Risk of bias assessments in individual participant data meta-analyses of test accuracy and prediction models: a review shows improvements are needed.

OBJECTIVES: Risk of bias assessments are important in meta-analyses of both aggregate and individual participant data (IPD). There is limited evidence on whether and how risk of bias of included studies or datasets in IPD meta-analyses (IPDMAs) is assessed. We review how risk of bias is currently assessed, reported, and incorporated in IPDMAs of test accuracy and clinical prediction model studies and provide recommendations for improvement. STUDY DESIGN AND SETTING: We searched PubMed (January 2018-May 2020) to identify IPDMAs of test accuracy and prediction models, then elicited whether each IPDMA assessed risk of bias of included studies and, if so, how assessments were reported and subsequently incorporated into the IPDMAs. RESULTS: Forty-nine IPDMAs were included. Nineteen of 27 (70%) test accuracy IPDMAs assessed risk of bias, compared to 5 of 22 (23%) prediction model IPDMAs. Seventeen of 19 (89%) test accuracy IPDMAs used Quality Assessment of Diagnostic Accuracy Studies-2 (QUADAS-2), but no tool was used consistently among prediction model IPDMAs. Of IPDMAs assessing risk of bias, 7 (37%) test accuracy IPDMAs and 1 (20%) prediction model IPDMA provided details on the information sources (e.g., the original manuscript, IPD, primary investigators) used to inform judgments, and 4 (21%) test accuracy IPDMAs and 1 (20%) prediction model IPDMA provided information or whether assessments were done before or after obtaining the IPD of the included studies or datasets. Of all included IPDMAs, only seven test accuracy IPDMAs (26%) and one prediction model IPDMA (5%) incorporated risk of bias assessments into their meta-analyses. For future IPDMA projects, we provide guidance on how to adapt tools such as Prediction model Risk Of Bias ASsessment Tool (for prediction models) and QUADAS-2 (for test accuracy) to assess risk of bias of included primary studies and their IPD. CONCLUSION: Risk of bias assessments and their reporting need to be improved in IPDMAs of test accuracy and, especially, prediction model studies. Using recommended tools, both before and after IPD are obtained, will address this.

A Systematic Review of Deep-Learning Methods for Intracranial Aneurysm Detection in CT Angiography.

Background: Subarachnoid hemorrhage resulting from cerebral aneurysm rupture is a significant cause of morbidity and mortality. Early identification of aneurysms on Computed Tomography Angiography (CTA), a frequently used modality for this purpose, is crucial, and artificial intelligence (AI)-based algorithms can improve the detection rate and minimize the intra- and inter-rater variability. Thus, a systematic review and meta-analysis were conducted to assess the diagnostic accuracy of deep-learning-based AI algorithms in detecting cerebral aneurysms using CTA. Methods: PubMed (MEDLINE), Embase, and the Cochrane Library were searched from January 2015 to July 2023. Eligibility criteria involved studies using fully automated and semi-automatic deep-learning algorithms for detecting cerebral aneurysms on the CTA modality. Eligible studies were assessed using the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) guidelines and the Quality Assessment of Diagnostic Accuracy Studies 2 (QUADAS-2) tool. A diagnostic accuracy meta-analysis was conducted to estimate pooled lesion-level sensitivity, size-dependent lesion-level sensitivity, patient-level specificity, and the number of false positives per image. An enhanced FROC curve was utilized to facilitate comparisons between the studies. Results: Fifteen eligible studies were assessed. The findings indicated that the methods exhibited high pooled sensitivity (0.87, 95% confidence interval: 0.835 to 0.91) in detecting intracranial aneurysms at the lesion level. Patient-level sensitivity was not reported due to the lack of a unified patient-level sensitivity definition. Only five studies involved a control group (healthy subjects), whereas two provided information on detection specificity. Moreover, the analysis of size-dependent sensitivity reported in eight studies revealed that the average sensitivity for small aneurysms (<3 mm) was rather low (0.56). Conclusions: The studies included in the analysis exhibited a high level of accuracy in detecting intracranial aneurysms larger than 3 mm in size. Nonetheless, there is a notable gap that necessitates increased attention and research focus on the detection of smaller aneurysms, the use of a common test dataset, and an evaluation of a consistent set of performance metrics.

