In:
European Journal of Cardiovascular Nursing, Oxford University Press (OUP), Vol. 20, No. 2 ( 2021-04-13), p. 167-174
Abstract:
A more accurate and simpler scoring systems for early risk stratification of acute myocardial infarction at admission can accelerate and improve decision-making. Aim To develop and validate a simple physiological prognostic scoring system for early risk stratification in patients with acute myocardial infarction. Methods Easily accessible physiological vital signs and demographic characteristics of patients with acute myocardial infarction at the time of presentation in the multicentre Retrospective Evaluation of Acute Chest Pain study were used to develop a multivariate logistic regression model predicting 12 and 24-month mortality. The study population consisted of 2619 patients from seven hospitals and was divided into a 70% sample for model derivation and a 30% sample for model validation. A nomogram was created to enable prospective risk stratification for clinical care. Results The simple physiological prognostic scoring system consisted of age, heart rate, body mass index and Killip class. The area under the receiver operating characteristic curve of the simple physiological prognostic scoring system was superior to that of several risk scoring systems in clinical use. Net reclassification improvement, integrated discrimination improvement and decision curve analysis of the derivation set also revealed superior performance to the Global Registry of Acute Coronary Events score, and the Hosmer–Lemeshow test indicated good calibration for predicting mortality in patient with acute myocardial infarction in the validation set (P = 0.612). Conclusion This simple physiological prognostic scoring system may be a useful risk stratification tool for early assessment of patients with acute myocardial infarction.
Type of Medium:
Online Resource
ISSN:
1474-5151
,
1873-1953
DOI:
10.1177/1474515120952214
Language:
English
Publisher:
Oxford University Press (OUP)
Publication Date:
2021
detail.hit.zdb_id:
2099328-6
