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Drug Dosing in Renal Diseases: Estimation of Glomerular Filtration Rate Based on Serum Creatinine Concentration01:28

Drug Dosing in Renal Diseases: Estimation of Glomerular Filtration Rate Based on Serum Creatinine Concentration

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Glomerular filtration rate (GFR) can be estimated from serum creatinine using the modification of diet in renal disease (MDRD) formula or the chronic kidney disease–epidemiology collaboration (CKD–EPI) equation. Both methods are widely used in clinical practice to assess kidney function and guide treatment decisions.The MDRD equation does not require weight or height measurements and is normalized to the body surface area of 1.73 m², considered the average adult surface area.
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Factors Affecting Renal Clearance: Renal Impairment01:17

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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.
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Chronic Kidney Disease I: Introduction01:25

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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 (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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Drug Dosing in Renal Diseases: Dose Adjustments Based on Drug Clearance and Elimination Rate Constant01:25

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In patients with renal disease, dosage adjustments are necessary to maintain therapeutic plasma drug concentrations and prevent toxicity or subtherapeutic exposure. Renal impairment alters drug pharmacokinetics, especially in conditions like uremia, where changes such as prolonged elimination half-life and altered apparent volume of distribution can significantly affect drug disposition. These changes require careful modification of the dosing regimen to achieve the desired clinical...
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Related Experiment Video

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Ensemble machine learning for predicting renal function decline in chronic kidney disease: development and external

Hong Chen1, Yuping Huang1, Lizhen Chen2

  • 1Department of Nephrology, the 95th Hospital of Putian in China RongTong Medical Health Corporation, Putian, China.

Frontiers in Medicine
|November 12, 2025
PubMed
Summary
This summary is machine-generated.

A new machine learning model accurately predicts chronic kidney disease (CKD) progression, outperforming traditional methods. This tool enables early risk stratification for personalized CKD management.

Keywords:
chronic kidney disease progressionclinical decision supportmachine learningprecision nephrologyrisk prediction modeling

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Area of Science:

  • Nephrology
  • Artificial Intelligence in Medicine
  • Biostatistics

Background:

  • Chronic kidney disease (CKD) is a global health concern with significant morbidity and mortality.
  • Current predictive models for CKD progression often lack accuracy and generalizability.
  • Timely interventions are crucial for managing renal function decline in CKD patients.

Purpose of the Study:

  • To develop and validate an ensemble machine learning model for enhanced prediction of renal function decline in CKD patients.
  • To enable early and personalized interventions for CKD management.
  • To improve the accuracy and generalizability of CKD progression prediction.

Main Methods:

  • An ensemble machine learning model was developed using Random Forest, XGBoost, and LightGBM algorithms.
  • The model was trained and validated on data from 1,200 CKD patients, incorporating clinical, demographic, and laboratory data.
  • Performance was assessed using AUC, calibration metrics, and external validation across three medical centers.

Main Results:

  • The ensemble model achieved an AUC of 0.89, outperforming traditional Cox models (AUC: 0.82) and standard ML approaches (AUC: 0.85).
  • Key predictors included estimated glomerular filtration rate (eGFR), age, and urinary protein-creatinine ratio.
  • The model demonstrated excellent calibration and a 60.6% reduction in computational resource use.

Conclusions:

  • The developed machine learning model represents a significant advancement in predicting CKD progression.
  • It offers a reliable and generalizable tool for early risk stratification in CKD patients.
  • Integration into clinical workflows can facilitate proactive, data-driven interventions for improved CKD management.