Assessment Precision of CT Perfusion Imaging in the Detection of Acute Ischemic Stroke: A Systematic Review and Meta-Analysis.

Stroke, a prevalent medical emergency, comprises ischemic and hemorrhagic subtypes, with acute ischemic stroke (AIS) being a predominant type. The application of computed tomography perfusion (CTP) imaging has gained prominence due to its rapidity and accessibility in stroke evaluation. This study systematically reviews and conducts a meta-analysis of existing literature to assess the diagnostic accuracy of CTP in detecting AIS and predicting hemorrhagic transformation (HT). Employing Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, an extensive search was conducted across electronic databases and relevant radiology journals. Studies conducted between 2007 and 2023 that fulfilled predetermined inclusion criteria underwent quality assessment using the Quality Assessment of Diagnostic Accuracy Studies 2 (QUADAS 2) tool. Cochrane diagnostic accuracy tools were used for data extraction. Thirteen studies involving a total of 1014 patients were included in the analysis. The diagnostic performance of CTP in predicting HT demonstrated high sensitivity (86.7%) and moderate specificity (77.8%), resulting in an overall accuracy of 79.1%. The negative predictive value (NPV) was notably high (92.9%), signifying its efficacy in excluding patients at risk of HT. The positive predictive value (PPV) was comparatively lower (60.3%), highlighting the need for clinical context when making thrombolysis decisions. The false positive rate was 16.2%, while the false negative rate was minimal (9.8%). Subgroup analysis underscored consistent sensitivity and specificity across diverse imaging metrics. The findings of this study emphasize the promising diagnostic accuracy of CTP imaging in predicting HT subsequent to AIS. This non-invasive technique can aid treatment decisions and patient management strategies. By effectively assessing perfusion status and offering predictive insights, CTP imaging improves stroke intervention choices, especially in identifying patients with a lower risk of HT.

The value of dual-energy computed tomography (DECT) in the diagnosis of urinary calculi: a systematic review and meta-analysis of retrospective studies.

Objective: Dual-energy computed tomography (DECT) imaging technology opens a new idea and method for analyzing stone composition, which can obtain several quantitative parameters reflecting tissue-related information and energy images different from traditional images. However, the application of DECT in diagnosing urinary calculi remains unknown. This study aims to evaluate the value of DECT in diagnosing urinary calculi by meta-analysis. Methods: PubMed, EMBASE, Web of Science, and the Cochrane Library were searched to articles published from the establishment of the databases to April 18, 2023. We reviewed the articles on the diagnosis of urinary calculi detected by DECT, established standards, screened the articles, and extracted data. Two researchers carried out data extraction and the Cohen's unweighted kappa was estimated for inter-investigator reliability. The quality of the literature was evaluated by the diagnostic test accuracy quality evaluation tool (QUADAS-2). The heterogeneity and threshold effects were analyzed by Meta-Disc 1.4 software, and the combined sensitivity, specificity, positive likelihood ratio, negative likelihood ratio, and diagnostic ratio were calculated. The combined receiver-operating characteristic (ROC) curve was drawn, and the value of DECT in the diagnosis of urinary calculi was evaluated by the area under the curve (AUC). The meta-analysis was registered at PROSPERO (CRD42023418204). Results: One thousand and twenty-seven stones were detected in 1,223 samples from 10 diagnostic tests. The analyzed kappa alternated between 0.78-0.85 for the document's retrieval and detection procedure. The sensitivity of DECT in the diagnosis of urinary calculi was 0.94 (95% CI [0.92-0.96]). The positive likelihood ratio (PLR) of DECT in the diagnosis of urinary stones was 0.91 (95% CI [0.88-0.94]), and the negative likelihood ratio (NLR) was 0.08 (95% CI [0.05-0.11]). The specificity of DECT for detecting urinary calculi was 0.91 (95% CI [0.88-0.94]). The area under the curve of the summary receiver operator characteristic (SROC) was 0.9875. The sensitivity of dual-energy CT in the diagnosis of urinary calculi diameter <3 mm was 0.94 (95% CI [0.91-0.96]). The PLR of DECT in the diagnosis of urinary stones diameter <3 mm was 10.79 (95% CI [5.25 to 22.17]), and the NLR was 0.08 (95% CI [0.05-0.13]). The specificity of DECT for detecting urinary calculi <3 mm was 0.91 (95% CI [0.87-0.94]). The SROC was 0.9772. Conclusion: The DECT has noble application value in detecting urinary calculi.

