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Procalcitonin for diagnosing infected pancreatic necrosis: a systematic review and meta-analysis

In brief

Procalcitonin detects infected pancreatic necrosis with 82% sensitivity and 84% specificity

Across 16 studies involving 2,156 patients, procalcitonin correctly identified about 82% of infected cases and correctly ruled out infection in about 84% of patients without it. Serial measurements performed better than a single admission test, but evidence certainty was low, so the proposed cutoff range needs prospective validation before guiding care.

Journal
BMC gastroenterology (Q2)
Published
13 August 2026
Study design
Systematic review / meta-analysis of RCTs
Evidence level
Level 1, High (CEBM 1a)
Authors
Tianrong He, Yangfan Zhang, Xiaoling Wen, Zuopeng Zhang
PMID
42881372
DOI
10.1186/s12876-026-05216-3

Why clinicians should know about it

  • Picked for Microbiology (medical) (top studies of the week, 18 October 2026): Procalcitonin diagnostic performance for infected necrosis
  • Picked for Biochemistry (medical) (top studies of the week, 18 October 2026): Procalcitonin diagnostic meta‑analysis for infected necrosis

Abstract

BACKGROUND: Infected pancreatic necrosis (IPN) ranks among the most severe local complications of acute pancreatitis (AP) and plays a central role in driving AP-related death. Procalcitonin (PCT), a serum biomarker that responds preferentially to bacterial infection, has theoretical promise for differentiating infected from sterile necrosis. Earlier systematic reviews, though, are limited by small sample sizes, shallow subgroup analysis, and a lack of formal evidence grading. An updated synthesis that focuses specifically on IPN as the clinical endpoint is missing. AIM: This meta-analysis primarily aimed to evaluate the diagnostic performance of PCT for detecting IPN in patients with AP. Prediction of severe acute pancreatitis (SAP) was assessed as a secondary analysis, and evidence on PCT-guided antibiotic therapy was summarized as an exploratory analysis. METHODS: PubMed, Embase, and the Cochrane Library were searched for original studies that assessed PCT for diagnosing IPN (primary objective), predicting severe AP (SAP; secondary analysis), or guiding antibiotic use in AP patients. Pooled sensitivity, specificity, likelihood ratios, and diagnostic odds ratios were calculated through a bivariate random-effects model. Hierarchical summary receiver operating characteristic (HSROC) curves were plotted. Pre-specified subgroup analyses covered PCT cutoff value, sampling time, study design, and reference standard. QUADAS-2 was used for bias assessment; evidence certainty was rated with the GRADE-DTA framework. RESULTS: Thirty-nine studies met inclusion criteria: 16 on PCT for IPN diagnosis (2,156 patients; 512 IPN cases), 20 on PCT for SAP prediction (3,386 patients; 934 SAP cases), and 3 randomized controlled trials on PCT-guided antibiotic therapy. For IPN diagnosis, pooled sensitivity was 0.82 (95% CI: 0.75-0.87), pooled specificity 0.84 (95% CI: 0.78-0.89), and area under the curve (AUC) 0.90 (95% CI: 0.87-0.92); GRADE certainty was low. For SAP prediction, pooled sensitivity was 0.83, specificity 0.76, AUC 0.87; evidence certainty was low. A cutoff of 1.0-3.5 ng/mL gave the best trade-off between sensitivity and specificity (AUC 0.92). Serial dynamic monitoring (AUC 0.93) outperformed single admission measurement (AUC 0.85) by a wide margin. CONCLUSIONS: PCT shows good overall ability to identify IPN and may be incorporated as an adjunctive biomarker for IPN risk stratification. A cutoff range of 1.0-3.5 ng/mL and serial monitoring may help clinical risk stratification. These cutoff-related findings require external validation in prospective multicenter studies with pre-specified cutoffs and standardized adjudicated reference criteria integrating clinical, radiologic, microbiologic, and procedural evidence, with culture confirmation incorporated when drainage or necrosectomy is clinically performed.

Abstract as published, via PubMed.

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For healthcare professionals. The summary is generated by AI from the published abstract, and the evidence level is assigned automatically from the study design on the Oxford CEBM hierarchy. Neither is medical advice. Read the full paper before changing practice.