Predictive Value of C-Reactive Protein for Major Complications after Major Abdominal Surgery: A Systematic Review and Pooled-Analysis

Jennifer Straatman, Annelieke M K Harmsen, Miguel A Cuesta, Johannes Berkhof, Elise P Jansma and Donald L van der Peet

PLOS ONE, 2015, vol. 10, issue 7, 1-14

Abstract: Background: Early diagnosis and treatment of complications after major abdominal surgery can decrease associated morbidity and mortality. Postoperative CRP levels have shown a strong correlation with complications. Aim of this systematic review and pooled-analysis was to assess postoperative values of CRP as a marker for major complications and construct a prediction model. Study design: A systematic review was performed for CRP levels as a predictor for complications after major abdominal surgery (MAS). Raw data was obtained from seven studies, including 1427 patients. A logit regression model assessed the probability of major complications as a function of CRP levels on the third postoperative day. Two practical cut-offs are proposed: an optimal cut-off for safe discharge in a fast track protocol and another for early identification of patients with increased risk for major complications. Results: A prediction model was calculated for major complications as a function of CRP levels on the third postoperative day. Based on the model several cut-offs for CRP are proposed. For instance, a two cut-off system may be applied, consisting of a safe discharge criterion with CRP levels below 75 mg/L, with a negative predictive value of 97.2%. A second cut-off is set at 215 mg/L (probability 20%) and serves as a predictor of complications, indicating additional CT-scan imaging. Conclusions: The present study provides insight in the interpretation of CRP levels after major abdominal surgery, proposing a prediction model for major complications as a function of CRP on postoperative day 3. Cut-offs for CRP may be implemented for safe early-discharge in a fast-track protocol and, secondly as a threshold for additional examinations, such as CT-scan imaging, even in absence of clinical signs, to confirm or exclude major complications. The prediction model allows for setting a cut-off at the discretion of individual surgeons or surgical departments.

Date: 2015
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Persistent link: https://EconPapers.repec.org/RePEc:plo:pone00:0132995

DOI: 10.1371/journal.pone.0132995

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