scholarly journals Comparative accuracy of cervical cancer screening strategies in healthy asymptomatic women: a systematic review and network meta-analysis

2022 ◽  
Vol 12 (1) ◽  
Author(s):  
Teruhiko Terasawa ◽  
Satoyo Hosono ◽  
Seiju Sasaki ◽  
Keika Hoshi ◽  
Yuri Hamashima ◽  
...  
Keyword(s):  
Systematic Review ◽  
Meta Analysis ◽  
Intraepithelial Neoplasia ◽  
Test Accuracy ◽  
Cervical Intraepithelial Neoplasia Grade ◽  
Validity Assessment ◽  
Screening Strategies ◽  
Comparative Accuracy ◽  
Paired Design ◽  
Asymptomatic Women

AbstractTo compare all available accuracy data on screening strategies for identifying cervical intraepithelial neoplasia grade ≥ 2 in healthy asymptomatic women, we performed a systematic review and network meta-analysis. MEDLINE and EMBASE were searched up to October 2020 for paired-design studies of cytology and testing for high-risk genotypes of human papillomavirus (hrHPV). The methods used included a duplicate assessment of eligibility, double extraction of quantitative data, validity assessment, random-effects network meta-analysis of test accuracy, and GRADE rating. Twenty-seven prospective studies (185,269 subjects) were included. The combination of cytology (atypical squamous cells of undetermined significance or higher grades) and hrHPV testing (excepting genotyping for HPV 16 or 18 [HPV16/18]) with the either-positive criterion (OR rule) was the most sensitive/least specific, whereas the same combination with the both-positive criterion (AND rule) was the most specific/least sensitive. Compared with standalone cytology, non-HPV16/18 hrHPV assays were more sensitive/less specific. Two algorithms proposed for primary cytological testing or primary hrHPV testing were ranked in the middle as more sensitive/less specific than standalone cytology and the AND rule combinations but more specific/less sensitive than standalone hrHPV testing and the OR rule combination. Further research is needed to assess these results in population-relevant outcomes at the program level.

scholarly journals Accuracy of Smartphone Images of the Cervix After Acetic Acid Application for Diagnosing Cervical Intraepithelial Neoplasia Grade 2 or Greater in Women With Positive Cervical Screening: A Systematic Review and Meta-Analysis

2021 ◽  
pp. 1711-1721
Author(s):  
Emma R. Allanson ◽  
Natacha Phoolcharoen ◽  
Mila P. Salcedo ◽  
Bryan Fellman ◽  
Kathleen M. Schmeler
Keyword(s):  
Systematic Review ◽  
Acetic Acid ◽  
Positive Predictive Value ◽  
Cervical Intraepithelial Neoplasia ◽  
Predictive Value ◽  
Precancerous Lesions ◽  
Meta Analysis ◽  
Cervical Screening ◽  
Intraepithelial Neoplasia ◽  
Cervical Intraepithelial Neoplasia Grade

PURPOSE Smartphones are used in cervical screening for visual inspection after acetic acid or Lugol's iodine (VIA/VILI) application to capture and share images to improve the sensitivity and interobserver variability of VIA/VILI. We undertook a systematic review and meta-analysis assessing the diagnostic accuracy of smartphone images of the cervix at the time of VIA/VILI (termed S-VIA) in the detection of precancerous lesions in women undergoing cervical screening. METHODS This systematic review was conducted in accordance with Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. Studies from January 1, 2010, to June 30, 2020, were assessed. MEDLINE/PubMed, Embase, CINAHL, Cochrane, and LILACS were searched. Cohort and cross-sectional studies were considered. S-VIA was compared with the reference standard of histopathology. We excluded studies where additional technology was added to the smartphone including artificial intelligence, enhanced visual assessment, and other algorithms to automatically diagnose precancerous lesions. The primary outcome was the accuracy of S-VIA for the diagnosis of cervical intraepithelial neoplasia grade 2 or greater (CIN 2+). Data were extracted, and we plotted the sensitivity, specificity, negative predictive value, and positive predictive value of S-VIA using forest plots. This study was prospectively registered with The International Prospective Register of Systematic Reviews:CRD42020204024. RESULTS Six thousand three studies were screened, 71 full texts assessed, and eight studies met criteria for inclusion, with six included in the final meta-analysis. The sensitivity of S-VIA for the diagnosis of CIN 2+ was 74.56% (95% CI, 70.16 to 78.95; I2 61.30%), specificity was 61.75% (95% CI, 56.35 to 67.15; I2 95.00%), negative predictive value was 93.71% (95% CI, 92.81 to 94.61; I2 0%), and positive predictive value was 26.97% (95% CI, 24.13 to 29.81; I2 61.3%). CONCLUSION Our results suggest that S-VIA has accuracy in the detection of CIN 2+ and may provide additional support to health care providers delivering care in low-resource settings.

