Artificial intelligence-assisted diagnosis and prognostication in low ejection fraction using electrocardiograms in inpatient department: a pragmatic randomized controlled trial
Abstract Background Early diagnosis of low ejection fraction (EF) remains challenging despite being a treatable condition. This study aimed to evaluate the effectiveness of an electrocardiogram (ECG)-based artificial intelligence (AI)-assisted clinical decision support tool in improving the early di...
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2025-06-01
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| Online Access: | https://doi.org/10.1186/s12916-025-04190-z |
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| author | Dung-Jang Tsai Chin Lin Wei-Ting Liu Chiao-Chin Lee Chiao-Hsiang Chang Wen-Yu Lin Yu-Lan Liu Da-Wei Chang Ping-Hsuan Hsieh Chien-Sung Tsai Yuan-Hao Chen Yi-Jen Hung Chin-Sheng Lin |
| author_facet | Dung-Jang Tsai Chin Lin Wei-Ting Liu Chiao-Chin Lee Chiao-Hsiang Chang Wen-Yu Lin Yu-Lan Liu Da-Wei Chang Ping-Hsuan Hsieh Chien-Sung Tsai Yuan-Hao Chen Yi-Jen Hung Chin-Sheng Lin |
| author_sort | Dung-Jang Tsai |
| collection | DOAJ |
| description | Abstract Background Early diagnosis of low ejection fraction (EF) remains challenging despite being a treatable condition. This study aimed to evaluate the effectiveness of an electrocardiogram (ECG)-based artificial intelligence (AI)-assisted clinical decision support tool in improving the early diagnosis of low EF among inpatient patients under non-cardiologist care. Methods We conducted a pragmatic randomized controlled trial at an academic medical center in Taiwan. 13,631 inpatient patients were randomized to either the intervention group (n = 6,840) receiving AI-generated ECG results or the control group (n = 6,791) following standard care. The primary outcome was the incidence of newly diagnosed low EF (≤ 50%) within 30 days following the ECG. Secondary outcomes included echocardiogram utilization rates, positive predictive value for low EF detection, and cardiology consultation rates. Statistical analysis included hazard ratios (HR) with 95% confidence intervals (CI) for time-to-event outcomes and chi-square tests for categorical variables. Results The intervention significantly increased the detection of newly diagnosed low EF in the overall cohort (1.5% vs. 1.1%, HR 1.50, 95% CI: 1.11–2.03, P = 0.023), with a more pronounced effect among AI-identified high-risk patients (13.0% vs. 8.9%, HR 1.55, 95% CI: 1.08–2.21). While overall echocardiogram utilization remained similar between groups (17.1% vs. 17.3%, HR 1.00, 95% CI: 0.92–1.09), the intervention group demonstrated higher positive predictive value for identifying low EF among patients receiving echocardiogram (34.2% vs. 20.2%, p < 0.001). Post-hoc analysis revealed increased cardiology consultation rates among high-risk patients in the intervention group (29.3% vs. 23.5%, p = 0.027). Conclusions Implementation of an AI-ECG algorithm enhanced the early diagnosis of low EF in the inpatient setting, primarily by improving diagnostic efficiency rather than increasing overall healthcare utilization. The tool was particularly effective in identifying high-risk patients who benefited from increased specialist consultation and more targeted diagnostic testing. Trial registration ClinicalTrials.gov Identifier: NCT05117970. |
| format | Article |
| id | doaj-art-da43eb82bd8946669e11ebece30a0397 |
| institution | DOAJ |
| issn | 1741-7015 |
| language | English |
| publishDate | 2025-06-01 |
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| spelling | doaj-art-da43eb82bd8946669e11ebece30a03972025-08-20T02:39:44ZengBMCBMC Medicine1741-70152025-06-0123111210.1186/s12916-025-04190-zArtificial intelligence-assisted diagnosis and prognostication in low ejection fraction using electrocardiograms in inpatient department: a pragmatic randomized controlled trialDung-Jang Tsai0Chin Lin1Wei-Ting Liu2Chiao-Chin Lee3Chiao-Hsiang Chang4Wen-Yu Lin5Yu-Lan Liu6Da-Wei Chang7Ping-Hsuan Hsieh8Chien-Sung Tsai9Yuan-Hao Chen10Yi-Jen Hung11Chin-Sheng Lin12Medical Technology Education Center, School of Medicine, National Defense Medical CenterMedical Technology Education Center, School of Medicine, National Defense Medical CenterDivision of Cardiology, Department of Internal Medicine, Tri-Service General Hospital, National Defense Medical CenterDivision of Cardiology, Department of Internal Medicine, Tri-Service General Hospital, National Defense Medical CenterDivision of Cardiology, Department of Internal Medicine, Tri-Service General Hospital, National Defense Medical CenterDivision of Cardiology, Department of Internal Medicine, Tri-Service General Hospital, National Defense Medical CenterDivision of Cardiology, Department of Internal Medicine, Tri-Service General Hospital, National Defense Medical CenterDivision of Cardiology, Department of Internal