Use of Smartphones to Detect Diabetic Retinopathy: Scoping Review and Meta-Analysis of Diagnostic Test Accuracy Studies (Preprint)

BACKGROUND Diabetic retinopathy (DR), a common complication of diabetes mellitus, is the leading cause of impaired vision in adults worldwide. Smartphone ophthalmoscopy involves using a smartphone camera for digital retinal imaging. Utilizing smartphones to detect DR is potentially more affordable, accessible, and easier to use than conventional methods. OBJECTIVE This study aimed to determine the diagnostic accuracy of various smartphone ophthalmoscopy approaches for detecting DR in diabetic patients. METHODS We performed an electronic search on the Medical Literature Analysis and Retrieval System Online (MEDLINE), EMBASE, and Cochrane Library for literature published from January 2000 to November 2018. We included studies involving diabetic patients, which compared the diagnostic accuracy of smartphone ophthalmoscopy for detecting DR to an accurate or commonly employed reference standard, such as indirect ophthalmoscopy, slit-lamp biomicroscopy, and tabletop fundus photography. Two reviewers independently screened studies against the inclusion criteria, extracted data, and assessed the quality of included studies using the Quality Assessment of Diagnostic Accuracy Studies–2 tool, with disagreements resolved via consensus. Sensitivity and specificity were pooled using the random effects model. A summary receiver operating characteristic (SROC) curve was constructed. This review is reported in line with the Preferred Reporting Items for a Systematic Review and Meta-analysis of Diagnostic Test Accuracy Studies guidelines. RESULTS In all, nine studies involving 1430 participants were included. Most studies were of high quality, except one study with limited applicability because of its reference standard. The pooled sensitivity and specificity for detecting any DR was 87% (95% CI 74%-94%) and 94% (95% CI 81%-98%); mild nonproliferative DR (NPDR) was 39% (95% CI 10%-79%) and 95% (95% CI 91%-98%); moderate NPDR was 71% (95% CI 57%-81%) and 95% (95% CI 88%-98%); severe NPDR was 80% (95% CI 49%-94%) and 97% (95% CI 88%-99%); proliferative DR (PDR) was 92% (95% CI 79%-97%) and 99% (95% CI 96%-99%); diabetic macular edema was 79% (95% CI 63%-89%) and 93% (95% CI 82%-97%); and referral-warranted DR was 91% (95% CI 86%-94%) and 89% (95% CI 56%-98%). The area under SROC curve ranged from 0.879 to 0.979. The diagnostic odds ratio ranged from 11.3 to 1225. CONCLUSIONS We found heterogeneous evidence showing that smartphone ophthalmoscopy performs well in detecting DR. The diagnostic accuracy for PDR was highest. Future studies should standardize reference criteria and classification criteria and evaluate other available forms of smartphone ophthalmoscopy in primary care settings.

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Use of Smartphones to Detect Diabetic Retinopathy: Scoping Review and Meta-Analysis of Diagnostic Test Accuracy Studies

Journal of Medical Internet Research ◽

10.2196/16658 ◽

2020 ◽

Vol 22 (5) ◽

pp. e16658

Author(s):

Choon Han Tan ◽

Bhone Myint Kyaw ◽

Helen Smith ◽

Colin S Tan ◽

Lorainne Tudor Car

Keyword(s):

