BackgroundIn the era of evidence based medicine, with systematic reviews as its cornerstone, adequate quality assessment tools should be available. There is currently a lack of a systematically developed and evaluated tool for the assessment of diagnostic accuracy studies. The aim of this project was to combine empirical evidence and expert opinion in a formal consensus method to develop a tool to be used in systematic reviews to assess the quality of primary studies of diagnostic accuracy.MethodsWe conducted a Delphi procedure to develop the quality assessment tool by refining an initial list of items. Members of the Delphi panel were experts in the area of diagnostic research. The results of three previously conducted reviews of the diagnostic literature were used to generate a list of potential items for inclusion in the tool and to provide an evidence base upon which to develop the tool.ResultsA total of nine experts in the field of diagnostics took part in the Delphi procedure. The Delphi procedure consisted of four rounds, after which agreement was reached on the items to be included in the tool which we have called QUADAS. The initial list of 28 items was reduced to fourteen items in the final tool. Items included covered patient spectrum, reference standard, disease progression bias, verification bias, review bias, clinical review bias, incorporation bias, test execution, study withdrawals, and indeterminate results. The QUADAS tool is presented together with guidelines for scoring each of the items included in the tool.ConclusionsThis project has produced an evidence based quality assessment tool to be used in systematic reviews of diagnostic accuracy studies. Further work to determine the usability and validity of the tool continues.

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).

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