Cargando…
Clinical Features and Predictive Nomogram of Acute Kidney Injury in Aging Population Infected with SARS-CoV-2 Omicron Variant
BACKGROUND: Since little is known about the acute kidney injury (AKI) in aging population infected with SARS-CoV-2 Omicron variant, we investigated the incidence, clinical features, risk factors and mid-term outcomes of AKI in hospitalized geriatric patients with and without COVID-19 and established...
Autores principales: | , , , , , , , , , , , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Dove
2023
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10362882/ https://www.ncbi.nlm.nih.gov/pubmed/37484995 http://dx.doi.org/10.2147/JIR.S413318 |
_version_ | 1785076529618747392 |
---|---|
author | Zhang, Yumei Li, Xin Zhang, Suning Chen, Wei Lu, Jianxin Xie, Yingxin Wu, Shengbin Zhuang, Feng Bi, Xiao Chu, Mingzi Wang, Feng Huang, Yemin Ding, Feng Hu, Chun Pan, Yu |
author_facet | Zhang, Yumei Li, Xin Zhang, Suning Chen, Wei Lu, Jianxin Xie, Yingxin Wu, Shengbin Zhuang, Feng Bi, Xiao Chu, Mingzi Wang, Feng Huang, Yemin Ding, Feng Hu, Chun Pan, Yu |
author_sort | Zhang, Yumei |
collection | PubMed |
description | BACKGROUND: Since little is known about the acute kidney injury (AKI) in aging population infected with SARS-CoV-2 Omicron variant, we investigated the incidence, clinical features, risk factors and mid-term outcomes of AKI in hospitalized geriatric patients with and without COVID-19 and established a prediction model for mortality. METHODS: A real-time data from the Shanghai Ninth People’s Hospital information system of inpatients with COVID-19 from 1 April 2022 to 30 June 2022 were extracted. Clinical spectrum, laboratory results, and clinical prognosis were included for the risk analyses. Moreover, Cox and Lasso regression analyses were applied to predict the 90-day death and a nomogram was established. RESULTS: A total of 1607 SARS-CoV-2 infected patients were enrolled; hypertension was the most common comorbidity, followed by chronic cardiovascular disease, diabetes mellitus, and lung disease. Most of the participants were non-vaccinated and the mean age of patients was 82.6 years old (range, 60–103 years). The AKI incidence was higher in relatively older patients (16.29% vs 3.63% in patients older than 80 years and 60 to 80 years, respectively). Linear regression models identified some variables associated with the incidence of AKI, such as older age, clinical spectrum, D-dimer level, number of comorbidities, baseline eGFR, and antibiotic or corticosteroid treatment. In this cohort, 11 patients died in-hospital and 21 patients died at 90-day follow-up. The predictive nomogram of 90-day death achieved a good C-index of 0.823 by using 5 predictor variables: ICU admission, D-dimer, peak of serum creatinine, rate of serum creatinine decline and white blood cell count (WBC). CONCLUSION: Older age, clinical spectrum, D-dimer level, number of comorbidities, baseline eGFR, and antibiotic or corticosteroid treatment are clinical risk factors for the incidence of AKI in geriatric COVID-19 patients. The prediction nomogram achieved an excellent performance at the prediction of 90-day mortality. |
format | Online Article Text |
id | pubmed-10362882 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Dove |
record_format | MEDLINE/PubMed |
spelling | pubmed-103628822023-07-23 Clinical Features and Predictive Nomogram of Acute Kidney Injury in Aging Population Infected with SARS-CoV-2 Omicron Variant Zhang, Yumei Li, Xin Zhang, Suning Chen, Wei Lu, Jianxin Xie, Yingxin Wu, Shengbin Zhuang, Feng Bi, Xiao Chu, Mingzi Wang, Feng Huang, Yemin Ding, Feng Hu, Chun Pan, Yu J Inflamm Res Original Research BACKGROUND: Since little is known about the acute kidney injury (AKI) in aging population infected with SARS-CoV-2 Omicron variant, we investigated the incidence, clinical features, risk factors and mid-term outcomes of AKI in hospitalized geriatric patients with and without COVID-19 and established a prediction model for mortality. METHODS: A real-time data from the Shanghai Ninth People’s Hospital information system of inpatients with COVID-19 from 1 April 2022 to 30 June 2022 were extracted. Clinical spectrum, laboratory results, and clinical prognosis were included for the risk analyses. Moreover, Cox and Lasso regression analyses were applied to predict the 90-day death and a nomogram was established. RESULTS: A total of 1607 SARS-CoV-2 infected patients were enrolled; hypertension was the most common comorbidity, followed by chronic cardiovascular disease, diabetes mellitus, and lung disease. Most of the participants were non-vaccinated and the mean age of patients was 82.6 years old (range, 60–103 years). The AKI incidence was higher in relatively older patients (16.29% vs 3.63% in patients older than 80 years and 60 to 80 years, respectively). Linear