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

Study Overview
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Brief Summary
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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.

Official Title
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The Diagnostic Value of the First Clinical Impression of Patients Presenting to the Emergency Department

Conditions
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Emergencies

Intervention / Treatment
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* Diagnostic Test: Machine Learning Prediction


Participation Criteria
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Eligibility Criteria
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Inclusion Criteria: Patients presenting to the emergency department between 2019-09-01 and 2020-02-28. Exclusion Criteria: None.

Ages Eligible for Study
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Minimum Age: 18 Years

Sexes Eligible for Study
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All

Accepts Healthy Volunteers
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Accepts Healthy Volunteers

Study Plan
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How is the study designed?
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Arms and Interventions

| Participant Group/Arm | Intervention/Treatment |
| --- | --- |
| Shortness of breath<br> | Diagnostic Test: Machine Learning Prediction<br>* Machine Learning Prediction<br>|
| Extremity pathologies<br> | Diagnostic Test: Machine Learning Prediction<br>* Machine Learning Prediction<br>|
| Abdominal pain<br> | Diagnostic Test: Machine Learning Prediction<br>* Machine Learning Prediction<br>|
| Urological pathologies<br> | Diagnostic Test: Machine Learning Prediction<br>* Machine Learning Prediction<br>|
| Chest pain<br> | Diagnostic Test: Machine Learning Prediction<br>* Machine Learning Prediction<br>|
| Back pain<br> | Diagnostic Test: Machine Learning Prediction<br>* Machine Learning Prediction<br>|

What is the study measuring?
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Primary Outcome Measures

| Outcome Measure | Measure Description | Time Frame |
| --- | --- | --- |
| AUROC for Classification of Shortness of Breath | AUROC for Classification of Shortness of Breath | 2019-09-01 to 2020-02-28 | 
| AUROC for Classification of Extremity Pathologies | AUROC for Classification of Extremity Pathologies | 2019-09-01 to 2020-02-28 | 
| AUROC for Classification of Abdominal Pain | AUROC for Classification of Abdominal Pain | 2019-09-01 to 2020-02-28 | 
| AUROC for Classification of Urological Pathologies | AUROC for Classification of Urological Pathologies | 2019-09-01 to 2020-02-28 | 
| AUROC for Classification of Chest Pain | AUROC for Classification of Chest Pain | 2019-09-01 to 2020-02-28 | 
| AUROC for Classification of Back Pain | AUROC for Classification of Back Pain | 2019-09-01 to 2020-02-28 | 

Secondary Outcome Measures

| Outcome Measure | Measure Description | Time Frame |
| --- | --- | --- |
| AUROC for Classification of Hospital Admission | AUROC for Classification of Hospital Admission | 2019-09-01 to 2020-02-28 | 
| Confusion Matrix | Confusion Matrix Results: true positives, true negatives, false positive, false negatives and values calculated from these results. | 2019-09-01 to 2020-02-28 | 
| Descriptive Statistics | Descriptive Statistics (e. g. age in years) | 2019-09-01 to 2020-02-28 |