Diagnostic accuracy of serological tests for dermatitis herpetiformis: systematic review and Bayesian meta-analysis

Abstract Background The diagnosis of Dermatitis Herpetiformis (DH) relies on both clinical and serological tests. Accurate diagnostic tools are critical for effective management. This systematic review and meta-analysis aimed to evaluate the diagnostic accuracy of serological tests for DH, focusing on the impact of gluten-free diet (GFD) status on test performance, using Direct Immunofluorescence (DIF) as the reference standard. Methods This review follows the PRISMA-DTA guidelines. The PICO framework was used to define the review question. Databases, including PubMed, SCOPUS, EMBASE, and Web of Science were comprehensively searched until July 14, 2023. The risk of bias was assessed using QUADAS-2. Bayesian meta-analyses were conducted using the MetaBayesDTA tool. Results A total of 25 studies were included in the systematic review, with 7 studies included in the meta-analysis. The pooled sensitivity (SE) and specificity (SP) estimates varied by test type and population characteristics, highlighting the significant impact of GFD status on test accuracy and false positivity rate. The meta-analysis on the sample that included patients with celiac disease (CeD) who had not adhered to a GFD, revealed that the ELISA for IgA- tTG (SE: 0.90, SE 95% CrI: 0.71–0.99; SP: 0.14, 95% CrI: 0.03–0.44) and IIF for IgA-EMA (SE: 0.89, 95% CrI: 0.32–0.99; SP: 0.13, 95% CrI: 0.01–0.71) demonstrated high sensitivity but low specificity. Instead, ELISA for IgA- eTG (SE: 0.46, 95% CrI: 0.05–0.91; SP: 0.60, 95% CrI: 0.14–0.95) exhibited a lower sensitivity but a higher specificity than others tests. Conclusions Selecting appropriate diagnostic tests based on clinical context is crucial for accurate DH diagnosis. Selecting appropriate diagnostic tests based on clinical context is crucial for accurate DH diagnosis. Incorporating the GFD status of patients and combining IIF for IgA EMA and ELISA for IgA tTG tests in diagnostic algorithms can improve sensitivity and specificity, leading to better patient outcomes. Future research should focus on standardizing study designs and improving patient selection methods to enhance diagnostic accuracy. Trial registration PROSPERO CRD42023444060.