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Related Concept Videos

Dialysis01:27

Dialysis

309
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).
Acute kidney injury develops suddenly and can be caused by pre-renal causes (e.g., hypovolemia, shock), intrinsic renal causes (e.g., acute tubular necrosis), or post-renal causes (e.g., urinary obstruction). In contrast, chronic renal failure progresses gradually over time and is often...
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Factors Affecting Renal Clearance: Renal Impairment01:17

Factors Affecting Renal Clearance: Renal Impairment

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Renal dysfunction significantly impairs the renal clearance of drugs, leading to potential complications in drug therapy. Renal failure, which can be caused by various factors, poses a significant challenge in the elimination of drugs from the body.
One condition associated with renal failure is uremia. Uremia is characterized by impaired glomerular filtration and fluid accumulation in the body. This condition hinders the renal clearance of drugs, resulting in drug accumulation and potential...
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Antihypertensive Drugs: Direct Renin Inhibitors01:25

Antihypertensive Drugs: Direct Renin Inhibitors

629
The renin-angiotensin-aldosterone system (RAAS) is an intricate physiological pathway involving numerous enzymes and hormones, including renin, angiotensin-converting enzyme (ACE), angiotensin I and II, and aldosterone. Imbalances within this system increase the production of angiotensin II and aldosterone. Increased angiotensin II levels promote vasoconstriction and blood pressure elevation. Concurrently, higher aldosterone levels stimulate sodium and water reabsorption in the kidneys,...
629

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[Artificial intelligence and acute kidney injury].

Fabian Perschinka, Andreas Peer, Michael Joannidis1

  • 1Gemeinsame Einrichtung für Internistische Notfall- und Intensivmedizin, Department Innere Medizin, Medizinische Universität Innsbruck, Anichstraße 35, 6020, Innsbruck, Österreich. michael.joannidis@i-med.ac.at.

Medizinische Klinik, Intensivmedizin Und Notfallmedizin
|February 23, 2024
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Summary

Artificial intelligence (AI) shows promise in predicting and classifying acute kidney injury (AKI) in intensive care units. However, current AI models face challenges with data limitations and transparency, impacting physician trust and clinical implementation.

Keywords:
AKI PhenotypesAlgorithmsForecastingKidney failureMachine learning

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Area of Science:

  • Artificial intelligence (AI) applications in critical care medicine.
  • Focus on predictive and classification models for acute kidney injury (AKI).

Context:

  • Digitalization is transforming intensive care units (ICUs).
  • AI is being explored for predicting and phenotyping AKI in critically ill patients.
  • Current AI models primarily use serum creatinine and urinary output, with known limitations.

Purpose:

  • To evaluate the current state and challenges of AI in AKI prediction and classification.
  • To assess the performance of AI models using metrics like AUROC.
  • To identify AI-specific shortcomings hindering clinical integration.

Summary:

  • AI models for AKI prediction show variable performance (AUROC 0.650-0.900), influenced by prediction time and AKI criteria (KDIGO vs. AKIN).
  • Phenotyping by AI aids risk stratification for mortality and RRT but lacks etiological and therapeutic insights.
  • Limitations include inability to incorporate recent therapeutic changes/biomarkers and lack of model transparency, hindering physician trust.

Impact:

  • Successful AI integration in ICUs hinges on overcoming data limitations and enhancing model interpretability.
  • Physician trust is crucial for adopting AI-driven alerts for AKI.
  • Clinicians remain essential for patient management, integrating AI insights with clinical judgment.