Comparison of Outcomes of Mild and Severe Community- and Hospital-Acquired Acute Kidney Injury

Acute kidney injury in hospitalized children in 20 hospitals of China's Hunan Province: a cross-sectional survey

OBJECTIVE

The incidence of acute kidney injury (AKI) in pediatric patients has been increasing over the years, and AKI significantly impacts children's health and quality of life. This article reviews the current epidemiological research on pediatric AKI.

METHODS

The clinical data of hospitalized children aged 0 to 14 years from 20 different hospitals in Hunan Province, China, collected from December 2017 to February 2018, were analyzed. The incidence rate, misdiagnosis rate, main causes, and medical costs of AKI in hospitalized children were examined.

RESULTS

A total of 29,639 patients were included, with an AKI incidence rate of 4.34% (1286/29,639). Among the 1286 AKI patients, 863 (67.11%) were classified as AKI stage 1324 (25.19%) as AKI stage 2, and 99 (7.7%) as AKI stage 3. AKI patients had significantly longer hospital stays [6.0 (4.0, 10) days vs. 6.0 (4.0, 8.0) days, p < 0.001] and higher hospitalization costs [3375.22 (1600, 6083.83) yuan vs. 2729.4 (1659.45, 8216.65) yuan, p = 0.003] than non-AKI patients. The mortality rate (1.2% vs. 0.1%, p < 0.001), intensive care unit (ICU) transfer rate (8.7% vs. 5.97%, p < 0.001), and use of invasive mechanical ventilation (3.6% vs. 1%, p < 0.001) were significantly greater in patients with AKI than in those without AKI patients. The etiology of AKI varied among different age groups, and dehydration, diarrhea, and shock were the main causes of pre-renal AKI.

CONCLUSION

The incidence and missed diagnosis rates of AKI in hospitalized children were high. AKI prolongs hospital stays, increases hospitalization costs, and increases the risk of mortality in children.

Time Series AI Model for Acute Kidney Injury Detection Based on a Multicenter Distributed Research Network: Development and Verification Study

Background

Acute kidney injury (AKI) is a marker of clinical deterioration and renal toxicity. While there are many studies offering prediction models for the early detection of AKI, those predicting AKI occurrence using distributed research network (DRN)-based time series data are rare.

Objective

In this study, we aimed to detect the early occurrence of AKI by applying an interpretable long short-term memory (LSTM)-based model to hospital electronic health record (EHR)-based time series data in patients who took nephrotoxic drugs using a DRN.

Methods

We conducted a multi-institutional retrospective cohort study of data from 6 hospitals using a DRN. For each institution, a patient-based data set was constructed using 5 drugs for AKI, and an interpretable multivariable LSTM (IMV-LSTM) model was used for training. This study used propensity score matching to mitigate differences in demographics and clinical characteristics. Additionally, the temporal attention values of the AKI prediction model's contribution variables were demonstrated for each institution and drug, with differences in highly important feature distributions between the case and control data confirmed using 1-way ANOVA.

Results

This study analyzed 8643 and 31,012 patients with and without AKI, respectively, across 6 hospitals. When analyzing the distribution of AKI onset, vancomycin showed an earlier onset (median 12, IQR 5-25 days), and acyclovir was the slowest compared to the other drugs (median 23, IQR 10-41 days). Our temporal deep learning model for AKI prediction performed well for most drugs. Acyclovir had the highest average area under the receiver operating characteristic curve score per drug (0.94), followed by acetaminophen (0.93), vancomycin (0.92), naproxen (0.90), and celecoxib (0.89). Based on the temporal attention values of the variables in the AKI prediction model, verified lymphocytes and calcvancomycin ium had the highest attention, whereas lymphocytes, albumin, and hemoglobin tended to decrease over time, and urine pH and prothrombin time tended to increase.

Conclusions

Early surveillance of AKI outbreaks can be achieved by applying an IMV-LSTM based on time series data through an EHR-based DRN. This approach can help identify risk factors and enable early detection of adverse drug reactions when prescribing drugs that cause renal toxicity before AKI occurs.

An overview of circulating and urinary biomarkers capable of predicting the transition of acute kidney injury to chronic kidney disease

INTRODUCTION

Acute kidney injury (AKI) defined by a substantial decrease in kidney function within hours to days and is often irreversible with higher risk to chronic kidney disease (CKD) transition.

