Indicators of Bacterial Vaginosis: A Systematic Literature Review - Udjianto; Noorhamdani; Rahardjo, Bambang; Zuhriyah, Lilik (South Eastern European Journal of Public Health, 2024)
Bacterial Vaginosis (BV) is a common vaginal infection with a prevalence of 23-29%. Accurate diagnosis and appropriate treatment are important to prevent complications. Antibiotic treatment often faces recurrence and resistance. This study aims to identify and explore indicators of BV diagnosis based on scientific literature in relation to the use of probiotics in the management of Bacterial Vaginosis, providing a comprehensive understanding of the methods and criteria for BV diagnosis. Systematic literature review was conducted through a comprehensive search and 1405 articles were obtained from the Scopus and Web of Science databases (2019-2024). Articles were selected based on inclusion and exclusion criteria, assessed for quality using MMAT, and 27 articles were extracted using NVivo. Thematic analysis was conducted to identify indicators of BV diagnosis. Various indicators were used in the diagnosis of BV, including Amsel criteria, Nugent score, vaginal pH, clinical symptoms, pro-inflammatory cytokines, clue cells, predominance of anaerobic bacteria, increased specific pathogenic bacteria, decreased Lactobacillary grade, and increased exfoliation of vaginal epithelium. Amsel criteria and Nugent score are most commonly used. Findings highlight the importance of using validated indicators to improve the accuracy of BV diagnosis. The implication is the need for standardization and optimization of diagnosis by utilizing a combination of indicators. Standardization of diagnosis based on valid indicators can aid appropriate treatment and prevention of complications. This study contributes to a comprehensive review of BV diagnosis indicators. Standardization of indicators and development of more accurate and efficient diagnostic approaches can improve vaginal health and quality of life for women. Further research is needed to strengthen the findings and explore potential new biomarkers as well as the integration of multi-omics and machine learning approaches.