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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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Analysis of Nephron Composition and Function in the Adult Zebrafish Kidney
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Renal function trajectory over time and adverse clinical outcomes.

Badrul Munir Sohel1, Nahid Rumana2, Masaki Ohsawa3

  • 1Centre for Public Health, Queen's University Belfast, Belfast, UK.

Clinical and Experimental Nephrology
|January 6, 2016
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Summary

Monitoring the rate of change in kidney function, not just static eGFR levels, can identify chronic kidney disease (CKD) patients at higher risk for adverse outcomes like End Stage Renal Disease (ESRD). This dynamic approach aids targeted interventions.

Keywords:
Adverse outcomeChronic kidney diseaseRenal function change

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Area of Science:

  • Nephrology
  • Public Health
  • Biostatistics

Background:

  • Chronic kidney disease (CKD) presents a significant global health burden, impacting morbidity, mortality, and healthcare systems.
  • Current CKD staging, based on estimated glomerular filtration rate (eGFR) or kidney damage markers, identifies many patients but lacks precision for targeted interventions.
  • Existing static definitions of kidney function fail to capture the progressive nature of CKD.

Purpose of the Study:

  • To explore the potential of assessing the rate of change in kidney function over time to identify high-risk CKD patients.
  • To summarize evidence supporting the concept of dynamic changes in kidney function and their impact on adverse outcomes.
  • To discuss the feasibility of utilizing longitudinal eGFR data in the context of digital health records.

Main Methods:

  • Review and synthesis of existing evidence on dynamic changes in kidney function.
  • Analysis of the relationship between the magnitude of kidney function change and future adverse outcomes.
  • Consideration of the role of digitalization in capturing longitudinal health data.

Main Results:

  • Dynamic changes in kidney function over time are a significant factor in predicting adverse outcomes.
  • The rate of decline in kidney function is a crucial metric for risk stratification.
  • Digital health records facilitate the collection of longitudinal data necessary for dynamic assessment.

Conclusions:

  • Assessing the rate of change in kidney function offers a more refined approach to identifying CKD patients at high risk for End Stage Renal Disease (ESRD), cardiovascular disease (CVD), and mortality.
  • This dynamic perspective complements static staging, enabling more personalized and effective clinical management strategies.
  • The integration of longitudinal data analysis into routine clinical practice is essential for proactive CKD management.