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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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Renal Failure: Dose Adjustments01:11

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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.
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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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Renal Drug Clearance: Comparison Between Renal Excretion Methods01:08

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Renal clearance is a critical parameter encompassing kidney filtration, secretion, and reabsorption processes. It is calculated using a specific equation to determine the rate at which the kidneys clear a drug.
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Renal Drug Clearance: Overview01:06

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Renal clearance is a crucial parameter in pharmacokinetics that quantifies the rate at which the kidneys excrete a drug. It represents a constant fraction of the central volume of distribution containing the drug that the kidney eliminates per unit of time.
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Kidney Transplant II: Surgical Procedure01:26

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Preoperative ManagementThe primary goals of preoperative management in kidney transplantation are to optimize the patient’s metabolic state and prepare them for surgery through diet adjustments, necessary dialysis, and tailored medical treatment. This phase also involves comprehensive infection screening and patient education about the surgical procedure and postoperative care to improve outcomes and adherence.Medical ManagementA comprehensive evaluation is required for both the living...
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Related Experiment Video

Updated: Jan 16, 2026

Normothermic Ex Vivo Kidney Perfusion for the Preservation of Kidney Grafts prior to Transplantation
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Predicting offer burden to optimize batch sizes in simultaneously expiring kidney offers.

Sean Berry1, Berk Görgülü2, Sait Tunç3

  • 1Department of Mechanical, Industrial and Mechatronics Engineering, Toronto Metropolitan University, Toronto, ON, Canada.

Frontiers in Artificial Intelligence
|October 6, 2025
PubMed
Summary

Machine learning models can predict kidney offers, reducing transplant delays. This improves kidney allocation efficiency by personalizing offer batches, benefiting both patients and professionals.

Keywords:
AI interpretabilitydecision supportmachine learningorgan nonusesimultaneously expiring offerssurvival models

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Area of Science:

  • Nephrology
  • Transplant Surgery
  • Data Science

Background:

  • Kidney allocation faces challenges with timely and efficient deceased donor kidney placement.
  • Traditional sequential offer systems result in significant delays and kidney nonuse.
  • Simultaneously expiring offers improve efficiency but fixed batch sizes can be problematic.

Purpose of the Study:

  • To develop and evaluate machine learning-based survival models for predicting the number of offers a deceased donor kidney requires for acceptance.
  • To optimize the efficiency of simultaneously expiring organ offer systems in kidney allocation.

Main Methods:

  • Utilized a national organ offer dataset of over 16,000 deceased donor kidneys.
  • Engineered predictive features from donor profiles and recipient pool characteristics.
  • Trained and evaluated multiple survival models, including Random Survival Forest, using time-dependent concordance indices.

Main Results:

  • The Random Survival Forest model achieved a high performance with a time-dependent C-index of 0.882.
  • Key predictors included waitlist characteristics like Estimated Post-Transplant Survival (EPTS) and time on dialysis.
  • Integration into a dynamic system could reduce average placement delays from 17.37 h to 1.59 h.

Conclusions:

  • Survival-based predictive modeling significantly enhances the efficiency of simultaneously expiring offers in kidney allocation.
  • Personalized batch sizes based on predicted offer burden reduce delays and notification overload for transplant professionals.
  • Data-driven tools are crucial for improving operational efficiency and practical implementation in organ allocation systems.