Carl Moons and colleagues provide a checklist and background explanation for critically appraising and extracting data from systematic reviews of prognostic and diagnostic prediction modelling studies. Please see later in the article for the Editors' Summary

Background: Clinical prediction models combine several predictors (risk or prognostic factors) to estimate the risk whether a particular condition is present (diagnostic model) or whether a certain event will occur in the future (prognostic model). Large numbers of diagnostic and prognostic prediction model studies are published each year and a tool facilitating their quality assessment is needed, e.g. to support systematic reviews and evidence syntheses.Objective: To introduce and describe the development of PROBAST, a tool for assessing the risk of bias and applicability of prediction model studies.Methods: Web-based Delphi procedure (involving 40 experts in the field of prediction model research) and refinement of the tool through piloting. The scope of PROBAST was determined with consideration of existing risk of bias tools and reporting guidelines, such as CHARMS, QUADAS, QUIPS, and TRIPOD.Results: After seven Delphi rounds, a final tool was developed which utilises a domain-based structure supported by signalling questions. PROBAST assesses the risk of bias of prediction model studies and any concerns for their applicability. Studies that PROBAST can be used for include those developing, validating, and extending a prediction model. We define risk of bias to occur when shortcomings in the study design, conduct or analysis lead to systematically distorted estimates of model predictive performance or to an inadequate model to address the research question. The predictive performance is typically evaluated using calibration and discrimination, and sometimes (notably in diagnostic model studies) classification measures. Applicability refers to the extent to which the prediction model study matches the systematic review question in terms of the target population, predictors, or outcomes of interest. PROBAST comprises 20 signalling questions grouped into four domains: participant selection, predictors, outcome, and analysis.Conclusions: PROBAST can be used to assess the risk of bias and any concerns for applicability of studies developing, validating or extending (adjusting) prediction, both diagnostic and prognostic, models.

Types of Predictors, Outcomes, and Modeling TechniquesPROBAST can be used to assess any type of diagnostic or prognostic prediction model aimed at individualized predictions regardless of the predictors used; outcomes being predicted; or methods used to develop, validate, or update (for example, extend) the model.Predictors range from demographic characteristics, medical history, and physical examination results; to imaging results, electrophysiology, blood, urine, or tissue measurements, and disease stages or characteristics; to results from "omics" and other new biological measurements. Predictors are also called covariates, risk indicators, prognostic factors, determinants, index test results, or independent variables (4, 6 -8, 49, 50, 55, 56, 57).PROBAST distinguishes between candidate predic-Prediction model external validation: These studies aim to assess the predictive performance of existing prediction models using data external to the development sample (i.e., from different participants).Adopted from the TRIPOD (Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis) and CHARMS (CHecklist for critical Appraisal and data extraction for systematic Reviews of prediction Modelling Studies) guidance (8, 16).

Walter Bouwmeester and colleagues investigated the reporting and methods of prediction studies in 2008, in six high-impact general medical journals, and found that the majority of prediction studies do not follow current methodological recommendations.

BackgroundThe World Health Organisation estimates that by 2030 there will be approximately 350 million people with type 2 diabetes. Associated with renal complications, heart disease, stroke and peripheral vascular disease, early identification of patients with undiagnosed type 2 diabetes or those at an increased risk of developing type 2 diabetes is an important challenge. We sought to systematically review and critically assess the conduct and reporting of methods used to develop risk prediction models for predicting the risk of having undiagnosed (prevalent) or future risk of developing (incident) type 2 diabetes in adults.MethodsWe conducted a systematic search of PubMed and EMBASE databases to identify studies published before May 2011 that describe the development of models combining two or more variables to predict the risk of prevalent or incident type 2 diabetes. We extracted key information that describes aspects of developing a prediction model including study design, sample size and number of events, outcome definition, risk predictor selection and coding, missing data, model-building strategies and aspects of performance.ResultsThirty-nine studies comprising 43 risk prediction models were included. Seventeen studies (44%) reported the development of models to predict incident type 2 diabetes, whilst 15 studies (38%) described the derivation of models to predict prevalent type 2 diabetes. In nine studies (23%), the number of events per variable was less than ten, whilst in fourteen studies there was insufficient information reported for this measure to be calculated. The number of candidate risk predictors ranged from four to sixty-four, and in seven studies it was unclear how many risk predictors were considered. A method, not recommended to select risk predictors for inclusion in the multivariate model, using statistical significance from univariate screening was carried out in eight studies (21%), whilst the selection procedure was unclear in ten studies (26%). Twenty-one risk prediction models (49%) were developed by categorising all continuous risk predictors. The treatment and handling of missing data were not reported in 16 studies (41%).ConclusionsWe found widespread use of poor methods that could jeopardise model development, including univariate pre-screening of variables, categorisation of continuous risk predictors and poor handling of missing data. The use of poor methods affects the reliability of the prediction model and ultimately compromises the accuracy of the probability estimates of having undiagnosed type 2 diabetes or the predicted risk of developing type 2 diabetes. In addition, many studies were characterised by a generally poor level of reporting, with many key details to objectively judge the usefulness of the models often omitted.

