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Diagnostic performance of urinary IgG antibody detection: A novel approach for population screening of strongyloidiasis.

ABSTRACT: The diagnosis of strongyloidiasis by coprological methods has a low sensitivity, underestimating the prevalence of Strongyloides stercoralis in endemic areas. Serodiagnostic tests for strongyloidiasis have shown robust diagnostic properties. However, these methods require a blood draw, an invasive and labor-intensive sample collection method, especially in the resource-limited settings where S. stercoralis is endemic. Our study examines a urine-based assay for strongyloidiasis and compares its diagnostic accuracy with coprological and serological methods. Receiver operating characteristic (ROC) curve analyses determined the diagnostic sensitivity (D-Sn) and specificity (D-Sp) of the urine ELISA, as well as estimates its positive predictive value and diagnostic risk. The likelihood ratios of obtaining a positive test result (LR+) or a negative test result (LR-) were calculated for each diagnostic positivity threshold. The urine ELISA assay correlated significantly with the serological ELISA assay for strongyloidiasis, with a D-Sn of 92.7% and a D-Sp of 40.7%, when compared to coprological methods. Moreover, the urine ELISA IgG test had a detection rate of 69%, which far exceeds the coprological method (28%). The likelihood of a positive diagnosis of strongyloidiasis by the urine ELISA IgG test increased significantly with increasing units of IgG detected in urine. The urine ELISA IgG assay for strongyloidiasis assay has a diagnostic accuracy comparable to serological assay, both of which are more sensitive than coprological methods. Since the collection of urine is easy and non-invasive, the urine ELISA IgG assay for strongyloidiasis could be used to screen populations at risk for strongyloidiasis in S. stercoralis endemic areas.

SUBMITTER: Eamudomkarn C

PROVIDER: S-EPMC6037348 | biostudies-literature | 2018

REPOSITORIES: biostudies-literature

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Diagnostic performance of urinary IgG antibody detection: A novel approach for population screening of strongyloidiasis.

Eamudomkarn Chatanun C Sithithaworn Paiboon P Kamamia Christine C Yakovleva Anna A Sithithaworn Jiraporn J Kaewkes Sasithorn S Techasen Anchalee A Loilome Watcharin W Yongvanit Puangrat P Wangboon Chompunoot C Saichua Prasert P Itoh Makoto M M Bethony Jeffrey J

PloS one 20180709 7

The diagnosis of strongyloidiasis by coprological methods has a low sensitivity, underestimating the prevalence of Strongyloides stercoralis in endemic areas. Serodiagnostic tests for strongyloidiasis have shown robust diagnostic properties. However, these methods require a blood draw, an invasive and labor-intensive sample collection method, especially in the resource-limited settings where S. stercoralis is endemic. Our study examines a urine-based assay for strongyloidiasis and compares its d ...[more]

PMID: 29985913

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Project description:The World Health Organization has recommended prevaccination screening for prior dengue infection as the preferred approach prior to vaccination with the dengue vaccine CYD-TDV. These screening tests need to be highly specific and sensitive, and deliverable at the point-of-care. We evaluate here the sensitivity and specificity of the newly developed OnSite Dengue IgG rapid diagnostic test (RDT). A retrospective double-blind study of the sensitivity and specificity of the OnSite Dengue IgG RDT was performed using a sample panel consisting of archived serum specimens collected during CYD-TDV clinical trials in Latin American and Asia, with the reference serostatus for each sample determined by an algorithm using measured dengue PRNT90, PRNT50, and NS1 IgG ELISA. An additional panel of dengue seronegative samples positive for other flaviviruses and infections was used to assess cross-reactivity. Samples were included from 579 participants; 346 in the specificity panel and 233 in the sensitivity panel. The OnSite dengue IgG RDT exhibited a specificity of 98.0% (95% CI = 95.9 to 99.2) and sensitivity of 95.3% (95% CI = 91.7 to 97.6). The sensitivity for samples exhibiting a multitypic immune profile (PRNT90-positive to >1 dengue serotype) was 98.8% while for monotypic immune samples (PRNT90-positive to a single dengue serotype) it was 88.1%. The OnSite dengue IgG RDT showed minimal to no cross-reactivity to related flaviviruses. These findings support the use of the OnSite dengue IgG RDT to determine dengue serostatus in CYD-TDV prevaccination screening. IMPORTANCE Dengue remains a significant public health issue, with over 5.2 million cases reported to the World Health Organization (WHO) in 2019. The tetravalent dengue vaccine (CYD-TDV) is currently licensed for use in those aged ≥9 years; however, vaccinees with no previous exposure to dengue experience an increased risk of hospitalized and severe dengue upon subsequent heterotypic infection. Consequently, WHO recommends screening for prior dengue infection before vaccination. Screening tests for previous infection need to be highly specific and sensitive, and deliverable at the point-of-care. High sensitivity ensures that the largest number of individuals with previous infection can be identified and vaccinated, while high specificity prevents the inadvertent vaccination of those without previous infection. This study of the OnSite Dengue IgG Rapid Test, which was explicitly developed to meet this need, found that it had both high specificity (98.0% [95% CI = 95.9 to 99.2]) and sensitivity (95.3% [95% CI = 91.7 to 97.6]).

Project description:Purpose To determine the feasibility of using a deep learning approach to detect cartilage lesions (including cartilage softening, fibrillation, fissuring, focal defects, diffuse thinning due to cartilage degeneration, and acute cartilage injury) within the knee joint on MR images. Materials and Methods A fully automated deep learning-based cartilage lesion detection system was developed by using segmentation and classification convolutional neural networks (CNNs). Fat-suppressed T2-weighted fast spin-echo MRI data sets of the knee of 175 patients with knee pain were retrospectively analyzed by using the deep learning method. The reference standard for training the CNN classification was the interpretation provided by a fellowship-trained musculoskeletal radiologist of the presence or absence of a cartilage lesion within 17 395 small image patches placed on the articular surfaces of the femur and tibia. Receiver operating curve (ROC) analysis and the κ statistic were used to assess diagnostic performance and intraobserver agreement for detecting cartilage lesions for two individual evaluations performed by the cartilage lesion detection system. Results The sensitivity and specificity of the cartilage lesion detection system at the optimal threshold according to the Youden index were 84.1% and 85.2%, respectively, for evaluation 1 and 80.5% and 87.9%, respectively, for evaluation 2. Areas under the ROC curve were 0.917 and 0.914 for evaluations 1 and 2, respectively, indicating high overall diagnostic accuracy for detecting cartilage lesions. There was good intraobserver agreement between the two individual evaluations, with a κ of 0.76. Conclusion This study demonstrated the feasibility of using a fully automated deep learning-based cartilage lesion detection system to evaluate the articular cartilage of the knee joint with high diagnostic performance and good intraobserver agreement for detecting cartilage degeneration and acute cartilage injury. © RSNA, 2018 Online supplemental material is available for this article .

Dataset Information

Diagnostic performance of urinary IgG antibody detection: A novel approach for population screening of strongyloidiasis.

Publications

Diagnostic performance of urinary IgG antibody detection: A novel approach for population screening of strongyloidiasis.

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