Review of Statistical and Methodological Issues in the Forensic Prediction of Malingering from Validity Tests: Part II-Methodological Issues.

Forensic neuropsychological examinations to detect malingering in patients with neurocognitive, physical, and psychological dysfunction have tremendous social, legal, and economic importance. Thousands of studies have been published to develop and validate methods to forensically detect malingering based largely on approximately 50 validity tests, including embedded and stand-alone performance and symptom validity tests. This is Part II of a two-part review of statistical and methodological issues in the forensic prediction of malingering based on validity tests. The Part I companion paper explored key statistical issues. Part II examines related methodological issues through conceptual analysis, statistical simulations, and reanalysis of findings from prior validity test validation studies. Methodological issues examined include the distinction between analog simulation and forensic studies, the effect of excluding too-close-to-call (TCTC) cases from analyses, the distinction between criterion-related and construct validation studies, and the application of the Revised Quality Assessment of Diagnostic Accuracy Studies tool (QUADAS-2) in all Test of Memory Malingering (TOMM) validation studies published within approximately the first 20 years following its initial publication to assess risk of bias. Findings include that analog studies are commonly confused for forensic validation studies, and that construct validation studies are routinely presented as if they were criterion-reference validation studies. After accounting for the exclusion of TCTC cases, actual classification accuracy was found to be well below claimed levels. QUADAS-2 results revealed that extant TOMM validation studies all had a high risk of bias, with not a single TOMM validation study with low risk of bias. Recommendations include adoption of well-established guidelines from the biomedical diagnostics literature for good quality criterion-referenced validation studies and examination of implications for malingering determination practices. Design of future studies may hinge on the availability of an incontrovertible reference standard of the malingering status of examinees.

Machine and Deep Learning for Tuberculosis Detection on Chest X-Rays: Systematic Literature Review.

