QUADAS-C: A Tool for Assessing Risk of Bias in Comparative Diagnostic Accuracy Studies.

Comparative diagnostic test accuracy studies assess and compare the accuracy of 2 or more tests in the same study. Although these studies have the potential to yield reliable evidence regarding comparative accuracy, shortcomings in the design, conduct, and analysis may bias their results. The currently recommended quality assessment tool for diagnostic test accuracy studies, QUADAS-2 (Quality Assessment of Diagnostic Accuracy Studies-2), is not designed for the assessment of test comparisons. The QUADAS-C (Quality Assessment of Diagnostic Accuracy Studies-Comparative) tool was developed as an extension of QUADAS-2 to assess the risk of bias in comparative diagnostic test accuracy studies. Through a 4-round Delphi study involving 24 international experts in test evaluation and a face-to-face consensus meeting, an initial version of the tool was developed that was revised and finalized following a pilot study among potential users. The QUADAS-C tool retains the same 4-domain structure of QUADAS-2 (Patient Selection, Index Test, Reference Standard, and Flow and Timing) and comprises additional questions to each QUADAS-2 domain. A risk-of-bias judgment for comparative accuracy requires a risk-of-bias judgment for the accuracy of each test (resulting from QUADAS-2) and additional criteria specific to test comparisons. Examples of such additional criteria include whether participants either received all index tests or were randomly assigned to index tests, and whether index tests were interpreted with blinding to the results of other index tests. The QUADAS-C tool will be useful for systematic reviews of diagnostic test accuracy addressing comparative questions. Furthermore, researchers may use this tool to identify and avoid risk of bias when designing a comparative diagnostic test accuracy study.

A tutorial on individualized treatment effect prediction from randomized trials with a binary endpoint.

Randomized trials typically estimate average relative treatment effects, but decisions on the benefit of a treatment are possibly better informed by more individualized predictions of the absolute treatment effect. In case of a binary outcome, these predictions of absolute individualized treatment effect require knowledge of the individual's risk without treatment and incorporation of a possibly differential treatment effect (ie, varying with patient characteristics). In this article, we lay out the causal structure of individualized treatment effect in terms of potential outcomes and describe the required assumptions that underlie a causal interpretation of its prediction. Subsequently, we describe regression models and model estimation techniques that can be used to move from average to more individualized treatment effect predictions. We focus mainly on logistic regression-based methods that are both well-known and naturally provide the required probabilistic estimates. We incorporate key components from both causal inference and prediction research to arrive at individualized treatment effect predictions. While the separate components are well known, their successful amalgamation is very much an ongoing field of research. We cut the problem down to its essentials in the setting of a randomized trial, discuss the importance of a clear definition of the estimand of interest, provide insight into the required assumptions, and give guidance with respect to modeling and estimation options. Simulated data illustrate the potential of different modeling options across scenarios that vary both average treatment effect and treatment effect heterogeneity. Two applied examples illustrate individualized treatment effect prediction in randomized trial data.

Transparent Reporting of Multivariable Prediction Models in Journal and Conference Abstracts: TRIPOD for Abstracts.

Clear and informative reporting in titles and abstracts is essential to help readers and reviewers identify potentially relevant studies and decide whether to read the full text. Although the TRIPOD (Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis) statement provides general recommendations for reporting titles and abstracts, more detailed guidance seems to be desirable. The authors present TRIPOD for Abstracts, a checklist and corresponding guidance for reporting prediction model studies in abstracts. A list of 32 potentially relevant items was the starting point for a modified Delphi procedure involving 110 experts, of whom 71 (65%) participated in the web-based survey. After 2 Delphi rounds, the experts agreed on 21 items as being essential to report in abstracts of prediction model studies. This number was reduced by merging some of the items. In a third round, participants provided feedback on a draft version of TRIPOD for Abstracts. The final checklist contains 12 items and applies to journal and conference abstracts that describe the development or external validation of a diagnostic or prognostic prediction model, or the added value of predictors to an existing model, regardless of the clinical domain or statistical approach used.

Clinical research study implementation of case-finding strategies for heart failure and chronic obstructive pulmonary disease in the elderly with reduced exercise tolerance or dyspnea: A cluster randomized trial.