Diabetic Retinopathy ◽

Diagnostic Accuracy ◽

Diagnostic Test ◽

Sensitivity And Specificity ◽

Meta Analysis ◽

Diagnostic Test Accuracy ◽

Fundus Photography ◽

Diabetic Patients ◽

Test Accuracy ◽

Reference Standard

Background Diabetic retinopathy (DR), a common complication of diabetes mellitus, is the leading cause of impaired vision in adults worldwide. Smartphone ophthalmoscopy involves using a smartphone camera for digital retinal imaging. Utilizing smartphones to detect DR is potentially more affordable, accessible, and easier to use than conventional methods. Objective This study aimed to determine the diagnostic accuracy of various smartphone ophthalmoscopy approaches for detecting DR in diabetic patients. Methods We performed an electronic search on the Medical Literature Analysis and Retrieval System Online (MEDLINE), EMBASE, and Cochrane Library for literature published from January 2000 to November 2018. We included studies involving diabetic patients, which compared the diagnostic accuracy of smartphone ophthalmoscopy for detecting DR to an accurate or commonly employed reference standard, such as indirect ophthalmoscopy, slit-lamp biomicroscopy, and tabletop fundus photography. Two reviewers independently screened studies against the inclusion criteria, extracted data, and assessed the quality of included studies using the Quality Assessment of Diagnostic Accuracy Studies–2 tool, with disagreements resolved via consensus. Sensitivity and specificity were pooled using the random effects model. A summary receiver operating characteristic (SROC) curve was constructed. This review is reported in line with the Preferred Reporting Items for a Systematic Review and Meta-analysis of Diagnostic Test Accuracy Studies guidelines. Results In all, nine studies involving 1430 participants were included. Most studies were of high quality, except one study with limited applicability because of its reference standard. The pooled sensitivity and specificity for detecting any DR was 87% (95% CI 74%-94%) and 94% (95% CI 81%-98%); mild nonproliferative DR (NPDR) was 39% (95% CI 10%-79%) and 95% (95% CI 91%-98%); moderate NPDR was 71% (95% CI 57%-81%) and 95% (95% CI 88%-98%); severe NPDR was 80% (95% CI 49%-94%) and 97% (95% CI 88%-99%); proliferative DR (PDR) was 92% (95% CI 79%-97%) and 99% (95% CI 96%-99%); diabetic macular edema was 79% (95% CI 63%-89%) and 93% (95% CI 82%-97%); and referral-warranted DR was 91% (95% CI 86%-94%) and 89% (95% CI 56%-98%). The area under SROC curve ranged from 0.879 to 0.979. The diagnostic odds ratio ranged from 11.3 to 1225. Conclusions We found heterogeneous evidence showing that smartphone ophthalmoscopy performs well in detecting DR. The diagnostic accuracy for PDR was highest. Future studies should standardize reference criteria and classification criteria and evaluate other available forms of smartphone ophthalmoscopy in primary care settings.

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Use of smartphones for detecting diabetic retinopathy: a protocol for a scoping review of diagnostic test accuracy studies

BMJ Open ◽

10.1136/bmjopen-2018-028811 ◽

2019 ◽

Vol 9 (12) ◽

pp. e028811 ◽

Cited By ~ 1

Author(s):

Choon Han Tan ◽

Willie-Henri Quah ◽

Colin S H Tan ◽

Helen Smith ◽

Lorainne Tudor Car

Keyword(s):

Diabetic Retinopathy ◽

Diagnostic Test ◽

Scoping Review ◽

Diagnostic Test Accuracy ◽

Fundus Photography ◽

Primary Data ◽

Cochrane Library ◽

Future Research ◽

Diabetic Patients ◽

Test Accuracy

IntroductionDiabetic retinopathy (DR) is a common microvascular complication of diabetes mellitus and the leading cause of impaired vision in adults worldwide. Early detection and treatment for DR could improve patient outcomes. Traditional methods of detecting DR include the gold standard Early Treatment Diabetic Retinopathy Study seven standard fields fundus photography, ophthalmoscopy and slit-lamp biomicroscopy. These modalities can be expensive, difficult to access and require involvement of specialised healthcare professionals. With the development of mobile phone technology, there is a growing interest in their use for DR identification as this approach is potentially more affordable, accessible and easier to use. Smartphones can be employed in a variety of ways for ophthalmoscopy including the use of smartphone camera, various attachments and artificial intelligence for obtaining and grading of retinal images. The aim of this scoping review is to determine the diagnostic test accuracy of various smartphone ophthalmoscopy approaches for detecting DR in diabetic patients.Methods and analysisWe will perform an electronic search of MEDLINE, Embase and Cochrane Library for literature published from 2000 onwards. Two reviewers will independently analyse studies for eligibility and assess study quality using the QUADAS-2 tool. Data for a 2⨉2 contingency table will be extracted. If possible, we will pool sensitivity and specificity data using the random-effects model and construct a summary receiver operating characteristic curve. In case of high heterogeneity, we will present the findings narratively. Subgroup analysis and sensitivity analysis will be performed where appropriate.Ethics and disseminationThis scoping review aims to provide an overview of smartphone ophthalmoscopy in DR identification. It will present findings on the accuracy of smartphone ophthalmoscopy in detecting DR, identify gaps in the literature and provide recommendations for future research. This review does not require ethical approval as we will not collect primary data.

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Ultrasonography for the diagnosis of extra-cranial carotid occlusion – diagnostic test accuracy meta-analysis

VASA ◽

10.1024/0301-1526/a000850 ◽

2020 ◽

Vol 49 (3) ◽

pp. 195-204

Author(s):

Djamila M. Rojoa ◽

Ahmad Q. D. Lodhi ◽

Nikos Kontopodis ◽

Christos V. Ioannou ◽

Nicos Labropoulos ◽

...

