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AbstractObjectiveTo examine the validity and findings of studies that examine the accuracy of algorithm based smartphone applications (“apps”) to assess risk of skin cancer in suspicious skin lesions.DesignSystematic review of diagnostic accuracy studies.Data sourcesCochrane Central Register of Controlled Trials, MEDLINE, Embase, CINAHL, CPCI, Zetoc, Science Citation Index, and online trial registers (from database inception to 10 April 2019).Eligibility criteria for selecting studiesStudies of any design that evaluated algorithm based smartphone apps to assess images of skin lesions suspicious for skin cancer. Reference standards included histological diagnosis or follow-up, and expert recommendation for further investigation or intervention. Two authors independently extracted data and assessed validity using QUADAS-2 (Quality Assessment of Diagnostic Accuracy Studies 2 tool). Estimates of sensitivity and specificity were reported for each app.ResultsNine studies that evaluated six different identifiable smartphone apps were included. Six verified results by using histology or follow-up (n=725 lesions), and three verified results by using expert recommendations (n=407 lesions). Studies were small and of poor methodological quality, with selective recruitment, high rates of unevaluable images, and differential verification. Lesion selection and image acquisition were performed by clinicians rather than smartphone users. Two CE (Conformit Europenne) marked apps are available for download. No published peer reviewed study was found evaluating the TeleSkin skinScan app. SkinVision was evaluated in three studies (n=267, 66 malignant or premalignant lesions) and achieved a sensitivity of 80% (95% confidence interval 63% to 92%) and a specificity of 78% (67% to 87%) for the detection of malignant or premalignant lesions. Accuracy of the SkinVision app verified against expert recommendations was poor (three studies).ConclusionsCurrent algorithm based smartphone apps cannot be relied on to detect all cases of melanoma or other skin cancers. Test performance is likely to be poorer than reported here when used in clinically relevant populations and by the intended users of the apps. The current regulatory process for awarding the CE marking for algorithm based apps does not provide adequate protection to the public.Systematic review registrationPROSPERO CRD42016033595.

Systematic reviews of diagnostic test accuracy (DTA) studies are fundamental to the decision making process in evidence based medicine. Although such studies are regarded as high level evidence, these reviews are not always reported completely and transparently. Suboptimal reporting of DTA systematic reviews compromises their validity and generalisability, and subsequently their value to key stakeholders. An extension of the PRISMA (preferred reporting items for systematic review and meta-analysis) statement was recently developed to improve the reporting quality of DTA systematic reviews. The PRISMA-DTA statement has 27 items, of which eight are unmodified from the original PRISMA statement. This article provides an explanation for the 19 new and modified items, along with their meaning and rationale. Examples of complete reporting are used for each item to illustrate best practices.

Publication bias and other sample size effects are issues for meta-analyses of test accuracy, as for randomized trials. We investigate limitations of standard funnel plots and tests when applied to meta-analyses of test accuracy and look for improved methods. Type I and type II error rates for existing and alternative tests of sample size effects were estimated and compared in simulated meta-analyses of test accuracy. Type I error rates for the Begg, Egger, and Macaskill tests are inflated for typical diagnostic odds ratios (DOR), when disease prevalence differs from 50% and when thresholds favor sensitivity over specificity or vice versa. Regression and correlation tests based on functions of effective sample size are valid, if occasionally conservative, tests for sample size effects. Empirical evidence suggests that they have adequate power to be useful tests. When DORs are heterogeneous, however, all tests of funnel plot asymmetry have low power. Existing tests that use standard errors of odds ratios are likely to be seriously misleading if applied to meta-analyses of test accuracy. The effective sample size funnel plot and associated regression test of asymmetry should be used to detect publication bias and other sample size related effects.

Screening for congenital heart defects relies on antenatal ultrasonography and postnatal clinical examination; however, life-threatening defects often are not detected. We prospectively assessed the accuracy of pulse oximetry as a screening test for congenital heart defects. In six maternity units in the UK, asymptomatic newborn babies (gestation >34 weeks) were screened with pulse oximetry before discharge. Infants who did not achieve predetermined oxygen saturation thresholds underwent echocardiography. All other infants were followed up to 12 months of age by use of regional and national registries and clinical follow-up. The main outcome was the sensitivity and specificity of pulse oximetry for detection of critical congenital heart defects (causing death or requiring invasive intervention before 28 days) or major congenital heart disease (causing death or requiring invasive intervention within 12 months of age). 20 055 newborn babies were screened and 53 had major congenital heart disease (24 critical), a prevalence of 2·6 per 1000 livebirths. Analyses were done on all babies for whom a pulse oximetry reading was obtained. Sensitivity of pulse oximetry was 75·00% (95% CI 53·29–90·23) for critical cases and 49·06% (35·06–63·16) for all major congenital heart defects. In 35 cases, congenital heart defects were already suspected after antenatal ultrasonography, and exclusion of these reduced the sensitivity to 58·33% (27·67–84·83) for critical cases and 28·57% (14·64–46·30) for all cases of major congenital heart defects. False-positive results were noted for 169 (0·8%) babies (specificity 99·16%, 99·02–99·28), of which six cases were significant, but not major, congenital heart defects, and 40 were other illnesses that required urgent medical intervention. Pulse oximetry is a safe, feasible test that adds value to existing screening. It identifies cases of critical congenital heart defects that go undetected with antenatal ultrasonography. The early detection of other diseases is an additional advantage. National Institute for Health Research Health Technology Assessment programme.

