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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 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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Chronic Kidney Disease III: Interprofessional Care01:28

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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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Chronic Kidney Disease IV: Nursing Management01:18

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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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Purpose of Health Records I01:11

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The vital purpose of health records is to provide a complete and accurate account of a patient's medical history, including communication, diagnostic and therapeutic orders, care planning, research, and quality review.
Here's a breakdown of how health records serve these purposes:
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Purpose of Health Records II01:19

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Health records serve various essential purposes in the healthcare system. Here are some key purposes:
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Extracting Deep Phenotypes for Chronic Kidney Disease Using Electronic Health Records.

Duc Thanh Anh Luong1, Dinh Tran1, Wilson D Pace2

  • 1University at Buffalo.

EGEMS (Washington, DC)
|June 23, 2018
PubMed
Summary
This summary is machine-generated.

Identifying chronic kidney disease (CKD) subtypes from electronic health records (EHR) is crucial for personalized treatment. This study uses a probabilistic model to uncover distinct CKD phenotypes, aiding in better risk stratification and targeted therapies.

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Area of Science:

  • Nephrology
  • Biostatistics
  • Health Informatics

Background:

  • Chronic kidney disease (CKD) is a prevalent global health issue with variable progression rates.
  • Phenotypic subtyping of CKD is essential for improved risk stratification and tailored patient treatments.
  • Electronic health records (EHR) offer a rich data source for identifying CKD subtypes.

Purpose of the Study:

  • To leverage EHR data for identifying meaningful phenotypic subtypes of CKD.
  • To extract disease severity profiles for CKD while controlling for confounding factors.
  • To analyze the clinical relevance of discovered CKD subtypes.

Main Methods:

  • Utilized a probabilistic model to identify precise phenotypes from CKD patient EHR data.
  • Decomposed estimated glomerular filtration rate (eGFR) trajectories into disease subtype, covariate, and individual long-term/short-term effects.
  • Analyzed clinical relevance of identified disease subtypes.

Main Results:

  • Discovered distinct CKD disease subtypes using the probabilistic subtyping model.
  • Presented clinical health markers associated with identified CKD subtypes.
  • Demonstrated the utility of large EHR datasets for retrospective deep phenotype identification.

Conclusions:

  • The identified CKD subtypes and associated markers can inform risk prediction.
  • The probabilistic model provides a framework for deep phenotyping in CKD.
  • Further research can expand the model for enhanced CKD management.