دورية أكاديمية
أعرض في EDS

Artificial Intelligence-Based Electrocardiographic Biomarker for Outcome Prediction in Patients With Acute Heart Failure: Prospective Cohort Study.

التفاصيل البيبلوغرافية
العنوان: Artificial Intelligence-Based Electrocardiographic Biomarker for Outcome Prediction in Patients With Acute Heart Failure: Prospective Cohort Study.
المؤلفون: Cho Y; Division of Cardiology, Department of Internal Medicine, Seoul National University Bundang Hospital, Seoul National University College of Medicine, Seongnam, Gyeonggi-do, Republic of Korea.; ARPI Inc, Seongnam, Gyeonggi-do, Republic of Korea., Yoon M; Division of Cardiology, Department of Internal Medicine, Seoul National University Bundang Hospital, Seoul National University College of Medicine, Seongnam, Gyeonggi-do, Republic of Korea., Kim J; ARPI Inc, Seongnam, Gyeonggi-do, Republic of Korea.; Department of Emergency Medicine, Seoul National University Bundang Hospital, Seongnam, Gyeonggi-do, Republic of Korea., Lee JH; Division of Cardiology, Department of Internal Medicine, Seoul National University Bundang Hospital, Seoul National University College of Medicine, Seongnam, Gyeonggi-do, Republic of Korea., Oh IY; Division of Cardiology, Department of Internal Medicine, Seoul National University Bundang Hospital, Seoul National University College of Medicine, Seongnam, Gyeonggi-do, Republic of Korea., Lee CJ; Division of Cardiology, Department of Internal Medicine, Severance Hospital, Yonsei University College of Medicine, Seoul, Republic of Korea., Kang SM; Division of Cardiology, Department of Internal Medicine, Severance Hospital, Yonsei University College of Medicine, Seoul, Republic of Korea., Choi DJ; Division of Cardiology, Department of Internal Medicine, Seoul National University Bundang Hospital, Seoul National University College of Medicine, Seongnam, Gyeonggi-do, Republic of Korea.
المصدر: Journal of medical Internet research [J Med Internet Res] 2024 Jul 03; Vol. 26, pp. e52139. Date of Electronic Publication: 2024 Jul 03.
نوع المنشور: Journal Article; Observational Study
اللغة: English
بيانات الدورية: Publisher: JMIR Publications Country of Publication: Canada NLM ID: 100959882 Publication Model: Electronic Cited Medium: Internet ISSN: 1438-8871 (Electronic) Linking ISSN: 14388871 NLM ISO Abbreviation: J Med Internet Res Subsets: MEDLINE
أسماء مطبوعة: Publication: <2011- > : Toronto : JMIR Publications
Original Publication: [Pittsburgh, PA? : s.n., 1999-
مواضيع طبية MeSH: Artificial Intelligence* , Biomarkers*/blood , Electrocardiography*/methods , Heart Failure*/physiopathology , Heart Failure*/mortality, Aged ; Female ; Humans ; Male ; Middle Aged ; Acute Disease ; Prognosis ; Prospective Studies ; Republic of Korea ; Retrospective Studies
مستخلص: Background: Although several biomarkers exist for patients with heart failure (HF), their use in routine clinical practice is often constrained by high costs and limited availability.
Objective: We examined the utility of an artificial intelligence (AI) algorithm that analyzes printed electrocardiograms (ECGs) for outcome prediction in patients with acute HF.
Methods: We retrospectively analyzed prospectively collected data of patients with acute HF at two tertiary centers in Korea. Baseline ECGs were analyzed using a deep-learning system called Quantitative ECG (QCG), which was trained to detect several urgent clinical conditions, including shock, cardiac arrest, and reduced left ventricular ejection fraction (LVEF).
Results: Among the 1254 patients enrolled, in-hospital cardiac death occurred in 53 (4.2%) patients, and the QCG score for critical events (QCG-Critical) was significantly higher in these patients than in survivors (mean 0.57, SD 0.23 vs mean 0.29, SD 0.20; P<.001). The QCG-Critical score was an independent predictor of in-hospital cardiac death after adjustment for age, sex, comorbidities, HF etiology/type, atrial fibrillation, and QRS widening (adjusted odds ratio [OR] 1.68, 95% CI 1.47-1.92 per 0.1 increase; P<.001), and remained a significant predictor after additional adjustments for echocardiographic LVEF and N-terminal prohormone of brain natriuretic peptide level (adjusted OR 1.59, 95% CI 1.36-1.87 per 0.1 increase; P<.001). During long-term follow-up, patients with higher QCG-Critical scores (>0.5) had higher mortality rates than those with low QCG-Critical scores (<0.25) (adjusted hazard ratio 2.69, 95% CI 2.14-3.38; P<.001).
Conclusions: Predicting outcomes in patients with acute HF using the QCG-Critical score is feasible, indicating that this AI-based ECG score may be a novel biomarker for these patients.
Trial Registration: ClinicalTrials.gov NCT01389843; https://clinicaltrials.gov/study/NCT01389843.
(©Youngjin Cho, Minjae Yoon, Joonghee Kim, Ji Hyun Lee, Il-Young Oh, Chan Joo Lee, Seok-Min Kang, Dong-Ju Choi. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 03.07.2024.)
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فهرسة مساهمة: Keywords: acute heart failure; artificial intelligence; deep learning; electrocardiography
سلسلة جزيئية: ClinicalTrials.gov NCT01389843
المشرفين على المادة: 0 (Biomarkers)
تواريخ الأحداث: Date Created: 20240703 Date Completed: 20240703 Latest Revision: 20240720
رمز التحديث: 20240720
مُعرف محوري في PubMed: PMC11255523
DOI: 10.2196/52139
PMID: 38959500
قاعدة البيانات: MEDLINE
الوصف
تدمد:1438-8871
DOI:10.2196/52139