Benford's law and metabolomics: A tale of numbers and blood

Leading digit bias in hemoglobin thresholds for red cell transfusion

BACKGROUND

Leading digit bias is a heuristic whereby humans overemphasize the left-most digit when evaluating numbers (e.g., 9.99 vs. 10.00). The bias might affect the interpretation of hemoglobin results and influence red cell transfusion in hospitalized patients.

STUDY DESIGN AND METHODS

Adults who received a red cell transfusion while registered at the University Health Network (Toronto, Canada) between January 1, 2016 and January 1, 2022 (n = 6 years) were included. The primary analysis excluded apheresis, red cell disorders, radiology suites, and operating rooms. The primary comparison was a regression discontinuity analysis of transfusion occurrence above and below the hemoglobin threshold of 79 g/L (local units). Additional analyses tested other leading digit and control thresholds (71, 81, and 91 g/L). Secondary analyses explored temporal covariates and clinical subgroups.

RESULTS

A total of 211,872 red cell transfusions were identified over the study period (median pre-transfusion hemoglobin 76 g/L; interquartile range = 69-92 g/L), with 107,790 inpatient transfusions in the primary analysis. The 79 g/L threshold showed 815 fewer red cell units above the threshold (95% confidence interval [CI]: -1215 to -415). The 69 g/L threshold showed 2813 fewer transfused units (95% CI: -4407 to -1220), and 89 g/L showed 40 fewer units (95% CI: -408 to 328). The effect was accentuated during daytime, weekday, and May-June months, persisted in analyses including all transfusions, and was absent at control thresholds.

CONCLUSION

Leading digit bias might have a modest influence on the decision to transfuse red cells. The findings may inform practice guidelines and quasi-experimental study design in transfusion research.

Metabolomics for personalized medicine: the input of analytical chemistry from biomarker discovery to point-of-care tests

Metabolomics refers to the large-scale detection, quantification, and analysis of small molecules (metabolites) in biological media. Although metabolomics, alone or combined with other omics data, has already demonstrated its relevance for patient stratification in the frame of research projects and clinical studies, much remains to be done to move this approach to the clinical practice. This is especially true in the perspective of being applied to personalized/precision medicine, which aims at stratifying patients according to their risk of developing diseases, and tailoring medical treatments of patients according to individual characteristics in order to improve their efficacy and limit their toxicity. In this review article, we discuss the main challenges linked to analytical chemistry that need to be addressed to foster the implementation of metabolomics in the clinics and the use of the data produced by this approach in personalized medicine. First of all, there are already well-known issues related to untargeted metabolomics workflows at the levels of data production (lack of standardization), metabolite identification (small proportion of annotated features and identified metabolites), and data processing (from automatic detection of features to multi-omic data integration) that hamper the inter-operability and reusability of metabolomics data. Furthermore, the outputs of metabolomics workflows are complex molecular signatures of few tens of metabolites, often with small abundance variations, and obtained with expensive laboratory equipment. It is thus necessary to simplify these molecular signatures so that they can be produced and used in the field. This last point, which is still poorly addressed by the metabolomics community, may be crucial in a near future with the increased availability of molecular signatures of medical relevance and the increased societal demand for participatory medicine.