Literature DB >> 28814479

Early prediction of hospital admission for emergency department patients: a comparison between patients younger or older than 70 years.

Jacinta A Lucke1,2, Jelle de Gelder2, Fleur Clarijs1, Christian Heringhaus1, Anton J M de Craen2, Anne J Fogteloo3, Gerard J Blauw2,4, Bas de Groot1, Simon P Mooijaart2,5.   

Abstract

OBJECTIVE: The aim of this study was to develop models that predict hospital admission to ED of patients younger and older than 70 and compare their performance.
METHODS: Prediction models were derived in a retrospective observational study of all patients≥18 years old visiting the ED of a university hospital during the first 6 months of 2012. Patients were stratified into two age groups (<70 years old and ≥70 years old). Multivariable logistic regression analysis was used to identify predictors of hospital admission among factors available immediately after patient arrival to the ED. Validation of the prediction models was performed on patients presenting to the ED during the second half of the year 2012.
RESULTS: 10 807 patients were included in the derivation and 10 480 in the validation cohorts. The strongest independent predictors of hospital admission among the 8728 patients <70 years old were age, sex, triage category, mode of arrival, performance of blood tests, chief complaint, ED revisit, type of specialist, phlebotomised blood sample and all vital signs. The area under the curve (AUC) of the validation cohort for those <70 years old was 0.86 (95% CI 0.85 to 0.87). Among the 2079 patients ≥70 years, the same factors were predictive, except for gender, type of specialist and heart rate; the AUC was 0.77 (95% CI 0.75 to 0.79). The prediction models could identify a group of 10% of patients with the highest risk in whom hospital admission was predicted at ED triage, with a positive predictive value (PPV) of 71% (95% CI 68% to 74%) in younger patients and PPV of 87% (95% CI 81% to 92%) in older patients.
CONCLUSION: Demographic and clinical factors readily available early in the ED visit can be useful in identifying patients who are likely to be admitted to the hospital. While the model for the younger patients had a higher AUC, the model for older patients had a higher PPV in identifying the patients at highest risk for admission. Of note, heart rate was not a useful predictor in the older patients. © Article author(s) (or their employer(s) unless otherwise stated in the text of the article) 2018. All rights reserved. No commercial use is permitted unless otherwise expressly granted.

Entities:  

Keywords:  aged; emergency department; epidemiology.; geriatrics; hospitalizations; research

Mesh:

Year:  2017        PMID: 28814479     DOI: 10.1136/emermed-2016-205846

Source DB:  PubMed          Journal:  Emerg Med J        ISSN: 1472-0205            Impact factor:   2.740


  11 in total

Review 1.  An exhaustive review and analysis on applications of statistical forecasting in hospital emergency departments.

Authors:  Muhammet Gul; Erkan Celik
Journal:  Health Syst (Basingstoke)       Date:  2018-11-19

2.  Development of a low-dimensional model to predict admissions from triage at a pediatric emergency department.

Authors:  Fiona Leonard; John Gilligan; Michael J Barrett
Journal:  J Am Coll Emerg Physicians Open       Date:  2022-07-15

3.  Predicting hospital admission at emergency department triage using machine learning.

Authors:  Woo Suk Hong; Adrian Daniel Haimovich; R Andrew Taylor
Journal:  PLoS One       Date:  2018-07-20       Impact factor: 3.240

4.  Predicting Admissions From a Paediatric Emergency Department - Protocol for Developing and Validating a Low-Dimensional Machine Learning Prediction Model.

Authors:  Fiona Leonard; John Gilligan; Michael J Barrett
Journal:  Front Big Data       Date:  2021-04-16

5.  Examining the association between paramedic transport to the emergency department and hospital admission: a population-based cohort study.

Authors:  Ryan P Strum; Fabrice I Mowbray; Andrew Worster; Walter Tavares; Matthew S Leyenaar; Rebecca H Correia; Andrew P Costa
Journal:  BMC Emerg Med       Date:  2021-10-12

Review 6.  Machine learning in patient flow: a review.

Authors:  Rasheed El-Bouri; Thomas Taylor; Alexey Youssef; Tingting Zhu; David A Clifton
Journal:  Prog Biomed Eng (Bristol)       Date:  2021-02-22

7.  Predicting inhospital admission at the emergency department: a systematic review.

Authors:  Anniek Brink; Jelmer Alsma; Lodewijk Aam van Attekum; Wichor M Bramer; Robert Zietse; Hester Lingsma; Stephanie Ce Schuit
Journal:  Emerg Med J       Date:  2021-10-28       Impact factor: 2.740

8.  Assessing the Generalizability of a Clinical Machine Learning Model Across Multiple Emergency Departments.

Authors:  Alexander J Ryu; Santiago Romero-Brufau; Ray Qian; Heather A Heaton; David M Nestler; Shant Ayanian; Thomas C Kingsley
Journal:  Mayo Clin Proc Innov Qual Outcomes       Date:  2022-04-26

9.  Comparing the effectiveness of three scoring systems in predicting adult patient outcomes in the emergency department.

Authors:  Xiaojun Wei; Haoli Ma; Ruining Liu; Yan Zhao
Journal:  Medicine (Baltimore)       Date:  2019-02       Impact factor: 1.817

10.  Characteristics of non-conveyed patients in emergency medical services (EMS): a one-year prospective descriptive and comparative study in a region of Sweden.

Authors:  Erik Höglund; Magnus Andersson-Hagiwara; Agneta Schröder; Margareta Möller; Emma Ohlsson-Nevo
Journal:  BMC Emerg Med       Date:  2020-08-10
View more

北京卡尤迪生物科技股份有限公司 © 2022-2023.