High-sensitivity cardiac troponin T (hs-cTnT) 0-hour/1-hour algorithm
The high-sensitivity cardiac troponin T (hs-cTnT) 0-hour/1-hour algorithm uses hs-cTnT blood concentration at presentation and its absolute 1h change to stratify patients suspected of acute myocardial infarction.

The Calculator is intended to be used for patients presenting to the Emergency Department with Chest Pain aged 18 years or older.
Forskende forfattere: Reichlin T, Schindler C, Drexler B, Twerenbold R, Reiter M, Zellweger C, Moehring B, Ziller R, Hoeller R, Gimenez MR, Haaf P, Potocki M, Wildi K, Balmelli C, Freese M, Stelzig C, Freidank H, Osswald S, and Mueller C.
Versjon: 1.44
  • Offentlig
  • Kardiologi
  • {{ modelType }}
V-1.44-2426.24.05.31
(01)08719327522776(8012)v1.44(4326)240531(240)2426
Last ned Brukerhåndbok og konsultere Tiltenkt bruk.

{{section.title}}

Beregne resultatet

Angi flere parametere for å utføre beregningen

Absolute change of hs-cTnT: ng/L within the first hour

{{ resultSubheader }}
{{ chart.title }}
Resultatintervall {{ additionalResult.min }} til {{ additionalResult.max }}

Betinget informasjon


Model performance:
The high-sensitivity cardiac troponin T(hs-cTnT) 0-hour/1-hour algorithm was reported to achieve a very high negative predictive value for acute myocardial infarction in the rule-out zone, to achieve a high positive predictive value in the rule-in zone, and to be very effective by triaging approximately 75% of patients presenting with suspected acute myocardial infarction to the ED to either rule-out or rule-in classifications.1,2

In 2016, the algorithm was further validated by Mueller et al. in an external mulicenter cohort of 1,282 patients with a 17% rate of acute myocardial infarction.3 Use of hs-cTn assays at presentation and 1 hour later in classified 63% of patients as having no acute myocardial infarction, with a 99.1% NPV (95% CI 98.2% to 99.7%); 14% as having acute myocardial infarction, with a PPV of 77% (95% CI 70.4% to 83.0%); and 22.5% as having an indeterminate classification after 1 hour of testing.

Related references:

  1. Reichlin T, Schindler C, Drexler B, et al. One-hour rule-out and rule-in ofacute myocardial infarction using high-sensitivity cardiac troponin T. Arch Intern Med. 2012;172:1211-1218.
     
  2. Reichlin T, Twerenbold R, Wildi K, et al. Prospective validation of a 1-hour algorithm to rule-out and rule-in acute myocardial infarction usinga high-sensitivity cardiac troponin T assay. CMAJ. 2015;187:E243-252.
     
  3. Mueller C, Giannitsis E, Christ M, et al. Multicenter Evaluation of a 0-Hour/1-Hour Algorithm in the Diagnosis of Myocardial Infarction With High-Sensitivity Cardiac Troponin T. Ann Emerg Med. 2016;68(1):76-87.e4.

{{ file.classification }}
PRO
Merknad
Notater er bare synlige i resultatnedlastingen og lagres ikke av Evidencio.

Beregninger alene bør aldri diktere pasientbehandlingen, og kan ikke erstatte faglig skjønn. Se hele vår disclaimer.

Underliggende modeller En del av
logo

Logg inn for å aktivere Evidencios utskriftsfunksjoner.

For å kunne bruke Evidencios utskriftsfunksjoner må du være logget inn.
Hvis du ikke har en Evidencio Community-konto, kan du opprette en gratis personlig konto på:

https://www.evidencio.com/registration

Trykte resultater - Eksempler {{ new Date().toLocaleString() }}


Fordeler med Evidencio Community Account


With an Evidencio Community account you can:

  • Create and publish your own prediction models.
  • Share your prediction models with your colleagues, research group, organization or the world.
  • Review and provide feedback on models that have been shared with you.
  • Validate your models and validate models from other users.
  • Find models based on Title, Keyword, Author, Institute, or MeSH classification.
  • Use and save prediction models and their data.
  • Use patient specific protocols and guidelines based on sequential models and decision trees.
  • Stay up-to-date with new models in your field as they are published.
  • Create your own lists of favorite models and topics.

A personal Evidencio account is free, with no strings attached!
Join us and help create clarity, transparency, and efficiency in the creation, validation, and use of medical prediction models.


Ansvarsfraskrivelse: Beregninger alene bør aldri være styrende for pasientbehandlingen, og kan ikke erstatte faglig skjønn.
Evidencio v3.29 © 2015 - 2024 Evidencio. All Rights Reserved