This article describes the Grading of Recommendations Assessment, Development and Evaluation (GRADE) Working Group's framework of moving from test accuracy to patient or population-important outcomes. We focus on the common scenario when studies directly evaluating the effect of diagnostic and other tests or strategies on health outcomes are not available or are not providing the best available evidence. Using practical examples, we explored how guideline developers and other decision makers can use information from test accuracy to develop a recommendation by linking evidence that addresses downstream consequences. Guideline panels should develop an analytic framework that summarizes the actions that follow from applying a test and the consequences. We describe GRADE's current thinking about the overall certainty of the evidence (also known as quality of the evidence or confidence in the estimates) arising from consideration of the often complex pathways that involve multiple tests and management options. Each link in the evidence can—and often does—lower the overall certainty of the evidence required to formulate recommendations and make decisions about tests. The frequency with which an outcome occurs and its importance will influence whether or not a particular step in the linked evidence is critical to decision-making. Overall certainty may be expressed by the weakest critical step in the linked evidence. The linked approach to addressing optimal testing will often require the use of decision analytic approaches. We present an example that involves decision modeling in a GRADE Evidence to Decision framework for cervical cancer screening. However, because resources and time of guideline developers may be limited, we describe alternative, pragmatic strategies for developing recommendations addressing test use.

Suicide risk is greatly increased in schizophrenia. Detection of those at risk is clinically important. To identify risk factors for suicide in schizophrenia. The international literature on case-control and cohort studies of patients with schizophrenia or related conditions in which suicide was reported as an outcome was systematically reviewed. Studies were identified through searching electronic databases and reference lists, and by consulting experts. Twenty-nine eligible studies were identified. Factors with robust evidence of increased risk of suicide were previous depressive disorders (OR=3.03, 95% CI 2.06-4.46), previous suicide attempts (OR=4.09, 95% CI 2.79-6.01), drug misuse (OR=3.21, 95% CI 1.99-5.17), agitation or motor restlessness (OR=2.61, 95% CI 1.54-4.41), fear of mental disintegration (OR=12.1, 95% CI 1.89-81.3), poor adherence to treatment (OR=3.75, 95% CI 2.20-6.37) and recent loss (OR=4.03, 95% CI 1.37-11.8). Reduced risk was associated with hallucinations (OR=0.50, 95% CI 0.35-0.71). Prevention of suicide in schizophrenia is likely to result from treatment of affective symptoms, improving adherence to treatment, and maintaining special vigilance in patients with risk factors, especially after losses.

Background The aim of the study was to investigate the development of evidence-based monitoring strategies in a population with progressive or recurrent disease. A simulation study of monitoring strategies using a new biomarker (ELF) for the detection of liver cirrhosis in people with known liver fibrosis was undertaken alongside a randomised controlled trial (ELUCIDATE). Methods Existing data and expert opinion were used to estimate the progression of disease and the performance of repeat testing with ELF. Knowledge of the true disease status in addition to the observed test results for a cohort of simulated patients allowed various monitoring strategies to be implemented, evaluated and validated against trial data. Results Several monitoring strategies ranging in complexity were successfully modelled and compared regarding the timing of detection of disease, the duration of monitoring, and the predictive value of a positive test result. The results of sensitivity analysis showed the importance of accurate data to inform the simulation. Results of the simulation were similar to those from the trial. Conclusion Monitoring data can be simulated and strategies compared given adequate knowledge of disease progression and test performance. Such exercises should be carried out to ensure optimal strategies are evaluated in trials thus reducing research waste. Monitoring data can be generated and monitoring strategies can be assessed if data is available on the monitoring test performance and the test variability. This work highlights the data necessary and the general method for evaluating the performance of monitoring strategies, allowing appropriate strategies to be selected for evaluation. Modelling work should be conducted prior to full scale investigation of monitoring strategies, allowing optimal monitoring strategies to be assessed.

