The Diagnostic Value of the First Clinical Impression of Patients Presenting to the Emergency Department (PREKEYDIA)
NCT ID: NCT05597059
Last Updated: 2022-10-31
Study Results
The study team has not published outcome measurements, participant flow, or safety data for this trial yet. Check back later for updates.
Basic Information
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COMPLETED
1506 participants
OBSERVATIONAL
2019-09-01
2021-02-01
Brief Summary
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One examination modality, which in addition to the medical history and clinical examination, could quickly provide information about the condition of the patient, their clinical picture and severity of illness, is the first clinical impression of the patient (so-called "first impression" or "end-of-bed view"). This describes the first sensory impression that the medical staff gathers from a patient. This includes visual (e.g., facial expression, gait, breathing), auditory (e.g., voice pitch, shortness of breath when speaking), and olfactory (e.g., smell of exhaled air, body odor) impressions. Clinical practice shows that a great deal of important additional information can be gathered through this first clinical impression, which, together with the history and clinical examination of the emergency patient, provides valuable clues to the underlying condition.
To date, however, only scattered data and study results exist in the medical literature on the value of the first clinical impression in the care of emergency patients. In the present prospective observational study, the study attempts to evaluate the predictive value of the first clinical impression in identifying a leading symptom and other important clinical parameters.
Detailed Description
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Conditions
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Study Design
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COHORT
PROSPECTIVE
Study Groups
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Shortness of breath
Machine Learning Prediction
Machine Learning Prediction
Extremity pathologies
Machine Learning Prediction
Machine Learning Prediction
Abdominal pain
Machine Learning Prediction
Machine Learning Prediction
Urological pathologies
Machine Learning Prediction
Machine Learning Prediction
Chest pain
Machine Learning Prediction
Machine Learning Prediction
Back pain
Machine Learning Prediction
Machine Learning Prediction
Interventions
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Machine Learning Prediction
Machine Learning Prediction
Eligibility Criteria
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Inclusion Criteria
Exclusion Criteria
18 Years
ALL
Yes
Sponsors
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Kepler University Hospital
OTHER
Responsible Party
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Locations
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Kepler University Hospital
Linz, Upper Austria, Austria
Countries
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Other Identifiers
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PREKEYDIA
Identifier Type: -
Identifier Source: org_study_id