Creating ethnicity-specific reference intervals for lab tests from EHR data

Abstract

USOne Sentence Summary: A novel method for defining population-specific reference intervals of common clinical laboratory tests from electronical health records has better prognostic value than existing reference intervals.. CC-BY-NC-ND 4.0 International license peer-reviewed) is the author/funder. It is made available under a The copyright holder for this preprint (which was not . http://dx.doi.org/10.1101/213892 doi: bioRxiv preprint first posted online Nov. 4, 2017; 2 AbstractThe results of clinical lab tests are an essential component of medical decision-making. To guide interpretation, test results are returned with reference intervals defined by the range in which 95% of values occur in healthy individuals. Clinical laboratories often set their own reference intervals to accommodate local population and instruments variations. This approach is costly and can be biased. We describe a novel data-driven method for using electronic health record data to extract healthy patients' information to define reference intervals. We found that the distributions of many clinical lab tests differ among self-identified racial and ethnic groups (SIREs) in healthy patients. Finally, we derived SIRE-specific reference intervals and provide evidence that these intervals have clinical prognostic value. Specifically, we show that for two lab tests, serum creatinine level and hemoglobin A1C, SIRE-specific reference intervals are more predictive for need for dialysis and development type 2 diabetes than existing reference intervals.

Current clinical use of polygenic scores will risk exacerbating health disparities

“…Defining ethnicity-specific reference intervals is clearly an important problem that can provide fundamental interpretability gains with implications for some major health benefits (e.g. need for dialysis and development of Type 2 diabetes based on ethnicity-specific serum creatinine and hemoglobin A1C reference intervals, respectively) 68 . Simply put, some biomarkers or clinical tests scale directly with health outcomes independent of ancestry, and many others may have distributional differences by ancestry but are equally valid after centering with respect to a readily collected population reference.…”

Using Complete Enumeration to Derive “One-Size-Fits-All” Versus “Subgroup-Specific” Diagnostic Rules for Substance Use Disorder

“…This is not to say that such “customized” diagnostic rules could not be useful. A potential clinical application of these customized diagnostic rules, consistent with the use of racial/ethnic-specific reference ranges in various clinical medicine diagnostic tests (e.g., Rappoport et al, 2019), would be to assess an individual using the full DSM-5 AUD criteria set (or full overall optimal solution) and then apply optimization-derived subalgorithms (i.e., subsets of criteria and diagnostic thresholds) that differ by their given demographic characteristics. This practice would serve to maintain a standard assessment across individuals, rather than requiring the clinician to know the exact subgroup diagnostic rule upfront, which would arguably result in considerable clinician burden.…”

The critical needs and challenges for genetic architecture studies in Africa

“…However, interpretability gaps for current clinical tests are less acutely and consistently worse in non-European populations than genetic risk prediction; the underlying biology remains the same, such that for all diseases, drugs do not routinely work many-fold better in European than African-descent populations. Further, new population-specific interpretation of common clinical lab tests enables better prognostic value than existing reference intervals [80]. In contrast, the most significant and highest frequency genetic variants from GWAS used to predict genetic risk are not likely to be the same across populations, even when the underlying causal variants are the same.…”