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Urinary cell cycle biomarkers for the prediction of renal non-recovery in patients with septic acute kidney injury: a prospective study. | LitMetric

Urinary cell cycle biomarkers for the prediction of renal non-recovery in patients with septic acute kidney injury: a prospective study.

Clin Exp Nephrol

Department of Surgical Intensive Critical Unit, Beijing Chao-Yang Hospital, Capital Medical University, 8 Gongren Tiyuchang Nanlu, Chaoyang District, Beijing, 100020, China.

Published: December 2023

Background: Poor prognosis has been associated with the absence of renal recovery after acute kidney injury (AKI). This study aimed to investigate whether urinary biomarkers at 0 and 24 h could be used independently or in conjunction with a clinical model to predict renal non-recovery in septic AKI.

Methods: A prospective observational study was conducted to measure the urinary levels of insulin-like growth factor-binding protein 7 (IGFBP7) and tissue inhibitor of metalloproteinase-2 (TIMP-2) at the time of AKI diagnosis (0 h) and 24 h later. Renal non-recovery within 7 days was defined as the outcome. The predictive value of urinary biomarkers for renal non-recovery in septic AKI was assessed using the area under the curve (AUC).

Results: A total of 198 individuals with septic AKI were included in the final analysis. Among them, 38.9% (n = 77) did not experience renal recovery within 7 days. The combination of urinary IGFBP7 and TIMP-2 at the initial time point demonstrated prognostic value for non-recovery of renal function, with an AUC of 0.782. When [TIMP-2]*[IGFBP7] was measured at 0 h, the clinical prognostic model, incorporating AKI stage 2-3 and the non-renal sequential organ failure assessment score, showed an improved AUC of 0.822 (with a sensitivity of 88.3% and specificity of 59.5%).

Conclusions: The combination of urinary [TIMP-2]*[IGFBP7] at 0 h exhibited moderate predictive ability for renal non-recovery in cases of septic AKI. However, there is potential to enhance the prognostic capabilities of the [TIMP-2]*[IGFBP7]-clinical prediction model.

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Source
http://dx.doi.org/10.1007/s10157-023-02397-zDOI Listing

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