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Factors Affecting Renal Clearance: Renal Impairment

Factors Affecting Renal Clearance: Renal Impairment

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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A Fully Automated Artificial Intelligence-Based Approach to Predict Renal Function After Radical or Partial

Nour Abdallah¹, Nityam Rathi¹, Nicholas Heller²

¹Glickman Urological and Kidney Institute, Cleveland, OH.

|February 6, 2025

Summary

This summary is machine-generated.

An artificial intelligence (AI) model accurately predicts postoperative kidney function, matching a clinical model. This AI tool offers a precise, automated method for estimating glomerular filtration rate (GFR) after kidney surgery.

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Early Detection of Drug-Induced Renal Hemodynamic Dysfunction Using Sonographic Technology in Rats

Early Detection of Drug-Induced Renal Hemodynamic Dysfunction Using Sonographic Technology in Rats

Published on: March 11, 2016

Area of Science:

Nephrology
Artificial Intelligence in Medicine
Medical Imaging

Background:

The American Urologic Association recommends estimating postoperative glomerular filtration rate (GFR) to guide kidney tumor treatment.
Partial nephrectomy (PN) is preferred over radical nephrectomy (RN) if GFR is predicted to fall below 45 ml/min/1.73 m² postoperatively.
Existing validated models for GFR prediction have limited clinical adoption.

Purpose of the Study:

To evaluate the accuracy of an artificial intelligence (AI) model in predicting postoperative GFR compared to a validated clinical model.
To assess the utility of AI in automating GFR estimation for patients undergoing nephrectomy.
To determine if AI prediction can match the performance of established clinical methods.

Main Methods:

A deep learning segmentation model was used to determine split-renal function from preoperative CT scans.
Postoperative GFR was estimated using AI-driven calculations based on preoperative GFR and split-renal function.
A validated clinical model was also used to estimate postoperative GFR.
Both AI and clinical model estimations were compared against measured postoperative GFR using correlation coefficients and logistic regression for predicting GFR < 45.

Main Results:

The AI model achieved a correlation coefficient of 0.75, while the clinical model achieved 0.77 when compared to measured postoperative GFR.
Both models demonstrated similar performance in predicting postoperative GFR < 45, with areas under the curve of 0.89 for AI and 0.9 for the clinical model.
The study included 300 patients undergoing nephrectomy for renal tumors.

Conclusions:

The fully automated AI model for predicting new baseline renal function is as accurate as a validated clinical model.
The AI approach eliminates the need for additional clinical details or manual measurements, streamlining the prediction process.
This AI tool offers a viable, automated alternative for postoperative GFR estimation in clinical practice.