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Acute Kidney Injury IV: Diagnostic Studies and Prevention01:30

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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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Acute Kidney Injury I: Introduction01:22

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Introduction:Acute Kidney Injury (AKI) describes a swift decrease in kidney function occurring over hours to days, characterized by the kidneys' failure to remove waste products from the bloodstream. This leads to dangerous complications like metabolic acidosis, fluid overload, and electrolyte imbalances, such as hyperkalemia, which can cause life-threatening arrhythmias. AKI is common in both hospital and outpatient settings, often triggered by dehydration, sepsis, or exposure to nephrotoxic...
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Acute Kidney Injury II: Pathophysiology01:29

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Acute kidney injury (AKI) causes are categorized into three primary categories based on the location of the injury: prerenal, intrarenal (or intrinsic), and postrenal causes. This classification guides clinical management and illustrates how different pathways can impair kidney function.Etiology and Pathophysiology of Acute Kidney Injury1. Prerenal causesEtiology: Prerenal Acute Kidney Injury, the most common type, occurs when reduced blood flow to the kidneys decreases filtration capacity...
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Acute Kidney Injury III: Clinical Manifestations01:29

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Acute Kidney Injury (AKI) progresses through distinct clinical phases: the oliguric, diuretic, and recovery phases, each marked by unique manifestations and challenges.Oliguric Phase:The oliguric phase is the initial stage of AKI, typically lasting 10 to 14 days. This phase is marked by a significant reduction in urine output, usually less than 400 mL per day, indicating decreased kidney function. Fluid retention is a prominent feature, leading to symptoms such as edema, hypertension, and...
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Acute Kidney Injury V: Interprofessional Care01:20

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Acute Kidney Injury (AKI) requires a collaborative healthcare approach to restore renal function and prevent complications. Essential management strategies involve monitoring fluid and electrolyte balance, adjusting medications, initiating dialysis when necessary, and providing nutritional support.Fluid and Electrolyte ManagementFluid Monitoring: Regularly monitoring body weight, central venous pressure, and urine output helps detect fluid imbalances early. Patient intake and output are...
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Related Experiment Video

Updated: Jan 11, 2026

A Large Animal Model for Acute Kidney Injury by Temporary Bilateral Renal Artery Occlusion
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Performance of Supervised Machine Learning Models for Cardiac Surgery-Associated Acute Kidney Injury in Children:

Orkun Baloglu1,2, Izzet T Akbasli1, Ayse Morca1

  • 1Department of Heart, Vascular, and Thoracic, Division of Cardiology and Cardiovascular Medicine, Children's Institute, Cleveland Clinic Children's Center for Artificial Intelligence (C4AI), Cleveland, OH.

Pediatric Critical Care Medicine : a Journal of the Society of Critical Care Medicine and the World Federation of Pediatric Intensive and Critical Care Societies
|November 10, 2025
PubMed
Summary
This summary is machine-generated.

Machine learning models for predicting cardiac surgery-associated acute kidney injury (CS-AKI) showed variable external performance. Generalizability challenges persist, likely due to center-specific practice variations in pediatric cardiac surgery patients.

Keywords:
acute kidney injurycardiac surgerycongenital heart diseasepediatricssupervised machine learning

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Area of Science:

  • Cardiology
  • Nephrology
  • Artificial Intelligence in Medicine

Background:

  • Cardiac surgery-associated acute kidney injury (CS-AKI) is a significant complication in pediatric patients.
  • Predictive models are crucial for early intervention and improved outcomes.

Purpose of the Study:

  • To develop and validate supervised machine learning (ML) models for predicting CS-AKI in pediatric patients.
  • To assess the generalizability of these ML models across different centers.

Main Methods:

  • Retrospective cohort analysis of 1100 pediatric patients undergoing cardiac surgery across four centers.
  • Development of 40 ML models using various algorithms and prediction outcomes (any CS-AKI, severe CS-AKI).
  • External validation using a separate center or random data split; SHapley Additive exPlanations for variable importance.

Main Results:

  • CS-AKI occurred in 49.1% of patients, with severe CS-AKI in 23.1%.
  • External validation performance varied widely across all 40 ML models for both any and severe CS-AKI.
  • Key predictors included preoperative serum creatinine, cardiopulmonary bypass, aortic cross-clamp duration, weight, and age.

Conclusions:

  • External performance of ML models for CS-AKI prediction varies, indicating challenges in generalizability.
  • Center-based differences in clinical practice may contribute to the observed variations in model performance.