دورية أكاديمية
Identification and risk stratification of coronary disease by artificial intelligence-enabled ECG.
| العنوان: | Identification and risk stratification of coronary disease by artificial intelligence-enabled ECG. |
|---|---|
| المؤلفون: | Awasthi S; Anumana, Inc, One Main Street, Cambridge, MA, USA.; nference, Inc, One Main Street, Cambridge, MA, USA., Sachdeva N; Anumana, Inc, One Main Street, Cambridge, MA, USA.; nference, Inc, One Main Street, Cambridge, MA, USA., Gupta Y; Anumana, Inc, One Main Street, Cambridge, MA, USA.; nference, Inc, One Main Street, Cambridge, MA, USA., Anto AG; Anumana, Inc, One Main Street, Cambridge, MA, USA.; nference, Inc, One Main Street, Cambridge, MA, USA., Asfahan S; Anumana, Inc, One Main Street, Cambridge, MA, USA.; nference, Inc, One Main Street, Cambridge, MA, USA., Abbou R; Anumana, Inc, One Main Street, Cambridge, MA, USA.; nference, Inc, One Main Street, Cambridge, MA, USA., Bade S; Anumana, Inc, One Main Street, Cambridge, MA, USA.; nference, Inc, One Main Street, Cambridge, MA, USA., Sood S; Anumana, Inc, One Main Street, Cambridge, MA, USA.; nference, Inc, One Main Street, Cambridge, MA, USA., Hegstrom L; Anumana, Inc, One Main Street, Cambridge, MA, USA.; nference, Inc, One Main Street, Cambridge, MA, USA., Vellanki N; nference, Inc, One Main Street, Cambridge, MA, USA.; Beth Israel Deaconess Medical Center, Boston, MA, USA., Alger HM; Anumana, Inc, One Main Street, Cambridge, MA, USA.; nference, Inc, One Main Street, Cambridge, MA, USA., Babu M; Anumana, Inc, One Main Street, Cambridge, MA, USA.; nference, Inc, One Main Street, Cambridge, MA, USA., Medina-Inojosa JR; Mayo Clinic, Rochester, MN, USA., McCully RB; Mayo Clinic, Rochester, MN, USA., Lerman A; Mayo Clinic, Rochester, MN, USA., Stampehl M; Novartis Pharmaceuticals Corporation, East Hanover, NJ, USA., Barve R; Anumana, Inc, One Main Street, Cambridge, MA, USA.; nference, Inc, One Main Street, Cambridge, MA, USA., Attia ZI; Mayo Clinic, Rochester, MN, USA., Friedman PA; Mayo Clinic, Rochester, MN, USA., Soundararajan V; Anumana, Inc, One Main Street, Cambridge, MA, USA.; nference, Inc, One Main Street, Cambridge, MA, USA., Lopez-Jimenez F; Mayo Clinic, Rochester, MN, USA. |
| المصدر: | EClinicalMedicine [EClinicalMedicine] 2023 Oct 24; Vol. 65, pp. 102259. Date of Electronic Publication: 2023 Oct 24 (Print Publication: 2023). |
| نوع المنشور: | Journal Article |
| اللغة: | English |
| بيانات الدورية: | Publisher: The Lancet Country of Publication: England NLM ID: 101733727 Publication Model: eCollection Cited Medium: Internet ISSN: 2589-5370 (Electronic) Linking ISSN: 25895370 NLM ISO Abbreviation: EClinicalMedicine Subsets: PubMed not MEDLINE |
| أسماء مطبوعة: | Original Publication: [London] : The Lancet, [2018]- |
| مستخلص: | Background: Atherosclerotic cardiovascular disease (ASCVD) is the leading cause of death worldwide, driven primarily by coronary artery disease (CAD). ASCVD risk estimators such as the pooled cohort equations (PCE) facilitate risk stratification and primary prevention of ASCVD but their accuracy is still suboptimal. Methods: Using deep electronic health record data from 7,116,209 patients seen at 70+ hospitals and clinics across 5 states in the USA, we developed an artificial intelligence-based electrocardiogram analysis tool (ECG-AI) to detect CAD and assessed the additive value of ECG-AI-based ASCVD risk stratification to the PCE. We created independent ECG-AI models using separate neural networks including subjects without known history of ASCVD, to identify coronary artery calcium (CAC) score ≥300 Agatston units by computed tomography, obstructive CAD by angiography or procedural intervention, and regional left ventricular akinesis in ≥1 segment by echocardiogram, as a reflection of possible prior myocardial infarction (MI). These were used to assess the utility of ECG-AI-based ASCVD risk stratification in a retrospective observational study consisting of patients with PCE scores and no prior ASCVD. The study period covered all available digitized EHR data, with the first available ECG in 1987 and the last in February 2023. Findings: ECG-AI for identifying CAC ≥300, obstructive CAD, and regional akinesis achieved area under the receiver operating characteristic (AUROC) values of 0.88, 0.85, and 0.94, respectively. An ensembled ECG-AI identified 3, 5, and 10-year risk for acute coronary events and mortality independently and additively to PCE. Hazard ratios for acute coronary events over 3-years in patients without ASCVD that tested positive on 1, 2, or 3 versus 0 disease-specific ECG-AI models at cohort entry were 2.41 (2.14-2.71), 4.23 (3.74-4.78), and 11.75 (10.2-13.52), respectively. Similar stratification was observed in cohorts stratified by PCE or age. Interpretation: ECG-AI has potential to address unmet need for accessible risk stratification in patients in whom PCE under, over, or insufficiently estimates ASCVD risk, and in whom risk assessment over time periods shorter than 10 years is desired. Funding: Anumana. Competing Interests: SA, NS, RA, YG, AGA, SA, SB, SS, LH, NV, HA, MB, RB, and VS were employees of nference, inc. and/or Anumana, inc. at the time the work was conducted and held vested or unvested stock. MS is an employee and stockholder of Novartis Pharmaceuticals Corporation. PAF, ZIA, and FLJ are advisors to Anumana. In conjunction with Mayo Clinic, PAF, ZIA, FLJ, and JMRI have filed patents related to the application of AI to the ECG for diagnosis and risk stratification. (© 2023 Mayo Clinic Foundation.) |
| References: | J Am Coll Cardiol. 2021 Oct 19;78(16):1573-1583. (PMID: 34649694) Nat Rev Cardiol. 2021 Jul;18(7):465-478. (PMID: 33526938) Circulation. 2019 Sep 10;140(11):e563-e595. (PMID: 30879339) Front Cardiovasc Med. 2022 Apr 06;9:849223. (PMID: 35463761) Ann Intern Med. 2018 Jul 3;169(1):20-29. (PMID: 29868850) Circulation. 2021 Aug 10;144(6):410-422. (PMID: 34247495) Patterns (N Y). 2021 May 12;2(6):100255. (PMID: 34179842) Circulation. 2022 Aug 23;146(8):585-586. (PMID: 35994564) JAMA Netw Open. 2020 Oct 1;3(10):e2023242. (PMID: 33119108) Circ Arrhythm Electrophysiol. 2020 Mar;13(3):e007988. (PMID: 32064914) Nat Med. 2019 Jan;25(1):70-74. (PMID: 30617318) Circulation. 2014 Jun 24;129(25 Suppl 2):S49-73. (PMID: 24222018) Am J Cardiol. 2022 Jul 1;174:40-47. (PMID: 35487777) Biomedicines. 2022 Feb 07;10(2):. (PMID: 35203603) Circulation. 2019 Sep 10;140(11):e596-e646. (PMID: 30879355) Lancet. 2019 Sep 7;394(10201):861-867. (PMID: 31378392) J Grad Med Educ. 2012 Sep;4(3):279-82. (PMID: 23997866) Front Cardiovasc Med. 2022 Oct 05;9:976769. (PMID: 36277775) Circulation. 2021 Feb 23;143(8):e254-e743. (PMID: 33501848) |
| فهرسة مساهمة: | Keywords: Artificial intelligence; Atherosclerotic cardiovascular disease; Cardiovascular risk; Coronary artery disease; ECG-AI |
| تواريخ الأحداث: | Date Created: 20231218 Latest Revision: 20231219 |
| رمز التحديث: | 20231219 |
| مُعرف محوري في PubMed: | PMC10725070 |
| DOI: | 10.1016/j.eclinm.2023.102259 |
| PMID: | 38106563 |
| قاعدة البيانات: | MEDLINE |
Full Text via ClinicalKey 
Full Text Finder | تدمد: | 2589-5370 |
|---|---|
| DOI: | 10.1016/j.eclinm.2023.102259 |