Resumen de Manifestaciones clínicas y predictores de gravedad en pacientes adultos con infección respiratoria aguda por coronavirus SARS-CoV-2
Fernando Saldías Peñafiel, Alejandro Peñaloza Tapia, Daniela Farías Nesvadba, Katia Farcas Oksenberg, Antonia Reyes Sánchez, Josefina Cortés Meza, Bárbara Lara Hernández, Pablo Aguilera Fuenzalida, Isabel Leiva Rodríguez
Background: In December 2019, coronavirus disease 2019 (COVID-19) emerged in Wuhan city and spread rapidly throughout China and the world. Aim: To describe the clinical features, risk factors, and predictors of hospitalization in adult patients treated for acute respiratory infections associated with coronavirus SARS-CoV-2. Material and Methods: Descriptive prospective study of ambulatory and hospitalized adult patients with confirmed COVID-19 attended between April 1 and May 31, 2020. Clinical features, chronic comorbidities and demographic data were recorded, and patients were followed for two months as outpatients. Results: We assessed 1,022 adults aged 41 ± 14 years (50% men) with laboratory-confirmed COVID-19. One-third had comorbidities, specially hypertension (12.5%), hypothyroidism (6.6%), asthma (5.4%) and diabetes (4.5%). Hospital admission was required in 11%, 5.2% were admitted to critical care unit and 0.9% were connected to mechanical ventilation. Common symptoms included fatigue (55.4%), fever (52.5%), headache (68.6%), anosmia/dysgeusia (53.2%), dry cough (53.4%), dyspnea (27.4%) and diarrhea (35.5%). One third of patients reported persistence of symptoms at one-month follow-up, specially fatigue, cough and dyspnea. In the multivariate analysis, age, fever, cough, dyspnea and immunosuppression were associated with hospitalization and ICU admission. Age, male sex and moderate-severe dyspnea were associated with requirement of mechanical ventilation. The main predictors of prolonged clinical course were female sex, presence of comorbidities, history of dyspnea, cough, myalgia and abdominal pain. Conclusions: Clinical features of COVID-19 were highly unspecific. Prediction models for severity, will help medical decision making at the primary care setting.