AREAS COVERED

The authors discuss the diagnostic and predictive utilities of serum and urinary biomarkers on AKI and on the risk of AKI-to-CKD progression. The authors focus on the relevant literature covering evidence of circulating and urinary biomarkers' capability to predict the transition of AKI to CKD.

EXPERT OPINION

Based on the different modalities of serum and urinary biomarkers, multiple biomarker panel seems to be potentially useful to distinguish between various types of AKI, to detect the severity and the risk of AKI progression, to predict the clinical outcome and evaluate response to the therapy. Serum/urinary neutrophil gelatinase-associated lipocalin (NGAL), serum/urinary uromodulin, serum extracellular high mobility group box-1 (HMGB-1), serum cystatin C and urinary liver-type fatty acid-binding protein (L-FABP) were the most effective in the prediction of AKI-to-CKD transition regardless of etiology and the presence of critical state in patients. The current clinical evidence on the risk assessments of AKI progression is mainly based on the utility of combination of functional, injury and stress biomarkers, mainly NGAL, L-FABP, HMGB-1 and cystatin C.

Characteristics, Risk Factors, and Outcomes in Acute Kidney Injury Patients: A Retrospective Cross-Sectional Study, Palestine

BACKGROUND

Acute kidney injury (AKI) is a major medical problem affecting patients' quality of life and healthcare costs.

OBJECTIVES

This study evaluated the severity, risk factors, and outcomes of patients diagnosed with acute kidney injury (AKI), including community-acquired AKI (CA-AKI) and hospital-acquired AKI (HA-AKI), who were admitted to tertiary institutions in Palestine.

METHODS

This retrospective cross-sectional study was conducted at multiple tertiary care hospitals in Palestine by reviewing patient charts from January 2020 to March 2023. The study included all patients aged ≥18 years who were admitted to the hospital and diagnosed with AKI at admission (CA-AKI) or who developed AKI 48 hours after admission (HA-AKI). Patients with incomplete medical records and those with no reported creatinine levels during their stay, pregnant women, kidney transplant patients, and end-stage renal disease patients were excluded. Data were analyzed using SPSS v22.0. The incidence of AKI in each group was compared using the chi-squared test.

RESULTS

This study included 259 participants. HA-AKI was present in 27.3% of the patients, while CA-AKI was 72.7%. The most common stage among patients was stage 3 (55.7%, HA-AKI) (42.9%, CA-AKI), and the most common comorbidity contributing to AKI was CKD. NSAIDs, ACE-I/ARBs, and DIURETICs were the most nephrotoxic drugs contributing to AKI. Patients with hyperphosphatemia, hyperkalemia, severe metabolic acidosis, or stage 3 AKI require renal replacement therapy. In addition, our findings revealed a significant association among AKI mortality, age, and heart disease.

CONCLUSION

CA-AKI was more prevalent than HA-AKI in Palestinian patients admitted for AKI. Risk factors for AKI included diabetes, CKD, and medications (antibiotics, NSAID, diuretics, and ACE-I/ARB). Preventive measures, medication management, and disease state management are necessary to minimize AKI during hospital admission or in the community.

Exosome-Based Drug Delivery: Translation from Bench to Clinic

Exosome-based drug delivery is emerging as a promising field with the potential to revolutionize therapeutic interventions. Exosomes, which are small extracellular vesicles released by various cell types, have attracted significant attention due to their unique properties and natural ability to transport bioactive molecules. These nano-sized vesicles, ranging in size from 30 to 150 nm, can effectively transport a variety of cargoes, including proteins, nucleic acids, and lipids. Compared to traditional drug delivery systems, exosomes exhibit unique biocompatibility, low immunogenicity, and reduced toxicity. In addition, exosomes can be designed and tailored to improve targeting efficiency, cargo loading capacity, and stability, paving the way for personalized medicine and precision therapy. However, despite the promising potential of exosome-based drug delivery, its clinical application remains challenging due to limitations in exosome isolation and purification, low loading efficiency of therapeutic cargoes, insufficient targeted delivery, and rapid elimination in circulation. This comprehensive review focuses on the transition of exosome-based drug delivery from the bench to clinic, highlighting key aspects, such as exosome structure and biogenesis, cargo loading methods, surface engineering techniques, and clinical applications. It also discusses challenges and prospects in this emerging field.