Dataset Information

Comparative study of Amsel's criteria and Nugent scoring for diagnosis of bacterial vaginosis in a tertiary care hospital, Nepal.

ABSTRACT:

Background

The most common pathological cause of abnormal vaginal discharge in reproductive-aged women is bacterial vaginosis (BV). Amsel's criteria and Nugent scoring systems are commonly employed approaches for the diagnosis of BV. Despite the Nugent scoring system being the gold standard method for diagnosing BV, Amsel's criteria are generally preferred in clinical setup owing to the fact Nugent scoring requires considerable time and expert microscopist. This study was conducted to determine the diagnostic value of Amsel's criteria by comparing it with the Nugent scoring system.

Methods

This was a descriptive cross-sectional study conducted at Tribhuvan University Teaching Hospital, Nepal from October 2016 to September 2017. Vaginal specimens were collected from a total of 141 women presenting with abnormal vaginal discharge. The sensitivity, specificity, positive predictive value, and negative predictive value of Amsel's criteria were calculated, and each component of Amsel's criteria was compared to the Nugent scoring system.

Results

The sensitivity, specificity, positive predictive value, and negative predictive value of Amsel's criteria were 50%, 98.2%, 87.5%, and 88.8% respectively. The clue cells showed 100% specificity and vaginal discharge with pH > 4.5 had 89.3% sensitivity while compared with Nugent's scoring system.

Conclusions

Amsel's criteria can be used as an adjunct method to Nugent scoring for the diagnosis of BV in the hands of skilled manpower in resources limited countries. The presence of clue cell and positive whiff test of Amsel's criteria shows good match with Nugent's score.

SUBMITTER: Bhujel R

PROVIDER: S-EPMC8369704 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:BackgroundOne of the most common and recurrent vaginal infections is bacterial vaginosis (BV). The diagnosis is based on changes to the "normal" vaginal microbiome; however, the normal microbiome appears to differ according to reproductive status and ethnicity, and even among individuals within these groups. The Amsel criteria and Nugent score test are widely used for diagnosing BV; however, these tests are based on different criteria, and so may indicate distinct changes in the vaginal microbial community. Nevertheless, few studies have compared the results of these test against metagenomics analysis.MethodsVaginal flora samples from 77 participants were classified according to the Amsel criteria and Nugent score test. The microbiota composition was analyzed using 16S ribosome RNA gene amplicon sequencing. Bioinformatics analysis and multivariate statistical analysis were used to evaluate the microbial diversity and function.ResultsOnly 3 % of the participants diagnosed BV negative using the Amsel criteria (A-) were BV-positive according to the Nugent score test (N+), while over half of the BV-positive patients using the Amsel criteria (A+) were BV-negative according to the Nugent score test (N-). Thirteen genera showed significant differences in distribution among BV status defined by BV tests (e.g., A - N-, A + N- and A + N+). Variations in the four most abundant taxa, Lactobacillus, Gardnerella, Prevotella, and Escherichia, were responsible for most of this dissimilarity. Furthermore, vaginal microbial diversity differed significantly among the three groups classified by the Nugent score test (N-, N+, and intermediate flora), but not between the Amsel criteria groups. Numerous predictive microbial functions, such as bacterial chemotaxis and bacterial invasion of epithelial cells, differed significantly among multiple BV test, but not between the A- and A+ groups.ConclusionsMetagenomics analysis can greatly expand our current understanding of vaginal microbial diversity in health and disease. Metagenomics profiling may also provide more reliable diagnostic criteria for BV testing.

Project description:Bacterial vaginosis (BV) is caused by the excessive and imbalanced growth of bacteria in vagina, affecting 30 to 50% of women. Gram staining followed by Nugent scoring based on bacterial morphotypes under the microscope is considered the gold standard for BV diagnosis; this method is often labor-intensive and time-consuming, and results vary from person to person. We developed and optimized a convolutional neural network (CNN) model and evaluated its ability to automatically identify and classify three categories of Nugent scores from microscope images. The CNN model was first established with a panel of microscopic images with Nugent scores determined by experts. The model was trained by minimizing the cross-entropy loss function and optimized by using a momentum optimizer. The separate test sets of images collected from three hospitals were evaluated by the CNN model. The CNN model consisted of 25 convolutional layers, 2 pooling layers, and a fully connected layer. The model obtained 82.4% sensitivity and 96.6% specificity with the 5,815 validation images when altered vaginal flora and BV were considered the positive samples, which was better than the rates achieved by top-level technologists and obstetricians in China. The capability of our model for generalization was so strong that it exhibited 75.1% accuracy in three categories of Nugent scores on the independent test set of 1,082 images, which was 6.6% higher than the average of three technologists, who are hold bachelor's degrees in medicine and are qualified to make diagnostic decisions. When three technologists ran one specimen in triplicate, the precision of three categories of Nugent scores was 54.0%. One hundred three samples diagnosed by two technologists on different days showed a repeatability of 90.3%. The CNN model outperformed human health care practitioners in terms of accuracy and stability for three categories of Nugent score diagnosis. The deep learning model may offer translational applications in automating diagnosis of bacterial vaginosis with proper supporting hardware.