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Diagnostic Performance of a Molecular Test versus Clinician Assessment of Vaginitis.

ABSTRACT: Vaginitis is a common complaint, diagnosed either empirically or using Amsel's criteria and wet mount microscopy. This study sought to determine characteristics of an investigational test (a molecular test for vaginitis), compared to reference, for detection of bacterial vaginosis, Candida spp., and Trichomonas vaginalis Vaginal specimens from a cross-sectional study were obtained from 1,740 women (?18 years old), with vaginitis symptoms, during routine clinic visits (across 10 sites in the United States). Specimens were analyzed using a commercial PCR/fluorogenic probe-based investigational test that detects bacterial vaginosis, Candida spp., and Trichomonas vaginalis Clinician diagnosis and in-clinic testing (Amsel's test, potassium hydroxide preparation, and wet mount) were also employed to detect the three vaginitis causes. All testing methods were compared to the respective reference methods (Nugent Gram stain for bacterial vaginosis, detection of the Candida gene its2, and Trichomonas vaginalis culture). The investigational test, clinician diagnosis, and in-clinic testing were compared to reference methods for bacterial vaginosis, Candida spp., and Trichomonas vaginalis The investigational test resulted in significantly higher sensitivity and negative predictive value than clinician diagnosis or in-clinic testing. In addition, the investigational test showed a statistically higher overall percent agreement with each of the three reference methods than did clinician diagnosis or in-clinic testing. The investigational test showed significantly higher sensitivity for detecting vaginitis, involving more than one cause, than did clinician diagnosis. Taken together, these results suggest that a molecular investigational test can facilitate accurate detection of vaginitis.

SUBMITTER: Schwebke JR

PROVIDER: S-EPMC5971525 | biostudies-literature | 2018 Jun

REPOSITORIES: biostudies-literature

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Diagnostic Performance of a Molecular Test versus Clinician Assessment of Vaginitis.

Schwebke Jane R JR Gaydos Charlotte A CA Nyirjesy Paul P Paradis Sonia S Kodsi Salma S Cooper Charles K CK

Journal of clinical microbiology 20180525 6

Vaginitis is a common complaint, diagnosed either empirically or using Amsel's criteria and wet mount microscopy. This study sought to determine characteristics of an investigational test (a molecular test for vaginitis), compared to reference, for detection of bacterial vaginosis, <i>Candida</i> spp., and <i>Trichomonas vaginalis</i> Vaginal specimens from a cross-sectional study were obtained from 1,740 women (≥18 years old), with vaginitis symptoms, during routine clinic visits (across 10 sit ...[more]

PMID: 29643195

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Project description:Vaginitis may be diagnosed as bacterial vaginosis, vulvovaginal candidiasis, trichomoniasis, or coinfection. A new molecular test assays the vaginal microbiome and organisms that cause three common infections. The objective of the trial was to evaluate the clinical accuracy of the investigational test for vaginal swabs collected by patients (self) or clinicians. The primary and secondary outcomes were to compare the investigational test with reference methods for the three most common causes of vaginitis and compare clinician-collected with self-collected swabs.We conducted a cross-sectional study in which women with symptoms of vaginitis were recruited at ten clinical centers and consented to the investigation between May and September 2015. The woman collected a vaginal swab, sheathed, and then handed it to the clinician. These swabs were to evaluate how self-collected swabs compared with clinician-collected swabs. The clinician collected an investigational test swab and reference test swabs. From 1,740 symptomatic patients, clinician-collected and self-collected vaginal swabs were evaluated by the molecular test and six tests. The reference methods for bacterial vaginosis were Nugent's score and Amsel's criteria for intermediate Nugent results. The reference methods for Candida infection were isolation of any potential Candida microorganisms from inoculation of two culture media: chromogenic and Sabouraud agar and sequencing. The reference methods for trichomoniasis were wet mount and culture.For clinician-collected swabs, by reference methods, bacterial vaginosis was diagnosed in 56.5%, vaginal candidiasis in 32.8%, trichomoniasis in 8%, and none of the three infections in 24% with a coinfection rate of 20%. The investigational test sensitivity was 90.5% (95% confidence interval [CI] 88.3-92.2%) and specificity was 85.8% (95% CI 83.0-88.3%) for bacterial vaginosis. The investigational test sensitivity was 90.9% (95% CI 88.1-93.1%) and specificity was 94.1% (95% CI 92.6-95.4%) for the Candida group. Sensitivity for Candida glabrata was 75.9% (95% CI 57.9-87.8%) and specificity was 99.7% (95% CI 99.3-99.9%). Investigational test sensitivity was 93.1% (95% CI 87.4-96.3%) and specificity was 99.3% (95% CI 98.7-99.6%) for trichomoniasis. Results from self-collected swabs were similar to clinician-collected swabs.A molecular-based test using vaginal swabs collected by clinicians or patients can accurately diagnose most common bacterial, fungal, and protozoan causes of vaginitis. Women and their clinicians seeking accurate diagnosis and appropriate selection of efficacious treatment for symptoms of vaginitis might benefit from this molecular test.

Project description:ImportanceAn increasing number of machine learning (ML)-based clinical decision support systems (CDSSs) are described in the medical literature, but this research focuses almost entirely on comparing CDSS directly with clinicians (human vs computer). Little is known about the outcomes of these systems when used as adjuncts to human decision-making (human vs human with computer).ObjectivesTo conduct a systematic review to investigate the association between the interactive use of ML-based diagnostic CDSSs and clinician performance and to examine the extent of the CDSSs' human factors evaluation.Evidence reviewA search of MEDLINE, Embase, PsycINFO, and grey literature was conducted for the period between January 1, 2010, and May 31, 2019. Peer-reviewed studies published in English comparing human clinician performance with and without interactive use of an ML-based diagnostic CDSSs were included. All metrics used to assess human performance were considered as outcomes. The risk of bias was assessed using Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2) and Risk of Bias in Non-Randomised Studies-Intervention (ROBINS-I). Narrative summaries were produced for the main outcomes. Given the heterogeneity of medical conditions, outcomes of interest, and evaluation metrics, no meta-analysis was performed.FindingsA total of 8112 studies were initially retrieved and 5154 abstracts were screened; of these, 37 studies met the inclusion criteria. The median number of participating clinicians was 4 (interquartile range, 3-8). Of the 107 results that reported statistical significance, 54 (50%) were increased by the use of CDSSs, 4 (4%) were decreased, and 49 (46%) showed no change or an unclear change. In the subgroup of studies carried out in representative clinical settings, no association between the use of ML-based diagnostic CDSSs and improved clinician performance could be observed. Interobserver agreement was the commonly reported outcome whose change was the most strongly associated with CDSS use. Four studies (11%) reported on user feedback, and, in all but 1 case, clinicians decided to override at least some of the algorithms' recommendations. Twenty-eight studies (76%) were rated as having a high risk of bias in at least 1 of the 4 QUADAS-2 core domains, and 6 studies (16%) were considered to be at serious or critical risk of bias using ROBINS-I.Conclusions and relevanceThis systematic review found only sparse evidence that the use of ML-based CDSSs is associated with improved clinician diagnostic performance. Most studies had a low number of participants, were at high or unclear risk of bias, and showed little or no consideration for human factors. Caution should be exercised when estimating the current potential of ML to improve human diagnostic performance, and more comprehensive evaluation should be conducted before deploying ML-based CDSSs in clinical settings. The results highlight the importance of considering supported human decisions as end points rather than merely the stand-alone CDSSs outputs.

Dataset Information

Diagnostic Performance of a Molecular Test versus Clinician Assessment of Vaginitis.

Publications

Diagnostic Performance of a Molecular Test versus Clinician Assessment of Vaginitis.

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