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Related Concept Videos

Dialysis01:27

Dialysis

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Renal failure occurs when the kidneys lose their ability to filter waste products from the blood effectively. It can be classified into two types: acute renal failure (ARF) and chronic renal failure (CRF).
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Pulse rhythm01:30

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Pulse rhythm refers to the pattern of pulsations within specific intervals, offering valuable insights into the regularity or irregularity of the heart's beats as observed through the pattern of pulsation within specific intervals. A regular pulse exhibits a consistent heart rate with uniform waveforms and pulsation force, variations of which can be classified as normal, weak, or bounding.
Conversely, an irregular pulse pattern is termed dysrhythmia, stemming from disruptions in cardiac...
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Related Experiment Video

Updated: May 12, 2025

A Retrograde Implantation Approach for Peritoneal Dialysis Catheter Placement in Mice
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Explainable machine learning algorithm to predict cardiovascular event in patients undergoing peritoneal dialysis.

Qiqi Yan1,2, Guiling Liu1,2, Ruifeng Wang1,2

  • 1Department of Nephrology, The Second Affiliated Hospital of Anhui Medical University, Hefei, China.

BMC Medical Informatics and Decision Making
|April 23, 2025
PubMed
Summary
This summary is machine-generated.

The random survival forest (RSF) model shows superior performance in predicting cardiovascular events (CVE) for patients on peritoneal dialysis (PD). This machine learning approach aids in identifying high-risk individuals for better management.

Keywords:
Cardiovascular eventMachine learningPeritoneal dialysisPredictive modelRandom survival forest

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Area of Science:

  • Nephrology and Cardiovascular Medicine
  • Biostatistics and Machine Learning

Background:

  • Cardiovascular events (CVE) are a major cause of morbidity and mortality in patients undergoing peritoneal dialysis (PD).
  • Accurate prediction of CVE risk is crucial for timely intervention and improved patient outcomes.

Purpose of the Study:

  • To compare the predictive performance of machine learning algorithms (extreme gradient boosting and random survival forest) against Cox proportional hazard regression for cardiovascular events in PD patients.
  • To identify key predictors of CVE in this patient population.

Main Methods:

  • A cohort of 318 patients undergoing PD catheterization was retrospectively analyzed.
  • Patients were randomly assigned to training (70%) and validation (30%) sets.
  • Cox regression, XGBoost, and RSF models were developed and validated using time-dependent area under the curve (AUC) and concordance index (C-index).

Main Results:

  • The random survival forest (RSF) model demonstrated superior predictive performance with a C-index of 0.725 and favorable time-dependent AUC values (1-year: 0.812, 3-year: 0.836, 5-year: 0.706) in the validation set.
  • Key predictors identified included platelet count, age, 4-hour dialysate to creatinine ratio (4hD/Pcr), left atrium diameter, and left ventricular diameter.
  • A significant difference in cumulative CVE-free survival was observed between high-risk and low-risk groups stratified by the RSF model.

Conclusions:

  • The random survival forest (RSF) model offers a robust and potentially superior method for assessing cardiovascular event risk in patients on peritoneal dialysis.
  • This machine learning approach can aid clinicians in stratifying patients and implementing personalized preventive strategies.