Machine learning for diagnosis of myocardial infarction using cardiac troponin concentrations
Doudesis, Dimitrios, Lee, Kuan Ken, Boeddinghaus, Jasper, Bularga, Anda, Ferry, Amy V., Tuck, Chris, Lowry, Matthew T.H., Lopez-Ayala, Pedro, Nestelberger, Thomas, Koechlin, Luca, Bernabeu, Miguel O., Neubeck, Lis, Anand, Atul, Schulz, Karen, Apple, Fred S., Parsonage, William, Greenslade, Jaimi H., Cullen, Louise, Pickering, John W., Than, Martin P., Gray, Alasdair, Mueller, Christian, Mills, Nicholas L., Richards, A. Mark, Pemberton, Chris, Troughton, Richard W., Aldous, Sally J., Brown, Anthony F.T., Dalton, Emily, Hammett, Chris, Hawkins, Tracey, O’Kane, Shanen, Parke, Kate, Ryan, Kimberley, Schluter, Jessica, Wild, Karin, Wussler, Desiree, Miró, Òscar, Martin-Sanchez, F. Javier, Keller, Dagmar I., Christ, Michael, Buser, Andreas, Giménez, Maria Rubini, Barker, Stephanie, Blades, Jennifer, Chapman, Andrew R., Fujisawa, Takeshi, Kimenai, Dorien M., Leung, Jeremy, Li, Ziwen, McDermott, Michael, Newby, David E., Schulberg, Stacey D., Shah, Anoop S.V., Sorbie, Andrew, Soutar, Grace, Strachan, Fiona E., Taggart, Caelan, Vicencio, Daniel Perez, Wang, Yiqing, Wereski, Ryan, Williams, Kelly, Weir, Christopher J., Berry, Colin, Reid, Alan, Maguire, Donogh, Collinson, Paul O., Sandoval, Yader, & other, and (2023) Machine learning for diagnosis of myocardial infarction using cardiac troponin concentrations. Nature Medicine, 29(5), pp. 1201-1210.
Open access copy at publisher website
Description
<p>Although guidelines recommend fixed cardiac troponin thresholds for the diagnosis of myocardial infarction, troponin concentrations are influenced by age, sex, comorbidities and time from symptom onset. To improve diagnosis, we developed machine learning models that integrate cardiac troponin concentrations at presentation or on serial testing with clinical features and compute the Collaboration for the Diagnosis and Evaluation of Acute Coronary Syndrome (CoDE-ACS) score (0–100) that corresponds to an individual’s probability of myocardial infarction. The models were trained on data from 10,038 patients (48% women), and their performance was externally validated using data from 10,286 patients (35% women) from seven cohorts. CoDE-ACS had excellent discrimination for myocardial infarction (area under curve, 0.953; 95% confidence interval, 0.947–0.958), performed well across subgroups and identified more patients at presentation as low probability of having myocardial infarction than fixed cardiac troponin thresholds (61 versus 27%) with a similar negative predictive value and fewer as high probability of having myocardial infarction (10 versus 16%) with a greater positive predictive value. Patients identified as having a low probability of myocardial infarction had a lower rate of cardiac death than those with intermediate or high probability 30 days (0.1 versus 0.5 and 1.8%) and 1 year (0.3 versus 2.8 and 4.2%; P < 0.001 for both) from patient presentation. CoDE-ACS used as a clinical decision support system has the potential to reduce hospital admissions and have major benefits for patients and health care providers.</p>
Impact and interest:
Citation counts are sourced monthly from Scopus and Web of Science® citation databases.
These databases contain citations from different subsets of available publications and different time periods and thus the citation count from each is usually different. Some works are not in either database and no count is displayed. Scopus includes citations from articles published in 1996 onwards, and Web of Science® generally from 1980 onwards.
Citations counts from the Google Scholar™ indexing service can be viewed at the linked Google Scholar™ search.
