Abstract
We report the clinical case of a 76- year-old patient who was admitted to the emergency department due to respiratory distress. He had a history of Systemic arterial hypertension, type 2 diabetes mellitus and hypothyroidism. The patient reports symptoms that began three days prior to admission, including dyspnea, fever, myalgia, arthralgia, headache and respiratory distress that was exacerbated 1 day prior to admission. Physical examination revealed a conscious patient with the following vital signs: blood pressure 120/62mmHg, heart rate 109bpm, respiratory rate 28rpm, temperature 38.8°C, arterial oxygen saturation 76%, dyspnea, use of accessory muscles, tachycardia, tachypnea, which met the operational definition for Covid-19. Upon admission to the service, a pulmonary ultrasound was performed in which subpleural consolidations were identified, predominantly in the right hemithorax, as well as an air bronchogram and little pleural effusion. Based on these observations, the patient was admitted to the unit, where management with supplemental oxygen was started using a face mask with a reservoir bag. A chest X-ray was performed in which a diffuse interstitial alveolar pattern was identified in both hemithoraxes. Later a simple chest tomography was performed in which changes compatible with CO-RADS 5 were identified. He continued with established management and during his stay in the service, he evolved to deterioration to need advanced management via air. This study suggests that pulmonary ultrasound may be an effective and reliable tool in the initial evaluation of patients with respiratory distress in the context of Covid-19 pneumonia and that the identification of subpleural consolidations represent a prognostic factor of the disease: identifying them quickly after admission suggests a worse evolution of the disease, thus prompting us to make correct and concrete decisions regarding the management and treatment of these patients, without having to wait for complementary studies such as chest radiography and even tomography.
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