Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Factors Affecting Renal Clearance: Renal Impairment

Factors Affecting Renal Clearance: Renal Impairment

Renal dysfunction significantly impairs the renal clearance of drugs, leading to potential complications in drug therapy. Renal failure, which can be caused by various factors, poses a significant challenge in the elimination of drugs from the body.
One condition associated with renal failure is uremia. Uremia is characterized by impaired glomerular filtration and fluid accumulation in the body. This condition hinders the renal clearance of drugs, resulting in drug accumulation and potential...

Renal Failure: Dose Adjustments

Renal Failure: Dose Adjustments

In patients with renal impairment, drugs undergo significant changes in their pharmacokinetics, which require dosage adjustments to ensure safe and effective therapy.
Reduced renal clearance and elimination rate are common outcomes of renal impairment. These alterations lead to a prolonged elimination half-life and an altered apparent volume of distribution for drugs. As a result, dosage adjustments are typically necessary to maintain optimal drug levels in the body.
However, dosage adjustments...

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

Underutilization of an Objective Vascular Screening Test in Patients with Diabetic Foot Ulcers.

Annals of vascular surgery·2026

Same author

Reevaluating the 2.5 total-to-ionized calcium threshold during RCA-CKRT.

Critical care (London, England)·2026

Same author

Generative AI in Admission Notes and Diagnostic Completeness: A Pilot Study.

Applied clinical informatics·2026

Same author

KDOQI US Commentary on the KDIGO 2026 Clinical Practice Guideline for the Management of Anemia in CKD.

American journal of kidney diseases : the official journal of the National Kidney Foundation·2026

Same author

Thyroid Function with Continuous KRT: A Prospective Study.

Kidney360·2026

Same author

Target Trial Emulation of Vaccine Effectiveness in 5- to 17-years-olds with Prior SARS-CoV-2 Infection.

Nature communications·2026

Same journal

Safety, Pharmacokinetics, and Pharmacodynamics of SHR6508, a Calcium-Sensing Receptor Agonist, in Maintenance Hemodialysis Patients With Secondary Hyperparathyroidism: A Multicenter, Randomized, Double-Blind, Placebo-Controlled Phase 1 Study.

Kidney medicine·2026

Same journal

Shared Decision-Making in CKD: Lessons From Advance Care Planning in Taiwan.

Kidney medicine·2026

Same journal

Effects of SGLT2I Therapy on Tubular Reabsorption and Tubular Epithelial Stress Injury in Patients With CKD.

Kidney medicine·2026

Same journal

Perspectives of Caregivers of Kidney Transplant Recipients and Transplant Candidates About Kidneys From Donors With Hepatitis C Virus Infection.

Kidney medicine·2026

Same journal

Concurrent Mantle Cell Lymphoma and Paraneoplastic Membranous Nephropathy.

Kidney medicine·2026

Same journal

Evolving Comorbidity Burden and Clinical Characteristics in Peritoneal Dialysis: A 17-Year Nationwide Study.

Kidney medicine·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Jun 5, 2025

A Large Animal Model for Acute Kidney Injury by Temporary Bilateral Renal Artery Occlusion

A Large Animal Model for Acute Kidney Injury by Temporary Bilateral Renal Artery Occlusion

Published on: February 2, 2021

Predicting Nephrotoxic Acute Kidney Injury in Hospitalized Adults: A Machine Learning Algorithm.

Benjamin R Griffin^1,2, Avinash Mudireddy³, Benjamin D Horne^4,5

¹Division of Nephrology, Department of Medicine, University of Iowa Carver College of Medicine, Iowa City, IA.

Kidney Medicine

|December 5, 2024

Summary

This summary is machine-generated.

Machine learning updated the pediatric NINJA program to predict acute kidney injury (AKI) in adults. The new model significantly reduced false alerts for nephrotoxic AKI, enabling more targeted interventions.

Keywords:

Drug-induced acute kidney injury machine learning nephrotoxicity

More Related Videos

Supervised Machine Learning for Semi-Quantification of Extracellular DNA in Glomerulonephritis

Supervised Machine Learning for Semi-Quantification of Extracellular DNA in Glomerulonephritis

Published on: June 18, 2020

Early Detection of Drug-Induced Renal Hemodynamic Dysfunction Using Sonographic Technology in Rats

Early Detection of Drug-Induced Renal Hemodynamic Dysfunction Using Sonographic Technology in Rats

Published on: March 11, 2016

Related Experiment Videos

Last Updated: Jun 5, 2025

A Large Animal Model for Acute Kidney Injury by Temporary Bilateral Renal Artery Occlusion

A Large Animal Model for Acute Kidney Injury by Temporary Bilateral Renal Artery Occlusion

Published on: February 2, 2021

Supervised Machine Learning for Semi-Quantification of Extracellular DNA in Glomerulonephritis

Supervised Machine Learning for Semi-Quantification of Extracellular DNA in Glomerulonephritis

Published on: June 18, 2020

Early Detection of Drug-Induced Renal Hemodynamic Dysfunction Using Sonographic Technology in Rats

Early Detection of Drug-Induced Renal Hemodynamic Dysfunction Using Sonographic Technology in Rats

Published on: March 11, 2016

Area of Science:

Nephrology
Artificial Intelligence
Clinical Informatics

Background:

Acute kidney injury (AKI) is a frequent complication in hospitalized adults, posing significant clinical challenges for prediction and prevention.
Existing predictive models often struggle with accuracy in adult populations, necessitating advanced approaches.

Purpose of the Study:

To adapt and enhance the pediatric Nephrotoxic Injury Negated by Just-in Time Action (NINJA) program using machine learning for improved prediction of nephrotoxic AKI in adults.
To evaluate the performance of a recurrent neural network (RNN) gated recurrent unit (GRU) model in identifying adult patients at high risk for AKI following nephrotoxic exposure.

Main Methods:

A retrospective cohort study involving adult patients admitted to the University of Iowa Hospital between 2017 and 2022.
Data included 85 variables: demographics, laboratory tests, vital signs, and medications. AKI was defined by a serum creatinine increase of ≥0.3 mg/dL.
A RNN-GRU model was trained on 85% of the data and tested on the remaining 15%, with performance assessed using precision, recall, negative predictive value, and AUC.

Main Results:

The study analyzed 14,480 patients and 37,300 high-nephrotoxin exposure events; 29% of exposures led to AKI within 48 hours.
The RNN-GRU model achieved a precision of 0.60 for AKI prediction, reducing false alerts from 2.5 to 0.7 per AKI case.
Key risk factors identified included lowest hemoglobin, lowest blood pressure, and highest white blood cell count, along with specific medications like acyclovir and piperacillin-tazobactam.

Conclusions:

The developed RNN-GRU model significantly decreases false alerts for nephrotoxic AKI in adults.
This advancement supports the potential translation of the NINJA program to adult healthcare settings, facilitating more precise interventions.
Further research should incorporate comprehensive clinical variables and drug-specific data for enhanced predictive accuracy.