BACKGROUND: Tuberculosis (TB) was the leading infectious cause of mortality globally prior to COVID-19 and chest radiography has an important role in the detection, and subsequent diagnosis, of patients with this disease. The conventional experts reading has substantial within- and between-observer variability, indicating poor reliability of human readers. Substantial efforts have been made in utilizing various artificial intelligence-based algorithms to address the limitations of human reading of chest radiographs for diagnosing TB. OBJECTIVE: This systematic literature review (SLR) aims to assess the performance of machine learning (ML) and deep learning (DL) in the detection of TB using chest radiography (chest x-ray [CXR]). METHODS: In conducting and reporting the SLR, we followed the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. A total of 309 records were identified from Scopus, PubMed, and IEEE (Institute of Electrical and Electronics Engineers) databases. We independently screened, reviewed, and assessed all available records and included 47 studies that met the inclusion criteria in this SLR. We also performed the risk of bias assessment using Quality Assessment of Diagnostic Accuracy Studies version 2 (QUADAS-2) and meta-analysis of 10 included studies that provided confusion matrix results. RESULTS: Various CXR data sets have been used in the included studies, with 2 of the most popular ones being Montgomery County (n=29) and Shenzhen (n=36) data sets. DL (n=34) was more commonly used than ML (n=7) in the included studies. Most studies used human radiologist's report as the reference standard. Support vector machine (n=5), k-nearest neighbors (n=3), and random forest (n=2) were the most popular ML approaches. Meanwhile, convolutional neural networks were the most commonly used DL techniques, with the 4 most popular applications being ResNet-50 (n=11), VGG-16 (n=8), VGG-19 (n=7), and AlexNet (n=6). Four performance metrics were popularly used, namely, accuracy (n=35), area under the curve (AUC; n=34), sensitivity (n=27), and specificity (n=23). In terms of the performance results, ML showed higher accuracy (mean ~93.71%) and sensitivity (mean ~92.55%), while on average DL models achieved better AUC (mean ~92.12%) and specificity (mean ~91.54%). Based on data from 10 studies that provided confusion matrix results, we estimated the pooled sensitivity and specificity of ML and DL methods to be 0.9857 (95% CI 0.9477-1.00) and 0.9805 (95% CI 0.9255-1.00), respectively. From the risk of bias assessment, 17 studies were regarded as having unclear risks for the reference standard aspect and 6 studies were regarded as having unclear risks for the flow and timing aspect. Only 2 included studies had built applications based on the proposed solutions. CONCLUSIONS: Findings from this SLR confirm the high potential of both ML and DL for TB detection using CXR. Future studies need to pay a close attention on 2 aspects of risk of bias, namely, the reference standard and the flow and timing aspects. TRIAL REGISTRATION: PROSPERO CRD42021277155; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=277155.

Canine olfactory detection of SARS-CoV-2-infected humans-a systematic review.

PURPOSE: To complement conventional testing methods for severe acute respiratory syndrome coronavirus type 2 infections, dogs' olfactory capability for true real-time detection has been investigated worldwide. Diseases produce specific scents in affected individuals via volatile organic compounds. This systematic review evaluates the current evidence for canine olfaction as a reliable coronavirus disease 2019 screening tool. METHODS: Two independent study quality assessment tools were used: the QUADAS-2 tool for the evaluation of laboratory tests' diagnostic accuracy, designed for systematic reviews, and a general evaluation tool for canine detection studies, adapted to medical detection. Various study design, sample, dog, and olfactory training features were considered as potential confounding factors. RESULTS: Twenty-seven studies from 15 countries were evaluated. Respectively, four and six studies had a low risk of bias and high quality: the four QUADAS-2 nonbiased studies resulted in ranges of 81%-97% sensitivity and 91%-100% specificity. The six high-quality studies, according to the general evaluation system, revealed ranges of 82%-97% sensitivity and 83%-100% specificity. The other studies contained high bias risks and applicability and/or quality concerns. CONCLUSIONS: Standardization and certification procedures as used for canine explosives detection are needed for medical detection dogs for the optimal and structured usage of their undoubtful potential.

The quality of diagnostic studies used for the diagnostic criteria of periprosthetic joint infections.

BACKGROUND: Although periprosthetic joint infection (PJI) is a serious complication following a total joint arthroplasty procedure, there remains uncertainty regarding the diagnosis of PJI due to the lack of a globally accepted, standardized definition. The goal of this review is to critically analyze the quality of the evidence used for the novel 2018 MSIS PJI definition and identify gaps and limitations with using the Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2) tool. METHODS: References from the modified 2018 MSIS definition for PJI by Parvizi et al. were retrieved and manually reviewed. A total of 11 studies were assessed using a validated QUADAS-2 tool. RESULTS: Many included studies had an unclear or high risk of bias for the Index Test domain due to a lack of blinding and lack of prespecified thresholds. A majority of studies utilized Youden's J statistic to optimize the thresholds which may diminish external validity. Likewise, several studies were assessed to have an unclear and high risk of bias for the Flow and Timing domain primarily due to a lack of reporting and a large number of exclusions. Overall, there was a low risk of bias for the choice of reference standard, its conduct and interpretation, as well as for the Patient Selection domain. CONCLUSION: Although the literature used for the MSIS 2018 PJI definition is fraught with potential sources of bias, there may be a trend toward an improvement in the quality of evidence when compared to the earlier definition of PJI.

