Literature DB >> 28993178

A user-friendly risk-score for predicting in-hospital cardiac arrest among patients admitted with suspected non ST-elevation acute coronary syndrome - The SAFER-score.

Jonas Faxén1, Marlous Hall2, Chris P Gale3, Johan Sundström4, Bertil Lindahl5, Tomas Jernberg6, Karolina Szummer7.   

Abstract

AIM: To develop a simple risk-score model for predicting in-hospital cardiac arrest (CA) among patients hospitalized with suspected non-ST elevation acute coronary syndrome (NSTE-ACS).
METHODS: Using the Swedish Web-system for Enhancement and Development of Evidence-based care in Heart disease Evaluated According to Recommended Therapies (SWEDEHEART), we identified patients (n=242 303) admitted with suspected NSTE-ACS between 2008 and 2014. Logistic regression was used to assess the association between 26 candidate variables and in-hospital CA. A risk-score model was developed and validated using a temporal cohort (n=126 073) comprising patients from SWEDEHEART between 2005 and 2007 and an external cohort (n=276 109) comprising patients from the Myocardial Ischaemia National Audit Project (MINAP) between 2008 and 2013.
RESULTS: The incidence of in-hospital CA for NSTE-ACS and non-ACS was lower in the SWEDEHEART-derivation cohort than in MINAP (1.3% and 0.5% vs. 2.3% and 2.3%). A seven point, five variable risk score (age ≥60 years (1 point), ST-T abnormalities (2 points), Killip Class >1 (1 point), heart rate <50 or ≥100bpm (1 point), and systolic blood pressure <100mmHg (2 points) was developed. Model discrimination was good in the derivation cohort (c-statistic 0.72) and temporal validation cohort (c-statistic 0.74), and calibration was reasonable with a tendency towards overestimation of risk with a higher sum of score points. External validation showed moderate discrimination (c-statistic 0.65) and calibration showed a general underestimation of predicted risk.
CONCLUSIONS: A simple points score containing five variables readily available on admission predicts in-hospital CA for patients with suspected NSTE-ACS.
Copyright © 2017 Elsevier B.V. All rights reserved.

Entities:  

Keywords:  Acute coronary syndrome; In-hospital cardiac arrest; Non-ST elevation acute coronary syndrome; Risk score; Risk stratification

Mesh:

Year:  2017        PMID: 28993178     DOI: 10.1016/j.resuscitation.2017.10.004

Source DB:  PubMed          Journal:  Resuscitation        ISSN: 0300-9572            Impact factor:   5.262


  3 in total

1.  Machine learning for early prediction of in-hospital cardiac arrest in patients with acute coronary syndromes.

Authors:  Ting Ting Wu; Xiu Quan Lin; Yan Mu; Hong Li; Yang Song Guo
Journal:  Clin Cardiol       Date:  2021-02-14       Impact factor: 3.287

2.  Decision tree model for predicting in-hospital cardiac arrest among patients admitted with acute coronary syndrome.

Authors:  Hong Li; Ting Ting Wu; Dong Liang Yang; Yang Song Guo; Pei Chang Liu; Yuan Chen; Li Ping Xiao
Journal:  Clin Cardiol       Date:  2019-09-11       Impact factor: 2.882

3.  Association of left anterior descending artery involvement on clinical outcomes among patients with STEMI presenting with and without out-of-hospital cardiac arrest.

Authors:  Mia Bertic; Christopher B Fordyce; Nima Moghaddam; John Cairns; Martha Mackay; Joel Singer; Terry Lee; Michele Perry-Arnesen; Wendy Tocher; Graham Wong
Journal:  Open Heart       Date:  2020-03-04
  3 in total

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