Clinical and laboratory features of COVID-19: Predictors of severe prognosis
•Decrease in %LUC (Large unstained cells%) value predicts severe SARS-CoV-2 infection.•Laboratory parameters associated with the severe illness in COVID-19 patients.•The optimal cut-off values of relevant parameters to define risk of critical illness.•The relevant coagulation abnormalities to predic...
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| Veröffentlicht in: | International immunopharmacology 2020-11, Vol.88, p.106950-106950, Article 106950 |
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| creator | 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 |
| description | •Decrease in %LUC (Large unstained cells%) value predicts severe SARS-CoV-2 infection.•Laboratory parameters associated with the severe illness in COVID-19 patients.•The optimal cut-off values of relevant parameters to define risk of critical illness.•The relevant coagulation abnormalities to predict severe patients with COVID-19.
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.
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.
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 |
| doi_str_mv | 10.1016/j.intimp.2020.106950 |
| format | Article |
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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.
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.
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).
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.</description><identifier>ISSN: 1567-5769</identifier><identifier>EISSN: 1878-1705</identifier><identifier>DOI: 10.1016/j.intimp.2020.106950</identifier><identifier>PMID: 32919217</identifier><language>eng</language><publisher>Netherlands: Elsevier B.V</publisher><subject>Adolescent ; Adult ; Aged ; Aged, 80 and over ; Albumins ; Biomarkers - blood ; Clinical Laboratory Techniques - methods ; Coronavirus Infections - diagnosis ; Coronavirus Infections - drug therapy ; Coronavirus Infections - mortality ; Coronaviruses ; COVID-19 ; COVID-19 Testing ; Creatinine ; Critical Care - methods ; Critical Illness ; Dimers ; Female ; Ferritin ; Hemoglobin ; Humans ; Intensive Care Units ; L-Lactate dehydrogenase ; Laboratories ; Lactate dehydrogenase ; Lactic acid ; Leukocytes ; Leukocytes (neutrophilic) ; Logistic Models ; Lymphocytes ; Male ; Medical Records ; Middle Aged ; Monocytes ; Pandemics ; Parameter identification ; Patients ; Pneumonia, Viral - diagnosis ; Pneumonia, Viral - drug therapy ; Pneumonia, Viral - mortality ; Predictive Value of Tests ; Prognosis ; Regression analysis ; Regression models ; Retrospective Studies ; ROC Curve ; Tertiary Care Centers ; Turkey ; Urea ; Viral diseases ; Young Adult</subject><ispartof>International immunopharmacology, 2020-11, Vol.88, p.106950-106950, Article 106950</ispartof><rights>2020 Elsevier B.V.</rights><rights>Copyright © 2020 Elsevier B.V. All rights reserved.</rights><rights>Copyright Elsevier BV Nov 2020</rights><rights>2020 Elsevier B.V. All rights reserved. 2020 Elsevier B.V.</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c557t-76c62a87444653226af9068600605e03aad4a2a298eda4aafd3a00846164ef23</citedby><cites>FETCH-LOGICAL-c557t-76c62a87444653226af9068600605e03aad4a2a298eda4aafd3a00846164ef23</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://dx.doi.org/10.1016/j.intimp.2020.106950$$EHTML$$P50$$Gelsevier$$H</linktohtml><link.rule.ids>230,314,780,784,885,3550,27924,27925,45995</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/32919217$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Bastug, Aliye</creatorcontrib><creatorcontrib>Bodur, Hurrem</creatorcontrib><creatorcontrib>Erdogan, Serpil</creatorcontrib><creatorcontrib>Gokcinar, Derya</creatorcontrib><creatorcontrib>Kazancioglu, Sumeyye</creatorcontrib><creatorcontrib>Kosovali, Behiye Deniz</creatorcontrib><creatorcontrib>Ozbay, Bahadır Orkun</creatorcontrib><creatorcontrib>Gok, Gamze</creatorcontrib><creatorcontrib>Turan, Isil Ozkocak</creatorcontrib><creatorcontrib>Yilmaz, Gulsen</creatorcontrib><creatorcontrib>Gonen, Canan Cam</creatorcontrib><creatorcontrib>Yilmaz, Fatma Meric</creatorcontrib><title>Clinical and laboratory features of COVID-19: Predictors of severe prognosis</title><title>International immunopharmacology</title><addtitle>Int Immunopharmacol</addtitle><description>•Decrease in %LUC (Large unstained cells%) value predicts severe SARS-CoV-2 infection.•Laboratory parameters associated with the severe illness in COVID-19 patients.•The optimal cut-off values of relevant parameters to define risk of critical illness.•The relevant coagulation abnormalities to predict severe patients with COVID-19.
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.
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.
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).
