PortalPublikationenPersonalized diagnosis in suspected myocardial infarction

Personalized diagnosis in suspected myocardial infarction

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Personalized diagnosis in suspected myocardial infarction. / Neumann, Johannes Tobias; Twerenbold, Raphael; Ojeda, Francisco; Aldous, Sally J; Allen, Brandon R; Apple, Fred S; Babel, Hugo; Christenson, Robert H; Cullen, Louise; Di Carluccio, Eleonora; Doudesis, Dimitrios; Ekelund, Ulf; Giannitsis, Evangelos; Greenslade, Jaimi; Inoue, Kenji; Jernberg, Tomas; Kavsak, Peter; Keller, Till; Lee, Kuan Ken; Lindahl, Bertil; Lorenz, Thiess; Mahler, Simon A; Mills, Nicholas L; Mokhtari, Arash; Parsonage, William; Pickering, John W; Pemberton, Christopher J; Reich, Christoph; Richards, A Mark; Sandoval, Yader; Than, Martin P; Toprak, Betül; Troughton, Richard W; Worster, Andrew; Zeller, Tanja; Ziegler, Andreas; Blankenberg, Stefan; ARTEMIS study group.

in: CLIN RES CARDIOL, Jahrgang 112, Nr. 9, 09.2023, S. 1288-1301.

Publikationen: SCORING: Beitrag in Fachzeitschrift/Zeitung › SCORING: Zeitschriftenaufsatz › Forschung › Begutachtung

Harvard

Neumann, JT, Twerenbold, R, Ojeda, F, Aldous, SJ, Allen, BR, Apple, FS, Babel, H, Christenson, RH, Cullen, L, Di Carluccio, E, Doudesis, D, Ekelund, U, Giannitsis, E, Greenslade, J, Inoue, K, Jernberg, T, Kavsak, P, Keller, T, Lee, KK, Lindahl, B, Lorenz, T, Mahler, SA, Mills, NL, Mokhtari, A, Parsonage, W, Pickering, JW, Pemberton, CJ, Reich, C, Richards, AM, Sandoval, Y, Than, MP, Toprak, B, Troughton, RW, Worster, A, Zeller, T, Ziegler, A, Blankenberg, S & ARTEMIS study group 2023, 'Personalized diagnosis in suspected myocardial infarction', CLIN RES CARDIOL, Jg. 112, Nr. 9, S. 1288-1301. https://doi.org/10.1007/s00392-023-02206-3

APA

Neumann, J. T., Twerenbold, R., Ojeda, F., Aldous, S. J., Allen, B. R., Apple, F. S., Babel, H., Christenson, R. H., Cullen, L., Di Carluccio, E., Doudesis, D., Ekelund, U., Giannitsis, E., Greenslade, J., Inoue, K., Jernberg, T., Kavsak, P., Keller, T., Lee, K. K., ... ARTEMIS study group (2023). Personalized diagnosis in suspected myocardial infarction. CLIN RES CARDIOL, 112(9), 1288-1301. https://doi.org/10.1007/s00392-023-02206-3

Vancouver

Neumann JT, Twerenbold R, Ojeda F, Aldous SJ, Allen BR, Apple FS et al. Personalized diagnosis in suspected myocardial infarction. CLIN RES CARDIOL. 2023 Sep;112(9):1288-1301. https://doi.org/10.1007/s00392-023-02206-3

