EVALUATION STUDY
JOURNAL ARTICLE
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Association between acute kidney injury and serum procalcitonin levels and their diagnostic usefulness in critically ill patients.

Scientific Reports 2019 March 19
Procalcitonin (PCT) is a useful marker for the diagnosis of systemic inflammatory response syndrome. In addition, PCT is affected by renal function. However, few studies have investigated the relationship between PCT and the development of acute kidney injury (AKI). Hence, we investigated whether serum PCT levels at the time of admission were associated with the development of AKI and clinical outcomes. A total of 790 patients in whom PCT was measured on admission to the intensive care unit (ICU) were analyzed retrospectively. We attempted to investigate whether serum PCT levels measured at the time of admission could be used as a risk factor for the development of AKI in septic and nonseptic patients or as a risk factor for all-cause mortality, and diagnostic usefulness of PCT was further assessed. Serum PCT levels were significantly higher in patients with AKI than in those without AKI (P < 0.001). After multivariable adjustment for clinical factors, laboratory findings, and comorbidities, PCT as a continuous variable showed a significant association with AKI (OR 1.006, 95% CI [1.000-1.011]; P = 0.035). However, PCT was not effective in predicting mortality. The cut-off value of PCT for the prediction of AKI incidence was calculated to be 0.315 ng/ml, with sensitivity and specificity of 60.9% and 56.9%, respectively. The odds ratios (ORs) from an equation adjusted for optimum thresholds of PCT levels for developing AKI with and without sepsis were 2.422 (1.222-4.802, P = 0.011) and 1.798 (1.101-2.937, P = 0.019), respectively. However, there were no absolute differences between the pre- and posttest probabilities after including the PCT value for AKI development. This study suggests that the PCT value was higher in AKI patients than in non-AKI patients, but PCT measurement at the time of admission did not improve the prediction model for AKI.

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