Dataset Information

A new framework for the documentation and interpretation of oral food challenges in population-based and clinical research.

ABSTRACT:

Background

The conduct of oral food challenges as the preferred diagnostic standard for food allergy (FA) was harmonized over the last years. However, documentation and interpretation of challenge results, particularly in research settings, are not sufficiently standardized to allow valid comparisons between studies. Our aim was to develop a diagnostic toolbox to capture and report clinical observations in double-blind placebo-controlled food challenges (DBPCFC).

Methods

A group of experienced allergists, paediatricians, dieticians, epidemiologists and data managers developed generic case report forms and standard operating procedures for DBPCFCs and piloted them in three clinical centres. The follow-up of the EuroPrevall/iFAAM birth cohort and other iFAAM work packages applied these methods.

Recommendations

A set of newly developed questionnaire or interview items capture the history of FA. Together with sensitization status, this forms the basis for the decision to perform a DBPCFC, following a standardized decision algorithm. A generic form including details about severity and timing captures signs and symptoms observed during or after the procedures. In contrast to the commonly used dichotomous outcome FA vs no FA, the allergy status is interpreted in multiple categories to reflect the complexity of clinical decision-making.

Conclusion

The proposed toolbox sets a standard for improved documentation and harmonized interpretation of DBPCFCs. By a detailed documentation and common terminology for communicating outcomes, these tools hope to reduce the influence of subjective judgment of supervising physicians. All forms are publicly available for further evolution and free use in clinical and research settings.

SUBMITTER: Grabenhenrich LB

PROVIDER: S-EPMC5324701 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Similar Datasets

Project description:Advancing technologies have facilitated the ever-widening application of genetic markers such as microsatellites into new systems and research questions in biology. In light of the data and experience accumulated from several years of using microsatellites, we present here a literature review that synthesizes the limitations of microsatellites in population genetic studies. With a focus on population structure, we review the widely used fixation (F ST) statistics and Bayesian clustering algorithms and find that the former can be confusing and problematic for microsatellites and that the latter may be confounded by complex population models and lack power in certain cases. Clustering, multivariate analyses, and diversity-based statistics are increasingly being applied to infer population structure, but in some instances these methods lack formalization with microsatellites. Migration-specific methods perform well only under narrow constraints. We also examine the use of microsatellites for inferring effective population size, changes in population size, and deeper demographic history, and find that these methods are untested and/or highly context-dependent. Overall, each method possesses important weaknesses for use with microsatellites, and there are significant constraints on inferences commonly made using microsatellite markers in the areas of population structure, admixture, and effective population size. To ameliorate and better understand these constraints, researchers are encouraged to analyze simulated datasets both prior to and following data collection and analysis, the latter of which is formalized within the approximate Bayesian computation framework. We also examine trends in the literature and show that microsatellites continue to be widely used, especially in non-human subject areas. This review assists with study design and molecular marker selection, facilitates sound interpretation of microsatellite data while fostering respect for their practical limitations, and identifies lessons that could be applied toward emerging markers and high-throughput technologies in population genetics.