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SummaryBackgroundPublic objection to autopsy has led to a search for minimally invasive alternatives. Imaging has potential, but its accuracy is unknown. We aimed to identify the accuracy of post-mortem CT and MRI compared with full autopsy in a large series of adult deaths.MethodsThis study was undertaken at two UK centres in Manchester and Oxford between April, 2006, and November, 2008. We used whole-body CT and MRI followed by full autopsy to investigate a series of adult deaths that were reported to the coroner. CT and MRI scans were reported independently, each by two radiologists who were masked to the autopsy findings. All four radiologists then produced a consensus report based on both techniques, recorded their confidence in cause of death, and identified whether autopsy was needed.FindingsWe assessed 182 unselected cases. The major discrepancy rate between cause of death identified by radiology and autopsy was 32% (95% CI 26–40) for CT, 43% (36–50) for MRI, and 30% (24–37) for the consensus radiology report; 10% (3–17) lower for CT than for MRI. Radiologists indicated that autopsy was not needed in 62 (34%; 95% CI 28–41) of 182 cases for CT reports, 76 (42%; 35–49) of 182 cases for MRI reports, and 88 (48%; 41–56) of 182 cases for consensus reports. Of these cases, the major discrepancy rate compared with autopsy was 16% (95% CI 9–27), 21% (13–32), and 16% (10–25), respectively, which is significantly lower (p<0·0001) than for cases with no definite cause of death. The most common imaging errors in identification of cause of death were ischaemic heart disease (n=27), pulmonary embolism (11), pneumonia (13), and intra-abdominal lesions (16).InterpretationWe found that, compared with traditional autopsy, CT was a more accurate imaging technique than MRI for providing a cause of death. The error rate when radiologists provided a confident cause of death was similar to that for clinical death certificates, and could therefore be acceptable for medicolegal purposes. However, common causes of sudden death are frequently missed on CT and MRI, and, unless these weaknesses are addressed, systematic errors in mortality statistics would result if imaging were to replace conventional autopsy.FundingPolicy Research Programme, Department of Health, UK.

The aims of severe perioperative bleeding management are three-fold. First, preoperative identification by anamesis and laboratory testing of those patients for whom the perioperative bleeding risk may be increased. Second, implementation of strategies for correcting preoperative anaemia and stabilisation of the macro- and microcirculations in order to optimise the patient's tolerance to bleeding. Third, targeted procoagulant interventions to reduce the amount of bleeding, morbidity, mortality and costs. The purpose of these guidelines is to provide an overview of current knowledge on the subject with an assessment of the quality of the evidence in order to allow anaesthetists throughout Europe to integrate this knowledge into daily patient care wherever possible. The Guidelines Committee of the European Society of Anaesthesiology (ESA) formed a task force with members of scientific subcommittees and individual expert members of the ESA. Electronic databases were searched without language restrictions from the year 2000 until 2012. These searches produced 20 664 abstracts. Relevant systematic reviews with meta-analyses, randomised controlled trials, cohort studies, case-control studies and cross-sectional surveys were selected. At the suggestion of the ESA Guideline Committee, the Scottish Intercollegiate Guidelines Network (SIGN) grading system was initially used to assess the level of evidence and to grade recommendations. During the process of guideline development, the official position of the ESA changed to favour the Grading of Recommendations Assessment, Development and Evaluation (GRADE) system. This report includes general recommendations as well as specific recommendations in various fields of surgical interventions. The final draft guideline was posted on the ESA website for four weeks and the link was sent to all ESA members. Comments were collated and the guidelines amended as appropriate. When the final draft was complete, the Guidelines Committee and ESA Board ratified the guidelines.