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.</description><subject>Adolescent</subject><subject>Adult</subject><subject>Aged</subject><subject>Aged, 80 and over</subject><subject>Albumins</subject><subject>Biomarkers - blood</subject><subject>Clinical Laboratory Techniques - methods</subject><subject>Coronavirus Infections - diagnosis</subject><subject>Coronavirus Infections - drug therapy</subject><subject>Coronavirus Infections - mortality</subject><subject>Coronaviruses</subject><subject>COVID-19</subject><subject>COVID-19 Testing</subject><subject>Creatinine</subject><subject>Critical Care - methods</subject><subject>Critical Illness</subject><subject>Dimers</subject><subject>Female</subject><subject>Ferritin</subject><subject>Hemoglobin</subject><subject>Humans</subject><subject>Intensive Care Units</subject><subject>L-Lactate dehydrogenase</subject><subject>Laboratories</subject><subject>Lactate dehydrogenase</subject><subject>Lactic acid</subject><subject>Leukocytes</subject><subject>Leukocytes (neutrophilic)</subject><subject>Logistic Models</subject><subject>Lymphocytes</subject><subject>Male</subject><subject>Medical Records</subject><subject>Middle Aged</subject><subject>Monocytes</subject><subject>Pandemics</subject><subject>Parameter identification</subject><subject>Patients</subject><subject>Pneumonia, Viral - diagnosis</subject><subject>Pneumonia, Viral - drug therapy</subject><subject>Pneumonia, Viral - mortality</subject><subject>Predictive Value of Tests</subject><subject>Prognosis</subject><subject>Regression analysis</subject><subject>Regression models</subject><subject>Retrospective Studies</subject><subject>ROC Curve</subject><subject>Tertiary Care Centers</subject><subject>Turkey</subject><subject>Urea</subject><subject>Viral diseases</subject><subject>Young Adult</subject><issn>1567-5769</issn><issn>1878-1705</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2020</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><recordid>eNp9UcFu1DAQtRCIlsIfIBSJC5dsx45jOxyQ0LZApZXKoeJqTZ1J8SprL3ayUv--3m4p0AMnW2_evJk3j7G3HBYcuDpdL3yY_Ga7ECD2kOpaeMaOudGm5hra5-XfKl23WnVH7FXOa4CCS_6SHTWi453g-pitlqMP3uFYYeirEa9jwimm22ognOZEuYpDtbz8cXFW8-5j9T1R710h3OOZdpSo2qZ4E2L2-TV7MeCY6c3De8KuvpxfLb_Vq8uvF8vPq9q1rZ5qrZwSaLSUUrWNEAqHDpRRAApaggaxlyhQdIZ6lIhD3yCAkYorSYNoTting-x2vt5Q7yhMCUe7TX6D6dZG9PbfSvA_7U3cWS0NdAaKwIcHgRR_zZQnu_HZ0ThioDhnK6QUQpQr72e9f0JdxzmF4q6wtDFle-gKSx5YLsWcEw2Py3Cw-7Ts2h7Ssvu07CGt0vbubyOPTb_j-eOUyjV3npLNzlNwJYREbrJ99P-fcAfOuaar</recordid><startdate>20201101</startdate><enddate>20201101</enddate><creator>Bastug, Aliye</creator><creator>Bodur, Hurrem</creator><creator>Erdogan, Serpil</creator><creator>Gokcinar, Derya</creator><creator>Kazancioglu, Sumeyye</creator><creator>Kosovali, Behiye Deniz</creator><creator>Ozbay, Bahadır Orkun</creator><creator>Gok, Gamze</creator><creator>Turan, Isil Ozkocak</creator><creator>Yilmaz, Gulsen</creator><creator>Gonen, Canan Cam</creator><creator>Yilmaz, Fatma Meric</creator><general>Elsevier B.V</general><general>Elsevier BV</general><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7QO</scope><scope>7T5</scope><scope>7U7</scope><scope>7U9</scope><scope>8FD</scope><scope>C1K</scope><scope>FR3</scope><scope>H94</scope><scope>P64</scope><scope>7X8</scope><scope>5PM</scope></search><sort><creationdate>20201101</creationdate><title>Clinical and laboratory features of COVID-19: Predictors of severe prognosis</title><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></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c557t-76c62a87444653226af9068600605e03aad4a2a298eda4aafd3a00846164ef23</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2020</creationdate><topic>Adolescent</topic><topic>Adult</topic><topic>Aged</topic><topic>Aged, 80 and over</topic><topic>Albumins</topic><topic>Biomarkers - blood</topic><topic>Clinical Laboratory Techniques - methods</topic><topic>Coronavirus Infections - diagnosis</topic><topic>Coronavirus Infections - drug therapy</topic><topic>Coronavirus Infections - mortality</topic><topic>Coronaviruses</topic><topic>COVID-19</topic><topic>COVID-19 Testing</topic><topic>Creatinine</topic><topic>Critical Care - methods</topic><topic>Critical Illness</topic><topic>Dimers</topic><topic>Female</topic><topic>Ferritin</topic><topic>Hemoglobin</topic><topic>Humans</topic><topic>Intensive Care Units</topic><topic>L-Lactate dehydrogenase</topic><topic>Laboratories</topic><topic>Lactate dehydrogenase</topic><topic>Lactic acid</topic><topic>Leukocytes</topic><topic>Leukocytes (neutrophilic)</topic><topic>Logistic