Diagnostic value of CRP for predicting the severity of acute pancreatitis: a systematic review and meta-analysis

Abstract

Background C-reactive protein (CRP) is a pentameric protein commonly used as a biomarker of inflammation or stress response which can be obtained during routine blood tests. Therefore, we conducted a systematic review and meta-analysis to explore its ability to predict the severity of acute pancreatitis (AP). This meta-analysis was registered in the PROSPERO system (registration number: CRD42022353769). Methods 41 studies with 6156 cases of acute pancreatitis, retrieved from PubMed, Cochrane Library, Springer, and Embase databases, were incorporated. We calculated the pooled estimates for predicting the severity of acute pancreatitis based on CRP levels. We also calculated the combined negative likelihood ratio (NLR), combined positive likelihood ratio (PLR) and combined diagnostic odds ratio (DOR) using a bivariate mixed model. Sensitivity analysis was used to examine the robustness of the results. Factors associated with heterogeneity were identified by meta-regression analysis. A summary operating characteristic (SROC) curve was generated to assess the diagnostic value of CRP in predicting severe acute pancreatitis. Fagan’s test was used to calculate likelihood ratios and post-test probabilities, and publication bias was gauged by asymmetry tests. Results SROC analysis yielded an AUC of 0.85 (95%CI: 0.81–0.88) with a sensitivity of 0.76 (95%CI: 0.69–0.83) and specificity of 0.79 (95%CI: 0.74–0.83). The combined NLR, PLR and DOR were 0.30 (0.23–0.40), 3.66 (2.94–4.55) and 12.19 (8.05–18.44) respectively. Sensitivity analysis demonstrated the stability of our results after omitting any study. Finally, meta-regression analysis indicated that the description of the reference test, prospective design, blinding method and spectrum of the disease could account for heterogeneity in this meta-analysis. Conclusion CRP has significant value as a biomarker for assessing AP severity. Besides, other parameters such as patient history, physical signs, and imaging should be considered to determine disease severity.