Personalized diagnosis in suspected myocardial infarction
Neumann, J.T. ; Ekelund, U. LU
; Mokhtari, A.
LU
and Blankenberg, S.
(2023)
In Clinical Research in Cardiology
112.
p.1288-1301
- 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... (More)
- 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). Graphical Abstract: [Figure not available: see fulltext.] © 2023, The Author(s). (Less)
Please use this url to cite or link to this publication:
https://lup.lub.lu.se/record/43de5e70-bf53-43d9-bc9f-357786f53a11
- author
- Neumann, J.T.
; Ekelund, U.
LU
; Mokhtari, A.
LU
and Blankenberg, S.
- author collaboration
- organization
- publishing date
- 2023
- type
- Contribution to journal
- publication status
- published
- subject
- keywords
- Acute myocardial infarction, Biomarker, Machine learning, Probability, Super learner, Troponin, Validation
- in
- Clinical Research in Cardiology
- volume
- 112
- pages
- 13 pages
- publisher
- Steinkopff
- external identifiers
-
- scopus:85156242150
- pmid:37131096
- ISSN
- 1861-0684
- DOI
- 10.1007/s00392-023-02206-3
- language
- English
- LU publication?
- yes
- id
- 43de5e70-bf53-43d9-bc9f-357786f53a11
- date added to LUP
- 2023-11-09 13:29:54
- date last changed
- 2023-11-10 03:00:03
@article{43de5e70-bf53-43d9-bc9f-357786f53a11,
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). Graphical Abstract: [Figure not available: see fulltext.] © 2023, The Author(s).}},
author = {{Neumann, J.T. and Ekelund, U. and Mokhtari, A. and Blankenberg, S.}},
issn = {{1861-0684}},
keywords = {{Acute myocardial infarction; Biomarker; Machine learning; Probability; Super learner; Troponin; Validation}},
language = {{eng}},
pages = {{1288--1301}},
publisher = {{Steinkopff}},
series = {{Clinical Research in Cardiology}},
title = {{Personalized diagnosis in suspected myocardial infarction}},
url = {{http://dx.doi.org/10.1007/s00392-023-02206-3}},
doi = {{10.1007/s00392-023-02206-3}},
volume = {{112}},
year = {{2023}},
}