Dataset Information

A clinical study on the changing dynamics of disease severity, management strategies and outcomes of COVID-19 in patients requiring haemodialysis.

ABSTRACT:

Introduction

In this observational study, we describe the change in the clinical profile and outcome of Corona Virus Disease 2019 (COVID-19) over the course of the outbreak, among patients requiring dialysis, including chronic haemodialysis therapy.

Methods

This is a single-centre prospective observational study of patients with COVID-19 (as confirmed by RT-PCR) and renal failure requiring haemodialysis. Their clinical profiles and outcomes were analysed, vis-à-vis the changing disease severity.

Findings

A total of 483 patients were included, of whom 416 had end-stage renal disease and were on maintenance haemodialysis. Patients who were symptomatic at presentation had significantly higher levels of Neutrophil-lymphocyte ratio (NLR) (p < 0.001), C-reactive protein (CRP) (p < 0.001), lactate dehydrogenase (LDH) (p < 0.001), higher degrees of lung involvement (p < 0.001) and required more respiratory support (p < 0.001). The overall mortality observed was 18.8%. In the late phases of the outbreak, there was a significant increase in disease severity without a statistically significant increase in mortality. Predictors of mortality on univariate analysis were age, diabetes mellitus, acute on chronic kidney disease, presence of symptoms on admission, NLR, CRP, LDH, computed tomography (CT) chest grades 3 and 4, and need for respiratory support; however, only age and the renal syndrome of acute on chronic kidney disease retained significance on multivariate analysis (p0.003 and p0.019, respectively).

Conclusion

Among patients on haemodialysis, higher mortality was observed in patients who were older, and among those with acute on chronic kidney disease. In the late phase of the outbreak, there was a statistically significant increase in disease severity without a corresponding increase in mortality.

SUBMITTER: Nithya G

PROVIDER: S-EPMC8164070 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Similar Datasets

Project description:The lack of available biomarkers for diagnosing and predicting different stages of coronavirus disease 2019 (COVID-19) is currently one of the main challenges that clinicians are facing. Recent evidence indicates that the plasma levels of specific miRNAs may be significantly modified in COVID-19 patients. Large-scale deep sequencing analysis of small RNA expression was performed on plasma samples from 40 patients hospitalized for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection (between March and May 2020) (median 13.50 [IQR 9–24] days since symptoms initiation) and 21 healthy noninfected individuals. Patients were categorized as hospitalized not requiring oxygen therapy (n = 6), hospitalized requiring low-flow oxygen (n = 23), and hospitalized requiring high-flow oxygen support (n = 11). A total of 1218 different micro(mi)RNAs were identified. When compared with healthy noninfected donors, SARS-CoV-2 infected patients showed significantly (fold change [FC] >1.2 and adjusted p [padj] <0.05) altered expression of 190 miRNAs. The top 10 differentially expressed (DE) miRNAs were miR-122-5p, let-7b-5p, miR-146a-5p, miR-342-3p, miR-146b-5p, miR-629-5p, miR-24-3p, miR-12136, let-7a-5p, and miR-191-5p, which displayed FC and padj values ranging from 153 to 5 and 2.51 × 10-32 to 2.21 × 10-21, respectively, which unequivocally diagnosed SARS-CoV-2 infection. No differences in blood cell counts and biochemical plasma parameters, including interleukin 6, ferritin and D-dimer, were observed between COVID-19 patients on high-flow oxygen therapy, low-flow oxygen therapy, or not requiring oxygen therapy. Notably, 31 significantly deregulated miRNAs were found when patients on high- and low-flow oxygen therapy were compared. Similarly, 6 DE miRNAs were identified between patients on high flow and those not requiring oxygen therapy. SARS-CoV-2 infection generates a specific miRNA signature in hospitalized patients. Furthermore, specific miRNA profiles are associated with COVID-19 prognosis in severe patients.

Project description:Using RNA-seq and high-resolution mass spectrometry we performed a comprehensive systems analysis on 128 plasma and leukocyte samples from hospitalized patients with or without COVID-19 (n=102 and 26 respectively) and with differing degrees of disease severity. We generated abundance measurements for over 17,000 transcripts, proteins, metabolites, and lipids and compiled them with clinical data into a curated relational database. This resource offers the unique opportunity to perform systems analysis and cross-ome correlations to both molecules and patient outcomes. In total 219 molecular features were mapped with high significance to COVID-19 status and severity, including those involved in processes such as complement system activation, dysregulated lipid transport, and B cell activation. In one example, we detected a trio of covarying molecules – citrate, plasmenyl-phosphatidylcholines, and gelsolin (GSN) – that offer both pathophysiological insight and potential novel therapeutic targets. Further, our data revealed in some cases, and supported in others, that several biological processes were dysregulated in COVID-19 patients including vessel damage, platelet activation and degranulation, blood coagulation, and acute phase response. For example, we observed that the coagulation-related protein, cellular fibronectin (cFN), was highly increased within COVID-19 patients and provide new evidence that the upregulated proteoform stems from endothelial cells, consistent with endothelial injury as a major activator of the coagulation cascade. The abundance of prothrombin, which is cleaved to form thrombin during clotting, was significantly reduced and correlated with severity and might help to explain the hyper coagulative environment of SARS-CoV-2 infection. From transcriptomic analysis of leukocytes, we concluded that COVID-19 patients with acute respiratory distress syndrome (ARDS) demonstrated a phenotype that overlapped with, but was distinct from, that found in patients with non-COVID-19-ARDS. To aid in the global efforts toward elucidation of disease pathophysiology and therapeutic development, we created a web-based tool with interactive visualizations allowing for easy navigation of this systems-level compendium of biomolecule abundance in relation to COVID-19 status and severity. Finally, we leveraged these multi-omic data to predict COVID-19 patient outcomes with machine learning, which highlighted the predictive power of these expansive molecular measurements beyond the standardized clinical estimate of 10-year survival Charlson score.