The application of Bayesian network meta-analyses in our search resulted in a large number of included studies, allowing for a full recognition of the diagnostic performance between sepsis biomarkers. Overall, presepsin exhibited the best pooled sensitivities, whereas CD64 showed the best pooled specificity for both Sepsis-1 and Sepsis-3 detection. After adjusting for study quality, study populations, types of specimens, year of the study conducted and sponsorship, we found that CD64 showed the best-pooled sensitivities and specificities, and we provided suggested cutoffs for presepsin and procalcitonin.
This study has several strengths. First, it provides an up-to-date overview of the diagnostic performance of biomarkers in detecting either Sepsis-3 or Sepsis-1 by including a number of recent studies. In two of the previous systematic reviews and meta-analyses, presepsin was found to have a marginally superior sensitivity than procalcitonin in detecting Sepsis-1; however, the limited numbers of studies included in these meta-analyses prevent a conclusive comparison.[6, 7] Second, our work covered not only well-studied biomarkers, such as procalcitonin, but also some novel biomarkers such as CD64, thus contributing to the accumulating body of knowledge on their current utility. Third, the Bayesian network meta-analyses we applied allowed for the simultaneous pooling of sensitivities and specificities and provided well-quantified credible intervals for relative diagnostic performance, which strengthened the evidence, with a superior reliability than conventional methods (Appendix pp. 6–7). In previous meta-analyses that applied different methods to evaluate the performance, the pooled sensitivities and specificities were not stable and were sometimes contradictory (Appendix pp. 106–108). Fourth, we applied rigorous criteria to verify the quality of the studies, and thoroughly examined their influence on the diagnostic performance with Bayesian network meta-regressions. Therefore, we were able to evaluate the biomarkers more confidently. Lastly, an objective optimal cutoff was achieved after adjusting the heterogeneity with multivariable Bayesian network meta-regression. The optimal points were visualized as a locally weighted scatterplot smoothing plot.
In this study, we pooled together numerous studies evaluating the biomarkers for severe sepsis and Sepsis-3 to provide up-to-date evidence for clinicians to re-evaluate these biomarkers. The newly revised definition of Sepsis-3 reflects a paradigm shift from implicitly diagnosing a systemic infection to explicitly identifying a severe infection associated with mortality. The biomarkers that were found to be associated with systemic infections, therefore, are not necessarily predictive of Sepsis-3. Therefore, the diagnostic performance of current biomarkers, especially procalcitonin, should be re-evaluated. To date, only a few studies have started to re-evaluate the biomarkers for Sepsis-3.[27, 132, 135]
Presepsin has been studied most extensively during the past decade with various degrees of investigation into biomarkers for the detection of sepsis. By adding indirect comparisons, our Bayesian network meta-analysis indicates that presepsin has a significantly better sensitivity than procalcitonin for the detection of both Sepsis-1 and Sepsis-3. Furthermore, presepsin has similar specificities to procalcitonin in terms of the detection of both Sepsis-1 and Sepsis-3, which could also be used for the guidance of antimicrobial therapies.[141, 142] Presepsin also shows a greater sensitivity than the currently used screening tools, such as quick Sepsis-related Organ Failure Assessment (qSOFA) and the systemic inflammatory response syndrome (SIRS) criteria. In a systematic review and meta-analysis, the qSOFA score and SIRS criteria showed a pooled sensitivity of 0.51 and 0.29 for the detection of Sepsis-3, respectively.
Due to its extensively varied performance across the studies, the diagnostic value of procalcitonin for Sepsis-1 was undetermined. The use of different study selection criteria has prevented a homogeneous conclusion being drawn in previous meta-analyses of procalcitonin (Appendix pp. 106–108). A univariable meta-analysis containing 2,097 patients in 2007 proclaimed the rather insufficient diagnostic capacity of procalcitonin for Sepsis-1, targeting the general population but excluding non-adult patients, in addition to a limited spectrum of patients. On the other hand, a more recent bivariate meta-analysis containing 3,487 patients in 2013 found that procalcitonin had an excellent diagnostic capacity, targeting a more general but heterogeneous population by including paediatric patients and patients with septic shock, as well as studies with only non-infectious SIRS as the reference group. In our study, we define an adult population without burn patients and use the subgroups of Sepsis-1 and Sepsis-3 to perform a better comparison of the performance of the biomarkers.
The potential confounding results in terms of the performance evaluation of these biomarkers have been extensively evaluated in our study. Suboptimal quality, study populations, types of serum specimens, and sponsorships were found to significantly bias the results. After adjusting these biases, through the application of the multivariable Bayesian network meta-regression, surprisingly, CD64 became the biomarker that performed best in the detection of both Sepsis-1 and Sepsis-3.
Neutrophil CD64 is an Fcγ receptor commonly expressed on monocytes, as well as occasionally on polymorphonuclear leukocytes. With a remarkably higher specificity for differentiating infectious from non-infectious SIRS, the potential superiority of neutrophil CD64 compared to procalcitonin was implied for use in antimicrobial therapies. However, the lack of a unifiable measuring unit for neutrophil CD64 means that the estimation of the optimal cutoff will needed to be determined in a future study.[145, 146]
The evaluation of the optimal cutoffs could assist clinicians in their decision making. In theory, a relevant diagnosis could be inferred during the synthesis of data from the cutoff parameters of the hierarchical summary receiver operating characteristic (HSROC) model, or in other words, the back-transformed bivariate model. However, it was assumed that no covariates asymmetrically affected the overall diagnostic accuracies. This assumption, in practice, could be violated by either including patients with different disease severities or non-SIRS controls. Another attempt was previously made from the previous meta-analysis, which suggested the use of 0.5-2.0 ng/mL as a cutoff range for procalcitonin detecting Sepsis-1 by finding the interquartile range (IQR) across studies.[9, 147] In this study, we provided suggested cutoffs by visualization of the optimal points, with the adjusted values plotted on a locally weighted scatterplot smoothing plot, thereby avoiding the need for an assumption or an adjustment for the confounders.
A few methodological issues in this study were noted. First, a gold standard for the diagnosis of sepsis does not currently exist, although both Sepsis-3 and Sepsis-1 do provide operative definitions for the description of life-threatening organ failure status or systemic inflammatory response. However, with the current revised operational definition of Sepsis-3, clinicians would be able to treat their patients more accurately. Nonetheless, attempts to validate biomarkers for Sepsis-3 are still needed to draw more reliable conclusions.
Second, while the large number of the studies included in this review contributed to a better confidence, it also resulted in a higher heterogeneity. Our Bayesian network meta-regression suggested that those strictly implementing random selection processes were generally associated with a decreased sensitivity. It is also compulsory to investigate whether the exclusion of patients with other conditions, including malignancy, status post-operation, kidney diseases, and pregnancy, is associated with a significant influence on the diagnostic performance of biomarkers in the future.