Clinical and CT patterns to predict EGFR mutation in patients with non-small cell lung cancer: A systematic literature review and meta-analysis.

PURPOSE: This study aims to determine if the presence of specific clinical and computed tomography (CT) patterns are associated with epidermal growth factor receptor (EGFR) mutation in patients with non-small cell lung cancer. METHODS: A systematic literature review and meta-analysis was carried out in 6 databases between January 2002 and July 2021. The relationship between clinical and CT patterns to detect EGFR mutation was measured and pooled using odds ratios (OR). These results were used to build several mathematical models to predict EGFR mutation. RESULTS: 34 retrospective diagnostic accuracy studies met the inclusion and exclusion criteria. The results showed that ground-glass opacities (GGO) have an OR of 1.86 (95%CI 1.34 -2.57), air bronchogram OR 1.60 (95%CI 1.38 - 1.85), vascular convergence OR 1.39 (95%CI 1.12 - 1.74), pleural retraction OR 1.99 (95%CI 1.72 - 2.31), spiculation OR 1.42 (95%CI 1.19 - 1.70), cavitation OR 0.70 (95%CI 0.57 - 0.86), early disease stage OR 1.58 (95%CI 1.14 - 2.18), non-smoker status OR 2.79 (95%CI 2.34 - 3.31), female gender OR 2.33 (95%CI 1.97 - 2.75). A mathematical model was built, including all clinical and CT patterns assessed, showing an area under the curve (AUC) of 0.81. CONCLUSIONS: GGO, air bronchogram, vascular convergence, pleural retraction, spiculated margins, early disease stage, female gender, and non-smoking status are significant risk factors for EGFR mutation. At the same time, cavitation is a protective factor for EGFR mutation. The mathematical model built acts as a good predictor for EGFR mutation in patients with lung adenocarcinoma.

Diagnostic accuracy of screening algorithms to identify persons with active pulmonary tuberculosis at prison entry: protocol of a systematic review and network meta-analysis.

Prison inmates are a high-risk group for tuberculosis (TB) infection and disease due to the increasing number of vulnerable fringe groups, risk factors (e.g., alcohol and drug addictions), contagious diseases (HIV, hepatitis), and their high-risk behavior. Compared to the general population, TB incidence and prevalence rates are significantly higher among prison inmates. Early identification of potentially infectious pulmonary TB (PTB) and targeted care of sick inmates are essential to effectively control TB within the prison system. The WHO recommends combining active and passive case-finding in prisons. No study has been published comparing the broad spectrum of screening tools using a diagnostic accuracy network meta-analysis (NMA). We aim to identify the most accurate TB case-finding algorithm at prison entry that is feasible in resource-limited prisons of high-burden TB countries and ensures continuous comprehensive TB detection services in such settings. Evidence generated by this NMA can provide important decision support in selecting the most (cost-) effective algorithms for screening methods for resource-limited settings in the short, medium, and long terms.

Magnetic resonance versus computed tomography for the detection of retroperitoneal lymph node metastasis due to testicular cancer: A systematic literature review.

INTRODUCTION: It is essential to see if MRI can be used as an alternative to CT for the detection of retroperitoneal lymphadenopathy in patients with testicular neoplasms. By doing so, the amount of radiation received by these young patients might be reduced. MATERIAL AND METHODS: A systematic literature review was carried out in 5 databases between January 1984 until December 2020. The articles included were randomized and non-randomized clinical trials, cross-sectional studies, cohort, case and control, and retrospective studies that compare the accuracy of MRI against CT to detect retroperitoneal lymph nodes in patients with testicular neoplasms. RESULTS: The search string initially retrieved 222 non duplicated papers from which a total of 3 studies of diagnostic accuracy were included for analysis. These articles evaluated a total of 127 patients with testicular neoplasm; the sample size per study ranged from 25 to 52 patients, with a mean age between 29-34 years. MRI presented a sensitivity ranging from 98-80% and specificity of 100 % when read by an experienced radiologist. However, when it was read by a radiologist with 1 year of experience, the sensitivity dropped to 78 % and specificity to 91%. CONCLUSION: This systematic literature review shows a knowledge gap since not much has been published regarding this topic; therefore, randomized clinical trials are mandatory. Research on when to use MRI over CT is necessary to reduce radiation exposure. The authors strongly suggest that readers start researching on this subject.

