Cargando…
Clinical and laboratory features of COVID-19: Predictors of severe prognosis
BACKGROUND: Coronavirus disease 2019 (COVID-19) emerged first in December 2019 in Wuhan, China and quickly spread throughout the world. Clinical and laboratory data are of importance to increase the success in the management of COVID-19 patients. METHODS: Data were obtained retrospectively from medi...
Autores principales: | , , , , , , , , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Elsevier B.V.
2020
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7480980/ https://www.ncbi.nlm.nih.gov/pubmed/32919217 http://dx.doi.org/10.1016/j.intimp.2020.106950 |
_version_ | 1783580509440311296 |
---|---|
author | Bastug, Aliye Bodur, Hurrem Erdogan, Serpil Gokcinar, Derya Kazancioglu, Sumeyye Kosovali, Behiye Deniz Ozbay, Bahadır Orkun Gok, Gamze Turan, Isil Ozkocak Yilmaz, Gulsen Gonen, Canan Cam Yilmaz, Fatma Meric |
author_facet | Bastug, Aliye Bodur, Hurrem Erdogan, Serpil Gokcinar, Derya Kazancioglu, Sumeyye Kosovali, Behiye Deniz Ozbay, Bahadır Orkun Gok, Gamze Turan, Isil Ozkocak Yilmaz, Gulsen Gonen, Canan Cam Yilmaz, Fatma Meric |
author_sort | Bastug, Aliye |
collection | PubMed |
description | BACKGROUND: Coronavirus disease 2019 (COVID-19) emerged first in December 2019 in Wuhan, China and quickly spread throughout the world. Clinical and laboratory data are of importance to increase the success in the management of COVID-19 patients. METHODS: Data were obtained retrospectively from medical records of 191 hospitalized patients diagnosed with COVID-19 from a tertiary single-center hospital between March and April 2020. Prognostic effects of variables on admission among patients who received intensive care unit (ICU) support and those who didn’t require ICU care were compared. RESULTS: Patients required ICU care (n = 46) were older (median, 71 vs. 43 years), with more underlying comorbidities (76.1% vs. 33.1%). ICU patients had lower lymphocytes, percentage of large unstained cell (%LUC), hemoglobin, total protein, and albumin, but higher leucocytes, neutrophils, neutrophil–lymphocyte ratio (NLR), monocyte-lymphocyte ratio (MLR), platelet-lymphocytes ratio (PLR), urea, creatinine, aspartate amino transferase (AST), lactate dehydrogenase (LDH), and D-dimer when compared with non-critically ill patients (p < 0.001). A logistic regression model was created to include ferritin, %LUC, NLR, and D-dimer. %LUC decrease and D-dimer increase had the highest odds ratios (0.093 vs 5.597, respectively) to predict severe prognosis. D-dimer, CRP, and NLR had the highest AUC in the ROC analysis (0.896, 0.874, 0.861, respectively). CONCLUSIONS: The comprehensive analysis of clinical and admission laboratory parameters to identify patients with severe prognosis is important not only for the follow-up of the patients but also to identify the pathophysiology of the disease. %LUC decrease and D-dimer, NLR, and CRP increases seem to be the most powerful laboratory predictors of severe prognosis. |
format | Online Article Text |
id | pubmed-7480980 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Elsevier B.V. |
record_format | MEDLINE/PubMed |
spelling | pubmed-74809802020-09-10 Clinical and laboratory features of COVID-19: Predictors of severe prognosis Bastug, Aliye Bodur, Hurrem Erdogan, Serpil Gokcinar, Derya Kazancioglu, Sumeyye Kosovali, Behiye Deniz Ozbay, Bahadır Orkun Gok, Gamze Turan, Isil Ozkocak Yilmaz, Gulsen Gonen, Canan Cam Yilmaz, Fatma Meric Int Immunopharmacol Article BACKGROUND: Coronavirus disease 2019 (COVID-19) emerged first in December 2019 in Wuhan, China and quickly spread throughout the world. Clinical and laboratory data are of importance to increase the success in the management of COVID-19 patients. METHODS: Data were obtained retrospectively