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Optimization of Treatment Priority of the Manchester Triage System

Optimization of Treatment Priority of the Manchester Triage System

Status
Completed
Phases
Unknown
Study type
Observational
Source
ClinicalTrials.gov
Registry ID
NCT05473988
Acronym
OPTIMTS
Enrollment
77976
Registered
2022-07-26
Start date
2022-06-01
Completion date
2022-07-31
Last updated
2023-05-03

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

Conditions

Triage

Brief summary

In the emergency department, the urgency for treating patients is determined according to the Manchester Triage System. The parameters collected in this process are deterministically translated into a treatment priority. The Manchester Triage System (MTS), which has been in use for at least 20 years, is a widely used, validated and standardized procedure for initial assessment in the emergency department - this initial assessment (triage) is done to prioritize medical assistance at a central point. Especially in emergency situations, critically endangered patients often require the deployment of a large part of the available staff at the same time - the medically correct triage of patients according to objective criteria in order to enable an adequate allocation of the available resources at the right time is the main objective. In the optimal case, each patient is treated by medical professionals within the time frame that is adequate for his/her health condition. Using artificial intelligence methods, it may be possible to increase the accuracy of treatment priority assignment. In the best case, incorrect prioritization of patients can be prevented and medical care can be ensured for those patients who actually need it most urgently. However, initial assessment, even if standardized and validated, still runs under limited resource conditions - time, space, material and personnel. Last but not least, the very idea of conducting an initial assessment limits its validity, and the results of the allocation fluctuate according to current research, although the determinants of this are currently unknown.

Interventions

OTHERAdmission to General Ward

Admission to General Ward

OTHERAdmission to Intensive Care Unit

Admission to Intensive Care Unit

OTHER30 Day Mortality

30 Day Mortality

Sponsors

Kepler University Hospital
Lead SponsorOTHER

Study design

Observational model
COHORT
Time perspective
RETROSPECTIVE

Eligibility

Sex/Gender
ALL
Age
18 Years to 120 Years

Inclusion criteria

* All adult patients that were triaged at the emergency department at the Kepler University Hospital in the period between 2015-12-01 to 2020-08-31.

Exclusion criteria

* None.

Design outcomes

Primary

MeasureTime frameDescription
AUROC for Classification of Admission to General Ward2015-12-01 to 2020-08-31AUROC for Classification of Admission to General Ward
AUROC for Classification of Admission to Intensive Care Unit2015-12-01 to 2020-08-31AUROC for Classification of Admission to Intensive Care Unit
AUROC for Classification of 30 Day Mortality2015-12-01 to 2020-08-31AUROC for Classification of 30 Day Mortality

Secondary

MeasureTime frameDescription
Confusion Matrix2015-12-01 to 2020-08-31Confusion Matrix Results: true positives, true negatives, false positive, false negatives and values calculated from these results.

Countries

Austria

Outcome results

None listed

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