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

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.

scholarly journals Predictors of lost to follow up from antiretroviral therapy among adults in sub-Saharan Africa: a systematic review and meta-analysis

2021 ◽  
Vol 10 (1) ◽  
Author(s):  
Hafte Kahsay Kebede ◽  
Lillian Mwanri ◽  
Paul Ward ◽  
Hailay Abrha Gesesew
Keyword(s):  
Risk Factors ◽  
Systematic Review ◽  
Meta Analysis ◽  
Sub Saharan Africa ◽  
Hiv Care ◽  
Care Services ◽  
Validity Assessment ◽  
Sub Saharan ◽  
Lost To Follow Up

Abstract Background It is known that ‘drop out’ from human immunodeficiency virus (HIV) treatment, the so called lost-to-follow-up (LTFU) occurs to persons enrolled in HIV care services. However, in sub-Saharan Africa (SSA), the risk factors for the LTFU are not well understood. Methods We performed a systematic review and meta-analysis of risk factors for LTFU among adults living with HIV in SSA. A systematic search of literature using identified keywords and index terms was conducted across five databases: MEDLINE, PubMed, CINAHL, Scopus, and Web of Science. We included quantitative studies published in English from 2002 to 2019. The Joanna Briggs Institute Meta-Analysis of Statistics Assessment and Review Instrument (JBI-MAStARI) was used for methodological validity assessment and data extraction. Mantel Haenszel method using Revman-5 software was used for meta-analysis. We demonstrated the meta-analytic measure of association using pooled odds ratio (OR), 95% confidence interval (CI) and heterogeneity using I2 tests. Results Thirty studies met the search criteria and were included in the meta-analysis. Predictors of LTFU were: demographic factors including being: (i) a male (OR = 1.2, 95% CI 1.1–1.3, I2 = 59%), (ii) between 15 and 35 years old (OR = 1.3, 95% CI 1.1–1.3, I2 = 0%), (iii) unmarried (OR = 1.2, 95% CI 1.2–1.3, I2 = 21%), (iv) a rural dweller (OR = 2.01, 95% CI 1.5–2.7, I2 = 40%), (v) unemployed (OR = 1.2, 95% CI 1.04–1.4, I2 = 58%); (vi) diagnosed with behavioral factors including illegal drug use(OR = 13.5, 95% CI 7.2–25.5, I2 = 60%), alcohol drinking (OR = 2.9, 95% CI 1.9–4.4, I2 = 39%), and tobacco smoking (OR = 2.6, 95% CI 1.6–4.3, I2 = 74%); and clinical diagnosis of mental illness (OR = 3.4, 95% CI 2.2–5.2, I2 = 1%), bed ridden or ambulatory functional status (OR = 2.2, 95% CI 1.5–3.1, I2 = 74%), low CD4 count in the last visit (OR = 1.4, 95% CI 1.1–1.9, I2 = 75%), tuberculosis co-infection (OR = 1.2, 95% CI 1.02–1.4, I2 = 66%) and a history of opportunistic infections (OR = 2.5, 95% CI 1.7–2.8, I2 = 75%). Conclusions The current review identifies demographic, behavioral and clinical factors to be determinants of LTFU. We recommend strengthening of HIV care services in SSA targeting the aforementioned group of patients. Trial registration Protocol: the PROSPERO Registration Number is CRD42018114418

scholarly journals Evaluating the diagnostic test accuracy of molecular xenomonitoring methods for characterising community burden of lymphatic filariasis

2021 ◽  
Author(s):  
Joseph Pryce ◽  
Lisa J Reimer
Keyword(s):  
Systematic Review ◽  
Lymphatic Filariasis ◽  
Meta Analysis ◽  
Test Accuracy ◽  
Sample Sizes ◽  
Pathogen Dna ◽  
Low Sensitivity ◽  
The Relationship ◽  
Molecular Xenomonitoring ◽  
Strong Linear Relationship

Abstract Background Molecular xenomonitoring (MX), the detection of pathogen DNA in mosquitoes, is a recommended approach to support lymphatic filariasis (LF) elimination efforts. Potential roles of MX include detecting presence of LF in communities and quantifying progress towards elimination of the disease. However, the relationship between MX results and human prevalence is poorly understood. Methods :We conducted a systematic review and meta-analysis from all previously conducted studies that reported the prevalence of filarial DNA in wild-caught mosquitoes (MX rate) and the corresponding prevalence of microfilaria (mf) in humans. We calculated a pooled estimate of MX sensitivity for detecting positive communities at a range of mf prevalence values and mosquito sample sizes. We conducted a linear regression to evaluate the relationship between mf prevalence and MX rate. Results We identified 24 studies comprising 144 study communities. MX had an overall sensitivity of 98.3% (95% CI 41.5, 99.9%) and identified 28 positive communities that were negative in the mf survey. Low sensitivity in some studies was attributed to small mosquito sample sizes (<1,000) and very low mf prevalence (<0.25%). Human mf prevalence and mass drug administration status accounted for approximately half of the variation in MX rate (R 2 = 0.49, p<0.001). Data from longitudinal studies showed that, within a given study area, there is a strong linear relationship between MX rate and mf prevalence (R 2 = 0.78, p < 0.001). Conclusion MX shows clear potential as tool for detecting communities where LF is present and as a predictor of human mf prevalence.

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