Home > Articles > Published articles > Simple predictive models identify patients with COVID-19 pneumonia and poor prognosis |
Date: | 2020 |
Abstract: | 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. 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. 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%. 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. |
Grants: | Instituto de Salud Carlos III PI20/00416 |
Rights: | Aquest document està subjecte a una llicència d'ús Creative Commons. Es permet la reproducció total o parcial, la distribució, la comunicació pública de l'obra i la creació d'obres derivades, fins i tot amb finalitats comercials, sempre i quan es reconegui l'autoria de l'obra original. |
Language: | Anglès |
Document: | Article ; recerca ; Versió publicada |
Published in: | PloS one, Vol. 15 (december 2020) , ISSN 1932-6203 |
14 p, 864.8 KB |
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