Risk Stratification With Chest Computed Tomography to Rule-out Suspected SARS-CoV-2 Infections of Unspecific Symptomatic Patients Before Hospital Admission
Risk Stratification With Chest Computed Tomography to Rule-out Suspected SARS-CoV-2 Infections of Unspecific Symptomatic Patients Before Hospital Admission
The study objective is to investigate the diagnostic value and consistency of chest CT as compared with comparison to RT-PCR assay in COVID-19 in patients which were stratified for hospital admission.
To prevent spreading of the new coronavirus (SARS-CoV-2) from patients who are infected but in whom infection was not detected by means of Reverse transcription polymerase chain reaction (RT-PCR) and who are to be admitted to ordinary wards of hospitals, we aimed to determine validity of exclusion of pneumonia immediately before admission by means of chest computed tomography.
Patients admitted to the emergency department of the university hospital Jena with Covid-19 symptoms (temperature > 37.5°C; respiratory and/or gastrointestinal symptoms) whose RT-PCR test resulted negative, undergo a chest CT scan. Those patients without pulmonary infiltrates can be safely ruled out for Covid-19. Thus, CT has perfect selectivity evidence regarding pulmonary infiltrates; it has limited selectivity concerning the pathogenesis of the infiltration.
The study objective is to investigate the diagnostic value and consistency of chest CT as compared with comparison to RT-PCR assay in COVID-19 in patients which were stratified for hospital admission.
The hypothesis is that chest CT has the greatest clinical evidence (no detection of lung infiltration) when the RT-PCT is tested negative. We assume that chest CT has a high sensitivity for diagnosis of respiratory dominant COVID-19. A pulmonary COVID-19 in epidemic areas can be best ruled out when chest CT is negative for the presence of infiltrations of the lung parenchyma. This is described by the SNOUT principle which is an acronym for 'Sensitive test when Negative rules OUT the disease' under the condition of a low pretest probability.
Inclusion Criteria:
Exclusion Criteria: