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Related Concept Videos

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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Drug Dosing in Renal Diseases: Measurement of Serum Creatinine Concentration and Clearance01:25

Drug Dosing in Renal Diseases: Measurement of Serum Creatinine Concentration and Clearance

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In healthy individuals, serum creatinine levels remain stable due to a balance between its constant production—primarily from muscle metabolism—and renal excretion. Creatinine is freely filtered by the glomeruli, making it a valuable marker for estimating renal function. When the glomerular filtration rate (GFR) decreases, the kidneys can only eliminate less creatinine, causing serum levels to rise.Serum creatinine concentration is widely used to estimate creatinine clearance...
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Serum Studies: Renal Function Tests01:24

Serum Studies: Renal Function Tests

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Renal function tests are crucial for assessing kidney health, monitoring disease progression, and evaluating the kidneys' efficiency in waste elimination, fluid balance, and electrolyte regulation. These tests offer critical insights into kidney function, even though routine measurements may appear normal until there is a significant decline in the glomerular filtration rate or GFR. Typically, signs of kidney impairment only become evident when the GFR falls to about 50% of its normal level.
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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.
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...
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Determination of Renal Drug Clearance: Graphical and Midpoint Methods01:07

Determination of Renal Drug Clearance: Graphical and Midpoint Methods

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Renal clearance, a crucial parameter in pharmacokinetics, can be determined using two different methods: the graphical method and the midpoint method. These methods provide insights into the rate of drug excretion by the kidneys and aid in assessing renal function.
The graphical method involves plotting the rate of drug excretion in urine against the plasma drug concentration. By analyzing the graph, the clearance can be calculated and obtained. Drugs rapidly excreted by the kidneys exhibit a...
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Renal Clearance01:23

Renal Clearance

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The glomerular filtration rate (GFR) is a critical marker of kidney function, reflecting the efficiency of filtration by the glomeruli. Renal clearance of specific substances, such as inulin or creatinine, is commonly used to measure GFR.
Renal clearance refers to the volume of plasma cleared of a specific substance, such as creatinine, per unit of time. To measure clearance, urine samples are collected over a 24-hour period during each bladder voiding, followed by a single blood sample at the...
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Estimation of Baseline Serum Creatinine with Machine Learning.

Erina Ghosh1, Larry Eshelman1, Stephanie Lanius1

  • 1Philips Research North America, Cambridge, Massachusetts, USA.

American Journal of Nephrology
|September 27, 2021
PubMed
Summary

A new machine learning model accurately predicts baseline serum creatinine, improving acute kidney injury detection. This model shows lower error and higher correlation compared to traditional methods.

Keywords:
Acute kidney injuryBaseline serum creatinineChronic kidney diseaseKidney functionMachine learning

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Area of Science:

  • Nephrology
  • Medical Informatics
  • Machine Learning

Background:

  • Accurate baseline serum creatinine is crucial for detecting acute kidney injury (AKI).
  • Current methods for estimating baseline creatinine may lack precision.
  • Machine learning offers potential for improved predictive accuracy.

Purpose of the Study:

  • To develop and validate a machine learning model for predicting baseline serum creatinine.
  • To compare the model's performance against established estimation methods.

Main Methods:

  • A gradient boosting machine learning model was developed using patient data from Mayo Clinic ICUs.
  • Internal validation was performed at Mayo Clinic, and external validation used the MIMIC III ICU cohort.
  • Model performance was evaluated by comparing predicted baseline creatinine with measured levels and against the Modification of Diet in Renal Disease (MDRD) equation.

Main Results:

  • The study included 44,370 patients from Mayo Clinic and 6,112 from MIMIC III.
  • The machine learning model utilized features such as chronic kidney disease, weight, height, and age.
  • The model demonstrated significantly lower mean absolute error (MAE) and higher intraclass correlation coefficient (ICC) compared to MDRD backcalculation in both cohorts.

Conclusions:

  • Machine learning models can estimate baseline serum creatinine with superior accuracy.
  • This approach offers a more reliable method for predicting baseline creatinine, aiding in AKI detection.