High-risk symptoms and quantitative faecal immunochemical test accuracy: Systematic review and meta-analysis

BACKGROUND: The relative accuracy of interferon-gamma release assays (IGRAs) and the tuberculin skin test (TST) in identifying latent tuberculosis infection (LTBI) is uncertain.OBJECTIVE: To perform a systematic review and meta-analysis to compare the sensitivity and specificity of IGRAs and TST for the prediction of progression to clinical tuberculosis (TB).METHODS: We searched electronic databases (e.g., MEDLINE and EMBASE) from December 2009 to September 2018 for prospective studies that followed up individuals who had undergone testing with commercial IGRAs and/or TST but had not received treatment based on the test result. The sensitivity and specificity estimates were pooled using a Bayesian bivariate random-effects model.RESULTS: Twenty-five studies, mostly with moderate to high risk of bias and a mean follow-up time ranging from 1 to 5 years were included. TST (10–15 mm) tended to have lower sensitivity and higher specificity than QuantiFERON® Gold In-Tube, T-SPOT®.TB and TST (5 mm). The evidence did not indicate that any test outperformed the others due to wide and overlapping 95% credible intervals.CONCLUSION: The evidence following individuals who had undergone testing for LTBI and had progressed to clinical TB is sparse. We did not find that IGRAs were superior to TST or vice versa; however, as our findings are based on a small number of studies with methodological limitations and great uncertainty around the pooled estimates, the results should be interpreted with caution.

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RISK FACTORS FOR FALSE-POSITIVE AND FALSE-NEGATIVE FECAL IMMUNOCHEMICAL TEST RESULTS IN COLORECTAL CANCER SCREENING: SYSTEMATIC REVIEW AND META-ANALYSIS

10.1055/s-0038-1637308 ◽

2018 ◽

Author(s):

CM de Klerk ◽

LM Vendrig ◽

PM Bossuyt ◽

E Dekker

Keyword(s):

Colorectal Cancer ◽

Risk Factors ◽

Systematic Review ◽

Cancer Screening ◽

Colorectal Cancer Screening ◽

False Positive ◽

Meta Analysis ◽

False Negative ◽

Test Results ◽

Immunochemical Test

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SERUM GLIAL FIBRILLARY ACIDIC PROTEIN IN THE DIFFERENTIAL DIAGNOSIS OF INTRACEREBRAL HEMORRHAGE AND ACUTE ISCHEMIC STROKE: A SYSTEMATIC REVIEW AND DIAGNOSTIC TEST ACCURACY META-ANALYSIS

10.26226/morressier.58e389add462b802923855f8 ◽

2017 ◽

Author(s):

Aristeidis Katsanos

Keyword(s):

Systematic Review ◽

Differential Diagnosis ◽

Ischemic Stroke ◽

Glial Fibrillary Acidic Protein ◽

Intracerebral Hemorrhage ◽

Acute Ischemic Stroke ◽

Diagnostic Test ◽

Meta Analysis ◽

Diagnostic Test Accuracy ◽

Test Accuracy

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Faculty Opinions recommendation of Comparison of Abiraterone Acetate and Docetaxel with Androgen Deprivation Therapy in High-risk and Metastatic Hormone-naïve Prostate Cancer: A Systematic Review and Network Meta-analysis.

Faculty Opinions – Post-Publication Peer Review of the Biomedical Literature ◽

10.3410/f.732006685.793538242 ◽

2017 ◽

Author(s):

Michael Glode

Keyword(s):

Prostate Cancer ◽

Systematic Review ◽

High Risk ◽

Androgen Deprivation Therapy ◽

Meta Analysis ◽

Abiraterone Acetate ◽

Androgen Deprivation

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Diagnostic test accuracy for use of machine learning in diagnosis of autism spectrum disorder: A Systematic Review and Meta-Analysis (Preprint)

10.2196/preprints.14108 ◽

2019 ◽

Author(s):

Sun Jae Moon ◽

Jin Seub Hwang ◽

Rajesh Kana ◽

John Torous ◽

Jung Won Kim

Keyword(s):

Machine Learning ◽

Systematic Review ◽

Autism Spectrum Disorder ◽

Meta Analysis ◽

Learning Algorithms ◽

Structural Mri ◽

Autism Spectrum ◽

Machine Learning Algorithms ◽

Spectrum Disorder ◽

Test Accuracy

BACKGROUND Over the recent years, machine learning algorithms have been more widely and increasingly applied in biomedical fields. In particular, its application has been drawing more attention in the field of psychiatry, for instance, as diagnostic tests/tools for autism spectrum disorder. However, given its complexity and potential clinical implications, there is ongoing need for further research on its accuracy. OBJECTIVE The current study aims to summarize the evidence for the accuracy of use of machine learning algorithms in diagnosing autism spectrum disorder (ASD) through systematic review and meta-analysis. METHODS MEDLINE, Embase, CINAHL Complete (with OpenDissertations), PsyINFO and IEEE Xplore Digital Library databases were searched on November 28th, 2018. Studies, which used a machine learning algorithm partially or fully in classifying ASD from controls and provided accuracy measures, were included in our analysis. Bivariate random effects model was applied to the pooled data in meta-analysis. Subgroup analysis was used to investigate and resolve the source of heterogeneity between studies. True-positive, false-positive, false negative and true-negative values from individual studies were used to calculate the pooled sensitivity and specificity values, draw SROC curves, and obtain area under the curve (AUC) and partial AUC. RESULTS A total of 43 studies were included for the final analysis, of which meta-analysis was performed on 40 studies (53 samples with 12,128 participants). A structural MRI subgroup meta-analysis (12 samples with 1,776 participants) showed the sensitivity at 0.83 (95% CI-0.76 to 0.89), specificity at 0.84 (95% CI -0.74 to 0.91), and AUC/pAUC at 0.90/0.83. An fMRI/deep neural network (DNN) subgroup meta-analysis (five samples with 1,345 participants) showed the sensitivity at 0.69 (95% CI- 0.62 to 0.75), the specificity at 0.66 (95% CI -0.61 to 0.70), and AUC/pAUC at 0.71/0.67. CONCLUSIONS Machine learning algorithms that used structural MRI features in diagnosis of ASD were shown to have accuracy that is similar to currently used diagnostic tools.

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