Epidemiology and Clinical Outcomes of Community-Acquired Acute Kidney Injury in the Emergency Department: A Multisite Retrospective Cohort Study.

RATIONALE & OBJECTIVE: The prevalence of community-acquired acute kidney injury (CA-AKI) in the United States and its clinical consequences are not well described. Our objective was to describe the epidemiology of CA-AKI and the associated clinical outcomes. STUDY DESIGN: Retrospective cohort study. SETTING & PARTICIPANTS: 178,927 encounters by 139,632 adults at 5 US emergency departments (EDs) between July 1, 2017, and December 31, 2022. PREDICTORS: CA-AKI identified using KDIGO (Kidney Disease: Improving Global Outcomes) serum creatinine (Scr)-based criteria. OUTCOMES: For encounters resulting in hospitalization, the in-hospital trajectory of AKI severity, dialysis initiation, intensive care unit (ICU) admission, and death. For all encounters, occurrence over 180 days of hospitalization, ICU admission, new or progressive chronic kidney disease, dialysis initiation, and death. ANALYTICAL APPROACH: Multivariable logistic regression analysis to test the association between CA-AKI and measured outcomes. RESULTS: For all encounters, 10.4% of patients met the criteria for any stage of AKI on arrival to the ED. 16.6% of patients admitted to the hospital from the ED had CA-AKI on arrival to the ED. The likelihood of AKI recovery was inversely related to CA-AKI stage on arrival to the ED. Among encounters for hospitalized patients, CA-AKI was associated with in-hospital dialysis initiation (OR, 6.2; 95% CI, 5.1-7.5), ICU admission (OR, 1.9; 95% CI, 1.7-2.0), and death (OR, 2.2; 95% CI, 2.0-2.5) compared with patients without CA-AKI. Among all encounters, CA-AKI was associated with new or progressive chronic kidney disease (OR, 6.0; 95% CI, 5.6-6.4), dialysis initiation (OR, 5.1; 95% CI, 4.5-5.7), subsequent hospitalization (OR, 1.1; 95% CI, 1.1-1.2) including ICU admission (OR, 1.2; 95% CI, 1.1-1.4), and death (OR, 1.6; 95% CI, 1.5-1.7) during the subsequent 180 days. LIMITATIONS: Residual confounding. Study implemented at a single university-based health system. Potential selection bias related to exclusion of patients without an available baseline Scr measurement. Potential ascertainment bias related to limited repeat Scr data during follow-up after an ED visit. CONCLUSIONS: CA-AKI is a common and important entity that is associated with serious adverse clinical consequences during the 6-month period after diagnosis. PLAIN-LANGUAGE SUMMARY: Acute kidney injury (AKI) is a condition characterized by a rapid decline in kidney function. There are many causes of AKI, but few studies have examined how often AKI is already present when patients first arrive to an emergency department seeking medical attention for any reason. We analyzed approximately 175,000 visits to Johns Hopkins emergency departments and found that AKI is common on presentation to the emergency department and that patients with AKI have increased risks of hospitalization, intensive care unit admission, development of chronic kidney disease, requirement of dialysis, and death in the first 6 months after diagnosis. AKI is an important condition for health care professionals to recognize and is associated with serious adverse outcomes.

Homogeneous and Label-Free Detection and Monitoring of Protein Kinase Activity Using the Impact Electrochemistry of Silver Nanoparticles.

Protein kinase activity correlates closely with that of many human diseases. However, the existing methods for quantifying protein kinase activity often suffer from limitations such as low sensitivity, harmful radioactive labels, high cost, and sophisticated detection procedures, underscoring the urgent need for sensitive and rapid detection methods. Herein, we present a simple and sensitive approach for the homogeneous detection of protein kinase activity based on nanoimpact electrochemistry to probe the degree of aggregation of silver nanoparticles (AgNPs) before and after phosphorylation. Phosphorylation, catalyzed by protein kinases, introduces two negative charges into the substrate peptide, leading to alterations in electrostatic interactions between the phosphorylated peptide and the negatively charged AgNPs, which, in turn, affects the aggregation status of AgNPs. Via direct electro-oxidation of AgNPs in nanoimpact electrochemistry experiments, protein kinase activity can be quantified by assessing the impact frequency. The present sensor demonstrates a broad detection range and a low detection limit for protein kinase A (PKA), along with remarkable selectivity. Additionally, it enables monitoring of PKA-catalyzed phosphorylation processes. In contrast to conventional electrochemical sensing methods, this approach avoids the requirement of complex labeling and washing procedures.

Multisite development and validation of machine learning models to predict severe outcomes and guide decision-making for emergency department patients with influenza.

Objective: Millions of Americans are infected by influenza annually. A minority seek care in the emergency department (ED) and, of those, only a limited number experience severe disease or death. ED clinicians must distinguish those at risk for deterioration from those who can be safely discharged. Methods: We developed random forest machine learning (ML) models to estimate needs for critical care within 24 h and inpatient care within 72 h in ED patients with influenza. Predictor data were limited to those recorded prior to ED disposition decision: demographics, ED complaint, medical problems, vital signs, supplemental oxygen use, and laboratory results. Our study population was comprised of adults diagnosed with influenza at one of five EDs in our university health system between January 1, 2017 and May 18, 2022; visits were divided into two cohorts to facilitate model development and validation. Prediction performance was assessed by the area under the receiver operating characteristic curve (AUC) and the Brier score. Results: Among 8032 patients with laboratory-confirmed influenza, incidence of critical care needs was 6.3% and incidence of inpatient care needs was 19.6%. The most common reasons for ED visit were symptoms of respiratory tract infection, fever, and shortness of breath. Model AUCs were 0.89 (95% CI 0.86-0.93) for prediction of critical care and 0.90 (95% CI 0.88-0.93) for inpatient care needs; Brier scores were 0.026 and 0.042, respectively. Importantpredictors included shortness of breath, increasing respiratory rate, and a high number of comorbid diseases. Conclusions: ML methods can be used to accurately predict clinical deterioration in ED patients with influenza and have potential to support ED disposition decision-making.

Rapid and Accurate Data Processing for Silver Nanoparticle Oxidation in Nano-Impact Electrochemistry.

In recent years, nano-impact electrochemistry (NIE) has attracted widespread attention as a new electroanalytical approach for the analysis and characterization of single nanoparticles in solution. The accurate analysis of the large volume of the experimental data is of great significance in improving the reliability of this method. Unfortunately, the commonly used data analysis approaches, mainly based on manual processing, are often time-consuming and subjective. Herein, we propose a spike detection algorithm for automatically processing the data from the direct oxidation of sliver nanoparticles (AgNPs) in NIE experiments, including baseline extraction, spike identification and spike area integration. The resulting size distribution of AgNPs is found to agree very well with that from transmission electron microscopy (TEM), showing that the current algorithm is promising for automated analysis of NIE data with high efficiency and accuracy.