Artificial Intelligence Compared to Radiologists for the Initial Diagnosis of Prostate Cancer on Magnetic Resonance Imaging: A Systematic Review and Recommendations for Future Studies.

Computer-aided diagnosis (CAD) of prostate cancer on multiparametric magnetic resonance imaging (mpMRI), using artificial intelligence (AI), may reduce missed cancers and unnecessary biopsies, increase inter-observer agreement between radiologists, and alleviate pressures caused by rising case incidence and a shortage of specialist radiologists to read prostate mpMRI. However, well-designed evaluation studies are required to prove efficacy above current clinical practice. A systematic search of the MEDLINE, EMBASE, and arXiv electronic databases was conducted for studies that compared CAD for prostate cancer detection or classification on MRI against radiologist interpretation and a histopathological reference standard, in treatment-naïve men with a clinical suspicion of prostate cancer. Twenty-seven studies were included in the final analysis. Due to substantial heterogeneities in the included studies, a narrative synthesis is presented. Several studies reported superior diagnostic accuracy for CAD over radiologist interpretation on small, internal patient datasets, though this was not observed in the few studies that performed evaluation using external patient data. Our review found insufficient evidence to suggest the clinical deployment of artificial intelligence algorithms at present. Further work is needed to develop and enforce methodological standards, promote access to large diverse datasets, and conduct prospective evaluations before clinical adoption can be considered.

Non-Invasive Biomarkers for Earlier Detection of Pancreatic Cancer-A Comprehensive Review.

Pancreatic ductal adenocarcinoma (PDAC) carries a deadly diagnosis, due in large part to delayed presentation when the disease is already at an advanced stage. CA19-9 is currently the most commonly utilized biomarker for PDAC; however, it lacks the necessary accuracy to detect precursor lesions or stage I PDAC. Novel biomarkers that could detect this malignancy with improved sensitivity (SN) and specificity (SP) would likely result in more curative resections and more effective therapeutic interventions, changing thus the present dismal survival figures. The aim of this study was to systematically and comprehensively review the scientific literature on non-invasive biomarkers in biofluids such as blood, urine and saliva that were attempting earlier PDAC detection. The search performed covered a period of 10 years (January 2010-August 2020). Data were extracted using keywords search in the three databases: MEDLINE, Web of Science and Embase. The Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2) tool was applied for study selection based on establishing the risk of bias and applicability concerns in Patient Selection, Index test (biomarker assay) and Reference Standard (standard-of-care diagnostic test). Out of initially over 4000 published reports, 49 relevant studies were selected and reviewed in more detail. In addition, we discuss the present challenges and complexities in the path of translating the discovered biomarkers into the clinical setting. Our systematic review highlighted several promising biomarkers that could, either alone or in combination with CA19-9, potentially improve earlier detection of PDAC. Overall, reviewed biomarker studies should aim to improve methodological and reporting quality, and novel candidate biomarkers should be investigated further in order to demonstrate their clinical usefulness. However, challenges and complexities in the path of translating the discovered biomarkers from the research laboratory to the clinical setting remain and would have to be addressed before a more realistic breakthrough in earlier detection of PDAC is achieved.

The search for a convenient procedure to detect one of the earliest signs of Alzheimer's disease: A systematic review of the prediction of brain amyloid status.

