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The Diagnostic Value of the First Clinical Impression of Patients Presenting to the Emergency Department (PREKEYDIA)

K

Kepler University Hospital

Status

Completed

Conditions

Emergencies

Treatments

Diagnostic Test: Machine Learning Prediction

Study type

Observational

Funder types

Other

Identifiers

NCT05597059
PREKEYDIA

Details and patient eligibility

About

Finding a diagnosis for acutely ill patients places high demands on emergency medical personnel. While anamnesis and clinical examination provide initial indications and allow a tentative diagnosis, both laboratory chemistry and imaging tests are used to confirm (or exclude) the tentative diagnosis. The more precise and targeted the additional laboratory chemical or radiological diagnosis, the more quickly and economically the causal treatment of the emergency patient can be initiated.

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.

Enrollment

1,506 patients

Sex

All

Ages

18+ years old

Volunteers

Accepts Healthy Volunteers

Inclusion criteria

  • Patients presenting to the emergency department between 2019-09-01 and 2020-02-28.

Exclusion criteria

  • None.

Trial design

1,506 participants in 6 patient groups

Shortness of breath
Treatment:
Diagnostic Test: Machine Learning Prediction
Extremity pathologies
Treatment:
Diagnostic Test: Machine Learning Prediction
Abdominal pain
Treatment:
Diagnostic Test: Machine Learning Prediction
Urological pathologies
Treatment:
Diagnostic Test: Machine Learning Prediction
Chest pain
Treatment:
Diagnostic Test: Machine Learning Prediction
Back pain
Treatment:
Diagnostic Test: Machine Learning Prediction

Trial contacts and locations

1

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Data sourced from clinicaltrials.gov

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