Keyword(s):

Systematic Review ◽

Diagnostic Test ◽

Sensitivity And Specificity ◽

Meta Analysis ◽

Diagnostic Test Accuracy ◽

Carotid Occlusion ◽

Test Accuracy ◽

Free Text ◽

Correct Identification ◽

Reference Standard

Summary: Background: The correct diagnosis of internal carotid artery (ICA) occlusion is crucial as it limits unnecessary intervention, whereas correct identification of patients with severe ICA stenosis is paramount in decision making and selecting patients who would benefit from intervention. We aimed to evaluate the accuracy of ultrasonography (US) in the diagnosis of ICA occlusion. Methods: We conducted a systematic review in compliance with the Preferred Reporting Items for a Systematic Review and Meta-analysis (PRISMA) of diagnostic test accuracy studies. We interrogated electronic bibliographic sources using a combination of free text and thesaurus terms to identify studies assessing the diagnostic accuracy of US in ICA occlusion. We used a mixed-effects logistic regression bivariate model to estimate summary sensitivity and specificity. We developed hierarchical summary receiver operating characteristic (HSROC) curves. Results: We identified 23 studies reporting a total of 5,675 arteries of which 722 were proven to be occluded by the reference standard. The reference standard was digital subtraction or cerebral angiography in all but two studies, which used surgery to ascertain a carotid occlusion. The pooled estimates for sensitivity and specificity were 0.97 (95% confidence interval (CI) 0.94 to 0.99) and 0.99 (95% CI 0.98 to 1.00), respectively. The diagnostic odds ratio was 3,846.15 (95% CI 1,375.74 to 10,752.65). The positive and negative likelihood ratio were 114.71 (95% CI 58.84 to 223.63) and 0.03 (95% CI 0.01 to 0.06), respectively. Conclusions: US is a reliable and accurate method in diagnosing ICA occlusion. US can be used as a screening tool with cross-sectional imaging being reserved for ambiguous cases.

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Observed Differences in Diagnostic Test Accuracy between Patient Subgroups: Is It Real or Due to Reference Standard Misclassification?

Clinical Chemistry ◽

10.1373/clinchem.2007.087403 ◽

2007 ◽

Vol 53 (10) ◽

pp. 1725-1729 ◽

Cited By ~ 15

Author(s):

Corné Biesheuvel ◽

Les Irwig ◽

Patrick Bossuyt

Keyword(s):

Clinical Practice ◽

Diagnostic Test ◽

Sensitivity And Specificity ◽

Diagnostic Test Accuracy ◽

Test Accuracy ◽

Reference Standard ◽

Target Condition ◽

Test Sensitivity ◽

The Past ◽

Patient Subgroups

Abstract Before a new test is introduced in clinical practice, its accuracy should be assessed. In the past decade, researchers have put an increased emphasis on exploring differences in test sensitivity and specificity between patient subgroups. If the reference standard is imperfect and the prevalence of the target condition differs among subgroups, apparent differences in test sensitivity and specificity between subgroups may be caused by reference standard misclassification. We provide guidance on how to determine whether observed differences may be explained by reference standard misclassification. Such misclassification may be ascertained by examining how the apparent sensitivity and specificity change with the prevalence of the target condition in the subgroups.

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The diagnostic accuracy of isothermal nucleic acid point-of-care tests for human coronaviruses: A systematic review and meta-analysis

Scientific Reports ◽

10.1038/s41598-020-79237-7 ◽

2020 ◽

Vol 10 (1) ◽

Cited By ~ 1

Author(s):

Pakpoom Subsoontorn ◽

Manupat Lohitnavy ◽

Chuenjid Kongkaew

Keyword(s):

Systematic Review ◽

Diagnostic Accuracy ◽

Diagnostic Test ◽

Point Of Care ◽

Meta Analysis ◽

Isothermal Amplification ◽

Diagnostic Test Accuracy ◽

Test Accuracy ◽

Index Test ◽

Point Of Care Tests

AbstractMany recent studies reported coronavirus point-of-care tests (POCTs) based on isothermal amplification. However, the performances of these tests have not been systematically evaluated. Cochrane Handbook for Systematic Reviews of Diagnostic Test Accuracy was used as a guideline for conducting this systematic review. We searched peer-reviewed and preprint articles in PubMed, BioRxiv and MedRxiv up to 28 September 2020 to identify studies that provide data to calculate sensitivity, specificity and diagnostic odds ratio (DOR). Quality Assessment of Diagnostic Accuracy Studies 2 (QUADAS-2) was applied for assessing quality of included studies and Preferred Reporting Items for a Systematic Review and Meta-analysis of Diagnostic Test Accuracy Studies (PRISMA-DTA) was followed for reporting. We included 81 studies from 65 research articles on POCTs of SARS, MERS and COVID-19. Most studies had high risk of patient selection and index test bias but low risk in other domains. Diagnostic specificities were high (> 0.95) for included studies while sensitivities varied depending on type of assays and sample used. Most studies (n = 51) used reverse transcription loop-mediated isothermal amplification (RT-LAMP) to diagnose coronaviruses. RT-LAMP of RNA purified from COVID-19 patient samples had pooled sensitivity at 0.94 (95% CI: 0.90–0.96). RT-LAMP of crude samples had substantially lower sensitivity at 0.78 (95% CI: 0.65–0.87). Abbott ID Now performance was similar to RT-LAMP of crude samples. Diagnostic performances by CRISPR and RT-LAMP on purified RNA were similar. Other diagnostic platforms including RT- recombinase assisted amplification (RT-RAA) and SAMBA-II also offered high sensitivity (> 0.95). Future studies should focus on the use of un-bias patient cohorts, double-blinded index test and detection assays that do not require RNA extraction.

