Combined detection of procalcitonin, heparin-binding protein, and interleukin-6 is a promising assay to diagnose and predict acute pancreatitis

Link to article at PubMed

J Clin Lab Anal. 2021 Jun 21:e23869. doi: 10.1002/jcla.23869. Online ahead of print.

ABSTRACT

BACKGROUND: Acute pancreatitis (AP), one of the most common clinical emergencies, is characterized by variable clinical features and inadequate diagnostic methods. At present, the commonly used indicators do not have high specificity and do not necessarily reflect disease severity. We therefore aimed to investigate diagnostic and prognostic value of plasma procalcitonin, heparin-binding protein, and interleukin-6 for acute pancreatitis by separate detection and joint detection.

METHODS: The study involved 451 participants, including 343 AP patients and 108 healthy controls. We analyzed the association of the three biomarkers with the severity and prognosis of AP.

RESULTS: A statistically significant increase in the mean plasma analyte levels was detected in the study group compared to the control group. Multivariate comparison showed that plasma levels of PCT, HBP, and IL-6 were all significantly different among the three groups at different sampling times (1st, 3rd, 7th, and 10th day of admission) (p < 0.01). The combination of the three indicators had significantly higher diagnostic value than either the individual markers or pairwise combinations (p < 0.001). The levels of the three were all significantly higher in severe acute pancreatitis (SAP) patients than in non-SAP patients (p < 0.001); meanwhile, patients with high levels had a worse prognosis than those with low levels (p < 0.05). In multivariate analysis adjusted for age and sex, high levels of PCT, HBP, and IL-6 were found to be independently associated with the development of AP.

CONCLUSIONS: It dramatically improved the diagnostic power of AP when PCT, HBP, and IL-6 were combined; high PCT, HBP, and IL-6 levels within 3 days of admission may be the potentially useful indicators for predicting SAP.

PMID:34151489 | DOI:10.1002/jcla.23869

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