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Coronary artery disease
Original research
European Society of Cardiology 0/1-hour algorithm (high-sensitivity cardiac troponin T) performance across distinct age groups
  1. Marissa J Millard1,
  2. Nicklaus P Ashburn1,
  3. Anna C Snavely1,2,
  4. Tara Hashemian1,
  5. Michael Supples1,
  6. Brandon Allen3,
  7. Robert Christenson4,
  8. Troy Madsen5,
  9. James McCord6,
  10. Bryn Mumma7,
  11. Jason Stopyra1,
  12. Richard Gentry Wilkerson8,
  13. Simon A Mahler1,9,10
  1. 1 Department of Emergency Medicine, Wake Forest University School of Medicine, Winston-Salem, North Carolina, USA
  2. 2 Department of Biostatistics and Data Science, Wake Forest University School of Medicine, Winston-Salem, North Carolina, USA
  3. 3 Department of Emergency Medicine, University of Florida College of Medicine, Gainesville, Florida, USA
  4. 4 Department of Pathology, University of Maryland School of Medicine, Baltimore, Maryland, USA
  5. 5 Department of Emergency Medicine, University of Utah School of Medicine, Salt Lake City, Utah, USA
  6. 6 Department of Cardiology, Henry Ford Hospital, Detroit, Michigan, USA
  7. 7 Department of Emergency Medicine, University of California Davis School of Medicine, Sacramento, California, USA
  8. 8 Department of Emergency Medicine, University of Maryland School of Medicine, Baltimore, Maryland, USA
  9. 9 Department of Epidemiology and Prevention, Wake Forest School of Medicine, Winston-Salem, North Carolina, USA
  10. 10 Department of Implementation Science, Wake Forest University School of Medicine, Winston-Salem, North Carolina, USA
  1. Correspondence to Marissa J Millard, Department of Emergency Medicine, Wake Forest University School of Medicine, Winston-Salem, NC 27157, USA; mmillard{at}wakehealth.edu

Abstract

Background To determine if the European Society of Cardiology 0/1-hour (ESC 0/1-h) algorithm with high-sensitivity cardiac troponin T (hs-cTnT) meets the ≥99% negative predictive value (NPV) safety threshold for 30-day cardiac death or myocardial infarction (MI) in older, middle-aged and young subgroups.

Methods We conducted a subgroup analysis of adult emergency department patients with chest pain prospectively enrolled from eight US sites (January 2017 to September 2018). Patients were stratified into rule-out, observation and rule-in zones using the hs-cTnT ESC 0/1-h algorithm and classified as older (≥65 years), middle aged (46–64 years) or young (21–45 years). Patients had 0-hour and 1-hour hs-cTnT measures (Roche Diagnostics) and a History, ECG, Age, Risk factor and Troponin (HEART) score. Fisher’s exact tests compared rule-out and 30-day cardiac death or MI rates between ages. NPVs with 95% CIs were calculated for the ESC 0/1-h algorithm with and without the HEART score.

Results Of 1430 participants, 26.9% (385/1430) were older, 57.4% (821/1430) middle aged and 15.7% (224/1430) young. Cardiac death or MI at 30 days occurred in 12.8% (183/1430). ESC 0/1-h algorithm ruled out 35.6% (137/385) of older, 62.1% (510/821) of middle-aged and 79.9% of (179/224) young patients (p<0.001). NPV for 30-day cardiac death or MI was 97.1% (95% CI 92.7% to 99.2%) among older patients, 98.4% (95% CI 96.9% to 99.3%) in middle-aged patients and 99.4% (95% CI 96.9% to 100%) among young patients. Adding a HEART score increased NPV to 100% (95% CI 87.7% to 100%) for older, 99.2% (95% CI 97.2% to 99.9%) for middle-aged and 99.4% (95% CI 96.6% to 100%) for young patients.

Conclusions In older and middle-aged adults, the hs-cTnT ESC 0/1-h algorithm was unable to reach a 99% NPV for 30-day cardiac death or MI unless combined with a HEART score.

Trial registration number NCT02984436.