from medical records of 191 hospitalized patients diagnosed with COVID-19 from a tertiary single-center hospital between March and April 2020. Prognostic effects of variables on admission among patients who received intensive care unit (ICU) support and those who didn’t require ICU care were compared. RESULTS: Patients required ICU care (n = 46) were older (median, 71 vs. 43 years), with more underlying comorbidities (76.1% vs. 33.1%). ICU patients had lower lymphocytes, percentage of large unstained cell (%LUC), hemoglobin, total protein, and albumin, but higher leucocytes, neutrophils, neutrophil–lymphocyte ratio (NLR), monocyte-lymphocyte ratio (MLR), platelet-lymphocytes ratio (PLR), urea, creatinine, aspartate amino transferase (AST), lactate dehydrogenase (LDH), and D-dimer when compared with non-critically ill patients (p < 0.001). A logistic regression model was created to include ferritin, %LUC, NLR, and D-dimer. %LUC decrease and D-dimer increase had the highest odds ratios (0.093 vs 5.597, respectively) to predict severe prognosis. D-dimer, CRP, and NLR had the highest AUC in the ROC analysis (0.896, 0.874, 0.861, respectively). CONCLUSIONS: The comprehensive analysis of clinical and admission laboratory parameters to identify patients with severe prognosis is important not only for the follow-up of the patients but also to identify the pathophysiology of the disease. %LUC decrease and D-dimer, NLR, and CRP increases seem to be the most powerful laboratory predictors of severe prognosis. Elsevier B.V. 2020-11 2020-09-09 /pmc/articles/PMC7480980/ /pubmed/32919217 http://dx.doi.org/10.1016/j.intimp.2020.106950 Text en © 2020 Elsevier B.V. All rights reserved. Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active. |
spellingShingle | Article Bastug, Aliye Bodur, Hurrem Erdogan, Serpil Gokcinar, Derya Kazancioglu, Sumeyye Kosovali, Behiye Deniz Ozbay, Bahadır Orkun Gok, Gamze Turan, Isil Ozkocak Yilmaz, Gulsen Gonen, Canan Cam Yilmaz, Fatma Meric Clinical and laboratory features of COVID-19: Predictors of severe prognosis |
title | Clinical and laboratory features of COVID-19: Predictors of severe prognosis |
title_full | Clinical and laboratory features of COVID-19: Predictors of severe prognosis |
title_fullStr | Clinical and laboratory features of COVID-19: Predictors of severe prognosis |
title_full_unstemmed | Clinical and laboratory features of COVID-19: Predictors of severe prognosis |
title_short | Clinical and laboratory features of COVID-19: Predictors of severe prognosis |
title_sort | clinical and laboratory features of covid-19: predictors of severe prognosis |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7480980/ https://www.ncbi.nlm.nih.gov/pubmed/32919217 http://dx.doi.org/10.1016/j.intimp.2020.106950 |
work_keys_str_mv | AT bastugaliye clinicalandlaboratoryfeaturesofcovid19predictorsofsevereprognosis AT bodurhurrem clinicalandlaboratoryfeaturesofcovid19predictorsofsevereprognosis AT erdoganserpil clinicalandlaboratoryfeaturesofcovid19predictorsofsevereprognosis AT gokcinarderya clinicalandlaboratoryfeaturesofcovid19predictorsofsevereprognosis AT kazancioglusumeyye clinicalandlaboratoryfeaturesofcovid19predictorsofsevereprognosis AT kosovalibehiyedeniz clinicalandlaboratoryfeaturesofcovid19predictorsofsevereprognosis AT ozbaybahadırorkun clinicalandlaboratoryfeaturesofcovid19predictorsofsevereprognosis AT gokgamze clinicalandlaboratoryfeaturesofcovid19predictorsofsevereprognosis AT turanisilozkocak clinicalandlaboratoryfeaturesofcovid19predictorsofsevereprognosis AT yilmazgulsen clinicalandlaboratoryfeaturesofcovid19predictorsofsevereprognosis AT gonencanancam clinicalandlaboratoryfeaturesofcovid19predictorsofsevereprognosis AT yilmazfatmameric clinicalandlaboratoryfeaturesofcovid19predictorsofsevereprognosis |