BACKGROUND: Heart failure (HF) and chronic obstructive pulmonary disease (COPD) often remain undiagnosed in older individuals, although both disorders inhibit functionality and impair health. The aim of the study was to assess the effectiveness of a case-finding strategy of these disorders. METHODS: This is a clustered randomized trial; 18 general practices from the vicinity of Utrecht, the Netherlands, were randomly allocated to a case-finding strategy or usual care. Multimorbid community subjects (≥65 years) with dyspnea or reduced exercise tolerance were eligible for inclusion. The case-finding strategy consisted of history taking, physical examination, blood tests, electrocardiography, spirometry, and echocardiography. Subsequent treatment decisions were at the discretion of the general practitioner. Questionnaires regarding health status and functionality were filled out at baseline and after 6 months of follow-up. Information regarding changes in medication and health care use during the 6 months follow-up was extracted. RESULTS: A total of 829 participants were randomized: 389 in the case-finding strategy group and 440 in the usual care group. More patients in the case-finding group received a new diagnosis of HF or COPD than the usual care group (cumulative incidence 34% vs 2% and 17% vs. 2%, respectively). Scores for health status, functionality, and health care use were similar between the 2 strategies after 6 months of follow-up. CONCLUSIONS: A case-finding strategy applied in primary care to multimorbid older people with dyspnea or reduced exercise tolerance resulted in a number of new diagnoses of HF and COPD but did not result in short-term improvement of health status compared to usual care.

Handling missing predictor values when validating and applying a prediction model to new patients.

Missing data present challenges for development and real-world application of clinical prediction models. While these challenges have received considerable attention in the development setting, there is only sparse research on the handling of missing data in applied settings. The main unique feature of handling missing data in these settings is that missing data methods have to be performed for a single new individual, precluding direct application of mainstay methods used during model development. Correspondingly, we propose that it is desirable to perform model validation using missing data methods that transfer to practice in single new patients. This article compares existing and new methods to account for missing data for a new individual in the context of prediction. These methods are based on (i) submodels based on observed data only, (ii) marginalization over the missing variables, or (iii) imputation based on fully conditional specification (also known as chained equations). They were compared in an internal validation setting to highlight the use of missing data methods that transfer to practice while validating a model. As a reference, they were compared to the use of multiple imputation by chained equations in a set of test patients, because this has been used in validation studies in the past. The methods were evaluated in a simulation study where performance was measured by means of optimism corrected C-statistic and mean squared prediction error. Furthermore, they were applied in data from a large Dutch cohort of prophylactic implantable cardioverter defibrillator patients.

Individual participant data meta-analysis to examine interactions between treatment effect and participant-level covariates: Statistical recommendations for conduct and planning.

Precision medicine research often searches for treatment-covariate interactions, which refers to when a treatment effect (eg, measured as a mean difference, odds ratio, hazard ratio) changes across values of a participant-level covariate (eg, age, gender, biomarker). Single trials do not usually have sufficient power to detect genuine treatment-covariate interactions, which motivate the sharing of individual participant data (IPD) from multiple trials for meta-analysis. Here, we provide statistical recommendations for conducting and planning an IPD meta-analysis of randomized trials to examine treatment-covariate interactions. For conduct, two-stage and one-stage statistical models are described, and we recommend: (i) interactions should be estimated directly, and not by calculating differences in meta-analysis results for subgroups; (ii) interaction estimates should be based solely on within-study information; (iii) continuous covariates and outcomes should be analyzed on their continuous scale; (iv) nonlinear relationships should be examined for continuous covariates, using a multivariate meta-analysis of the trend (eg, using restricted cubic spline functions); and (v) translation of interactions into clinical practice is nontrivial, requiring individualized treatment effect prediction. For planning, we describe first why the decision to initiate an IPD meta-analysis project should not be based on between-study heterogeneity in the overall treatment effect; and second, how to calculate the power of a potential IPD meta-analysis project in advance of IPD collection, conditional on characteristics (eg, number of participants, standard deviation of covariates) of the trials (potentially) promising their IPD. Real IPD meta-analysis projects are used for illustration throughout.

Diagnosing arrhythmogenic right ventricular cardiomyopathy by 2010 Task Force Criteria: clinical performance and simplified practical implementation.

