Johannes Tobias Neumann, Raphael Twerenbold, Francisco Ojeda, Sally J Aldous, Brandon R Allen, Fred S Apple, Hugo Babel, Robert H Christenson, Louise Cullen, Eleonora Di Carluccio, Dimitrios Doudesis, Ulf Ekelund, Evangelos Giannitsis, Jaimi Greenslade, Kenji Inoue, Tomas Jernberg, Peter Kavsak, Till Keller, Kuan Ken Lee, Bertil Lindahl, Thiess Lorenz, Simon A Mahler, Nicholas L Mills, Arash Mokhtari, William Parsonage, John W Pickering, Christopher J Pemberton, Christoph Reich, A Mark Richards, Yader Sandoval, Martin P Than, Betül Toprak, Richard W Troughton, Andrew Worster, Tanja Zeller, Andreas Ziegler, Stefan Blankenberg
BACKGROUND: In suspected myocardial infarction (MI), guidelines recommend using high-sensitivity cardiac troponin (hs-cTn)-based approaches. These require fixed assay-specific thresholds and timepoints, without directly integrating clinical information. Using machine-learning techniques including hs-cTn and clinical routine variables, we aimed to build a digital tool to directly estimate the individual probability of MI, allowing for numerous hs-cTn assays. METHODS: In 2,575 patients presenting to the emergency department with suspected MI, two ensembles of machine-learning models using single or serial concentrations of six different hs-cTn assays were derived to estimate the individual MI probability (ARTEMIS model)...
May 2, 2023: Clinical Research in Cardiology: Official Journal of the German Cardiac Society