regression models identified some variables associated with the incidence of AKI, such as older age, clinical spectrum, D-dimer level, number of comorbidities, baseline eGFR, and antibiotic or corticosteroid treatment. In this cohort, 11 patients died in-hospital and 21 patients died at 90-day follow-up. The predictive nomogram of 90-day death achieved a good C-index of 0.823 by using 5 predictor variables: ICU admission, D-dimer, peak of serum creatinine, rate of serum creatinine decline and white blood cell count (WBC). CONCLUSION: Older age, clinical spectrum, D-dimer level, number of comorbidities, baseline eGFR, and antibiotic or corticosteroid treatment are clinical risk factors for the incidence of AKI in geriatric COVID-19 patients. The prediction nomogram achieved an excellent performance at the prediction of 90-day mortality. Dove 2023-07-18 /pmc/articles/PMC10362882/ /pubmed/37484995 http://dx.doi.org/10.2147/JIR.S413318 Text en © 2023 Zhang et al. https://creativecommons.org/licenses/by-nc/3.0/This work is published and licensed by Dove Medical Press Limited. The full terms of this license are available at https://www.dovepress.com/terms.php and incorporate the Creative Commons Attribution – Non Commercial (unported, v3.0) License (http://creativecommons.org/licenses/by-nc/3.0/ (https://creativecommons.org/licenses/by-nc/3.0/) ). By accessing the work you hereby accept the Terms. Non-commercial uses of the work are permitted without any further permission from Dove Medical Press Limited, provided the work is properly attributed. For permission for commercial use of this work, please see paragraphs 4.2 and 5 of our Terms (https://www.dovepress.com/terms.php). |
spellingShingle | Original Research Zhang, Yumei Li, Xin Zhang, Suning Chen, Wei Lu, Jianxin Xie, Yingxin Wu, Shengbin Zhuang, Feng Bi, Xiao Chu, Mingzi Wang, Feng Huang, Yemin Ding, Feng Hu, Chun Pan, Yu Clinical Features and Predictive Nomogram of Acute Kidney Injury in Aging Population Infected with SARS-CoV-2 Omicron Variant |
title | Clinical Features and Predictive Nomogram of Acute Kidney Injury in Aging Population Infected with SARS-CoV-2 Omicron Variant |
title_full | Clinical Features and Predictive Nomogram of Acute Kidney Injury in Aging Population Infected with SARS-CoV-2 Omicron Variant |
title_fullStr | Clinical Features and Predictive Nomogram of Acute Kidney Injury in Aging Population Infected with SARS-CoV-2 Omicron Variant |
title_full_unstemmed | Clinical Features and Predictive Nomogram of Acute Kidney Injury in Aging Population Infected with SARS-CoV-2 Omicron Variant |
title_short | Clinical Features and Predictive Nomogram of Acute Kidney Injury in Aging Population Infected with SARS-CoV-2 Omicron Variant |
title_sort | clinical features and predictive nomogram of acute kidney injury in aging population infected with sars-cov-2 omicron variant |
topic | Original Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10362882/ https://www.ncbi.nlm.nih.gov/pubmed/37484995 http://dx.doi.org/10.2147/JIR.S413318 |
work_keys_str_mv | AT zhangyumei clinicalfeaturesandpredictivenomogramofacutekidneyinjuryinagingpopulationinfectedwithsarscov2omicronvariant AT lixin clinicalfeaturesandpredictivenomogramofacutekidneyinjuryinagingpopulationinfectedwithsarscov2omicronvariant AT zhangsuning clinicalfeaturesandpredictivenomogramofacutekidneyinjuryinagingpopulationinfectedwithsarscov2omicronvariant AT chenwei clinicalfeaturesandpredictivenomogramofacutekidneyinjuryinagingpopulationinfectedwithsarscov2omicronvariant AT lujianxin clinicalfeaturesandpredictivenomogramofacutekidneyinjuryinagingpopulationinfectedwithsarscov2omicronvariant AT xieyingxin clinicalfeaturesandpredictivenomogramofacutekidneyinjuryinagingpopulationinfectedwithsarscov2omicronvariant AT wushengbin clinicalfeaturesandpredictivenomogramofacutekidneyinjuryinagingpopulationinfectedwithsarscov2omicronvariant AT zhuangfeng clinicalfeaturesandpredictivenomogramofacutekidneyinjuryinagingpopulationinfectedwithsarscov2omicronvariant AT bixiao clinicalfeaturesandpredictivenomogramofacutekidneyinjuryinagingpopulationinfectedwithsarscov2omicronvariant AT chumingzi clinicalfeaturesandpredictivenomogramofacutekidneyinjuryinagingpopulationinfectedwithsarscov2omicronvariant AT wangfeng clinicalfeaturesandpredictivenomogramofacutekidneyinjuryinagingpopulationinfectedwithsarscov2omicronvariant AT huangyemin clinicalfeaturesandpredictivenomogramofacutekidneyinjuryinagingpopulationinfectedwithsarscov2omicronvariant AT dingfeng clinicalfeaturesandpredictivenomogramofacutekidneyinjuryinagingpopulationinfectedwithsarscov2omicronvariant AT huchun clinicalfeaturesandpredictivenomogramofacutekidneyinjuryinagingpopulationinfectedwithsarscov2omicronvariant AT panyu clinicalfeaturesandpredictivenomogramofacutekidneyinjuryinagingpopulationinfectedwithsarscov2omicronvariant |