INTRODUCTION: Convenient, cost-effective tests for amyloid beta (Aß) are needed to identify those at higher risk for developing Alzheimer's disease (AD). This systematic review evaluates recent models that predict dichotomous Aß. (PROSPERO: CRD42020144734). METHODS: We searched Embase and identified 73 studies from 29,581 for review. We assessed study quality using established tools, extracted information, and reported results narratively. RESULTS: We identified few high-quality studies due to concerns about Aß determination and analytical issues. The most promising convenient, inexpensive classifiers consist of age, apolipoprotein E genotype, cognitive measures, and/or plasma Aß. Plasma Aß may be sufficient if pre-analytical variables are standardized and scalable assays developed. Some models lowered costs associated with clinical trial recruitment or clinical screening. DISCUSSION: Conclusions about models are difficult due to study heterogeneity and quality. Promising prediction models used demographic, cognitive/neuropsychological, imaging, and plasma Aß measures. Further studies using standardized Aß determination, and improved model validation are required.

At what times during infection is SARS-CoV-2 detectable and no longer detectable using RT-PCR-based tests? A systematic review of individual participant data.

BACKGROUND: Tests for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) viral ribonucleic acid (RNA) using reverse transcription polymerase chain reaction (RT-PCR) are pivotal to detecting current coronavirus disease (COVID-19) and duration of detectable virus indicating potential for infectivity. METHODS: We conducted an individual participant data (IPD) systematic review of longitudinal studies of RT-PCR test results in symptomatic SARS-CoV-2. We searched PubMed, LitCOVID, medRxiv, and COVID-19 Living Evidence databases. We assessed risk of bias using a QUADAS-2 adaptation. Outcomes were the percentage of positive test results by time and the duration of detectable virus, by anatomical sampling sites. RESULTS: Of 5078 studies screened, we included 32 studies with 1023 SARS-CoV-2 infected participants and 1619 test results, from - 6 to 66 days post-symptom onset and hospitalisation. The highest percentage virus detection was from nasopharyngeal sampling between 0 and 4 days post-symptom onset at 89% (95% confidence interval (CI) 83 to 93) dropping to 54% (95% CI 47 to 61) after 10 to 14 days. On average, duration of detectable virus was longer with lower respiratory tract (LRT) sampling than upper respiratory tract (URT). Duration of faecal and respiratory tract virus detection varied greatly within individual participants. In some participants, virus was still detectable at 46 days post-symptom onset. CONCLUSIONS: RT-PCR misses detection of people with SARS-CoV-2 infection; early sampling minimises false negative diagnoses. Beyond 10 days post-symptom onset, lower RT or faecal testing may be preferred sampling sites. The included studies are open to substantial risk of bias, so the positivity rates are probably overestimated.

Potential diagnostic value of circulating miRNA for multiple myeloma: A meta-analysis.

BACKGROUND: Multiple myeloma (MM) is the second incurable hematological malignancy. In recent years, due to the rise of microRNA (miRNA), many scholars have participated in the study of its value in the diagnosis of MM, and have obtained good but inconsistent results. Therefore, in order to determine the role of miRNA in the early diagnosis of MM, we performed this meta-analysis. METHODS: We searched for related studies including PubMed, Web of Science, EMBASE, Cochrane Library, China National Knowledge Infrastructure (CNKI) and Wanfang Database as of July 20, 2020 to conduct this meta-analysis. To improve the accuracy, the quality assessment of Diagnostic Accuracy Study 2 (QUADAS-2) was used. We also applied random effects models to summarize sensitivity and specificity, positive likelihood ratio (PLR), negative likelihood ratio (NLR), diagnostic odds ratio (DOR) and area under the curve (AUC) to measure diagnostic values, and subgroup analysis used to discover potential sources of heterogeneity. RESULTS: We finally collected 32 studies from 15 articles that included a total of 2053 MM patients and 1118 healthy controls in this meta-analysis. The overall sensitivity, specificity, PLR, NLR, DOR and AUC were 0.81, 0.85, 5.5, 0.22, 25 and 0.90, respectively. Subgroup analysis shows that the down-regulation of microRNA clusters with larger samples size of plasma type could carry out a better diagnostic accuracy of MM patients. In addition, publication bias was not found. CONCLUSIONS: Circulating miRNA could be a potential non-invasive biomarker for early diagnosis of MM. However, multi-center, more rigorous, and larger-scale studies are needed to verify our conclusions.

