Background and aims Identification of SARS-CoV-2-infected patients at high-risk of poor prognosis is crucial. We aimed to establish predictive models for COVID-19 pneumonia severity in hospitalized patients. Methods Retrospective study of 430 patients admitted in Vall d’Hebron Hospital (Barcelona) between 03-12-2020 and 04-28-2020 due to COVID-19 pneumonia. Two models to identify the patients who required high-flow-oxygen-support were generated, one using baseline data and another with also follow-up analytical results. Calibration was performed by a 1000-bootstrap replication model. Results 249 were male, mean age 57.9 years. Overall, 135 (31.4%) required high-flow-oxygen-support . The baseline predictive model showed a ROC of 0.800 based on: SpO2/FiO2 (adjusted Hazard Ratio-aHR = 8), chest x-ray (aHR = 4), prior immunosuppressive therapy (aHR = 4), obesity (aHR = 2), IL-6 (aHR = 2), platelets (aHR = 0.5). The cut-off of 11 presented a specificity of 94.8%. The second model included changes on the analytical parameters: ferritin (aHR = 7.5 if ≥200ng/mL) and IL-6 (aHR = 18 if ≥64pg/mL) plus chest x-ray (aHR = 2) showing a ROC of 0.877. The cut-off of 12 exhibited a negative predictive value of 92%. Conclusions SpO2/FiO2 and chest x-ray on admission or changes on inflammatory parameters as IL-6 and ferritin allow us early identification of COVID-19 patients at risk of high-flow-oxygen-support that may benefit from a more intensive disease management.
【초록키워드】 COVID-19, therapy, Pneumonia, IL-6, obesity, severity, risk, ferritin, hospitalized patients, Replication, specificity, Predictive model, ROC, management, Chest, male, Patient, Platelet, age, Follow-up, disease, change, Admission, Intensive, immunosuppressive, COVID-19 patient, Negative predictive value, Barcelona, identification, Predictive, calibration, hazard, poor prognosis, SpO2/FiO2, cut-off, benefit, Result, identify, the patient, required, was performed, exhibited, adjusted, baseline, inflammatory parameter, SARS-CoV-2-infected patient, 【제목키워드】 Pneumonia, Predictive model, poor prognosis, simple, identify, patients with COVID-19,