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Predictive Tracking of Patient Flow in the Emergency Services During the Virus Winter Epidemics

Predictive Tracking of Patient Flow in the Emergency Services During the Virus Winter Epidemics

Status
Completed
Phases
Unknown
Study type
Observational
Source
ClinicalTrials.gov
Registry ID
NCT02858531
Acronym
PREDAFLU
Enrollment
760000
Registered
2016-08-08
Start date
2016-09-01
Completion date
2023-12-31
Last updated
2024-03-22

For informational purposes only — not medical advice. Sourced from public registries and may not reflect the latest updates. Terms

Conditions

Acute Renal Failure, Bronchiolitis, Child, Disease Outbreaks, Elderly

Brief summary

Epidemics and infectious diseases in general, punctuate much of the activity of an emergency service. The impact of winter infections is particularly important to vulnerable populations such as infant during bronchiolitis epidemics and the elderly during seasonal influenza. Each year, these epidemic phenomena lead to disorganization of emergency services and healthcare teams by lack of anticipation and organizational measures in particular to manage the approval of emergency services for the most vulnerable populations requiring hospitalization. For 2 years, the pediatric emergency department of St Etienne University Hospital has a decision support tool for the periods of winter epidemics. Through a retrospective analysis of Passages of Emergency summary, this tool provides an estimate of infants with bronchiolitis flow day to day, and the availability in real time of an abnormally high flow of patients to pediatric emergencies. These data can help to affirm that the epidemic begins in this hospital.

Interventions

OTHERdata retrieval

data retrieval with the Hospital Information System

Sponsors

Centre Hospitalier Universitaire de Saint Etienne
Lead SponsorOTHER

Study design

Observational model
COHORT
Time perspective
PROSPECTIVE

Eligibility

Sex/Gender
ALL
Age
1 Months to No maximum
Healthy volunteers
No

Inclusion criteria

* child \< 24 months with bronchiolitis * elderly \< 60 years with acute renal failure or breathing problem

Exclusion criteria

* refuse of transmission of their data

Design outcomes

Primary

MeasureTime frameDescription
build a predictive toolat inclusiona tool with different levels of alerts of the influx of people aged to emergencies during winter epidemics. Variables in the model : activity database in emergency services, computer data, virology database and average length of stay.

Secondary

MeasureTime frame
Difference between the estimated date and the effective date of the activity peak on the average length of stay of patients in the hospital of Saint Etienneat inclusion
Difference between the estimated date and the effective date of the activity peak on the average length of stay of patients in the other hospitalsat inclusion
Comparison virological databases with clinical diagnosis of patientsat inclusion
Percentage of elderly staying more than 10 hours in the emergency services.at inclusion

Countries

France

Outcome results

None listed

Source: ClinicalTrials.gov · Data processed: Feb 4, 2026