Evaluation of the Cepheid Xpert C. difficile diagnostic assay: an update meta-analysis

Abstract

Background: Accurate and rapid diagnosis of Clostridium difficile infection (CDI) is critical for effective patient management and implementation of infection control measures to prevent transmission.

Objectives: We updated our previous meta-analysis to provide a more reliable evidence base for the clinical diagnosis of Xpert C. difficile (Xpert C. difficile) assay.

Methods: We searched PubMed, EMBASE, Cochrane Library, Chinese National Knowledge Infrastructure (CNKI), and the Chinese Biomedical Literature Database (CBM) databases to identify studies according to predetermined criteria. STATA 13.0 software was used to analyze the tests for sensitivity, specificity, positive likelihood ratio, negative likelihood ratio, diagnostic odds ratio, and area under the summary receiver operating characteristic curves (AUC). QUADAS-2 was used to assess the quality of included studies with RevMan 5.2. Heterogeneity in accuracy measures was tested with Spearman correlation coefficient and chi-square. Meta-regressions and subgroup analyses were performed to figure out the potential sources of heterogeneity. Model diagnostics were used to evaluate the veracity of the data.

Results: A total of 26 studies were included in the meta-analysis. The pooled sensitivity (95% confidence intervals [CI]) for diagnosis was 0.97(0.95-0.98), and specificity was 0.96(0.95-0.97). The AUC was 0.99 (0.98-1.00). Model diagnostics confirmed the robustness of our meta-analysis's results. Significant heterogeneity was still observed when we pooled most of the accuracy measures of selected studies. Meta-regression and subgroup analyses showed that the sample size and type, ethnicity, and disease prevalence might be the conspicuous sources of heterogeneity.

Conclusions: The up-to-date meta-analysis showed the Xpert CD assay had good accuracy for detecting CDI. However, the diagnosis of CDI must combine clinical presentation with diagnostic testing to better answer the question of whether the patient actually has CDI in the future, and inclusion of preanalytical parameters and clinical outcomes in study design would provide a more objective evidence base.

Keywords: Clostridium infections; Meta-analysis; Nucleic acid amplification techniques.

Fig. 1

Flow chart of study selection

Fig. 2

Quality assessment of included studies

Fig. 3

Forest plots of the pooled sensitivity and specificity and SROC curve of Xpert CD for detection of CDI. a Forest plots of the pooled sensitivity and specificity. Each solid square represents an individual study. Error bars represent 95% CI. Diamond indicates the pooled sensitivity and specificity for all of the studies. b SROC curve

Fig. 4

Using of the likelihood ratio scatter matrix to aid in the decision of effect size. LUQ, left upper quadrant; RUQ, right upper quadrant; LLQ, left lower quadrant; RLQ, right lower quadrant; LRP, positive likelihood ratio; LRN, negative likelihood ratio

Fig. 5

Graphs for sensitivity analyses: a goodness of fit, b bivariate normality, c influence analysis, and evaluation of Xpert detection system of Clostridium difficile outlier detection

Fig. 6

Graph of Deeks’ funnel plot asymmetry test

Fig. 7

Univariate meta-regression and subgroup analysis for sensitivity and specificity. Factors with asterisk are potential sources of heterogeneity