Bibtex

@article{e395e3677a3b4552b6f425fe4668d53b,
title = "Personalized diagnosis in suspected myocardial infarction",
abstract = "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). Discriminative performance of the models was assessed using area under the receiver operating characteristic curve (AUC) and logLoss. Model performance was validated in an external cohort with 1688 patients and tested for global generalizability in 13 international cohorts with 23,411 patients.RESULTS: Eleven routinely available variables including age, sex, cardiovascular risk factors, electrocardiography, and hs-cTn were included in the ARTEMIS models. In the validation and generalization cohorts, excellent discriminative performance was confirmed, superior to hs-cTn only. For the serial hs-cTn measurement model, AUC ranged from 0.92 to 0.98. Good calibration was observed. Using a single hs-cTn measurement, the ARTEMIS model allowed direct rule-out of MI with very high and similar safety but up to tripled efficiency compared to the guideline-recommended strategy.CONCLUSION: We developed and validated diagnostic models to accurately estimate the individual probability of MI, which allow for variable hs-cTn use and flexible timing of resampling. Their digital application may provide rapid, safe and efficient personalized patient care.TRIAL REGISTRATION NUMBERS: Data of following cohorts were used for this project: BACC ( www.CLINICALTRIALS: gov ; NCT02355457), stenoCardia ( www.CLINICALTRIALS: gov ; NCT03227159), ADAPT-BSN ( www.australianclinicaltrials.gov.au ; ACTRN12611001069943), IMPACT ( www.australianclinicaltrials.gov.au , ACTRN12611000206921), ADAPT-RCT ( www.anzctr.org.au ; ANZCTR12610000766011), EDACS-RCT ( www.anzctr.org.au ; ANZCTR12613000745741); DROP-ACS ( https://www.umin.ac.jp , UMIN000030668); High-STEACS ( www.CLINICALTRIALS: gov ; NCT01852123), LUND ( www.CLINICALTRIALS: gov ; NCT05484544), RAPID-CPU ( www.CLINICALTRIALS: gov ; NCT03111862), ROMI ( www.CLINICALTRIALS: gov ; NCT01994577), SAMIE ( https://anzctr.org.au ; ACTRN12621000053820), SEIGE and SAFETY ( www.CLINICALTRIALS: gov ; NCT04772157), STOP-CP ( www.CLINICALTRIALS: gov ; NCT02984436), UTROPIA ( www.CLINICALTRIALS: gov ; NCT02060760).",
keywords = "Humans, Angina Pectoris, Biomarkers, Myocardial Infarction/diagnosis, ROC Curve, Troponin I, Troponin T, Clinical Studies as Topic",
author = "Neumann, {Johannes Tobias} and Raphael Twerenbold and Francisco Ojeda and Aldous, {Sally J} and Allen, {Brandon R} and Apple, {Fred S} and Hugo Babel and Christenson, {Robert H} and Louise Cullen and {Di Carluccio}, Eleonora and Dimitrios Doudesis and Ulf Ekelund and Evangelos Giannitsis and Jaimi Greenslade and Kenji Inoue and Tomas Jernberg and Peter Kavsak and Till Keller and Lee, {Kuan Ken} and Bertil Lindahl and Thiess Lorenz and Mahler, {Simon A} and Mills, {Nicholas L} and Arash Mokhtari and William Parsonage and Pickering, {John W} and Pemberton, {Christopher J} and Christoph Reich and Richards, {A Mark} and Yader Sandoval and Than, {Martin P} and Bet{\"u}l Toprak and Troughton, {Richard W} and Andrew Worster and Tanja Zeller and Andreas Ziegler and Stefan Blankenberg and {ARTEMIS study group}",
note = "{\textcopyright} 2023. The Author(s).",
year = "2023",
month = sep,
doi = "10.1007/s00392-023-02206-3",
language = "English",
volume = "112",
pages = "1288--1301",
journal = "CLIN RES CARDIOL",
issn = "1861-0684",
publisher = "D. Steinkopff-Verlag",
number = "9",

}

RIS

TY - JOUR

T1 - Personalized diagnosis in suspected myocardial infarction

AU - Neumann, Johannes Tobias

AU - Twerenbold, Raphael

AU - Ojeda, Francisco

AU - Aldous, Sally J

AU - Allen, Brandon R

AU - Apple, Fred S

AU - Babel, Hugo

AU - Christenson, Robert H

AU - Cullen, Louise

AU - Di Carluccio, Eleonora

AU - Doudesis, Dimitrios

AU - Ekelund, Ulf

AU - Giannitsis, Evangelos

AU - Greenslade, Jaimi

AU - Inoue, Kenji

AU - Jernberg, Tomas

AU - Kavsak, Peter

AU - Keller, Till

AU - Lee, Kuan Ken

AU - Lindahl, Bertil

AU - Lorenz, Thiess

AU - Mahler, Simon A

AU - Mills, Nicholas L

AU - Mokhtari, Arash

AU - Parsonage, William

AU - Pickering, John W

AU - Pemberton, Christopher J

AU - Reich, Christoph

AU - Richards, A Mark

AU - Sandoval, Yader

AU - Than, Martin P

AU - Toprak, Betül

AU - Troughton, Richard W

AU - Worster, Andrew

AU - Zeller, Tanja

AU - Ziegler, Andreas

AU - Blankenberg, Stefan

AU - ARTEMIS study group

N1 - © 2023. The Author(s).