AIMS: Arrhythmogenic right ventricular cardiomyopathy (ARVC) is diagnosed by a complex set of clinical tests as per 2010 Task Force Criteria (TFC). Avoiding misdiagnosis is crucial to prevent sudden cardiac death as well as unnecessary implantable cardioverter-defibrillator implantations. This study aims to validate the overall performance of the TFC in a real-world cohort of patients referred for ARVC evaluation. METHODS AND RESULTS: We included patients consecutively referred to our centres for ARVC evaluation. Patients were diagnosed by consensus of three independent clinical experts. Using this as a reference standard, diagnostic performance was measured for each individual criterion as well as the overall TFC classification. Of 407 evaluated patients (age 38 ± 17 years, 51% male), the expert panel diagnosed 66 (16%) with ARVC. The clinically observed TFC was false negative in 7/66 (11%) patients and false positive in 10/69 (14%) patients. Idiopathic outflow tract ventricular tachycardia was the most common alternative diagnosis. While the TFC performed well overall (sensitivity and specificity 92%), signal-averaged electrocardiogram (SAECG, P = 0.43), and several family history criteria (P ≥ 0.17) failed to discriminate. Eliminating these criteria reduced false positives without increasing false negatives (net reclassification improvement 4.3%, P = 0.019). Furthermore, all ARVC patients met at least one electrocardiogram (ECG) or arrhythmia criterion (sensitivity 100%). CONCLUSION: The TFC perform well but are complex and can lead to misdiagnosis. Simplification by eliminating SAECG and several family history criteria improves diagnostic accuracy. Arrhythmogenic right ventricular cardiomyopathy can be ruled out using ECG and arrhythmia criteria alone, hence these tests may serve as a first-line screening strategy among at-risk individuals.

PROBAST: A Tool to Assess the Risk of Bias and Applicability of Prediction Model Studies.

Clinical prediction models combine multiple predictors to estimate risk for the presence of a particular condition (diagnostic models) or the occurrence of a certain event in the future (prognostic models). PROBAST (Prediction model Risk Of Bias ASsessment Tool), a tool for assessing the risk of bias (ROB) and applicability of diagnostic and prognostic prediction model studies, was developed by a steering group that considered existing ROB tools and reporting guidelines. The tool was informed by a Delphi procedure involving 38 experts and was refined through piloting. PROBAST is organized into the following 4 domains: participants, predictors, outcome, and analysis. These domains contain a total of 20 signaling questions to facilitate structured judgment of ROB, which was defined to occur when shortcomings in study design, conduct, or analysis lead to systematically distorted estimates of model predictive performance. PROBAST enables a focused and transparent approach to assessing the ROB and applicability of studies that develop, validate, or update prediction models for individualized predictions. Although PROBAST was designed for systematic reviews, it can be used more generally in critical appraisal of prediction model studies. Potential users include organizations supporting decision making, researchers and clinicians who are interested in evidence-based medicine or involved in guideline development, journal editors, and manuscript reviewers.

PROBAST: A Tool to Assess Risk of Bias and Applicability of Prediction Model Studies: Explanation and Elaboration.

Prediction models in health care use predictors to estimate for an individual the probability that a condition or disease is already present (diagnostic model) or will occur in the future (prognostic model). Publications on prediction models have become more common in recent years, and competing prediction models frequently exist for the same outcome or target population. Health care providers, guideline developers, and policymakers are often unsure which model to use or recommend, and in which persons or settings. Hence, systematic reviews of these studies are increasingly demanded, required, and performed. A key part of a systematic review of prediction models is examination of risk of bias and applicability to the intended population and setting. To help reviewers with this process, the authors developed PROBAST (Prediction model Risk Of Bias ASsessment Tool) for studies developing, validating, or updating (for example, extending) prediction models, both diagnostic and prognostic. PROBAST was developed through a consensus process involving a group of experts in the field. It includes 20 signaling questions across 4 domains (participants, predictors, outcome, and analysis). This explanation and elaboration document describes the rationale for including each domain and signaling question and guides researchers, reviewers, readers, and guideline developers in how to use them to assess risk of bias and applicability concerns. All concepts are illustrated with published examples across different topics. The latest version of the PROBAST checklist, accompanying documents, and filled-in examples can be downloaded from www.probast.org.

Rapid Molecular Tests for Influenza, Respiratory Syncytial Virus, and Other Respiratory Viruses: A Systematic Review of Diagnostic Accuracy and Clinical Impact Studies.

We systematically reviewed available evidence from Embase, Medline, and the Cochrane Library on diagnostic accuracy and clinical impact of commercially available rapid (results <3 hours) molecular diagnostics for respiratory viruses as compared to conventional molecular tests. Quality of included studies was assessed using the Quality Assessment of Diagnostic Accuracy Studies criteria for diagnostic test accuracy (DTA) studies, and the Cochrane Risk of Bias Assessment and Risk of Bias in Nonrandomized Studies of Interventions criteria for randomized and observational impact studies, respectively. Sixty-three DTA reports (56 studies) were meta-analyzed with a pooled sensitivity of 90.9% (95% confidence interval [CI], 88.7%-93.1%) and specificity of 96.1% (95% CI, 94.2%-97.9%) for the detection of either influenza virus (n = 29), respiratory syncytial virus (RSV) (n = 1), influenza virus and RSV (n = 19), or a viral panel including influenza virus and RSV (n = 14). The 15 included impact studies (5 randomized) were very heterogeneous and results were therefore inconclusive. However, we suggest that implementation of rapid diagnostics in hospital care settings should be considered.

