Literature DB >> 29953281

Limitations of track and trigger systems and the National Early Warning Score. Part 2: sensitivity versus specificity.

Steven Grant1, Kevin Crimmons2.   

Abstract

The second article on the use of track and trigger scoring (TTS) and National Early Warning Scoring Systems (NEWS 1 and 2) discusses how their use in relation to some patients can be too sensitive and in the case of others it merely detects late deterioration. This raises concerns that TTS and NEWS focus on a single set of observations at one point in time. They, therefore, ignore the observational trends by failing to compare the latest readings against previous sets of vital signs. It is therefore important that nurses do not rely solely on these tools, but use them in conjunction with their physiological knowledge and clinical assessment to identify deteriorating patients, as well as those who do not require unnecessary escalation of care.

Entities:  

Keywords:  Acutely ill patients; National Early Warning Score; Track and trigger systems; Vital signs

Mesh:

Year:  2018        PMID: 29953281     DOI: 10.12968/bjon.2018.27.12.705

Source DB:  PubMed          Journal:  Br J Nurs        ISSN: 0966-0461


  4 in total

1.  Implementation of the National Early Warning Score in UK care homes: a qualitative evaluation.

Authors:  Siân Russell; Rachel Stocker; Robert Oliver Barker; Jennifer Liddle; Joy Adamson; Barbara Hanratty
Journal:  Br J Gen Pract       Date:  2020-10-29       Impact factor: 5.386

2.  Nurses' Experiences and Perceptions of two Early Warning Score systems to Identify Patient Deterioration-A Focus Group Study.

Authors:  Caroline S Langkjaer; Dorthe G Bove; Pernille B Nielsen; Kasper K Iversen; Morten H Bestle; Gitte Bunkenborg
Journal:  Nurs Open       Date:  2021-02-27

3.  What items should be included in an early warning score for remote assessment of suspected COVID-19? qualitative and Delphi study.

Authors:  Trisha Greenhalgh; Paul Thompson; Sietse Weiringa; Ana Luisa Neves; Laiba Husain; Merlin Dunlop; Alexander Rushforth; David Nunan; Simon de Lusignan; Brendan Delaney
Journal:  BMJ Open       Date:  2020-11-12       Impact factor: 2.692

4.  Explainable machine learning for real-time deterioration alert prediction to guide pre-emptive treatment.

Authors:  Aida Brankovic; Hamed Hassanzadeh; Norm Good; Kay Mann; Sankalp Khanna; Ahmad Abdel-Hafez; David Cook
Journal:  Sci Rep       Date:  2022-07-11       Impact factor: 4.996

  4 in total

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