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Related Concept Videos

Chronic Kidney Disease I: Introduction01:25

Chronic Kidney Disease I: Introduction

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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...
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Chronic Kidney Disease III: Interprofessional Care01:28

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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...
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Acute Kidney Injury I: Introduction01:22

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Introduction:Acute Kidney Injury (AKI) describes a swift decrease in kidney function occurring over hours to days, characterized by the kidneys' failure to remove waste products from the bloodstream. This leads to dangerous complications like metabolic acidosis, fluid overload, and electrolyte imbalances, such as hyperkalemia, which can cause life-threatening arrhythmias. AKI is common in both hospital and outpatient settings, often triggered by dehydration, sepsis, or exposure to nephrotoxic...
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Acute Kidney Injury IV: Diagnostic Studies and Prevention01:30

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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...
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Kidney Transplant I: Introduction01:28

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A kidney transplant is a surgical approach that involves replacing a non-functioning kidney with a healthy one from a donor. This procedure is often a treatment option for end-stage renal disease (ESRD) patients. The method requires careful recipient selection, including evaluating various medical and psychosocial factors. These criteria vary between transplant centers but generally include assessments of the patient's overall health, adherence to medical recommendations, and lifestyle...
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Acute Kidney Injury II: Pathophysiology01:29

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Acute kidney injury (AKI) causes are categorized into three primary categories based on the location of the injury: prerenal, intrarenal (or intrinsic), and postrenal causes. This classification guides clinical management and illustrates how different pathways can impair kidney function.Etiology and Pathophysiology of Acute Kidney Injury1. Prerenal causesEtiology: Prerenal Acute Kidney Injury, the most common type, occurs when reduced blood flow to the kidneys decreases filtration capacity...
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Related Experiment Video

Updated: Aug 31, 2025

Supervised Machine Learning for Semi-Quantification of Extracellular DNA in Glomerulonephritis
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Machine learning for risk stratification in kidney disease.

Faris F Gulamali1, Ashwin S Sawant, Girish N Nadkarni

  • 1Icahn School of Medicine at Mount Sinai, New York, New York, USA.

Current Opinion in Nephrology and Hypertension
|August 25, 2022
PubMed
Summary

Machine learning tools enhance chronic kidney disease risk stratification using genomics and electronic health records. These methods offer quantitative risk predictions and qualitative subphenotype characterization for personalized care.

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Area of Science:

  • Nephrology
  • Biomedical Informatics
  • Computational Biology

Background:

  • Chronic kidney disease (CKD) risk stratification is crucial for effective treatment and prevention.
  • Advancements in machine learning offer novel approaches to predict CKD progression and outcomes.

Purpose of the Study:

  • To review the role of machine learning (ML) in facilitating CKD risk stratification.
  • To identify how ML tools can be integrated into clinical practice for improved patient management.

Main Methods:

  • Genomics-based approaches, including polygenic risk scores using whole genome sequencing.
  • Multiomic methods integrating diverse biomarkers for prognostic trajectories.
  • Electronic Health Record (EHR)-based approaches using supervised and unsupervised ML.

Main Results:

  • Genomic and multiomic data provide quantitative relative risk and prognostic information.
  • Supervised ML on EHR data enables direct risk prediction from clinical information.
  • Unsupervised ML identifies distinct CKD subphenotypes for tailored care strategies.

Conclusions:

  • Machine learning, leveraging genomics and EHR data, significantly advances CKD risk stratification.
  • These ML tools provide both quantitative and qualitative insights for personalized medicine in nephrology.
  • Integrating ML into clinical workflows promises to optimize prevention and treatment strategies for CKD.