Antiplatelet Therapy After Noncardioembolic Stroke.

Background and Purpose- We assessed the efficacy and safety of antiplatelet agents after noncardioembolic stroke or transient ischemic attack and examined how these vary according to patients' demographic and clinical characteristics. Methods- We did a network meta-analysis (NMA) of data from 6 randomized trials of the effects of commonly prescribed antiplatelet agents in the long-term (≥3 months) secondary prevention of noncardioembolic stroke or transient ischemic attack. Individual patient data from 43 112 patients were pooled and reanalyzed. Main outcomes were serious vascular events (nonfatal stroke, nonfatal myocardial infarction, or vascular death), major bleeding, and net clinical benefit (serious vascular event or major bleeding). Subgroup analyses were done according to age, sex, ethnicity, hypertension, qualifying diagnosis, type of vessel involved (large versus small vessel disease), and time from qualifying event to randomization. Results- Aspirin/dipyridamole combination (RRNMA-adj, 0.83; 95% CI, 0.74-0.94) significantly reduced the risk of vascular events compared with aspirin, as did clopidogrel (RRNMA-adj, 0.88; 95% CI, 0.78-0.98), and aspirin/clopidogrel combination (RRNMA-adj, 0.83; 95% CI, 0.71-0.96). Clopidogrel caused significantly less major bleeding and intracranial hemorrhage than aspirin, aspirin/dipyridamole combination, and aspirin/clopidogrel combination. Aspirin/clopidogrel combination caused significantly more major bleeding than aspirin, aspirin/dipyridamole combination, and clopidogrel. Net clinical benefit was similar for clopidogrel and aspirin/dipyridamole combination (RRNMA-adj, 0.99; 95% CI, 0.93-1.05). Subgroup analyses showed no heterogeneity of treatment effectiveness across prespecified subgroups. The excess risk of major bleeding associated with aspirin/clopidogrel combination compared with clopidogrel alone was higher in patients aged <65 years than it was in patients ≥65 years (RRNMA-adj, 3.9 versus 1.7). Conclusions- Results favor clopidogrel and aspirin/dipyridamole combination for long-term secondary prevention after noncardioembolic stroke or transient ischemic attack, regardless of patient characteristics. Aspirin/clopidogrel combination was associated with a significantly higher risk of major bleeding compared with other antiplatelet regimens.

Performance of the Framingham risk models and pooled cohort equations for predicting 10-year risk of cardiovascular disease: a systematic review and meta-analysis.

BACKGROUND: The Framingham risk models and pooled cohort equations (PCE) are widely used and advocated in guidelines for predicting 10-year risk of developing coronary heart disease (CHD) and cardiovascular disease (CVD) in the general population. Over the past few decades, these models have been extensively validated within different populations, which provided mounting evidence that local tailoring is often necessary to obtain accurate predictions. The objective is to systematically review and summarize the predictive performance of three widely advocated cardiovascular risk prediction models (Framingham Wilson 1998, Framingham ATP III 2002 and PCE 2013) in men and women separately, to assess the generalizability of performance across different subgroups and geographical regions, and to determine sources of heterogeneity in the findings across studies. METHODS: A search was performed in October 2017 to identify studies investigating the predictive performance of the aforementioned models. Studies were included if they externally validated one or more of the original models in the general population for the same outcome as the original model. We assessed risk of bias for each validation and extracted data on population characteristics and model performance. Performance estimates (observed versus expected (OE) ratio and c-statistic) were summarized using a random effects models and sources of heterogeneity were explored with meta-regression. RESULTS: The search identified 1585 studies, of which 38 were included, describing a total of 112 external validations. Results indicate that, on average, all models overestimate the 10-year risk of CHD and CVD (pooled OE ratio ranged from 0.58 (95% CI 0.43-0.73; Wilson men) to 0.79 (95% CI 0.60-0.97; ATP III women)). Overestimation was most pronounced for high-risk individuals and European populations. Further, discriminative performance was better in women for all models. There was considerable heterogeneity in the c-statistic between studies, likely due to differences in population characteristics. CONCLUSIONS: The Framingham Wilson, ATP III and PCE discriminate comparably well but all overestimate the risk of developing CVD, especially in higher risk populations. Because the extent of miscalibration substantially varied across settings, we highly recommend that researchers further explore reasons for overprediction and that the models be updated for specific populations.

