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231226s2023 xx |||||o 00| ||eng c |
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|a 10.1016/j.clim.2023.109271
|2 doi
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|a pubmed24n1177.xml
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|a (DE-627)NLM353130842
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|a (NLM)36806705
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|a (PII)S1521-6616(23)00050-5
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|a DE-627
|b ger
|c DE-627
|e rakwb
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| 041 |
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|a eng
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| 100 |
1 |
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|a Yan, Jiayi
|e verfasserin
|4 aut
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| 245 |
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|a Adaptive immune dysfunction in patients with COVID-19 and impaired kidney function during the omicron surge
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|c 2023
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|a Text
|b txt
|2 rdacontent
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|a ƒaComputermedien
|b c
|2 rdamedia
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|a ƒa Online-Ressource
|b cr
|2 rdacarrier
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|a Date Completed 14.03.2023
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|a Date Revised 18.03.2023
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|a published: Print-Electronic
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|a Citation Status MEDLINE
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|a Copyright © 2023 Elsevier Inc. All rights reserved.
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|a BACKGROUND: Little is known about the characteristics of lymphocyte subsets and the association with patient outcomes in COVID-19 with and without impaired kidney function
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|a METHODS: Lymphocyte subsets were compared in COVID-19 patients with or without kidney dysfunction. The primary outcome was a composite of all-cause mortality or intensive care unit admission. Secondary outcomes included duration of viral shedding, length of hospital stay, and acute kidney injury
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| 520 |
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|a RESULTS: Lymphocyte subset cell counts demonstrated the lowest in patients with severe/critical COVID-19 and kidney dysfunction. Among all lymphocyte subset parameters, Th cell count was the most significant indicator for outcomes. ROC of the combined model of Th cell count and eGFR presented better predictive value than that of the other parameters. Th cell count <394.5 cells/μl and eGFR <87.5 ml/min/1·73m2 were independently associated with poor outcomes. The propensity score matching analysis revealed consistent results
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|a CONCLUSIONS: Reduced Th cell count and eGFR may be applied as promising predictive indicators for identifying COVID-19 patients with high risk and poor outcomes
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|a Journal Article
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|a Research Support, Non-U.S. Gov't
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|a COVID-19
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|a Impaired kidney function
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|a Lymphocyte subset
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|a Omicron
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| 650 |
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|a Outcomes
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|a Wang, Jieying
|e verfasserin
|4 aut
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|a Ding, Li
|e verfasserin
|4 aut
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|a Liu, Shang
|e verfasserin
|4 aut
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| 700 |
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|a Zhan, Yaping
|e verfasserin
|4 aut
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| 700 |
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|a Lu, Jiayue
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Li, Zhenyuan
|e verfasserin
|4 aut
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| 700 |
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|a Gu, Leyi
|e verfasserin
|4 aut
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| 700 |
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|a Li, Ping
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Zhu, Mingli
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Gao, Yuan
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Gong, XingRong
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Ban, Haiqun
|e verfasserin
|4 aut
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| 700 |
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|a Cai, Hong
|e verfasserin
|4 aut
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| 700 |
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|a Mou, Shan
|e verfasserin
|4 aut
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| 773 |
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|i Enthalten in
|t Clinical immunology (Orlando, Fla.)
|d 1999
|g 248(2023) vom: 17. März, Seite 109271
|w (DE-627)NLM098196855
|x 1521-7035
|7 nnns
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| 773 |
1 |
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|g volume:248
|g year:2023
|g day:17
|g month:03
|g pages:109271
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| 856 |
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|u http://dx.doi.org/10.1016/j.clim.2023.109271
|3 Volltext
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