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Metadta: A Stata Command for Meta-analysis and Meta-regression of Diagnostic Test Accuracy Data – A Tutorial.

10.21203/rs.3.rs-721114/v1 ◽

2021 ◽

Author(s):

Victoria Nyawira Nyaga ◽

Marc Arbyn

Keyword(s):

Bladder Cancer ◽

Diagnostic Test ◽

Sensitivity And Specificity ◽

Fixed Effects ◽

Meta Analysis ◽

Diagnostic Test Accuracy ◽

Forest Plot ◽

Test Accuracy ◽

Statistical Procedures ◽

Meta Regression

Abstract BackgroundAlthough statistical procedures for pooling of several epidemiological metrics are generally available in statistical packages, those for meta-analysis of diagnostic test accuracy studies including options for multivariate regression are lacking. Fitting regression models and the processing of the estimates often entails lengthy and tedious calculations. Therefore, packaging appropriate statistical procedures in a robust and user-friendly program is of great interest to the scientific community. Methodsmetadta is a statistical program for pooling of diagnostic accuracy test data in Stata. It implements both the bivariate random-effects and fixed-effects model, allows for meta-regression, and presents the results in tables, a forest plot and/or summary receiver operating characteristic (SROC) plot. For a model without covariates, it also quantifies heterogeneity using an I2 statistic that accounts for the mean-variance relationship, and correlation between sensitivity and specificity, a typical characteristic of diagnostic data. To demonstrate metadta, we applied the program on two published meta-analyses on: 1) the sensitivity and specificity of cytology and other markers including telomerase for primary diagnosis of bladder cancer; and 2) the accuracy of human papillomavirus testing on self-collected versus clinician-collected samples to detect cervical precancer.ResultsWithout requiring a continuity correction, metadta generated a pooled sensitivity and specificity of 0.77 [95% CI: 0.70, 0.82] and 0.91 [95% CI: 0.75, 0.97] respectively of telomerase for the diagnosis of primary bladder cancer. metadta allowed to assess the relative accuracy of human Papilloma virus (HPV) testing on self- versus clinician-taken specimens in matched studies taking into account two covariates. Under the condition of using assays based on target-amplification, HPV tests were similarly sensitive to detect cervical pre-cancer, irrespective of clinical setting. ConclusionThe metadta program implements state of art statistical procedures in an attempt to close the gap between methodological statisticians and systematic reviewers. With metadta, we hope to popularize even further, the use of appropriate statistical methods for diagnostic meta-analysis.

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A tutorial for meta-analysis of diagnostic tests for low-prevalence diseases: Bayesian models and software

Methodology ◽

10.5964/meth.4015 ◽

2020 ◽

Vol 16 (3) ◽

pp. 258-277

Author(s):

Johny J. Pambabay-Calero ◽

Sergio A. Bauz-Olvera ◽

Ana B. Nieto-Librero ◽

Maria Purificación Galindo-Villardón ◽

Ana B. Sánchez-García

Keyword(s):

Diagnostic Test ◽

Sensitivity And Specificity ◽

Meta Analysis ◽

Diagnostic Test Accuracy ◽

Mixed Effects Models ◽

Test Accuracy ◽

R Program ◽

Correct Model ◽

Analytical Integration ◽

Low Prevalence

Although measures such as sensitivity and specificity are used in the study of diagnostic test accuracy, these are not appropriate for integrating heterogeneous studies. Therefore, it is essential to assess in detail all related aspects prior to integrating a set of studies so that the correct model can then be selected. This work describes the scheme employed for making decisions regarding the use of the R, STATA and SAS statistical programs. We used the R Program Meta-Analysis of Diagnostic Accuracy package for determining the correlation between sensitivity and specificity. This package considers fixed, random and mixed effects models and provides excellent summaries and assesses heterogeneity. For selecting various cutoff points in the meta-analysis, we used the STATA module for meta-analytical integration of diagnostic test accuracy studies, which produces bivariate outputs for heterogeneity.

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