Candidate Biomarkers for the Diagnosis of Transient Ischemic Attack: A Systematic Review.

BACKGROUND AND PURPOSE: A rapid serum biomarker that confirms or rules out a transient ischemic attack (TIA) would be of great value in clinical practice. We aimed to systematically review current evidence for the diagnostic accuracy of blood biomarkers in the early diagnosis of TIA. METHODS: This is a systematic review with quality appraisal of individual studies using the QUADAS-2 tool. MEDLINE and EMBASE databases were searched up to May 1, 2017, to select primary diagnostic accuracy studies evaluating potential biomarkers in blood for the diagnosis of TIA or ischemic stroke. RESULTS: Of 4,215 studies retrieved, 78 met our eligibility criteria. Forty-five studies restricted their population to ischemic stroke patients, 32 included both TIA and ischemic stroke patients, and only one study was restricted to TIA patients. In total 62/78 (79.5%) studies had a case-control design comparing TIA or stroke patients with healthy subjects. Overall, 125 single biomarkers and 5 biomarker panels were studied, with a median number of participants per study of 92.0 (interquartile range 44.8-144.5), varying from 8 to 915. Sufficient information to extract 2 × 2 tables was available for 35 (44.9%) articles, and for 60 (48.0 %) biomarkers. Several markers, such as NR2A/B (antibodies), Parkinson 7, nucleoside diphosphate kinase A, ubiquitin fusion degradation protein-1, and heart-type fatty acid binding protein, have shown moderate to high diagnostic accuracy in multiple studies. CONCLUSIONS: Although the methodological quality of studies evaluating biomarkers of brain ischemia was poor, several biomarkers have shown the potential to detect transient brain ischemia in an early phase. Diagnostic accuracy studies in suspected cases of TIA are needed to determine their true clinical value.

Statistical approaches to identify subgroups in meta-analysis of individual participant data: a simulation study.

BACKGROUND: Individual participant data meta-analysis (IPD-MA) is considered the gold standard for investigating subgroup effects. Frequently used regression-based approaches to detect subgroups in IPD-MA are: meta-regression, per-subgroup meta-analysis (PS-MA), meta-analysis of interaction terms (MA-IT), naive one-stage IPD-MA (ignoring potential study-level confounding), and centred one-stage IPD-MA (accounting for potential study-level confounding). Clear guidance on the analyses is lacking and clinical researchers may use approaches with suboptimal efficiency to investigate subgroup effects in an IPD setting. Therefore, our aim is to overview and compare the aforementioned methods, and provide recommendations over which should be preferred. METHODS: We conducted a simulation study where we generated IPD of randomised trials and varied the magnitude of subgroup effect (0, 25, 50% relative reduction), between-study treatment effect heterogeneity (none, medium, large), ecological bias (none, quantitative, qualitative), sample size (50,100,200), and number of trials (5,10) for binary, continuous and time-to-event outcomes. For each scenario, we assessed the power, false positive rate (FPR) and bias of aforementioned five approaches. RESULTS: Naive and centred IPD-MA yielded the highest power, whilst preserving acceptable FPR around the nominal 5% in all scenarios. Centred IPD-MA showed slightly less biased estimates than naïve IPD-MA. Similar results were obtained for MA-IT, except when analysing binary outcomes (where it yielded less power and FPR < 5%). PS-MA showed similar power as MA-IT in non-heterogeneous scenarios, but power collapsed as heterogeneity increased, and decreased even more in the presence of ecological bias. PS-MA suffered from too high FPRs in non-heterogeneous settings and showed biased estimates in all scenarios. Meta-regression showed poor power (< 20%) in all scenarios and completely biased results in settings with qualitative ecological bias. CONCLUSIONS: Our results indicate that subgroup detection in IPD-MA requires careful modelling. Naive and centred IPD-MA performed equally well, but due to less bias of the estimates in the presence of ecological bias, we recommend the latter.

Computed tomography for diagnosis of acute appendicitis in adults.