  • Chest Pain
  • Acute Coronary Syndrome
  • Myocardial Infarction
  • Biomarkers

Data availability statement

No data are available. The data will not be made publicly available.

https://doi.org/10.1136/heartjnl-2023-323621

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    Data availability statement

    No data are available. The data will not be made publicly available.

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    Footnotes

    • X @Nick_Ashburn1

    • Contributors MJM contributed to conceptualisation, data curation, investigation, methodology and writing—original draft. NPA contributed to conceptualisation, data curation, formal analysis, investigation, methodology, supervision and writing—review and editing. ACS contributed to conceptualisation, data curation, formal analysis, investigation, methodology, resources, supervision and writing—review and editing. TH contributed to methodology, data curation, formal analysis and writing—review and editing. MS and JS contributed to conceptualisation, investigation, methodology, supervision and writing—review and editing. BA contributed to conceptualisation, data curation, funding acquisition, investigation, methodology, project administration, resources, supervision and writing—review and editing. RC, TM, JM, BM and RGW contributed to conceptualisation, investigation, methodology, project administration, resources, supervision and writing—review and editing. SAM contributed to conceptualisation, data curation, formal analysis, funding acquisition, investigation, methodology, project administration, resources, supervision and writing—review and editing, he is also the guarantor for this work.

    • Funding This investigator-initiated study was funded by Roche Diagnostics.

    • Competing interests ACS receives funding from Abbott, HRSA (1H2ARH399760100) and AHRQ (R01HS029017). JS receives research funding from HRSA (H2ARH39976-01-00), The Duke Endowment, Roche Diagnostics, Abbott Laboratories, Pathfast, Genetesis, Cytovale, Forest Devices, Vifor Pharma and Chiesi Farmaceutici. SAM receives funding/support from Roche Diagnostics, Abbott Laboratories, QuidelOrtho, Siemens, Grifols, Pathfast, Genetesis, Cytovale, The Duke Endowment, Beckman Coulter, Comprehensive Research Associates, Brainbox, HRSA (1H2ARH399760100) and AHRQ (R01HS029017). He is a consultant for Roche, QuidelOrtho, Abbott, Siemens, Genetesis, Inflammatix, Radiometer and Amgen and is the chief medical officer for Impathiq. BA receives research funding/support from Roche Diagnostics, Siemens and Beckman Coulter. He is a consultant for Roche Diagnostics. BM has research support from the NIH (5K08HL130546) and Roche Diagnostics. She has served as a consultant for Roche Diagnostics. JM receives research funding/support from Beckman Coulter, Roche Diagnostics, Abbott Laboratories and Siemens. He is a consultant for Beckman Coulter, Roche Diagnostics and Siemens. RGW received research funding from Regeneron Pharmaceuticals, Lilly USA, BioAge Labs, Roche Diagnostics, Global Blood Therapeutics, Novartis Pharmaceuticals, Egetis Therapeutics, EndPoint Health, Blade Therapeutics, Janssen R&D, ProvePharma and Pfizer. He has received research funding from CoapTech through an NIH/NIDDK grant (R44DK115325). He has received research support in the form of equipment and supplies from Cepheid and Eldon Biologicals. He is an uncompensated advisor to CSL Behring. RC is a consultant for and receives funding/support from Roche Diagnostics, Siemens Healthineers, Beckman Coulter Diagnostics, Becton Dickinson and Co, Quidel and Sphingotec.

    • Patient and public involvement Patients and/or the public were not involved in the design, or conduct, or reporting, or dissemination plans of this research.

    • Provenance and peer review Not commissioned; externally peer reviewed.

    • Supplemental material This content has been supplied by the author(s). It has not been vetted by BMJ Publishing Group Limited (BMJ) and may not have been peer-reviewed. Any opinions or recommendations discussed are solely those of the author(s) and are not endorsed by BMJ. BMJ disclaims all liability and responsibility arising from any reliance placed on the content. Where the content includes any translated material, BMJ does not warrant the accuracy and reliability of the translations (including but not limited to local regulations, clinical guidelines, terminology, drug names and drug dosages), and is not responsible for any error and/or omissions arising from translation and adaptation or otherwise.

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