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Related Concept Videos

Chronic Kidney Disease I: Introduction01:25

Chronic Kidney Disease I: Introduction

Chronic Kidney Disease (CKD) arises when the kidneys progressively lose their ability to function, ultimately leading to end-stage renal disease. At this advanced stage, the kidneys can no longer filter waste or maintain essential body functions, requiring renal replacement therapy (RRT) through dialysis or a kidney transplant for survival.Early-stage chronic kidney disease and detection challengesIn CKD's early stages, symptoms often remain absent because healthy nephrons compensate for...
Chronic Kidney Disease III: Interprofessional Care01:28

Chronic Kidney Disease III: Interprofessional Care

Chronic kidney disease (CKD) requires collaborative and comprehensive management. CKD progresses through stages and can lead to end-stage kidney disease (ESKD) if untreated. Interprofessional collaboration and patient education are crucial, enabling patients to manage their health and improve their quality of life.Diagnostic approach for chronic kidney diseaseThe diagnosis of CKD primarily focuses on the glomerular filtration rate (GFR), which assesses kidney function by measuring how well...
Chronic Kidney Disease II: Clinical Manifestations01:24

Chronic Kidney Disease II: Clinical Manifestations

Chronic Kidney Disease (CKD) progressively impairs multiple body systems due to the accumulation of uremic toxins, which disrupt cellular functions across various organs.Neurologic symptomsNeurologic symptoms often arise early in CKD, as uremic toxin buildup drives changes in cognitive and motor functions. Patients frequently experience fatigue, headache, confusion, difficulty concentrating, and, in severe cases, seizures. Peripheral neuropathy commonly manifests as burning sensations in the...
Acute Kidney Injury IV: Diagnostic Studies and Prevention01:30

Acute Kidney Injury IV: Diagnostic Studies and Prevention

Accurate diagnosis and effective prevention are critical in managing Acute Kidney Injury (AKI), which is linked to high mortality rates ranging from 10% to 80%. Timely recognition of at-risk patients and careful monitoring can significantly reduce the likelihood of kidney damage.Diagnostic Assessments:The diagnostic process starts with a comprehensive medical history to identify prerenal, intrarenal, and postrenal causes.Prerenal causes, such as dehydration, hypotension, or blood loss, should...
Chronic Kidney Disease IV: Nursing Management01:18

Chronic Kidney Disease IV: Nursing Management

Nursing management is essential for preventing complications, maintaining stability, and improving patients' quality of life in chronic kidney disease (CKD). By using a structured approach, nurses help slow CKD progression and support effective patient care​.1. Comprehensive patient assessmentEffective management begins with nurses reviewing the patient’s medical history, and identifying key risk factors like diabetes, hypertension, and nephrotoxic drug use. Nurses assess signs of fluid...
Acute Kidney Injury III: Clinical Manifestations01:29

Acute Kidney Injury III: Clinical Manifestations

Acute Kidney Injury (AKI) progresses through distinct clinical phases: the oliguric, diuretic, and recovery phases, each marked by unique manifestations and challenges.Oliguric Phase:The oliguric phase is the initial stage of AKI, typically lasting 10 to 14 days. This phase is marked by a significant reduction in urine output, usually less than 400 mL per day, indicating decreased kidney function. Fluid retention is a prominent feature, leading to symptoms such as edema, hypertension, and...

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Related Experiment Video

Updated: May 25, 2026

Mouse Model of Acute to Chronic Kidney Disease Transition Induced by Renal Ischemia/Reperfusion Injury
07:02

Mouse Model of Acute to Chronic Kidney Disease Transition Induced by Renal Ischemia/Reperfusion Injury

Published on: February 10, 2026

Machine Learning-Predicted Risk Trajectories for Incident Chronic Kidney Disease and Associations with Post-CKD

Aaron Boussina1, Amy M Sitapati1, Soo-Young Yoon2

  • 1Division of Biomedical Informatics, Department of Medicine, University of California San Diego, La Jolla, San Diego, CA, USA.

Journal of Medical Systems
|May 23, 2026
PubMed
Summary
This summary is machine-generated.

Predicting chronic kidney disease (CKD) risk is crucial. A new machine learning model identifies three distinct CKD risk trajectories, aiding early monitoring and intervention for better patient outcomes.

Keywords:
Cardiovascular eventsChronic kidney diseaseExtreme gradient boostingRisk trajectory

Related Experiment Videos

Last Updated: May 25, 2026

Mouse Model of Acute to Chronic Kidney Disease Transition Induced by Renal Ischemia/Reperfusion Injury
07:02

Mouse Model of Acute to Chronic Kidney Disease Transition Induced by Renal Ischemia/Reperfusion Injury

Published on: February 10, 2026

Area of Science:

  • Nephrology
  • Data Science
  • Predictive Analytics

Background:

  • Chronic kidney disease (CKD) risk accumulates before clinical detection, yet risk evaluation strategies are limited.
  • Understanding CKD risk trajectories over time is essential for proactive healthcare interventions.

Purpose of the Study:

  • To develop and validate a machine learning model for predicting incident chronic kidney disease (CKD) risk.
  • To identify distinct longitudinal CKD risk trajectories and their association with clinical outcomes.

Main Methods:

  • Utilized a large cohort (51,156 individuals) from the University of California health system (2012-2024).
  • Developed a debiased XGBoost model for CKD risk prediction.
  • Employed latent class growth mixture modeling to derive risk trajectories from predicted probabilities.

Main Results:

  • The XGBoost model demonstrated high predictive performance (AUC 0.95, C-index 0.93) for 5-year CKD occurrence.
  • Identified three distinct CKD risk trajectories: gradual (75.9%), progressive (10.1%), and rapid increase (14.0%).
  • Progressive and rapid risk trajectories were significantly associated with increased risk of eGFR decline, cardiovascular events, and all-cause mortality post-CKD onset.

Conclusions:

  • Machine learning offers robust prediction of incident CKD risk.
  • Distinct CKD risk trajectories correlate with varying post-diagnosis outcomes, emphasizing the need for early risk stratification and monitoring.
  • Early identification of high-risk individuals can guide timely interventions to mitigate CKD progression and associated complications.