Background: Acute kidney injury (AKI) can affect hospitalized patients with coronavirus disease 2019 (COVID-19), with estimates ranging between 0.5% and 40%. We performed a systematic review and meta-analysis of studies reporting incidence, mortality and risk factors for AKI in hospitalized COVID-19 patients.
Methods: We systematically searched 11 electronic databases until 29 May 2020 for studies in English reporting original data on AKI and kidney replacement therapy (KRT) in hospitalized COVID-19 patients. Incidences of AKI and KRT and risk ratios for mortality associated with AKI were pooled using generalized linear mixed and random-effects models. Potential risk factors for AKI were assessed using meta-regression. Incidences were stratified by geographic location and disease severity.
Results: A total of 3042 articles were identified, of which 142 studies were included, with 49 048 hospitalized COVID-19 patients including 5152 AKI events. The risk of bias of included studies was generally low. The pooled incidence of AKI was 28.6% [95% confidence interval (CI) 19.8-39.5] among hospitalized COVID-19 patients from the USA and Europe (20 studies) and 5.5% (95% CI 4.1-7.4) among patients from China (62 studies), whereas the pooled incidence of KRT was 7.7% (95% CI 5.1-11.4; 18 studies) and 2.2% (95% CI 1.5-3.3; 52 studies), respectively. Among patients admitted to the intensive care unit, the incidence of KRT was 20.6% (95% CI 15.7-26.7; 38 studies). Meta-regression analyses showed that age, male sex, cardiovascular disease, diabetes mellitus, hypertension and chronic kidney disease were associated with the occurrence of AKI; in itself, AKI was associated with an increased risk of mortality, with a pooled risk ratio of 4.6 (95% CI 3.3-6.5).
Conclusions: AKI and KRT are common events in hospitalized COVID-19 patients, with estimates varying across geographic locations. Additional studies are needed to better understand the underlying mechanisms and optimal treatment of AKI in these patients.
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http://dx.doi.org/10.1093/ckj/sfaa160 | DOI Listing |
Sci Rep
December 2024
Department of Mathematics, GC University, Lahore, Pakistan.
In this article, a nonlinear fractional bi-susceptible [Formula: see text] model is developed to mathematically study the deadly Coronavirus disease (Covid-19), employing the Atangana-Baleanu derivative in Caputo sense (ABC). A more profound comprehension of the system's intricate dynamics using fractional-order derivative is explored as the primary focus of constructing this model. The fundamental properties such as positivity and boundedness, of an epidemic model have been proven, ensuring that the model accurately reflects the realistic behavior of disease spread within a population.
View Article and Find Full Text PDFNat Commun
December 2024
State Key Laboratory of Quality Research in Chinese Medicine, Institute of Chinese Medical Sciences, University of Macau, Macau, China.
Lipid nanoparticles (LNPs) have proven effective in mRNA delivery, as evidenced by COVID-19 vaccines. Its key ingredient, ionizable lipids, is traditionally optimized by inefficient and costly experimental screening. This study leverages artificial intelligence (AI) and virtual screening to facilitate the rational design of ionizable lipids by predicting two key properties of LNPs, apparent pKa and mRNA delivery efficiency.
View Article and Find Full Text PDFNat Commun
December 2024
Laboratory of Aging Research and Cancer Drug Target, National Clinical Research Center for Geriatrics, West China Hospital, Sichuan University, Chengdu, China.
The immune escape capacities of XBB variants necessitate the authorization of vaccines with these antigens. In this study, we produce three recombinant trimeric proteins from the RBD sequences of Delta, BA.5, and XBB.
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December 2024
Lunenfeld-Tanenbaum Research Institute, Mount Sinai Hospital, Toronto, Ontario, M5G 1X5, Canada.
Highly mutable pathogens generate viral diversity that impacts virulence, transmissibility, treatment, and thwarts acquired immunity. We previously described C19-SPAR-Seq, a high-throughput, next-generation sequencing platform to detect SARS-CoV-2 that we here deployed to systematically profile variant dynamics of SARS-CoV-2 for over 3 years in a large, North American urban environment (Toronto, Canada). Sequencing of the ACE2 receptor binding motif and polybasic furin cleavage site of the Spike gene in over 70,000 patients revealed that population sweeps of canonical variants of concern (VOCs) occurred in repeating wavelets.
View Article and Find Full Text PDFNat Commun
December 2024
Whitehead Institute for Biomedical Research, Cambridge, MA, 02142, USA.
Although respiratory symptoms are the most prevalent disease manifestation of infection by Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2), nearly 20% of hospitalized patients are at risk for thromboembolic events. This prothrombotic state is considered a key factor in the increased risk of stroke, which is observed clinically during both acute infection and long after symptoms clear. Here, we develop a model of SARS-CoV-2 infection using human-induced pluripotent stem cell-derived endothelial cells (ECs), pericytes (PCs), and smooth muscle cells (SMCs) to recapitulate the vascular pathology associated with SARS-CoV-2 exposure.
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