| ID Code: | 250632 | ||||||
|---|---|---|---|---|---|---|---|
| Item Type: | Contribution to Journal (Journal Article) | ||||||
| Refereed: | Yes | ||||||
| ORCID iD: |
|
||||||
| Measurements or Duration: | 10 pages | ||||||
| DOI: | 10.1038/s41591-023-02325-4 | ||||||
| ISSN: | 1078-8956 | ||||||
| Pure ID: | 173245347 | ||||||
| Divisions: | Current > Research Centres > Centre for Healthcare Transformation Current > Research Centres > Australian Centre for Health Services Innovation Current > QUT Faculties and Divisions > Faculty of Health Current > Schools > School of Public Health & Social Work |
||||||
| Funding Information: | The research was funded with support from the National Institute for Health Research and NHSX (Grant AI_AWARD02322), the British Heart Foundation (Grant RG/20/10/34966) and Wellcome Leap. The analysis was performed within the Secure Data Environment provided by DataLoch ( https://dataloch.org/ ), which is funded by the Data Driven Innovation Programme within the Edinburgh and South East Scotland City Region Deal. D.D. is supported by the Medical Research Council (Grant MR/N013166/1). K.K.L. is supported by the British Heart Foundation (Clinical Research Training Fellowship FS/18/25/33454). J.B. is supported by grants from the University of Basel, the University Hospital of Basel, the Swiss Academy of Medical Sciences, the Gottfried and Julia Bangerter-Rhyner Foundation, the Swiss National Science Foundation (Grant P500PM_206636) and the Edinburgh Doctoral College (scholarship). A. Bularga is supported by the Medical Research Council (Clinical Research Training Fellowship MR/V007254/1). L.K. is supported by the Swiss Heart Foundation (grant), the University of Basel, the Swiss Academy of Medical Science, the Gottfried and Julia Bangerter-Rhyner Foundation and the Freiwillige Akademische Gesellschaft Basel. J.H.G. is supported by Advance Queensland (fellowship). C.M. has received research support from the Swiss National Science Foundation, the Swiss Heart Foundation, the Commission for Technology and Innovation and the University Hospital Basel. N.L.M. is supported by the British Heart Foundation (Chair Award CH/F/21/90010, Programme Grant RG/20/10/34966 and Research Excellent Award RE/18/5/34216). The views expressed in this publication are those of the authors and not necessarily those of the National Institute for Health Research, NHSX or the Department of Health and Social Care. K.K.L. has received honoraria from Abbott Diagnostics. J.B. has received honoraria from Siemens, Roche Diagnostics, Ortho Clinical Diagnostics and Quidel Corporation. P.L.-A. has received speaker’s honoraria or consultancy from Quidel paid to the institution outside the submitted work. L.K. has received honoraria from Roche Diagnostics and Siemens outside the submitted work. F.S.A. has consulted, advised or received honoraria from HyTest Ltd., AWE Medical, Werfen, Siemens Healthineers, Qorvo, Siemens Healthineers and Beckman Coulter. Hennepin Healthcare Research Institute has received research grants from Abbott Diagnostics, Abbott POC, Beckman Dickenson, Beckman Coulter, Ortho Clinical Diagnostics, Roche Diagnostics, Siemens Healthineers and Quidel outside the submitted work. L.C. has received honoraria or consultancy from Abbott Diagnostics, Beckman Coulter and Siemens Healthineers. J.W.P. has undertaken consultancy for Abbott Diagnostics. M.P.T. has received consulting fees or honoraria from Abbott Diagnostics, Roche Diagnostics and Siemens Healthineers; received funding for clinical research from Radiometer; and participated on a Data Safety Monitoring Board/Advisory Board for Abbott Diagnostics, Roche Diagnostics, Siemens Healthineers and Radiometer. C.M. has received research support from Abbott, Beckman Coulter, Brahms, Idorsia, LSI Medience Corporation, Novartis, Ortho Diagnostics, Quidel, Roche, Siemens, Singulex and Sphingotec outside the submitted work as well as speaker honoraria/consulting honoraria from Amgen, Astra Zeneca, Bayer, Boehringer Ingelheim, BMS, Idorsia, Novartis, Osler, Roche and Sanofi all paid to the institution. N.L.M. has received honoraria or consultancy from Abbott Diagnostics, Roche Diagnostics, Siemens Healthineers and LumiraDx. D.D., K.K.L. and N.L.M. are employed by the University of Edinburgh, which has filed a patent on the Collaboration for the Diagnosis and Evaluation of Acute Coronary Syndrome score (patent reference: GB2212464). The remainign authors declare no competing interests. All other authors have reported that they have no relationships relevant to the contents of this paper to disclose. | ||||||
| Copyright Owner: | 2023 The Authors | ||||||
| Copyright Statement: | This work is covered by copyright. Unless the document is being made available under a Creative Commons Licence, you must assume that re-use is limited to personal use and that permission from the copyright owner must be obtained for all other uses. If the document is available under a Creative Commons License (or other specified license) then refer to the Licence for details of permitted re-use. It is a condition of access that users recognise and abide by the legal requirements associated with these rights. If you believe that this work infringes copyright please provide details by email to qut.copyright@qut.edu.au | ||||||
| Deposited On: | 16 Jul 2024 10:44 | ||||||
| Last Modified: | 20 May 2026 04:17 |
Export: EndNote | Dublin Core | BibTeX
Repository Staff Only: item control page