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External Validation of the Myocardial-Ischaemic-injury-index Machine Learning Algorithm for the Early Diagnosis of Myocardial Infarction: a Multicentre Cohort Study

LANCET DIGITAL HEALTH(2024)

Univ Basel

Cited 1|Views5
Abstract
Background The myocardial-ischaemic-injury-index (MI3 ) is a novel machine learning algorithm for the early diagnosis of type 1 non -ST -segment elevation myocardial infarction (NSTEMI). The performance of MI3 , both when using early serial blood draws (eg, at 1 h or 2 h) and in direct comparison with guideline-recommended algorithms, remains unknown. Our aim was to externally validate MI3 and compare its performance with that of the European Society of Cardiology (ESC) 0/1h-algorithm. Methods In this secondary analysis of a multicentre international diagnostic cohort study, adult patients (age >18 years) presenting to the emergency department with symptoms suggestive of myocardial infarction were prospectively enrolled from April 21, 2006, to Feb 27, 2019 in 12 centres from five European countries (Switzerland, Spain, Italy, Poland, and Czech Republic). Patients were excluded if they presented with ST -segment -elevation myocardial infarction, did not have at least two serial high-sensitivity cardiac troponin I (hs-cTnI) measurements, or if the final diagnosis remained unclear. The final diagnosis was centrally adjudicated by two independent cardiologists using all available medical records, including serial hs-cTnI measurements and cardiac imaging. The primary outcome was type 1 NSTEMI. The performance of MI3 was directly compared with that of the ESC 0/1h-algorithm. Findings Among 6487 patients, (median age 610 years [IQR 490-730]; 2122 [33%] female and 4365 [67%] male), 882 (136%) patients had type 1 NSTEMI. The median time difference between the first and second hs-cTnI measurement was 600 mins (IQR 570-700). MI3 performance was very good, with an area under the receiver-operating-characteristic curve of 0961 (95% CI 0957 to 0965) and a good overall calibration (intercept -009 [-02 to 002]; slope 102 [097 to 108]). The originally defined MI3 score of less than 16 identified 4186 (645%) patients as low probability of having a type 1 NSTEMI (sensitivity 991% [95% CI 982 to 995]; negative predictive value [NPV] 998% [95% CI 996 to 999]) and an MI3 score of 497 or more identified 915 (141%) patients as high probability of having a type 1 NSTEMI (specificity 950% [943 to 955]; positive predictive value [PPV] 691% [660-720]). The sensitivity and NPV of the ESC 0/1h-algorithm were higher than that of MI3 (difference for sensitivity 088% [019 to 160], p=00082; difference for NPV 018% [005 to 032], p=0016), and the rule-out efficacy was higher for MI3 (11% difference, p<00001). Specificity and PPV for MI3 were superior (difference for specificity 380% [324 to 436], p<00001; difference for PPV 784% [586 to 997], p<00001), and the rule-in efficacy was higher for the ESC 0/1h-algorithm (54% difference, p<00001). Interpretation MI3 performs very well in diagnosing type 1 NSTEMI, demonstrating comparability to the ESC 0/1h-algorithm in an emergency department setting when using early serial blood draws. Copyright (c) 2024 The Author(s). Published by Elsevier Ltd. This is an Open Access article under the CC BY 4.0 license.
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Basel,Switzerland
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