Clinicians use chest imaging tests to diagnose COVID-19 disease, when awaiting RT-PCR test results, for example, or when RT-PCR results are negative, and the person has COVID-19 symptoms. Computed tomography (CT) scans use a computer to merge multiple X-ray images taken from different angles to produce a 2-D image that can be converted to a 3-D image. [...]25% of studies were published as preprints, which do not undergo the same rigorous checks as published studies.

The comparative analysis of the effect of several doses of statins against different intensities of physical exercise on arterial stiffness (a measure of cardiovascular risk) could shed light for clinicians on which method is most effective in preventing cardiovascular disease (CVD) and be used to inform shared decision-making between doctors and patients. This study was aimed at analyzing the effect, in high cardiometabolic risk patients, of different statins doses and exercise intensities on arterial stiffness (a measure of cardiovascular risk) by integrating all available direct and indirect evidence in network meta-analyses. We systematically searched MEDLINE, Embase, SPORTDiscus, Cochrane Central Register of Controlled Trials, Cochrane Database of Systematic Reviews, and Web of Science databases from their inception to February 28, 2020; for unpublished trials, we also searched ClinicalTrials.gov. We searched for studies concerning the effect of statins or physical exercise on arterial stiffness, measured by pulse wave velocity (PWV). For methodological quality assessment, Cochrane Collaboration's tool for assessing risk of bias (RoB2) was used. A network geometry graph was used to assess the strength of the evidence. Comparative evaluation of the interventions effect was performed by conducting a standard pairwise meta-analysis and a network meta-analysis (NMA) for direct and indirect comparisons between interventions and control/nonintervention. A total of 22 studies were included in the analyses (18 randomized controlled trials (RCTs) and 4 nonrandomized experimental studies), including 1,307 patients with high cardiometabolic risk from Asia (3 studies), Oceania (2 studies), Europe (10 studies), North America (5 studies), and South America (2 studies). The overall risk of bias assessed with RoB2 was high in all included studies. For standard pairwise meta-analysis and NMA, high-intensity exercise versus control (mean difference (MD) -0.56; 95% CI: -1.01, -0.11; p = 0.015 and -0.62; 95% CI: -1.20, -0.04; p = 0.038, respectively) and moderate statin dose versus control (MD -0.80, 95% CI: -1.59, -0.01; p = 0.048 and -0.73, 95% CI: -1.30, -0.15; p = 0.014, respectively) showed significant MDs. When nonrandomized experimental studies were excluded, the effect on high-intensity exercise versus control and moderate statin dose versus was slightly modified. The main limitation of this study was that the magnitude of the effect of the exercise interventions could be underestimated due to regression toward the mean bias because the baseline cardiometabolic risk profile of patients in the physical exercise intervention trials was healthier than those in the statins ones; consequently, more modest improvements in physical exercise interventions compared to statins interventions can be expected. Additionally, we might consider as limitations the small study sizes, the heterogeneous patient groups, the focus on a proxy endpoint (PWV), and the high risk of bias. In this NMA, we found that although many patients could benefit from statins for reducing CVD risk, our results support that, considering the beneficial effects of high-intensity exercise on arterial stiffness, it would be worthwhile to refocus our attention on this type of exercise as an effective tool for the prevention of CVD. PROSPERO CRD42019123120.

Abstract Context Urine steroid metabolomics, combining mass spectrometry-based steroid profiling and machine learning, has been described as a novel diagnostic tool for detection of adrenocortical carcinoma (ACC). Objective, Design, Setting This proof-of-concept study evaluated the performance of urine steroid metabolomics as a tool for postoperative recurrence detection after microscopically complete (R0) resection of ACC. Patients and Methods 135 patients from 14 clinical centers provided postoperative urine samples, which were analyzed by gas chromatography–mass spectrometry. We assessed the utility of these urine steroid profiles in detecting ACC recurrence, either when interpreted by expert clinicians or when analyzed by random forest, a machine learning-based classifier. Radiological recurrence detection served as the reference standard. Results Imaging detected recurrent disease in 42 of 135 patients; 32 had provided pre- and post-recurrence urine samples. 39 patients remained disease-free for ≥3 years. The urine “steroid fingerprint” at recurrence resembled that observed before R0 resection in the majority of cases. Review of longitudinally collected urine steroid profiles by 3 blinded experts detected recurrence by the time of radiological diagnosis in 50% to 72% of cases, improving to 69% to 92%, if a preoperative urine steroid result was available. Recurrence detection by steroid profiling preceded detection by imaging by more than 2 months in 22% to 39% of patients. Specificities varied considerably, ranging from 61% to 97%. The computational classifier detected ACC recurrence with superior accuracy (sensitivity = specificity = 81%). Conclusion Urine steroid metabolomics is a promising tool for postoperative recurrence detection in ACC; availability of a preoperative urine considerably improves the ability to detect ACC recurrence.