BACKGROUND: Diagnosing acute appendicitis (appendicitis) based on clinical evaluation, blood testing, and urinalysis can be difficult. Therefore, in persons with suspected appendicitis, abdominopelvic computed tomography (CT) is often used as an add-on test following the initial evaluation to reduce remaining diagnostic uncertainty. The aim of using CT is to assist the clinician in discriminating between persons who need surgery with appendicectomy and persons who do not. OBJECTIVES: Primary objective Our primary objective was to evaluate the accuracy of CT for diagnosing appendicitis in adults with suspected appendicitis. Secondary objectives Our secondary objectives were to compare the accuracy of contrast-enhanced versus non-contrast-enhanced CT, to compare the accuracy of low-dose versus standard-dose CT, and to explore the influence of CT-scanner generation, radiologist experience, degree of clinical suspicion of appendicitis, and aspects of methodological quality on diagnostic accuracy. SEARCH METHODS: We searched MEDLINE, Embase, and Science Citation Index until 16 June 2017. We also searched references lists. We did not exclude studies on the basis of language or publication status. SELECTION CRITERIA: We included prospective studies that compared results of CT versus outcomes of a reference standard in adults (> 14 years of age) with suspected appendicitis. We excluded studies recruiting only pregnant women; studies in persons with abdominal pain at any location and with no particular suspicion of appendicitis; studies in which all participants had undergone ultrasonography (US) before CT and the decision to perform CT depended on the US outcome; studies using a case-control design; studies with fewer than 10 participants; and studies that did not report the numbers of true-positives, false-positives, false-negatives, and true-negatives. Two review authors independently screened and selected studies for inclusion. DATA COLLECTION AND ANALYSIS: Two review authors independently collected the data from each study and evaluated methodological quality according to the Quality Assessment of Studies of Diagnostic Accuracy - Revised (QUADAS-2) tool. We used the bivariate random-effects model to obtain summary estimates of sensitivity and specificity. MAIN RESULTS: We identified 64 studies including 71 separate study populations with a total of 10,280 participants (4583 with and 5697 without acute appendicitis). Estimates of sensitivity ranged from 0.72 to 1.0 and estimates of specificity ranged from 0.5 to 1.0 across the 71 study populations. Summary sensitivity was 0.95 (95% confidence interval (CI) 0.93 to 0.96), and summary specificity was 0.94 (95% CI 0.92 to 0.95). At the median prevalence of appendicitis (0.43), the probability of having appendicitis following a positive CT result was 0.92 (95% CI 0.90 to 0.94), and the probability of having appendicitis following a negative CT result was 0.04 (95% CI 0.03 to 0.05). In subgroup analyses according to contrast enhancement, summary sensitivity was higher for CT with intravenous contrast (0.96, 95% CI 0.92 to 0.98), CT with rectal contrast (0.97, 95% CI 0.93 to 0.99), and CT with intravenous and oral contrast enhancement (0.96, 95% CI 0.93 to 0.98) than for unenhanced CT (0.91, 95% CI 0.87 to 0.93). Summary sensitivity of CT with oral contrast enhancement (0.89, 95% CI 0.81 to 0.94) and unenhanced CT was similar. Results show practically no differences in summary specificity, which varied from 0.93 (95% CI 0.90 to 0.95) to 0.95 (95% CI 0.90 to 0.98) between subgroups. Summary sensitivity for low-dose CT (0.94, 95% 0.90 to 0.97) was similar to summary sensitivity for standard-dose or unspecified-dose CT (0.95, 95% 0.93 to 0.96); summary specificity did not differ between low-dose and standard-dose or unspecified-dose CT. No studies had high methodological quality as evaluated by the QUADAS-2 tool. Major methodological problems were poor reference standards and partial verification primarily due to inadequate and incomplete follow-up in persons who did not have surgery. AUTHORS' CONCLUSIONS: The sensitivity and specificity of CT for diagnosing appendicitis in adults are high. Unenhanced standard-dose CT appears to have lower sensitivity than standard-dose CT with intravenous, rectal, or oral and intravenous contrast enhancement. Use of different types of contrast enhancement or no enhancement does not appear to affect specificity. Differences in sensitivity and specificity between low-dose and standard-dose CT appear to be negligible. The results of this review should be interpreted with caution for two reasons. First, these results are based on studies of low methodological quality. Second, the comparisons between types of contrast enhancement and radiation dose may be unreliable because they are based on indirect comparisons that may be confounded by other factors.

Overinterpretation and misreporting of prognostic factor studies in oncology: a systematic review.

