International Big Data Centre in Emergency Medicine

Institute of Sciences in Emergency Medicine - International Big Data Centre in Emergency Medicine

This observational study aims to use electronic health records to build an International Big Data Centre in Emergency Medicine, within the Institute of Sciences in Emergency Medicine (ISEM) at the Guangdong Provincial People's Hospital. The main questions it seeks to answer are not limited to the following:

• Identify the relationship between Emergency Department Length of Stay (EDLOS), Mortality, and Adverse Events (AE)
• Identify the risk factors associated with high mortality and AE rate among patients who experience prolonged EDLOS
• Other research questions related to emergency medicine, such as building prediction and cluster models for acute diseases

Study Overview

• Status: Recruiting
• Conditions: Emergency Medicine

Detailed Description

The relationship between the emergency department length-of-stay (EDLOS), mortality, and adverse events (AE) rate remains unclear and underestimated. EDLOS is the time elapsed between the time of arrival and the time of the release of a patient admitted to ED. Mortality is defined as short-term (14 hours, 48 hours, 7 days, 15 days) and 30 days death of patients managed in the ED discharged or admitted. An AE is an event that can result whether from medical mismanagement or happen by accident and causes physical and mental harm to the patient during the stay and remains after leaving. To separate the mismanagement from accidental causes i.e., compliant management but the unfortunate situation or persons, this study will refer to indicators that are objectively measurable and can be compared at different times throughout the patient's stay in the ED.

The hypothesis is that a prolonged stay of the patients in the ED especially without diagnosis or decision of admission is associated with increased mortality and AEs. This project will test this hypothesis through a prospective international multicenter study in contributing to whether a prolonged EDLOS is associated with an increased rate of adverse outcomes.

• Study Type: Observational
• Enrollment (Anticipated): 500000

Contacts and Locations

• Study Contact: Name: Abdelouahab Bellou, MD, PhD, MSc; Phone Number: +16176693707; Email: abellou402@gmail.com

Study Locations

• China
  • Guangdong
    • Guangzhou, Guangdong, China
      • Recruiting
      • Guangdong Provincial People's Hospital
      • Contact: Abdelouahab Bellou, MD; Phone Number: +16176693707; Email: abellou402@gmail.com

Participation Criteria

Eligibility Criteria

• Ages Eligible for Study: Child; Adult; Older Adult
• Accepts Healthy Volunteers: No
• Genders Eligible for Study: All

• Sampling Method: Non-Probability Sample

Study Population

The study population will be everybody who visited the Emergency Department.

Description

Inclusion Criteria:

• Every patient who ever visited Emergency Department from 2011-2022

Exclusion Criteria:

• Patients with missing demographic (age, gender, etc.) and triage data

Study Plan

How is the study designed?

Design Details

• Observational Models: Cohort
• Time Perspectives: Retrospective

Number of groups / cohorts

1

Cohorts and Interventions

Group / Cohort
All ED Patient; All patients visited the emergency department during the period.

What is the study measuring?

Primary Outcome Measures

Outcome Measure	Measure Description	Time Frame
In-hospital Mortality	IHM	From date of ED triage until the date of discharge or date of death from any cause, whichever came first, assessed up to 1 month

Secondary Outcome Measures

Outcome Measure	Measure Description	Time Frame
Number of Adverse Events	AE	From date of ED triage until the date of discharge or date of death from any cause, whichever came first, assessed up to 1 month

Collaborators and Investigators

• Sponsor: Guangdong Provincial People's Hospital
• Investigators: Principal Investigator: Abdelouahab Bellou, MD, PhD, MSc, Guangdong Provincial People's Hospital

Study record dates

Study Major Dates

• Study Start (Actual): January 1, 2011
• Primary Completion (Anticipated): December 1, 2027
• Study Completion (Anticipated): December 1, 2027

Study Registration Dates

• First Submitted: November 7, 2022
• First Submitted That Met QC Criteria: November 13, 2022
• First Posted (Actual): November 15, 2022

Study Record Updates

• Last Update Posted (Estimate): February 23, 2023
• Last Update Submitted That Met QC Criteria: February 21, 2023
• Last Verified: February 1, 2023

More Information

Terms related to this study

• Keywords: Artificial Intelligence; Machine Learning; Data Mining
• Additional Relevant MeSH Terms: Pathologic Processes; Disease Attributes; Emergencies
• Other Study ID Numbers: KY-N-2022-105-01

Plan for Individual participant data (IPD)

• Plan to Share Individual Participant Data (IPD)?: UNDECIDED

Drug and device information, study documents

• Studies a U.S. FDA-regulated drug product: No
• Studies a U.S. FDA-regulated device product: No