Study selection and characteristics
The systematic literature search retrieved 2,742 articles. After initial screening by the title and abstract, 2,688 articles were excluded. The full texts of the remaining 49 articles were examined and 21 studies were further excluded. Hence, 18 studies (17 prospective studies and 1 retrospective observation) with cfDNA analysis in adult blood samples at admission or at enrollment (published between 2006 and 2022) were included in the current meta-analysis (figure 1). There were 9 and 6 studies from Europe and Eastern Asia, respectively, with 2 studies from North America and 1 study from Middle East Asia. The cfDNA detection methods, include ELISA (2 studies), spectroscopy (2 studies), fluorescent-based assay (6 studies), and qPCR (8 studies). The main characteristics of these studies are summarized in Table 1. A total of 1,850 participants (healthy volunteers, patients with non-sepsis, and patients with sepsis) were enrolled with the number of participants across studies ranging from 3 to 221 cases. There were 8 and 4 studies that examined the prognosis and diagnostic outcomes, respectively, and the remaining 4 studies simultaneously analyzed for both prognosis and diagnostic outcomes (table 1).
Quality of enrolled studies
The QUIPS and QUADAS-2 were used to evaluate prognostic and diagnostic studies, respectively (19, 20), as illustrated in figure 2. In the QUIPS measurement, 12 included prognostic studies (22-24, 26-29, 32-34, 37, 39) with sepsis-related mortality were evaluated. Study participation and outcome measurement bias was identified as a concern in > 50% of the included studies. Many studies failed to specify whether they followed consecutive or random enrollment and reported no exclusion criteria. For outcome measurement, the risk of bias occurred due to the failure to clarify the time of measurement for the outcomes. There was a high-risk concern in 3 studies due to records of the hospital or global mortality which possibly did not relate to sepsis (figure 2A).
Eight studies with diagnostic outcomes (25, 27, 31, 32, 34, 35, 37, 38) were evaluated using QUADAS-2 measurement. For the index test domain of risk of bias, greater than 50% of studies were scored as unclear as these studies reported mean biomarker levels rather than AUC data, but cut-offs were not calculated. As well, it is also unclear if the index test was interpreted while blinded to the patient outcome. One study assigned a high risk for reference standard due to critically ill patients being included regardless of bacteremia, but all of the patients were not positive for blood culture. Four studies demonstrated an unclear risk of flow and timing bias due to the inappropriate timing of sample collection. Studies stated that biomarker was taken upon admission of study enrollment, but this specific time interval may vary for each patient and affect cfDNA levels studies. In this study, none of the studies had high concerns for applicability with respect to the reference standard (figure 2B).
The difference in cfDNA concentration between sepsis and non-sepsis
A total of 13 studies involving 941 samples were used to differentiate cfDNA concentrations of sepsis versus control. For the comparison, 9 and 6 studies were the comparison between sepsis and the healthy volunteers (22, 25, 26, 29-32, 34, 38) and sepsis versus non-sepsis in ICU (23, 30, 31, 34, 36, 37), respectively. In parallel, there were 4 studies that compared patients with sepsis versus non-sepsis with systemic inflammatory response syndrome (SIRS) in ICU (25, 27, 32, 38). With a comparison to the healthy control, cfDNA levels were significantly higher in patients with sepsis (630 participants) with the standardized mean difference (SMD)=1.02 (95% CI 0.46–1.57) I2= 84; the mean difference (MD) = 345.15 ng/mL (95%CI 200.86-489.44) and I2= 94% (figure 3A). In comparison with the ICU control group, there was also an increase in cfDNA levels in patients with sepsis as evaluated from 221 patients; SMD=1.03 (95%CI 0.65-1.40) I2=35%; MD= 163.98 ng/mL (95%CI 75.28-252.68) I2= 87% (figure 3A). With the comparison between sepsis versus the ICU SIRS group, there were 297 patients with SMD=0.74 (95%CI 0.42-1.06) I2=20%; MD= 255.46 ng/mL (95%CI 1.74-509.18) I2= 79% (figure 3A). Among these 3 studies (27, 31, 37), the analysis also exhibited an elevated concentration of cfDNA in patients with severe sepsis when compared between severe sepsis versus sepsis (212 patients, SMD=0.59 (95% CI 0.18–1.01) I2= 0%; MD= 91.36 ng/mL (95% CI -11.70-194.42) I2= 47%) (figure 3B).
Predictive performance of cfDNA for sepsis related mortality
The predictive value of cfDNA on sepsis-related mortality was reported by 13 studies involving 936 patients. There were 8 studies (23, 24, 26, 29, 32, 34, 38, 40) with a comparison of cfDNA levels between survival versus non-survival groups and 9 studies (24, 26-29, 33, 34, 37, 39) reporting the area under the receiver operating characteristic curve (AUC) for prediction of mortality. Of these 8 studies, there have been reported that sepsis non-survivors demonstrated significantly higher cfDNA levels than sepsis survivors (595 patients; SMD=1.26 (95% CI 0.08–2.45); MD=1601.44 ng/mL (95%CI 509.00-2693.88) with significant heterogeneity between the studies (I2 = 95%)) (figure 4A). For prediction of the sepsis-related mortality, the combined AUC of prediction from 9 studies was 0.76 (95% CI 0.70–0.86) (figure 4B). Subgroup analysis of cfDNA measurement for sepsis-related mortality was also analyzed (table 2).
Table 2 subgroup analysis of cfDNA measurements of sepsis survivors compared to non-survivors
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)
Diagnostic capacity of cfDNA for the diagnosis of sepsis
The diagnostic capacity of cfDNA to distinguish between sepsis versus non-sepsis in ICU was represented in 8 studies (25, 27, 31, 32, 34, 35, 37, 38) involving 493 patients. Of these included studies, there was only 1 study demonstrating the AUC value for sepsis diagnosis (27). The forest plots of sensitivity and specificity of cfDNA for diagnosis of patients with sepsis and the summary receiver operating characteristic curve (SROC) with pooled diagnostic utility were shown (figure 5A-B). The respective values of pooled sensitivity and specificity were 0.81 (95% CI 0.75 - 0.86; I2= 0%) and 0.72 (95% CI 0.65 - 0.78; I2= 90.8%), respectively, with pooled calculating diagnostic odd ratio (DOR) at 25.03 (95% CI 5.48 – 114.43, I2= 80.3%) and the pooled diagnostic accuracy represented by AUC was 0.80 (95% CI 0.70-0.90, I2=91%) (figure 5C).
Additionally, there were studies with subgroup analysis based on the separation into sepsis, non-sepsis, and SIRS in ICU cases. As such, in sepsis versus non-sepsis of ICU cases, the pooled sensitivity and specificity were 0.81 (95% CI 0.73-0.88, I2=8.9%) and 0.92 (95% CI 0.85-0.97, I2=0%), respectively, with the pooled DOR and pooled AUC value at 47.45 (95% CI 19.27-116.79, I2=0%) and 0.85 (95% CI 0.74-0.96; I2=85%), respectively (figure 5A-C). For sepsis versus SIRS of ICU cases, the pooled sensitivity and specificity were 0.82 (95% CI 0.75-0.88, I2=2.2%) and 0.63 (95% CI 0.54-0.71, I2=93%), respectively, with the pooled DOR and the pooled AUC at 11.48 (95% CI 1.76-74.89, I2=83.5%) and 0.75 (95% CI 0.59-0.90; I2=91%), respectively (figure 5A-C).