BACKGROUND: Cancer prognostic biomarkers have shown disappointing clinical applicability. The objective of this study was to classify and estimate how study results are overinterpreted and misreported in prognostic factor studies in oncology. METHODS: This systematic review focused on 17 oncology journals with an impact factor above 7. PubMed was searched for primary clinical studies published in 2015, evaluating prognostic factors. We developed a classification system, focusing on three domains: misleading reporting (selective, incomplete reporting, misreporting), misleading interpretation (unreliable statistical analysis, spin) and misleading extrapolation of the results (claiming irrelevant clinical applicability, ignoring uncertainty). RESULTS: Our search identified 10,844 articles. The 98 studies included investigated a median of two prognostic factors (Q1-Q3, 1-7). The prognostic factors' effects were selectively and incompletely reported in 35/98 and 24/98 full texts, respectively. Twenty-nine articles used linguistic spin in the form of strong statements. Linguistic spin rejecting non-significant results was found in 34 full-text results and 15 abstract results sections. One in five articles had discussion and/or abstract conclusions that were inconsistent with the study findings. Sixteen reports had discrepancies between their full-text and abstract conclusions. CONCLUSIONS: Our study provides evidence of frequent overinterpretation of findings of prognostic factor assessment in high-impact medical oncology journals.

Poor reporting of multivariable prediction model studies: towards a targeted implementation strategy of the TRIPOD statement.

BACKGROUND: As complete reporting is essential to judge the validity and applicability of multivariable prediction models, a guideline for the Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD) was introduced. We assessed the completeness of reporting of prediction model studies published just before the introduction of the TRIPOD statement, to refine and tailor its implementation strategy. METHODS: Within each of 37 clinical domains, 10 journals with the highest journal impact factor were selected. A PubMed search was performed to identify prediction model studies published before the launch of TRIPOD in these journals (May 2014). Eligible publications reported on the development or external validation of a multivariable prediction model (either diagnostic or prognostic) or on the incremental value of adding a predictor to an existing model. RESULTS: We included 146 publications (84% prognostic), from which we assessed 170 models: 73 (43%) on model development, 43 (25%) on external validation, 33 (19%) on incremental value, and 21 (12%) on combined development and external validation of the same model. Overall, publications adhered to a median of 44% (25th-75th percentile 35-52%) of TRIPOD items, with 44% (35-53%) for prognostic and 41% (34-48%) for diagnostic models. TRIPOD items that were completely reported for less than 25% of the models concerned abstract (2%), title (5%), blinding of predictor assessment (6%), comparison of development and validation data (11%), model updating (14%), model performance (14%), model specification (17%), characteristics of participants (21%), model performance measures (methods) (21%), and model-building procedures (24%). Most often reported were TRIPOD items regarding overall interpretation (96%), source of data (95%), and risk groups (90%). CONCLUSIONS: More than half of the items considered essential for transparent reporting were not fully addressed in publications of multivariable prediction model studies. Essential information for using a model in individual risk prediction, i.e. model specifications and model performance, was incomplete for more than 80% of the models. Items that require improved reporting are title, abstract, and model-building procedures, as they are crucial for identification and external validation of prediction models.

Neurocognitive functioning over the course of trauma-focused psychotherapy for PTSD: Changes in verbal memory and executive functioning.

OBJECTIVES: Individuals with post-traumatic stress disorder (PTSD) have neurocognitive deficits in verbal memory and executive functioning. In this study, we examined whether memory and executive functioning changed over the course of treatment and which clinical variables were associated with change. DESIGN: Neuropsychological assessments were administered at baseline and endpoint of a randomized controlled trial as secondary outcome. METHODS: Trauma survivors (n = 88) diagnosed with PTSD received trauma-focused psychotherapy within a 17-week randomized controlled trial. Neuropsychological tests were the California Verbal Learning Test, Rivermead Behavioural Memory Test, Stroop Color Word Test, and Trail Making Test. RESULTS: Significant, small- to medium-sized improvements in verbal memory, information processing speed, and executive functioning were found after trauma-focused psychotherapy (Cohen's d 0.16-0.68). Greater PTSD symptom decrease was significantly related to better post-treatment neurocognitive performance (all p < .005). Patients with comorbid depression improved more than patients with PTSD alone on interference tasks (p < .01). No differences emerged between treatment conditions and between patients on serotonergic antidepressants and those who were not. CONCLUSIONS: This study suggests that neurocognitive deficits in PTSD can improve over the course of trauma-focused psychotherapy and are therefore at least partly reversible. Improvements over treatment are in line with previous neuropsychological and neuroimaging studies and effect sizes exceed those of practice effects. Future research should determine whether these changes translate into improved functioning in the daily lives of the patients. PRACTITIONER POINTS: Patients with PTSD have difficulties performing verbal memory tasks (e.g., remembering a grocery list, recall of a story) and executive functioning tasks (e.g., shifting attention between two tasks, ignoring irrelevant information to complete a task). Verbal memory, information processing speed, and executive functioning significantly improved in patients with post-traumatic stress disorder over the course of trauma-focused psychotherapy. Improvements were equal in size for two different trauma-focused psychotherapies (Eye movement desensitization and reprocessing therapy and brief eclectic psychotherapy for PTSD). Medium-sized effects were found for recall of a story, whereas effects in other aspects of verbal memory, information processing speed, and executive functioning were small-sized. No causal attributions can be made because we could not include a control group without treatment for ethical reasons. Findings may be more reflective of patients who completed treatment than patients who prematurely dropped out as completers were overrepresented in our sample.