The estimation of diagnostic accuracy of tests for COVID-19: A scoping review.

OBJECTIVES: To assess the methodologies used in the estimation of diagnostic accuracy of SARS-CoV-2 real-time reverse transcription polymerase chain reaction (rRT-PCR) and other nucleic acid amplification tests (NAATs) and to evaluate the quality and reliability of the studies employing those methods. METHODS: We conducted a systematic search of English-language articles published December 31, 2019-June 19, 2020. Studies of any design that performed tests on ≥10 patients and reported or inferred correlative statistics were included. Studies were evaluated using elements of the Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2) guidelines. RESULTS: We conducted a narrative and tabular synthesis of studies organized by their reference standard strategy or comparative agreement method, resulting in six categorizations. Critical study details were frequently unreported, including the mechanism for patient/sample selection and researcher blinding to results, which lead to concern for bias. CONCLUSIONS: Current studies estimating test performance characteristics have imperfect study design and statistical methods for the estimation of test performance characteristics of SARS-CoV-2 tests. The included studies employ heterogeneous methods and overall have an increased risk of bias. Employing standardized guidelines for study designs and statistical methods will improve the process for developing and validating rRT-PCR and NAAT for the diagnosis of COVID-19.

Risk of bias in studies investigating novel diagnostic biomarkers for heart failure with preserved ejection fraction. A systematic review.

AIM: Diagnosing heart failure with preserved ejection fraction (HFpEF) in the non-acute setting remains challenging. Natriuretic peptides have limited value for this purpose, and a multitude of studies investigating novel diagnostic circulating biomarkers have not resulted in their implementation. This review aims to provide an overview of studies investigating novel circulating biomarkers for the diagnosis of HFpEF and determine their risk of bias (ROB). METHODS AND RESULTS: A systematic literature search for studies investigating novel diagnostic HFpEF circulating biomarkers in humans was performed up until 21 April 2020. Those without diagnostic performance measures reported, or performed in an acute heart failure population were excluded, leading to a total of 28 studies. For each study, four reviewers determined the ROB within the QUADAS-2 domains: patient selection, index test, reference standard, and flow and timing. At least one domain with a high ROB was present in all studies. Use of case-control/two-gated designs, exclusion of difficult-to-diagnose patients, absence of a pre-specified cut-off value for the index test without the performance of external validation, the use of inappropriate reference standards and unclear timing of the index test and/or reference standard were the main bias determinants. Due to the high ROB and different patient populations, no meta-analysis was performed. CONCLUSION: The majority of current diagnostic HFpEF biomarker studies have a high ROB, reducing the reproducibility and the potential for clinical care. Methodological well-designed studies with a uniform reference diagnosis are urgently needed to determine the incremental value of circulating biomarkers for the diagnosis of HFpEF.

Event-Related Potentials for Diagnosing Children and Adults With ADHD.

Objective: The diagnosis of ADHD is based on behavioral criteria, which allow for subjective variability and invite criticism regarding the reality of the disorder. In this situation, more objective criteria would be desirable. We review the scientific literature for diagnostic tests based on event-related potentials (ERPs). Method: Seven studies met the inclusion criteria of reporting the sensitivity and specificity of an ERP-based classifier discriminating participants with ADHD from healthy controls. Study quality was rated using the second version of the Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2) system. Results: Overall, study quality was acceptable. The largest biases were lack of representativeness and overfitting. Sensitivities and specificities ranged from 57% to 96%, and 63% to 92%, respectively. However, no two studies used the same diagnostic test. Conclusion: There is a serious lack of coordination in worldwide efforts to find more objective ERP-based criteria for the diagnosis of ADHD. Concerted action is needed.