scholarly journals PEMCRC anaphylaxis study protocol: a multicentre cohort study to derive and validate clinical decision models for the emergency department management of children with anaphylaxis

BMJ Open ◽  
2021 ◽  
Vol 11 (1) ◽  
pp. e037341
Author(s):  
Timothy E Dribin ◽  
Kenneth A Michelson ◽  
David Vyles ◽  
Mark I Neuman ◽  
David C Brousseau ◽  
...  
Keyword(s):  
Risk Factors ◽  
Emergency Department ◽  
Online Education ◽  
Prediction Models ◽  
Recursive Partitioning ◽  
Clinical Decision ◽  
Pediatric Emergency ◽  
Initial Reaction ◽  
Research Committee ◽  
Reaction Severity

IntroductionThere remain significant knowledge gaps about the management and outcomes of children with anaphylaxis. These gaps have led to practice variation regarding decisions to hospitalise children and length of observation periods following treatment with epinephrine. The objectives of this multicentre study are to (1) determine the prevalence of and risk factors for severe, persistent, refractory and biphasic anaphylaxis, as well as persistent and biphasic non-anaphylactic reactions; (2) derive and validate prediction models for emergency department (ED) discharge; and (3) determine data-driven lengths of ED and inpatient observation prior to discharge to home based on initial reaction severity.Methods and analysisThe study is being conducted through the Pediatric Emergency Medicine Collaborative Research Committee (PEMCRC). Children 6 months to less than 18 years of age presenting to 30 participating EDs for anaphylaxis from October 2015 to December 2019 will be eligible. The primary outcomes for each objective are (1) severe, persistent, refractory or biphasic anaphylaxis, as well as persistent or biphasic non-anaphylactic reactions; (2) safe ED discharge, defined as no receipt of acute anaphylaxis medications or hypotension beyond 4 hours from first administered dose of epinephrine; and (3) time from first to last administered dose of epinephrine and vasopressor cessation. Analyses for each objective include (1) descriptive statistics to estimate prevalence and generalised estimating equations that will be used to investigate risk factors for anaphylaxis outcomes, (2) least absolute shrinkage and selection operator regression and binary recursive partitioning to derive and validate prediction models of children who may be candidates for safe ED discharge, and (3) Kaplan-Meier analyses to assess timing from first to last epinephrine doses and vasopressor cessation based on initial reaction severity.Ethics and disseminationAll sites will obtain institutional review board approval; results will be published in peer-reviewed journals and disseminated via traditional and social media, blogs and online education platforms.

Machine Learning for Emergency Department Management

2019 ◽  
Vol 11 (3) ◽  
pp. 19-36 ◽  
Author(s):  
Sofia Benbelkacem ◽  
Farid Kadri ◽  
Baghdad Atmani ◽  
Sondès Chaabane
Keyword(s):  
Machine Learning ◽  
Emergency Department ◽  
Length Of Stay ◽  
Prediction Models ◽  
Pediatric Emergency ◽  
Machine Learning Techniques ◽  
Learning Techniques ◽  
Emergency Department Management ◽  
Increasing Demand ◽  
Set Up

Nowadays, emergency department services are confronted to an increasing demand. This situation causes emergency department overcrowding which often increases the length of stay of patients and leads to strain situations. To overcome this issue, emergency department managers must predict the length of stay. In this work, the researchers propose to use machine learning techniques to set up a methodology that supports the management of emergency departments (EDs). The target of this work is to predict the length of stay of patients in the ED in order to prevent strain situations. The experiments were carried out on a real database collected from the pediatric emergency department (PED) in Lille regional hospital center, France. Different machine learning techniques have been used to build the best prediction models. The results seem better with Naive Bayes, C4.5 and SVM methods. In addition, the models based on a subset of attributes proved to be more efficient than models based on the set of attributes.

scholarly journals Alert Override Patterns With a Medication Clinical Decision Support System in an Academic Emergency Department: Retrospective Descriptive Study (Preprint)

2020 ◽  
Author(s):  
Junsang Yoo ◽  
Jeonghoon Lee ◽  
Poong-Lyul Rhee ◽  
Dong Kyung Chang ◽  
Mira Kang ◽  
...  
Keyword(s):  
Risk Factors ◽  
Emergency Department ◽  
Logistic Regression ◽  
Decision Support ◽  
Decision Support System ◽  
Clinical Decision Support ◽  
Support System ◽  
Clinical Decision Support System ◽  
Clinical Decision ◽  
Broad Perspective

BACKGROUND Physicians’ alert overriding behavior is considered to be the most important factor leading to failure of computerized provider order entry (CPOE) combined with a clinical decision support system (CDSS) in achieving its potential adverse drug events prevention effect. Previous studies on this subject have focused on specific diseases or alert types for well-defined targets and particular settings. The emergency department is an optimal environment to examine physicians’ alert overriding behaviors from a broad perspective because patients have a wider range of severity, and many receive interdisciplinary care in this environment. However, less than one-tenth of related studies have targeted this physician behavior in an emergency department setting. OBJECTIVE The aim of this study was to describe alert override patterns with a commercial medication CDSS in an academic emergency department. METHODS This study was conducted at a tertiary urban academic hospital in the emergency department with an annual census of 80,000 visits. We analyzed data on the patients who visited the emergency department for 18 months and the medical staff who treated them, including the prescription and CPOE alert log. We also performed descriptive analysis and logistic regression for assessing the risk factors for alert overrides. RESULTS During the study period, 611 physicians cared for 71,546 patients with 101,186 visits. The emergency department physicians encountered 13.75 alerts during every 100 orders entered. Of the total 102,887 alerts, almost two-thirds (65,616, 63.77%) were overridden. Univariate and multivariate logistic regression analyses identified 21 statistically significant risk factors for emergency department physicians’ alert override behavior. CONCLUSIONS In this retrospective study, we described the alert override patterns with a medication CDSS in an academic emergency department. We found relatively low overrides and assessed their contributing factors, including physicians’ designation and specialty, patients’ severity and chief complaints, and alert and medication type.

Evaluation of Unscheduled Return Visits to the Pediatric Emergency Department and Risk Factors for Admission After Return Visit

2021 ◽  
Vol Publish Ahead of Print ◽  
Author(s):  
Funda Kurt ◽  
Damla Hanalioğlu ◽  
Fatmanur Can ◽  
Fatma Eren Kurtipek ◽  
Halil İbrahim Yakut ◽  
...  
Keyword(s):  
Risk Factors ◽  
Emergency Department ◽  
Pediatric Emergency ◽  
Pediatric Emergency Department ◽  
Return Visit ◽  
Return Visits
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