Medicine, Tri-Service General Hospital, National Defense Medical CenterSchool of Pharmacy, National Defense Medical CenterDivision of Cardiovascular Surgery, Department of Surgery, Tri-Service General Hospital, National Defense Medical CenterDepartment of Neurological Surgery, Tri-Service General Hospital, National Defense Medical CenterDivision of Endocrinology and Metabolism, Department of Internal Medicine, Tri-Service General Hospital, National Defense Medical CenterDivision of Cardiology, Department of Internal Medicine, Tri-Service General Hospital, National Defense Medical CenterAbstract Background Early diagnosis of low ejection fraction (EF) remains challenging despite being a treatable condition. This study aimed to evaluate the effectiveness of an electrocardiogram (ECG)-based artificial intelligence (AI)-assisted clinical decision support tool in improving the early diagnosis of low EF among inpatient patients under non-cardiologist care. Methods We conducted a pragmatic randomized controlled trial at an academic medical center in Taiwan. 13,631 inpatient patients were randomized to either the intervention group (n = 6,840) receiving AI-generated ECG results or the control group (n = 6,791) following standard care. The primary outcome was the incidence of newly diagnosed low EF (≤ 50%) within 30 days following the ECG. Secondary outcomes included echocardiogram utilization rates, positive predictive value for low EF detection, and cardiology consultation rates. Statistical analysis included hazard ratios (HR) with 95% confidence intervals (CI) for time-to-event outcomes and chi-square tests for categorical variables. Results The intervention significantly increased the detection of newly diagnosed low EF in the overall cohort (1.5% vs. 1.1%, HR 1.50, 95% CI: 1.11–2.03, P = 0.023), with a more pronounced effect among AI-identified high-risk patients (13.0% vs. 8.9%, HR 1.55, 95% CI: 1.08–2.21). While overall echocardiogram utilization remained similar between groups (17.1% vs. 17.3%, HR 1.00, 95% CI: 0.92–1.09), the intervention group demonstrated higher positive predictive value for identifying low EF among patients receiving echocardiogram (34.2% vs. 20.2%, p < 0.001). Post-hoc analysis revealed increased cardiology consultation rates among high-risk patients in the intervention group (29.3% vs. 23.5%, p = 0.027). Conclusions Implementation of an AI-ECG algorithm enhanced the early diagnosis of low EF in the inpatient setting, primarily by improving diagnostic efficiency rather than increasing overall healthcare utilization. The tool was particularly effective in identifying high-risk patients who benefited from increased specialist consultation and more targeted diagnostic testing. Trial registration ClinicalTrials.gov Identifier: NCT05117970.https://doi.org/10.1186/s12916-025-04190-zArtificial intelligenceElectrocardiogramRapid response systemsLow Ejection FractionRandomized clinical trialHigh-intensity care |
| spellingShingle | Dung-Jang Tsai Chin Lin Wei-Ting Liu Chiao-Chin Lee Chiao-Hsiang Chang Wen-Yu Lin Yu-Lan Liu Da-Wei Chang Ping-Hsuan Hsieh Chien-Sung Tsai Yuan-Hao Chen Yi-Jen Hung Chin-Sheng Lin Artificial intelligence-assisted diagnosis and prognostication in low ejection fraction using electrocardiograms in inpatient department: a pragmatic randomized controlled trial BMC Medicine Artificial intelligence Electrocardiogram Rapid response systems Low Ejection Fraction Randomized clinical trial High-intensity care |
| title | Artificial intelligence-assisted diagnosis and prognostication in low ejection fraction using electrocardiograms in inpatient department: a pragmatic randomized controlled trial |
| title_full | Artificial intelligence-assisted diagnosis and prognostication in low ejection fraction using electrocardiograms in inpatient department: a pragmatic randomized controlled trial |
| title_fullStr | Artificial intelligence-assisted diagnosis and prognostication in low ejection fraction using electrocardiograms in inpatient department: a pragmatic randomized controlled trial |
| title_full_unstemmed | Artificial intelligence-assisted diagnosis and prognostication in low ejection fraction using electrocardiograms in inpatient department: a pragmatic randomized controlled trial |
| title_short | Artificial intelligence-assisted diagnosis and prognostication in low ejection fraction using electrocardiograms in inpatient department: a pragmatic randomized controlled trial |
| title_sort | artificial intelligence assisted diagnosis and prognostication in low ejection fraction using electrocardiograms in inpatient department a pragmatic randomized controlled trial |
| topic | Artificial intelligence Electrocardiogram Rapid response systems Low Ejection Fraction Randomized clinical trial High-intensity care |
| url | https://doi.org/10.1186/s12916-025-04190-z |
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