Occurrence of acute kidney injury in adult patients hospitalized with COVID-19: A systematic review and meta-analysis

Global Scientific Trends in Continuous Renal Replacement Therapy from 2000 to 2023: A Bibliometric and Visual Analysis

INTRODUCTION

Continuous renal replacement therapy (CRRT) is one of the most widely used blood purification and organ support methods in the ICU. However, the development process, the current status, hotspots, and future trends of CRRT remain unclear.

METHOD

The WoSCC database was used to analyze CRRT research evolution and theme trends. VOSviewer was used to construct coauthorship, co-occurrence, co-citation, and network visualizations. CiteSpace is used to detect bursts for co-occurrence items. Several important subtopics were reviewed and discussed in more detail.

RESULTS

Global publications increased from 56 in 2000 to 398 in 2023, a 710.71% increase. Blood Purification published the most manuscripts, followed by the International Journal of Artificial Organs. The USA, the San Bortolo Hospital, and Bellomo were the most productive and impactful institution, country, and author, respectively. Based on co-occurrence cluster analysis, five clusters emerged: (1) clinical applications and management of CRRT; (2) sepsis and CRRT; (3) CRRT anticoagulant management; (4) CRRT and antibiotic pharmacokinetics and pharmacodynamics; and (5) comparison of CRRT and intermittent hemodialysis. COVID-19, initiation, ECOMO, cefepime, guidelines, cardiogenic shock, biomarker, and outcome were the latest high-frequency keywords or strongest bursts, indicating the emerging frontiers of CRRT.

CONCLUSIONS

There has been widespread publication and citation of CRRT research in the past 2 decades. We provide an overview of current trends, global collaboration patterns, basic knowledge, research hotspots, and emerging frontiers.

Kidney Disease and Proteomics: A Recent Overview of a Useful Tool for Improving Early Diagnosis

Kidney disease is a critical and potentially life-threatening degenerative condition that poses a significant global public health challenge due to its elevated rates of morbidity and mortality. It manifests primarily in two distinct clinical forms: acute kidney injury (AKI) and chronic kidney disease (CKD). The development of these conditions hinges on a multitude of factors, including the etiological agents and the presence of coexisting medical conditions. Despite disparities in their underlying pathogenic mechanisms, both AKI and CKD can progress to end-stage kidney disease (ESKD). This advanced stage is characterized by organ failure and its associated complications, greatly increasing the risk of mortality. There is an urgent need to delve into the pathogenic mechanisms underlying these diseases and to identify novel biomarkers that can facilitate earlier diagnosis. Such early detection is crucial for enhancing the efficacy of therapy and impeding disease progression. In this context, proteomic approaches have emerged as invaluable tools for uncovering potential new markers of different pathological conditions, including kidney diseases. In this chapter, we overview the recent discoveries achieved through diverse proteomic techniques aimed at identifying novel molecules that may play a pivotal role in kidney diseases such as diabetic kidney disease (DKD), IgA nephropathy (IgAN), CKD of unknown origin (CKDu), autosomal dominant polycystic kidney disease (ADPKD), lupus nephritis (LN), hypertensive nephropathy (HN), and COVID-19-associated acute kidney injury (COVID-AKI).

Development and validation of a nomogram for the early prediction of acute kidney injury in hospitalized COVID-19 patients

Introduction

Acute kidney injury (AKI) is a prevalent complication of coronavirus disease 2019 (COVID-19) and is closely linked with a poorer prognosis. The aim of this study was to develop and validate an easy-to-use and accurate early prediction model for AKI in hospitalized COVID-19 patients.

Methods

Data from 480 COVID-19-positive patients (336 in the training set and 144 in the validation set) were obtained from the public database of the Cancer Imaging Archive (TCIA). The least absolute shrinkage and selection operator (LASSO) regression method and multivariate logistic regression were used to screen potential predictive factors to construct the prediction nomogram. Receiver operating curves (ROC), calibration curves, as well as decision curve analysis (DCA) were adopted to assess the effectiveness of the nomogram. The prognostic value of the nomogram was also examined.

Results

A predictive nomogram for AKI was developed based on arterial oxygen saturation, procalcitonin, C-reactive protein, glomerular filtration rate, and the history of coronary artery disease. In the training set, the nomogram produced an AUC of 0.831 (95% confidence interval [CI]: 0.774-0.889) with a sensitivity of 85.2% and a specificity of 69.9%. In the validation set, the nomogram produced an AUC of 0.810 (95% CI: 0.737-0.871) with a sensitivity of 77.4% and a specificity of 78.8%. The calibration curve shows that the nomogram exhibited excellent calibration and fit in both the training and validation sets. DCA suggested that the nomogram has promising clinical effectiveness. In addition, the median length of stay (m-LS) for patients in the high-risk group for AKI (risk score ≥ 0.122) was 14.0 days (95% CI: 11.3-16.7 days), which was significantly longer than 8.0 days (95% CI: 7.1-8.9 days) for patients in the low-risk group (risk score <0.122) (hazard ratio (HR): 1.98, 95% CI: 1.55-2.53, p < 0.001). Moreover, the mortality rate was also significantly higher in the high-risk group than that in the low-risk group (20.6 vs. 2.9%, odd ratio (OR):8.61, 95%CI: 3.45-21.52).

Conclusions

The newly constructed nomogram model could accurately identify potential COVID-19 patients who may experience AKI during hospitalization at the very beginning of their admission and may be useful for informing clinical prognosis.