 patients with 34,803 emergency department (ED) visits to an academic medical center and 6492 ED visits from 4747 patients to a community hospital who had a 12-lead ECG to estimate ECG-K+ and serum laboratory potassium measurement (Lab-K+) within 1 h were included. ECG-K+ had mean absolute errors (MAEs) of ≤0.365 mmol/L. Area under receiver operating characteristic curves for ECG-K+ to predict moderate-to-severe hypokalemia (Lab-K+ ≤3 mmol/L) and moderate-to-severe hyperkalemia (Lab-K+ ≥ 6 mmol/L) were >0.85 and >0.95, respectively. The U-shaped relationships between K+ concentration and adverse outcomes were more prominent for ECG-K+ than for Lab-K+. ECG-K+ and Lab-K+ hyperkalemia were associated with high HRs for 30-day all-cause mortality. Compared to hypokalemic Lab-K+, patients with hypokalemic ECG-K+ had significantly higher risk for adverse outcomes after full confounder adjustment. In addition, patients with normal Lab-K+ but dyskalemic ECG-K+ (pseudo-positive) also exhibited more co-morbidities and had worse outcomes. Point-of-care bloodless AI ECG-K+ not only rapidly identified potentially severe hypo- and hyperkalemia, but also may serve as a biomarker for medical complexity and an independent predictor for adverse outcomes.
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Affiliation(s)
- Chin Lin
- Medical Technology Education Center, School of Medicine, National Defense Medical Center, Taipei, Taiwan, ROC.,Graduate Institute of Life Sciences, National Defense Medical Center, Taipei, Taiwan, ROC.,School of Public Health, National Defense Medical Center, Taipei, Taiwan, ROC
| | - Tom Chau
- Department of Medicine, Providence St. Vincent Medical Center, Portland, OR, USA
| | - Chin-Sheng Lin
- Medical Technology Education Center, School of Medicine, National Defense Medical Center, Taipei, Taiwan, ROC.,Division of Cardiology, Department of Internal Medicine, Tri-Service General Hospital, National Defense Medical Center, Taipei, Taiwan, ROC
| | - Hung-Sheng Shang
- Division of Clinical Pathology, Department of Pathology, Tri-Service General Hospital, National Defense Medical Center, Taipei, Taiwan, ROC
| | - Wen-Hui Fang
- Department of Family and Community Medicine, Department of Internal Medicine, Tri-Service General Hospital, National Defense Medical Center, Taipei, Taiwan, ROC
| | - Ding-Jie Lee
- Division of Nephrology, Department of Medicine, Tri-Service General Hospital, National Defense Medical Center, Taipei, Taiwan, ROC
| | - Chia-Cheng Lee
- Department of Medical Infromatics, Tri-Service General Hospital, National Defense Medical Center, Taipei, Taiwan, ROC.,Division of Colorectal Surgery, Department of Surgery, Tri-Service General Hospital, National Defense Medical Center, Taipei, Taiwan, ROC
| | - Shi-Hung Tsai
- Department of Emergency Medicine, Tri-Service General Hospital, National Defense Medical Center, Taipei, Taiwan, ROC
| | - Chih-Hung Wang
- Department of Otolaryngology-Head and Neck Surgery, Tri-Service General Hospital, National Defense Medical Center, Taipei, Taiwan, ROC.,Graduate Institute of Medical Sciences, National Defense Medical Center, Taipei, Taiwan, ROC
| | - Shih-Hua Lin
- Division of Nephrology, Department of Medicine, Tri-Service General Hospital, National Defense Medical Center, Taipei, Taiwan, ROC.
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