PY - 2023/9

Y1 - 2023/9

N2 - 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). Discriminative performance of the models was assessed using area under the receiver operating characteristic curve (AUC) and logLoss. Model performance was validated in an external cohort with 1688 patients and tested for global generalizability in 13 international cohorts with 23,411 patients.RESULTS: Eleven routinely available variables including age, sex, cardiovascular risk factors, electrocardiography, and hs-cTn were included in the ARTEMIS models. In the validation and generalization cohorts, excellent discriminative performance was confirmed, superior to hs-cTn only. For the serial hs-cTn measurement model, AUC ranged from 0.92 to 0.98. Good calibration was observed. Using a single hs-cTn measurement, the ARTEMIS model allowed direct rule-out of MI with very high and similar safety but up to tripled efficiency compared to the guideline-recommended strategy.CONCLUSION: We developed and validated diagnostic models to accurately estimate the individual probability of MI, which allow for variable hs-cTn use and flexible timing of resampling. Their digital application may provide rapid, safe and efficient personalized patient care.TRIAL REGISTRATION NUMBERS: Data of following cohorts were used for this project: BACC ( www.CLINICALTRIALS: gov ; NCT02355457), stenoCardia ( www.CLINICALTRIALS: gov ; NCT03227159), ADAPT-BSN ( www.australianclinicaltrials.gov.au ; ACTRN12611001069943), IMPACT ( www.australianclinicaltrials.gov.au , ACTRN12611000206921), ADAPT-RCT ( www.anzctr.org.au ; ANZCTR12610000766011), EDACS-RCT ( www.anzctr.org.au ; ANZCTR12613000745741); DROP-ACS ( https://www.umin.ac.jp , UMIN000030668); High-STEACS ( www.CLINICALTRIALS: gov ; NCT01852123), LUND ( www.CLINICALTRIALS: gov ; NCT05484544), RAPID-CPU ( www.CLINICALTRIALS: gov ; NCT03111862), ROMI ( www.CLINICALTRIALS: gov ; NCT01994577), SAMIE ( https://anzctr.org.au ; ACTRN12621000053820), SEIGE and SAFETY ( www.CLINICALTRIALS: gov ; NCT04772157), STOP-CP ( www.CLINICALTRIALS: gov ; NCT02984436), UTROPIA ( www.CLINICALTRIALS: gov ; NCT02060760).

AB - 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). Discriminative performance of the models was assessed using area under the receiver operating characteristic curve (AUC) and logLoss. Model performance was validated in an external cohort with 1688 patients and tested for global generalizability in 13 international cohorts with 23,411 patients.RESULTS: Eleven routinely available variables including age, sex, cardiovascular risk factors, electrocardiography, and hs-cTn were included in the ARTEMIS models. In the validation and generalization cohorts, excellent discriminative performance was confirmed, superior to hs-cTn only. For the serial hs-cTn measurement model, AUC ranged from 0.92 to 0.98. Good calibration was observed. Using a single hs-cTn measurement, the ARTEMIS model allowed direct rule-out of MI with very high and similar safety but up to tripled efficiency compared to the guideline-recommended strategy.CONCLUSION: We developed and validated diagnostic models to accurately estimate the individual probability of MI, which allow for variable hs-cTn use and flexible timing of resampling. Their digital application may provide rapid, safe and efficient personalized patient care.TRIAL REGISTRATION NUMBERS: Data of following cohorts were used for this project: BACC ( www.CLINICALTRIALS: gov ; NCT02355457), stenoCardia ( www.CLINICALTRIALS: gov ; NCT03227159), ADAPT-BSN ( www.australianclinicaltrials.gov.au ; ACTRN12611001069943), IMPACT ( www.australianclinicaltrials.gov.au , ACTRN12611000206921), ADAPT-RCT ( www.anzctr.org.au ; ANZCTR12610000766011), EDACS-RCT ( www.anzctr.org.au ; ANZCTR12613000745741); DROP-ACS ( https://www.umin.ac.jp , UMIN000030668); High-STEACS ( www.CLINICALTRIALS: gov ; NCT01852123), LUND ( www.CLINICALTRIALS: gov ; NCT05484544), RAPID-CPU ( www.CLINICALTRIALS: gov ; NCT03111862), ROMI ( www.CLINICALTRIALS: gov ; NCT01994577), SAMIE ( https://anzctr.org.au ; ACTRN12621000053820), SEIGE and SAFETY ( www.CLINICALTRIALS: gov ; NCT04772157), STOP-CP ( www.CLINICALTRIALS: gov ; NCT02984436), UTROPIA ( www.CLINICALTRIALS: gov ; NCT02060760).

KW - Humans

KW - Angina Pectoris

KW - Biomarkers

KW - Myocardial Infarction/diagnosis

KW - ROC Curve

KW - Troponin I

KW - Troponin T

KW - Clinical Studies as Topic

U2 - 10.1007/s00392-023-02206-3

DO - 10.1007/s00392-023-02206-3

M3 - SCORING: Journal article

C2 - 37131096

VL - 112

SP - 1288

EP - 1301

JO - CLIN RES CARDIOL

JF - CLIN RES CARDIOL

SN - 1861-0684

IS - 9

ER -