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Related Concept Videos

Chronic Kidney Disease I: Introduction01:25

Chronic Kidney Disease I: Introduction

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Chronic Kidney Disease (CKD) arises when the kidneys progressively lose their ability to function, ultimately leading to end-stage renal disease. At this advanced stage, the kidneys can no longer filter waste or maintain essential body functions, requiring renal replacement therapy (RRT) through dialysis or a kidney transplant for survival.Early-stage chronic kidney disease and detection challengesIn CKD's early stages, symptoms often remain absent because healthy nephrons compensate for...
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Chronic kidney disease (CKD) requires collaborative and comprehensive management. CKD progresses through stages and can lead to end-stage kidney disease (ESKD) if untreated. Interprofessional collaboration and patient education are crucial, enabling patients to manage their health and improve their quality of life.Diagnostic approach for chronic kidney diseaseThe diagnosis of CKD primarily focuses on the glomerular filtration rate (GFR), which assesses kidney function by measuring how well...
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Nursing management is essential for preventing complications, maintaining stability, and improving patients' quality of life in chronic kidney disease (CKD). By using a structured approach, nurses help slow CKD progression and support effective patient care​.1. Comprehensive patient assessmentEffective management begins with nurses reviewing the patient’s medical history, and identifying key risk factors like diabetes, hypertension, and nephrotoxic drug use. Nurses assess signs of...
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Chronic Kidney Disease II: Clinical Manifestations01:24

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Chronic Kidney Disease (CKD) progressively impairs multiple body systems due to the accumulation of uremic toxins, which disrupt cellular functions across various organs.Neurologic symptomsNeurologic symptoms often arise early in CKD, as uremic toxin buildup drives changes in cognitive and motor functions. Patients frequently experience fatigue, headache, confusion, difficulty concentrating, and, in severe cases, seizures. Peripheral neuropathy commonly manifests as burning sensations in the...
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Drug Dosing in Renal Diseases: Estimation of Glomerular Filtration Rate Based on Serum Creatinine Concentration01:28

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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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Type 2 diabetes, characterized by insulin resistance, arises when the insulin receptors on cells lose responsiveness to insulin, diminishing the cell's capacity to take up glucose, resulting in elevated blood glucose levels. To receive a diagnosis of Type 2 diabetes, a series of blood glucose tests are necessary to assess whether the blood glucose falls within normal parameters. If the result is out of the normal range, a patient may be diagnosed as prediabetic or diabetic, depending on the...
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Updated: Jan 13, 2026

Comparative Proteomic Analysis of Whole Kidney, Medulla, and Cortical Tubules in Diabetic Pathogenesis of Kidney Injury in Mice
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Predicting Chronic Kidney Disease in Type 2 Diabetes Using Natural Language Processing on Healthcare Data.

Juan F Navarro-González1,2,3,4, Leopoldo Pérez de Isla5, Gloria Cánovas Molina6

  • 1Unidad de Investigación y Servicio de Nefrología, Hospital Universitario Nuestra Señora de Candelaria, Santa Cruz de Tenerife, Spain.

Kidney Diseases (Basel, Switzerland)
|January 9, 2026
PubMed
Summary
This summary is machine-generated.

Predicting chronic kidney disease (CKD) in type 2 diabetes mellitus (T2DM) is possible using unstructured electronic health record (EHR) data. This study developed a 2-year CKD risk model for T2DM patients, highlighting the need for better EHR data quality.

Keywords:
Chronic kidney diseaseElectronic health recordsMachine learningNatural language processingPredictive modelReal-world dataType 2 diabetes mellitus

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Area of Science:

  • Medical Informatics
  • Nephrology
  • Data Science

Background:

  • Type 2 diabetes mellitus (T2DM) patients frequently develop major complications like chronic kidney disease (CKD).
  • Electronic Health Records (EHRs) contain unstructured data valuable for predicting T2DM complications.
  • Early detection of CKD in T2DM is crucial for effective management and risk stratification.

Purpose of the Study:

  • To assess the utility of unstructured EHR data, processed via Natural Language Processing (NLP) and Machine Learning (ML), for developing a predictive model of CKD in T2DM patients.
  • To create and validate a 2-year predictive model for CKD development in individuals with T2DM.
  • To integrate the best-performing predictive model into a web-based tool for early CKD detection and risk stratification.

Main Methods:

  • A multicenter retrospective study utilizing EHR data from eight Spanish hospitals (2013-2018).
  • Data extraction and analysis employed NLP and ML techniques (EHRead®) based on SNOMED CT terminology.
  • A logistic regression model was trained and validated to predict 2-year CKD risk in T2DM patients without CKD at baseline.

Main Results:

  • The study included 316,597 individuals with T2DM for model development.
  • A logistic regression model using 27 predictors achieved an AUC of 0.72, with reduced 8- and 10-predictor models showing comparable performance.
  • A clinically refined 8-predictor model was selected for implementation in a web-based tool.

Conclusions:

  • Unstructured EHR data, processed with NLP and ML, can effectively develop a predictive model for 2-year CKD risk in T2DM patients.
  • The developed model aids in early detection and risk stratification of CKD in T2DM.
  • Improving the completeness of EHR data is essential for enhancing the accuracy and utility of future predictive models.