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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 III: Interprofessional Care01:28

Chronic Kidney Disease III: Interprofessional Care

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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

Chronic Kidney Disease IV: Nursing Management

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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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Electrocardiogram01:29

Electrocardiogram

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An electrocardiogram (ECG or EKG) is a critical diagnostic tool that records the electrical signals produced by the heart during each heartbeat. This recording is achieved through electrodes placed strategically on the arms, legs, and chest. The electrocardiograph amplifies these signals and produces 12 distinct tracings, offering a comprehensive understanding of the heart's electrical activity.
Three major waveforms are present in a typical ECG recording: the P wave, the QRS complex, and...
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Chronic Kidney Disease II: Clinical Manifestations01:24

Chronic Kidney Disease II: Clinical Manifestations

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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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Acute Kidney Injury IV: Diagnostic Studies and Prevention01:30

Acute Kidney Injury IV: Diagnostic Studies and Prevention

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Accurate diagnosis and effective prevention are critical in managing Acute Kidney Injury (AKI), which is linked to high mortality rates ranging from 10% to 80%. Timely recognition of at-risk patients and careful monitoring can significantly reduce the likelihood of kidney damage.Diagnostic Assessments:The diagnostic process starts with a comprehensive medical history to identify prerenal, intrarenal, and postrenal causes.Prerenal causes, such as dehydration, hypotension, or blood loss, should...
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Related Experiment Video

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Deep learning-based electrocardiographic screening for chronic kidney disease.

Lauri Holmstrom1,2,3,4, Matthew Christensen1,4, Neal Yuan5

  • 1Department of Cardiology, Smidt Heart Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA.

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Summary

A new deep learning model can screen for chronic kidney disease (CKD) using electrocardiograms (ECGs). This AI tool shows promise for early CKD detection, especially in younger individuals and more advanced disease stages.

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Area of Science:

  • Artificial Intelligence in Medicine
  • Cardiology
  • Nephrology

Background:

  • Chronic kidney disease (CKD) is a widespread, often asymptomatic condition contributing to significant global morbidity and mortality.
  • Early detection of CKD is crucial for managing the disease and preventing severe complications.

Purpose of the Study:

  • To develop and validate a deep learning (DL) model for screening chronic kidney disease (CKD) using electrocardiogram (ECG) waveforms.
  • To assess the model's performance across different CKD stages and patient demographics.

Main Methods:

  • A DL model was developed and trained using a primary cohort of 111,370 patients with 247,655 ECGs.
  • The model was validated on an independent external cohort of 312,145 patients with 896,620 ECGs.
  • The model predicted the likelihood of a CKD diagnosis within one year of ECG acquisition.

Main Results:

  • The DL algorithm achieved an AUC of 0.767 in the test set and 0.709 in the external validation cohort for detecting any stage CKD.
  • Performance was consistent across CKD severity, with AUCs ranging from 0.753 to 0.783.
  • High performance was observed in patients under 60 years old (AUC 0.843 for 12-lead ECG).

Conclusions:

  • A deep learning algorithm effectively detects chronic kidney disease (CKD) from ECG waveforms.
  • The model demonstrates enhanced performance in younger patients and those with more severe CKD.
  • This ECG-based AI tool offers potential for augmenting current CKD screening strategies.