# Systematic review and meta-analysis on the diagnostic accuracy of various detection methods for porcine reproductive and respiratory syndrome virus

## TL;DR

This study compares different methods for detecting a virus in pigs and finds that molecular techniques are most accurate.

## Contribution

A systematic review and meta-analysis comparing diagnostic accuracy of PRRSV detection methods, identifying molecular amplification as optimal.

## Key findings

- Molecular amplification techniques showed highest sensitivity (0.97) and specificity (0.99) for PRRSV detection.
- Convergent diagnostic technologies had high accuracy with an AUC of 0.9910 and diagnostic odds ratio of 503.74.
- Traditional immunological techniques had lower performance compared to molecular methods, with an AUC of 0.9686.

## Abstract

Currently, many detection methods for porcine reproductive and respiratory syndrome virus have been developed, However, the optimal laboratory diagnostic method remains controversial. To evaluate the diagnostic accuracy of PRRSV detection methods based on systematic reviews and meta-analyses, and to determine the optimal strategy for laboratory detection of PRRSV.

Articles published between 1 January 2015 and 1 January 2025 were retrieved from multiple databases. Based on different detection methods, the articles were divided into three categories: traditional immunological techniques, molecular amplification techniques, and convergent diagnostic technologies. The sensitivity and specificity of each study were calculated. Diagnostic accuracy was assessed using threshold value definitions, ROC curve analysis, and statistical methods. Meta-analysis was performed using a random-effects model and pooled SROC curves. Stratified analysis and meta-regression were used to address effect size variability caused by differences in detection targets, tissue samples tested, and control trial designs.

A total of 55 articles on traditional immunological techniques (involving 17,359 samples), 90 articles on molecular amplification techniques (involving 21,362 samples), and 14 articles on convergent diagnostic technologies (involving 1,289 samples) were included in the meta-analysis. In the 55 studies on traditional immunological techniques, the overall sensitivity was 0.93–0.94 (95% CI), and the overall specificity was 0.92 (95% CI). The area under the ROC curve (AUC) was 0.9686, with an overall diagnostic odds ratio of 115.23 (95% CI 71.38-186.01). In 90 studies on molecular amplification techniques, the overall sensitivity was 0.97 (95% CI 0.96–0.97), and the overall specificity was 0.99 (95% CI 0.99–0.99). The AUC was 0.9951, with an overall diagnostic odds ratio of 1540 (95% CI 883.97-2684.10). In 14 studies on convergent technologies, the overall sensitivity was 0.95 (95% CI 0.92–0.96), and the overall specificity was 0.98 (95% CI 0.96–0.99). The AUC was 0.9910, with an overall diagnostic odds ratio of 503.74 (95% CI 152.78-1660.88).

The systematic review and meta-analysis results indicate that traditional immunological techniques, molecular amplification techniques, and convergent diagnostic technologies all exhibit high sensitivity and specificity. Among the three technological platforms, molecular amplification techniques consistently yielded the highest point estimates for sensitivity, specificity, and AUC, along with a markedly higher diagnostic odds ratio.

The online version contains supplementary material available at 10.1186/s40813-025-00482-1.