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2000
Volume 27, Issue 7
  • ISSN: 1386-2073
  • E-ISSN: 1875-5402

Abstract

Background: Acute kidney injury (AKI) is one of the most severe complications of sepsis. This study was conducted to analyze the role of urinary neutrophil gelatinase-associated lipocalin (uNGAL), urinary kidney injury molecular-1 (uKIM-1), and urinary angiotensinogen (uAGT) in the early diagnosis and mortality prediction of septic AKI. Methods: The prospective study enrolled 80 sepsis patients in the ICU and 100 healthy individuals and divided patients into an AKI group and a non-AKI group. uNGAL, uKIM-1, uAGT, serum creatinine/procalcitonin/C-reaction protein, and other indicators were determined, and clinical prediction scores were recorded. The sensitivity and specificity of uNGAL, uKIM-1, and uAGT in diagnosis and mortality prediction were analyzed by the receiver operator characteristic (ROC) curve and the area under the curve (AUC). Results: uNGAL, uKIM-1, and uAGT levels were higher in sepsis patients than healthy controls, higher in AKI patients than non-AKI patients, and higher in AKI-2 and AKI-3 patients than AKI-1 patients. At 0 h after admission, the combined efficacy of three indicators in septic AKI diagnosis (ROC-AUC: 0.770; sensitivity: 82.5%; specificity: 70.0%) was better than a single indicator. At 24 h, uNGAL, uKIM-1, and uAGT levels were higher in sepsis non-survivals than survivals and higher in septic AKI non-survivals than septic AKI survivals. The combined efficacy of three indicators in the prediction of sepsis/septic AKI mortality (ROC-AUC: 0.828/0.847; sensitivity: 71.4%/100.0%; specificity: 82.7%/66.7%) was better than a single indicator. Conclusion: uNGAL, uKIM-1, and uAGT levels were increased in septic AKI, and their combination helped the early diagnosis and mortality prediction.

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/content/journals/cchts/10.2174/0113862073263073231011060142
2024-05-01
2025-07-13
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  • Article Type:
    Research Article
Keyword(s): acute kidney injury; early diagnosis; mortality; Sepsis; severity assessment; uAGT; uKIM-1; uNGAL
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