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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 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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Acute Kidney Injury VI: Nursing Management01:22

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Acute Kidney Injury (AKI) results in an inability to maintain fluid, electrolyte, and acid-base balance. Effective nursing management is critical in improving patient outcomes and includes comprehensive patient assessment and targeted interventions.Comprehensive Patient AssessmentA detailed history collection is essential, focusing on any recent infections, nephrotoxic medication use, or chronic conditions such as hypertension and diabetes that may contribute to AKI. During the physical...
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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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A web-based machine-learning algorithm predicting postoperative acute kidney injury after total knee arthroplasty.

Sunho Ko1, Changwung Jo1, Chong Bum Chang2

  • 1Seoul National University College of Medicine, Seoul, South Korea.

Knee Surgery, Sports Traumatology, Arthroscopy : Official Journal of the ESSKA
|September 4, 2020
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Summary

A new web-based model predicts acute kidney injury (AKI) after total knee arthroplasty (TKA). Identifying risk factors helps surgeons prevent AKI and its progression to end-stage renal disease (ESRD).

Keywords:
Acute kidney injuryEnd-stage renal diseaseMachine learningPredictionTotal knee arthroplastyTotal knee replacement

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Area of Science:

  • Nephrology
  • Orthopedic Surgery
  • Medical Informatics

Background:

  • Acute kidney injury (AKI) is a significant complication following total knee arthroplasty (TKA).
  • AKI post-TKA is associated with an increased risk of progression to end-stage renal disease (ESRD).

Purpose of the Study:

  • To identify preoperative risk factors for AKI after TKA.
  • To develop and validate a web-based prediction model for postoperative AKI.
  • To assess the impact of AKI on the progression to ESRD.

Main Methods:

  • A cohort of 5757 patients undergoing TKA was analyzed.
  • A gradient boosting machine (GBM) model was developed using preoperative data from 5302 patients and validated on 455 patients.
  • Six key preoperative predictors were identified: serum creatinine, general anesthesia, male sex, ASA class >3, renin-angiotensin-aldosterone system inhibitor use, and no tranexamic acid use.

Main Results:

  • The developed model demonstrated good predictive performance (AUC 0.78, improved to 0.89 in external validation).
  • AKI patients had a 9.8-fold increased odds of progressing to ESRD compared to non-AKI patients.
  • The web-based model is accessible at https://safetka.net.

Conclusions:

  • A validated, web-based predictive model for AKI post-TKA was created using machine learning and six preoperative variables.
  • This tool can help improve both short- and long-term prognoses for TKA patients.
  • Preventing postoperative AKI is crucial to avoid progression to ESRD.