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Related Concept Videos

Factors Affecting Renal Clearance: Renal Impairment01:17

Factors Affecting Renal Clearance: Renal Impairment

61
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...
61

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Personalized Prediction of Long-Term Renal Function Prognosis Following Nephrectomy Using Interpretable Machine

Lingyu Xu1, Chenyu Li1,2, Shuang Gao3

  • 1Department of Nephrology, The Affiliated Hospital of Qingdao University, Qingdao, China.

JMIR Medical Informatics
|September 20, 2024
PubMed
Summary

Machine learning models predict acute kidney disease (AKD) and chronic kidney disease (CKD) after nephrectomy. An interpretable model identifies key risk factors, aiding personalized clinical strategies for renal function outcomes.

Keywords:
acute kidney diseaseacute kidney injurychronic kidney diseasemachine learningnephrectomy

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Area of Science:

  • Nephrology
  • Artificial Intelligence
  • Medical Informatics

Background:

  • Acute kidney injury (AKI) is a common complication after nephrectomy, with potential progression to acute kidney disease (AKD) and chronic kidney disease (CKD).
  • Predictive mechanisms for AKI to AKD/CKD transitions are not fully understood.
  • Interpretable machine learning (ML) offers insights into clinical features influencing long-term renal outcomes post-nephrectomy.

Purpose of the Study:

  • Evaluate postnephrectomy AKI, AKD, and CKD rates and analyze long-term renal outcome trajectories.
  • Interpret AKD and CKD predictive models using explainability algorithms (SHAP, LIME).
  • Develop a web-based tool for estimating AKD or CKD risk post-nephrectomy.

Main Methods:

  • Retrospective cohort study of 1559 patients undergoing nephrectomy (July 2012 - June 2019).
  • Eight ML algorithms were used to build predictive models for AKD and CKD, with data split into training, validation, and test sets.
  • Explainability plots (SHAP, LIME) and directed acyclic graphs were used for model interpretation; a web-based prediction tool was developed.

Main Results:

  • Incidence rates: AKI 21.7%, AKD 15.3%, CKD 10.6%.
  • Light Gradient-Boosting Machine (LightGBM) model showed high performance (AUC 0.97 for AKD, 0.96 for CKD).
  • Top AKD predictors: operative duration, hemoglobin, blood loss, urine protein, hematocrit. Top CKD predictors: baseline eGFR, pathology, renal function trajectories, age, total bilirubin.

Conclusions:

  • Interpretable ML models effectively identify patients at risk of AKD and CKD post-nephrectomy by highlighting critical features.
  • A web-based calculator utilizing the LightGBM model can support personalized, evidence-based clinical strategies for renal function management.