The Diagnostic Value of the First Clinical Impression of Patients Presenting to the Emergency Department (PREKEYDIA)
- Conditions
- Emergencies
- Interventions
- Diagnostic Test: Machine Learning Prediction
- Registration Number
- NCT05597059
- Lead Sponsor
- Kepler University Hospital
- Brief Summary
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.
- Detailed Description
Not available
Recruitment & Eligibility
- Status
- COMPLETED
- Sex
- All
- Target Recruitment
- 1506
- Patients presenting to the emergency department between 2019-09-01 and 2020-02-28.
- None.
Study & Design
- Study Type
- OBSERVATIONAL
- Study Design
- Not specified
- Arm && Interventions
Group Intervention Description Chest pain Machine Learning Prediction - Back pain Machine Learning Prediction - Urological pathologies Machine Learning Prediction - Abdominal pain Machine Learning Prediction - Shortness of breath Machine Learning Prediction - Extremity pathologies Machine Learning Prediction -
- Primary Outcome Measures
Name Time Method AUROC for Classification of Back Pain 2019-09-01 to 2020-02-28 AUROC for Classification of Back Pain
AUROC for Classification of Extremity Pathologies 2019-09-01 to 2020-02-28 AUROC for Classification of Extremity Pathologies
AUROC for Classification of Abdominal Pain 2019-09-01 to 2020-02-28 AUROC for Classification of Abdominal Pain
AUROC for Classification of Shortness of Breath 2019-09-01 to 2020-02-28 AUROC for Classification of Shortness of Breath
AUROC for Classification of Urological Pathologies 2019-09-01 to 2020-02-28 AUROC for Classification of Urological Pathologies
AUROC for Classification of Chest Pain 2019-09-01 to 2020-02-28 AUROC for Classification of Chest Pain
- Secondary Outcome Measures
Name Time Method AUROC for Classification of Hospital Admission 2019-09-01 to 2020-02-28 AUROC for Classification of Hospital Admission
Descriptive Statistics 2019-09-01 to 2020-02-28 Descriptive Statistics (e. g. age in years)
Confusion Matrix 2019-09-01 to 2020-02-28 Confusion Matrix Results: true positives, true negatives, false positive, false negatives and values calculated from these results.
Trial Locations
- Locations (1)
Kepler University Hospital
🇦🇹Linz, Upper Austria, Austria