Effect of Using the HEART Score in Patients With Chest Pain in the Emergency Department: A Stepped-Wedge, Cluster Randomized Trial.

BACKGROUND: The HEART (History, Electrocardiogram, Age, Risk factors, and initial Troponin) score is an easy-to-apply instrument to stratify patients with chest pain according to their short-term risk for major adverse cardiac events (MACEs), but its effect on daily practice is unknown. OBJECTIVE: To measure the effect of use of the HEART score on patient outcomes and use of health care resources. DESIGN: Stepped-wedge, cluster randomized trial. (ClinicalTrials.gov: NCT01756846). SETTING: Emergency departments in 9 Dutch hospitals. PATIENTS: Unselected patients with chest pain presenting at emergency departments in 2013 and 2014. INTERVENTION: All hospitals started with usual care. Every 6 weeks, 1 hospital was randomly assigned to switch to "HEART care," during which physicians calculated the HEART score to guide patient management. MEASUREMENTS: For safety, a noninferiority margin of a 3.0% absolute increase in MACEs within 6 weeks was set. Other outcomes included use of health care resources, quality of life, and cost-effectiveness. RESULTS: A total of 3648 patients were included (1827 receiving usual care and 1821 receiving HEART care). Six-week incidence of MACEs during HEART care was 1.3% lower than during usual care (upper limit of the 1-sided 95% CI, 2.1% [within the noninferiority margin of 3.0%]). In low-risk patients, incidence of MACEs was 2.0% (95% CI, 1.2% to 3.3%). No statistically significant differences in early discharge, readmissions, recurrent emergency department visits, outpatient visits, or visits to general practitioners were observed. LIMITATION: Physicians were hesitant to refrain from admission and diagnostic tests in patients classified as low risk by the HEART score. CONCLUSION: Using the HEART score during initial assessment of patients with chest pain is safe, but the effect on health care resources is limited, possibly due to nonadherence to management recommendations. PRIMARY FUNDING SOURCE: Netherlands Organisation for Health Research and Development.

Selection of individuals for genetic testing for familial hypercholesterolaemia: development and external validation of a prediction model for the presence of a mutation causing familial hypercholesterolaemia.

AIMS: Familial hypercholesterolaemia (FH) is an autosomal dominant disease that warrants early diagnosis to prevent premature cardiovascular disease (CVD). However, genetic testing to make a definite diagnosis is costly, and careful selection of eligible subjects is important. Unfortunately, accuracy of current diagnostic criteria is poor, especially in young individuals. We therefore developed and validated a model to predict the presence of an FH causing mutation in persons referred by general practitioners. METHODS AND RESULTS: All participants in the Dutch FH screening programme from 1994 to 2014 were included in the development cohort. The validation cohort consisted of consecutive patients, suspected for FH, attending the outpatient lipid clinic in Saguenay (Quebec) from 1993 to 2014. Cross-sectional data were available on medical history, lipid profile, and DNA analysis. Multivariable logistic regression analysis was used for model development. The primary outcome was the presence of a deleterious FH mutation. The development cohort comprised 26 167 FH patients and 37 939 unaffected relatives. Our final model included age; sex; levels of low-density lipoprotein cholesterol (LDL-C), high-density lipoprotein cholesterol, and triglycerides; history and age of CVD; use of statins; smoking; alcohol; and presence of hypertension. The area under the receiver operating characteristic curve (AUC) was 85.4% (95% CI: 85.0-85.9). The calibration slope was 1.02 (where 1.00 is optimal). In the validation cohort (1436 FH patients and 1767 unaffected persons), the AUC was 95.4% (95% CI: 94.7-96.1%) and the calibration slope 1.06. CONCLUSION: Our model showed good discrimination and calibration. We specifically expect our model to be of added value for young persons set against current diagnostic criteria, since LDL-C and age are now used as continuous predictors. The equation will be available as an online calculator to estimate the probability of the presence of an FH mutation in individual patients. This tool might aid physicians in the decision for referral of patients for molecular testing.