Models</topic><topic>Lymphocytes</topic><topic>Male</topic><topic>Medical Records</topic><topic>Middle Aged</topic><topic>Monocytes</topic><topic>Pandemics</topic><topic>Parameter identification</topic><topic>Patients</topic><topic>Pneumonia, Viral - diagnosis</topic><topic>Pneumonia, Viral - drug therapy</topic><topic>Pneumonia, Viral - mortality</topic><topic>Predictive Value of Tests</topic><topic>Prognosis</topic><topic>Regression analysis</topic><topic>Regression models</topic><topic>Retrospective Studies</topic><topic>ROC Curve</topic><topic>Tertiary Care Centers</topic><topic>Turkey</topic><topic>Urea</topic><topic>Viral diseases</topic><topic>Young Adult</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Bastug, Aliye</creatorcontrib><creatorcontrib>Bodur, Hurrem</creatorcontrib><creatorcontrib>Erdogan, Serpil</creatorcontrib><creatorcontrib>Gokcinar, Derya</creatorcontrib><creatorcontrib>Kazancioglu, Sumeyye</creatorcontrib><creatorcontrib>Kosovali, Behiye Deniz</creatorcontrib><creatorcontrib>Ozbay, Bahadır Orkun</creatorcontrib><creatorcontrib>Gok, Gamze</creatorcontrib><creatorcontrib>Turan, Isil Ozkocak</creatorcontrib><creatorcontrib>Yilmaz, Gulsen</creatorcontrib><creatorcontrib>Gonen, Canan Cam</creatorcontrib><creatorcontrib>Yilmaz, Fatma Meric</creatorcontrib><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>Biotechnology Research Abstracts</collection><collection>Immunology Abstracts</collection><collection>Toxicology Abstracts</collection><collection>Virology and AIDS Abstracts</collection><collection>Technology Research Database</collection><collection>Environmental Sciences and Pollution Management</collection><collection>Engineering Research Database</collection><collection>AIDS and Cancer Research Abstracts</collection><collection>Biotechnology and BioEngineering Abstracts</collection><collection>MEDLINE - Academic</collection><collection>PubMed Central (Full Participant titles)</collection><jtitle>International immunopharmacology</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Bastug, Aliye</au><au>Bodur, Hurrem</au><au>Erdogan, Serpil</au><au>Gokcinar, Derya</au><au>Kazancioglu, Sumeyye</au><au>Kosovali, Behiye Deniz</au><au>Ozbay, Bahadır Orkun</au><au>Gok, Gamze</au><au>Turan, Isil Ozkocak</au><au>Yilmaz, Gulsen</au><au>Gonen, Canan Cam</au><au>Yilmaz, Fatma Meric</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Clinical and laboratory features of COVID-19: Predictors of severe prognosis</atitle><jtitle>International immunopharmacology</jtitle><addtitle>Int Immunopharmacol</addtitle><date>2020-11-01</date><risdate>2020</risdate><volume>88</volume><spage>106950</spage><epage>106950</epage><pages>106950-106950</pages><artnum>106950</artnum><issn>1567-5769</issn><eissn>1878-1705</eissn><abstract>•Decrease in %LUC (Large unstained cells%) value predicts severe SARS-CoV-2 infection.•Laboratory parameters associated with the severe illness in COVID-19 patients.•The optimal cut-off values of relevant parameters to define risk of critical illness.•The relevant coagulation abnormalities to predict severe patients with COVID-19.
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.
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.
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).
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.</abstract><cop>Netherlands</cop><pub>Elsevier B.V</pub><pmid>32919217</pmid><doi>10.1016/j.intimp.2020.106950</doi><tpages>1</tpages><oa>free_for_read</oa></addata></record> |
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| source | MEDLINE; Elsevier ScienceDirect Journals Complete |
| subjects | Adolescent Adult Aged Aged, 80 and over Albumins Biomarkers - blood Clinical Laboratory Techniques - methods Coronavirus Infections - diagnosis Coronavirus Infections - drug therapy Coronavirus Infections - mortality Coronaviruses COVID-19 COVID-19 Testing Creatinine Critical Care - methods Critical Illness Dimers Female Ferritin Hemoglobin Humans Intensive Care Units L-Lactate dehydrogenase Laboratories Lactate dehydrogenase Lactic acid Leukocytes Leukocytes (neutrophilic) Logistic Models Lymphocytes Male Medical Records Middle Aged Monocytes Pandemics Parameter identification Patients Pneumonia, Viral - diagnosis Pneumonia, Viral - drug therapy Pneumonia, Viral - mortality Predictive Value of Tests Prognosis Regression analysis Regression models Retrospective Studies ROC Curve Tertiary Care Centers Turkey Urea Viral diseases Young Adult |
| title | Clinical and laboratory features of COVID-19: Predictors of severe prognosis |
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