Biomarkers for diagnosis of sepsis in patients with systemic inflammatory response syndrome: a systematic review and meta-analysis

Liu et al. SpringerPlus (2016) 5:2091 DOI 10.1186/s40064-016-3591-5 Open Access RESEARCH Biomarkers for diagnosis of sepsis in patients with systemic inflammatory response syndrome: a systematic review and meta‑analysis Yong Liu1†, Jun‑huan Hou2,3†, Qing Li2,3, Kui‑jun Chen2,3, Shu‑Nan Wang4 and Jian‑min Wang2,3* Abstract Background: Sepsis is one of the most common diseases that seriously threaten human health. Although a large number of markers related to sepsis have been reported in the last two decades, the diagnostic accuracy of these biomarkers remains unclear due to the lack of similar baselines among studies. Therefore, we conducted a large systematic review and meta-analysis to evaluate the diagnostic value of biomarkers from studies that included noninfectious systemic inflammatory response syndrome patients as a control group. Methods: We searched Medline, Embase and the reference lists of identified studies beginning in April 2014. The last retrieval was updated in September 2016. Results: Ultimately, 86 articles fulfilled the inclusion criteria. Sixty biomarkers and 10,438 subjects entered the final analysis. The areas under the receiver operating characteristic curves for the 7 most common biomarkers, including procalcitonin, C-reactive protein, interleukin 6, soluble triggering receptor expressed on myeloid cells-1, presepsin, lipopolysaccharide binding protein and CD64, were 0.85, 0.77, 0.79, 0.85, 0.88, 0.71 and 0.96, respectively. The remain‑ ing 53 biomarkers exhibited obvious variances in diagnostic value and methodological quality. Conclusions: Although some biomarkers displayed moderate or above moderate diagnostic value for sepsis, the limitations of the methodological quality and sample size may weaken these findings. Currently, we still lack an ideal biomarker to aid in the diagnosis of sepsis. In the future, biomarkers with better diagnostic value as well as a com‑ bined diagnosis using multiple biomarkers are expected to solve the challenge of the diagnosis of sepsis. Keywords: Biomarkers, Sepsis, Systemic inflammatory response syndrome, Diagnosis, Meta-analysis Background Epidemiological surveys indicate that sepsis is the leading cause of non-cardiac death in intensive care units and causes at least 30% of the deaths in patients who are septic (Levy et al. 2010). Along with the aging of the population, the incidence of sepsis shows an obvious increase in countries around the world (Wafaisade et al. 2011; Martin et al. 2003; Angus et al. 2001). An important aspect of *Correspondence: † Yong Liu and Jun-huan Hou contributed equally to this work 2 Research Institute of Surgery, Daping Hospital, Third Military Medical University, Chongqing 400042, People’s Republic of China Full list of author information is available at the end of the article improving survival rates in septic patients is early diagnosis, which is helpful to ensure timely treatment and to avoid deterioration of organ function. The classical method of diagnosis is based on signs of an inflammatory response and microbial cultures. However, doctors must wait for several days before getting culture results, and what is worse, negative culture results account for 30–40%. Because microbial cultures have the features of being time-consuming and having a low positive rate as well as being non-specific for systemic inflammatory response syndrome (SIRS), many patients may lose the opportunity of timely and effective treatment. Unlike microbial culture, biomarkers, primarily from the blood, © The Author(s) 2016. This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. Liu et al. SpringerPlus (2016) 5:2091 increase in the early stage of the inflammatory response and show different expression between non-infectious inflammation and sepsis. Over the last 20 years, many researchers have been dedicated to finding blood biomarkers for the early diagnosis of infection or sepsis, and they have obtained a substantial number of research results. However, due to the large amounts of experimental data and the inconsistency of the baselines among these studies, it is difficult for medical researchers and workers to make comparisons across various biomarkers or to identify biomarkers with potential diagnostic value. Therefore, we performed a large-scale meta-analysis to summarize potential biomarkers for the differential diagnosis between non-infectious SIRS and sepsis. Methods Literature search We conducted the first systematic retrieval from PubMed and Embase in April 2014. The basic retrieval scheme included the following three search keywords: ‘sepsis’, ‘systemic inflammatory response syndrome’ and ‘diagnosis’. Then, we excluded ‘review’, ‘erratum’, ‘editorial’ and ‘letter’ from the retrieval results. In addition, the reference lists of the included original studies and relevant meta-analysis articles were examined for any eligible documents that were missed. The last retrieval was updated in September 2016. The study protocol was approved by the ethics committee affiliated with Daping Hospital and did not require written informed consent from the patients. Selection criteria Articles were included if they evaluated the diagnostic accuracy of biomarkers for distinguishing patients with sepsis from those with non-infectious SIRS. Sepsis was defined as the coexistence of SIRS with infection, according to the diagnostic criteria proposed by the American College of Chest Physicians and the Society of Critical Care Medicine (Bone et al. 1992). We excluded articles that lacked non-infectious SIRS patients as a control group. We also eliminated studies with immunocompromised patients, hematologic patients or pediatric patients. Moreover, articles that could not provide sufficient data to build a 2 × 2 contingency table were likewise excluded. Data collection and quality assessment The data were extracted independently by two reviewers (YL and WX) using a pre-designed Microsoft Excel spreadsheet table that included the categories of methodological quality, methods of biomarker detection, features of the participants and results of diagnostic accuracy. If needed, the authors were contacted for any missing Page 2 of 10 information. We evaluated the quality of the included studies according to the Quality Assessment of Diagnostic Accuracy Studies (QUADAS). Because the analysis of the test results of the biomarkers did not involve clinical data, we omitted item 12 of QUADAS in the quality assessment. Discrepancies between the two reviewers were resolved by discussion with the third author (SHW). Data synthesis and statistical analysis The scheme of the systemati (...truncated)