An Artificial Intelligence-Based Alarm Strategy Facilitates Management of Acute Myocardial Infarction
Abstract
:1. Introduction
2. Method
2.1. Study Design and Setting
2.2. Study Population for AI-S Development
2.3. Definition of AMI, DtoB Time Metrics and STEMI Protocol
2.4. Data Collection
2.5. AI-S
2.5.1. First Part of AI-S: The Set-Up of AI-S
2.5.2. Second Part of AI-S: Automatic Active Alarm System with Notification by Short Message
2.6. Study Outcomes
2.7. Statistical Analysis
3. Results
3.1. The Development of AI-S
3.2. The Performance of AI-S for STEMI Detection
3.3. The Performance of AI-S for NSTEMI and Not-AMI Detection
3.4. Delayed/Misdiagnosed Cases by Front-Line Physicians
3.5. The DtoB Time Metrics before and after AI-S Implantation
3.6. The Performance of AI-S in Different Groups and Clinical Features
4. Discussion
5. Limitations and Strengths
6. Conclusions
Supplementary Materials
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
Abbreviations
References
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Prospective Validation Cohort | ||||
---|---|---|---|---|
STEMI | NSTEMI | not-AMI | Excluded | |
All dataset | ||||
Alarmed | 59 (100.0%) | 41 (62.1%) | 10 (0.1%) | 13 (100.0%) |
Strategy-1 | 41 | 0 | 1 | 2 |
Strategy-2 | 14 | 1 | 3 | 11 |
Strategy-3 | 4 | 40 | 6 | 0 |
Unalarmed | 0 (0.0%) | 25 (37.9%) | 14,161 (99.9%) | 0 (0.0%) |
Subset-1 | ||||
Alarmed | 4 (100.0%) | 6 (50.0%) | 1 (<0.1%) | 3 (100.0%) |
Strategy-1 | 3 | 0 | 0 | 1 |
Strategy-2 | 1 | 1 | 0 | 2 |
Strategy-3 | 0 | 5 | 1 | 0 |
Unalarmed | 0 (0.0%) | 6 (50.0%) | 3559 (>99.9%) | 0 (0.0%) |
Subset-2 | ||||
Alarmed | 55 (100.0%) | 35 (64.8%) | 9 (0.1%) | 10 (100.0%) |
Strategy-1 | 38 | 0 | 1 | 1 |
Strategy-2 | 13 | 0 | 3 | 9 |
Strategy-3 | 4 | 35 | 5 | 0 |
Unalarmed | 0 (0.0%) | 19 (35.2%) | 10,602 (99.9%) | 0 (0.0%) |
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Liu, W.-C.; Lin, C.; Lin, C.-S.; Tsai, M.-C.; Chen, S.-J.; Tsai, S.-H.; Lin, W.-S.; Lee, C.-C.; Tsao, T.-P.; Cheng, C.-C. An Artificial Intelligence-Based Alarm Strategy Facilitates Management of Acute Myocardial Infarction. J. Pers. Med. 2021, 11, 1149. https://doi.org/10.3390/jpm11111149
Liu W-C, Lin C, Lin C-S, Tsai M-C, Chen S-J, Tsai S-H, Lin W-S, Lee C-C, Tsao T-P, Cheng C-C. An Artificial Intelligence-Based Alarm Strategy Facilitates Management of Acute Myocardial Infarction. Journal of Personalized Medicine. 2021; 11(11):1149. https://doi.org/10.3390/jpm11111149
Chicago/Turabian StyleLiu, Wen-Cheng, Chin Lin, Chin-Sheng Lin, Min-Chien Tsai, Sy-Jou Chen, Shih-Hung Tsai, Wei-Shiang Lin, Chia-Cheng Lee, Tien-Ping Tsao, and Cheng-Chung Cheng. 2021. "An Artificial Intelligence-Based Alarm Strategy Facilitates Management of Acute Myocardial Infarction" Journal of Personalized Medicine 11, no. 11: 1149. https://doi.org/10.3390/jpm11111149
APA StyleLiu, W.-C., Lin, C., Lin, C.-S., Tsai, M.-C., Chen, S.-J., Tsai, S.-H., Lin, W.-S., Lee, C.-C., Tsao, T.-P., & Cheng, C.-C. (2021). An Artificial Intelligence-Based Alarm Strategy Facilitates Management of Acute Myocardial Infarction. Journal of Personalized Medicine, 11(11), 1149. https://doi.org/10.3390/jpm11111149