Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2023 Nov 16;12(11):743-752.
doi: 10.1093/ehjacc/zuad089.

Derivation and external validation of machine-learning models for risk stratification in chest pain with normal troponin

Agustín Fernández-Cisnal  1 , Pedro Lopez-Ayala  2 , Ernesto Valero  1 , Luca Koechlin  2 , Arturo Catarralá  3 , Jasper Boeddinghaus  2 , José Noceda  4 , Thomas Nestelberger  2 , Òscar Miró  5 , Núñez Julio  1 , Christian Mueller  2 , Juan Sanchis  1
Affiliations

Derivation and external validation of machine-learning models for risk stratification in chest pain with normal troponin

Agustín Fernández-Cisnal et al. Eur Heart J Acute Cardiovasc Care. .

Erratum in

Abstract

Aims: Risk stratification of patients with chest pain and a high-sensitivity cardiac troponin T (hs-cTnT) concentration <upper reference limit (URL) is challenging. The aim of this study was to develop and externally validate clinical models for risk prediction of 90-day death or myocardial infarction in patients presenting to the emergency department with chest pain and an initial hs-cTnT concentration <URL.

Methods and results: Four machine-learning-based models and one logistic regression (LR) model were trained on 4075 patients (single-centre Spanish cohort) and externally validated on 3609 patients (international prospective Advantageous Predictors of Acute Coronary syndromes Evaluation cohort). Models were compared with GRACE and HEART scores and a single undetectable hs-cTnT-based strategy (u-cTn; hs-cTnT < 5 ng/L and time from symptoms onset >180 min). Probability thresholds for safe discharge were derived in the derivation cohort. The endpoint occurred in 105 (2.6%) patients in the training set and 98 (2.7%) in the external validation set. Gradient boosting full (GBf) showed the best discrimination (area under the curve = 0.808). Calibration was good for the reduced neural network and LR models. Gradient boosting full identified the highest proportion of patients for safe discharge (36.7 vs. 23.4 vs. 27.2%; GBf vs. LR vs. u-cTn, respectively) with similar safety (missed endpoint per 1000 patients: 2.2 vs. 3.5 vs. 3.1, respectively). All derived models were superior to the HEART and GRACE scores (P < 0.001).

Conclusion: Machine-learning and LR prediction models were superior to the HEART, GRACE, and u-cTn for risk stratification of patients with chest pain and a baseline hs-cTnT <URL. Gradient boosting full models best balanced discrimination, calibration, and efficacy, reducing the need for serial hs-cTnT determination by more than one-third.

Clinical trial registration: ClinicalTrials.gov number, NCT00470587, https://clinicaltrials.gov/ct2/show/NCT00470587.

Keywords: Machine learning; Myocardial infarction; Prediction; Troponin.

PubMed Disclaimer

Conflict of interest statement

Conflict of interest: P.L.-A. received research grants from the Swiss Heart Foundation (FF20079 and FF21103) and speaker honoraria from Quidel, paid to the institution, outside the submitted work. L.K. received a research grant from the Swiss Heart Foundation, the University of Basel, the Swiss Academy of Medical Sciences, and the Gottfried and Julia Bangerter-Rhyner Foundation, as well as the ‘Freiwillige Akademische Gesellschaft Basel’, and speaker honoraria from Roche Diagnostics. J.B. received research grants from the University of Basel, the University Hospital of Basel, the Division of Internal Medicine, the Swiss Academy of Medical Sciences, the Gottfried and Julia Bangerter-Rhyner Foundation, and the Swiss National Science Foundation (P500PM_206636), and speaker honoraria from Siemens, Roche Diagnostics, Ortho Clinical Diagnostics, and Quidel Corporation. C.M. received research grants from the Swiss National Science Foundation, the Swiss Heart Foundation, the KTI, the European Union, the University Basel, the University Hospital Basel, Abbott, Astra Zeneca, Beckman Coulter, BRAHMS, Idorsia, Novartis, Ortho Clinical Diagnostics, Quidel, Roche, Siemens, Singulex, and Sphingotec, as well as speaker/consulting honoraria from Astra Zeneca, Bayer, Boehringer Ingelheim, BMS, Daiichi Sankyo, Idorsia, Osler, Novartis, Roche, Sanofi, Siemens, and Singulex, all paid to the institution. The remaining authors have nothing to disclose.

Associated data

LinkOut - more resources