Challenges and standards in reporting diagnostic and prognostic biomarker studies

Abstract

The discovery of novel biomarkers is a crucial goal in translational biomedical research. A complete and accurate reporting of biomarker studies, including quantitative prediction models, is fundamental to improve research quality and facilitate their potential incorporation into clinical practice. This paper reviews key problems, guidelines, and challenges in reporting biomarker studies, with an emphasis on diagnostic and prognostic applications in cardiovascular research. Recent advances and recommendations for aiding in peer review, research quality assessment, and the reproducibility of findings, such as diagnostic biosignatures, are discussed. An examination of research recently published in the area of cardiovascular biomarkers was implemented. Such a survey, which was based on a sample of papers deposited in PubMed Central, suggests that there is a need to improve the documentation of biomarker studies in terms of information completeness and clarity, as well as the application of more rigorous quantitative evaluation techniques. There is also room for improving practices in reporting data analysis and research limitations. This survey also suggests that, in comparison with other research areas, the cardiovascular biomarker research domain may not be taking advantage of existing standards for reporting diagnostic accuracy. The review concludes with a discussion of the challenges and recommendations.

Figure 1

Summary of survey results in terms of the proportions of papers complying with each of the reporting aspects shown in Table 5 . Yes = evidence found to suggest compliance with reporting aspect; A1 = report of study modality; A2 = report of predictive accuracy; A3 = comparative assessment; A4 = specification of pre‐processing; A5 = documentation of prediction model documentation; A6 = report of missing data; A7 = report of software; A8 = complete report of statistical significance; A9 = discussion of limitations; A10 = data availability; A11 = model cross‐validation; A12 = reference to reporting guidelines; A13 = discussion of clinical applicability.