1. Validation of the myocardial-ischaemic-injury-index machine learning algorithm to guide the diagnosis of myocardial infarction in a heterogenous population: a prespecified exploratory analysis
- Author
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Dimitrios Doudesis, Kuan Ken Lee, Jason Yang, Ryan Wereski, Anoop S V Shah, Athanasios Tsanas, Atul Anand, John W Pickering, Martin P Than, Nicholas L Mills, Fiona E Strachan, Christopher Tuck, Anoop SV Shah, Andrew R Chapman, Amy V Ferry, Anda Bularga, Caelan Taggart, Matthew TH Lowry, Filip Mendusic, Dorien M Kimenai, Dennis Sandeman, Philip D Adamson, Catherine L Stables, Catalina A Vallejos, Lucy Marshall, Stacey D Stewart, Takeshi Fujisawa, Mischa Hautvast, Jean McPherson, Lynn McKinlay, Ian Ford, David E Newby, Keith AA Fox, Colin Berry, Simon Walker, Christopher J Weir, Alasdair Gray, Paul O Collinson, Fred S Apple, Alan Reid, Anne Cruikshank, Iain Findlay, Shannon Amoils, David A McAllister, Donogh Maguire, Jennifer Stevens, John Norrie, Jack PM Andrews, Alastair Moss, Mohamed S Anwar, John Hung, Jonathan Malo, Colin Fischbacher, Bernard L Croal, Stephen J Leslie, Catriona Keerie, Richard A Parker, Allan Walker, Ronnie Harkess, Tony Wackett, Roma Armstrong, Laura Stirling, Claire MacDonald, Imran Sadat, Frank Finlay, Heather Charles, Pamela Linksted, Stephen Young, Bill Alexander, and Chris Duncan
- Subjects
Male ,troponin ,Troponin I ,Myocardial Infarction ,Medicine (miscellaneous) ,Health Informatics ,acute coronary syndrome ,Machine Learning ,Myocardial infarction ,machine learning ,Health Information Management ,Humans ,Decision Sciences (miscellaneous) ,Female ,Acute Coronary Syndrome ,Algorithms ,Biomarkers - Abstract
Background: We recently introduced the myocardial-ischemic-injury-index (MI3), a machine learning algorithm that predicts the likelihood of myocardial infarction in patients with suspected acute coronary syndrome. Whether this algorithm performs well in routine clinical practice or predicts subsequent events is unknown.Methods: MI3 was validated in a prespecified exploratory analysis from a multi-centre randomised trial that included consecutive patients with suspected acute coronary syndrome undergoing serial high-sensitivity cardiac troponin I measurement. Patients with ST-segment elevation myocardial infarction were excluded. MI3 incorporates age, sex, and two troponin measurements to compute a value (0-100) reflecting an individual’s likelihood of myocardial infarction during the index visit and estimates diagnostic performance metrics at the computed score. Model performance for an index diagnosis of myocardial infarction, and for subsequent myocardial infarction or cardiovascular death at one year was determined using previously defined low- and high-probability MI3 thresholds (1·6 and 49·7, respectively). Findings: In total, 20,761 patients (64±16 years, 46% women) were included of whom 3,272 (15·8%) had myocardial infarction. MI3 had an area under the receiver-operating-characteristic curve of 0·949 (95% confidence interval 0·946-0·952) identifying 12,983 (62·5%) patients as low-probability (sensitivity 99·3% [99·0-99·6%], negative predictive value 99·8% [99·8-99·9%]), and 2,961 (14·3%) as high-probability (specificity 95·0% [94·6-95·3%], positive predictive value 70·4% [68·7-72·0%]). At one year, subsequent myocardial infarction or cardiovascular death occurred more often in high-probability compared to low-probability patients (17·6% [520/2,961] versus 1·5% [197/12,983], PInterpretation: In consecutive patients undergoing serial cardiac troponin measurement for suspected acute coronary syndrome, the MI3 algorithm accurately estimates the likelihood of myocardial infarction and predicts subsequent adverse cardiovascular events.
- Published
- 2022
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