Evidence map›Paper›PMID 37484995›Full record

ArticleJournal of inflammation research2023

Clinical Features and Predictive Nomogram of Acute Kidney Injury in Aging Population Infected with SARS-CoV-2 Omicron Variant.

Yumei Zhang, Xin Li, Suning Zhang, Wei Chen, Jianxin Lu, Yingxin Xie, Shengbin Wu, Feng Zhuang, Xiao Bi, Mingzi Chu and 5 more

Open access · goldAbstract read
In one paragraph

Article in Journal of inflammation research, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
0.2field-weighted citation impact, top 49% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed, 1 citations in OpenAlex.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

15 authors at 2 institutions in 1 country.

Yumei Zhang *Division of Nephrology, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, People's Republic of China.
Xin Li *Division of Nephrology, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, People's Republic of China.
Suning ZhangDivision of Emergency, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, People's Republic of China.
Wei ChenDivision of Neurology, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, People's Republic of China.
Jianxin LuDivision of Nephrology, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, People's Republic of China.
Yingxin XieDivision of Nephrology, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, People's Republic of China.
Shengbin WuDivision of Nephrology, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, People's Republic of China.
Feng ZhuangDivision of Nephrology, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, People's Republic of China.
Xiao BiDivision of Nephrology, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, People's Republic of China.
Mingzi ChuDivision of Nephrology, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, People's Republic of China.
Feng WangDivision of Nephrology, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, People's Republic of China.
Yemin HuangDivision of Nephrology, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, People's Republic of China.
Feng DingDivision of Nephrology, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, People's Republic of China.
Chun HuDivision of Nephrology, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, People's Republic of China.
Yu PanDivision of Nephrology, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, People's Republic of China.
Shanghai Ninth People's Hospital · CNShanghai Jiao Tong University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

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.

Indexed as

acute kidney injuryagingCOVID-19Omicronpredictive nomogram

Identifiers

PMID37484995
PMCPMC10362882
OpenAlexW4384700160

What OpenQuestion holds

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.