Wellcome Open Research (Jul 2024)
Interpreting pathology test result values with comparators (< , >) in Electronic Health Records research: an OpenSAFELY short data report [version 2; peer review: 2 approved]
Abstract
Background Numeric results of pathology tests are sometimes returned as a range rather than a precise value, e.g. “ etc) are typically stored separately to the numeric values and often ignored, but may influence interpretation. Methods With the approval of NHS England we used routine clinical data from 24 million patients in OpenSAFELY to identify pathology tests with comparators commonly attached to result values. For each test we report: the proportion returned with comparators present, split by comparator type and geographic region; the specific numeric result values returned with comparators, and the associated reference limits. Results We identified 11 common test codes where at least one in four results had comparators. Three codes related to glomerular filtration rate (GFR) tests/calculations, with 31-45% of results returned with “≥” comparators. At least 90% of tests with numeric values 60 and 90 represented ranges (≥60 and ≥90 respectively) rather than exact values. The other tests - four blood tests (Nucleated red blood cell count, Plasma C reactive protein, Tissue transglutaminase immunoglobulin A, and Rheumatoid factor), two urine tests (albumin/microalbumin) and two faecal tests (calprotectin and quantitative faecal immunochemical test) - were returned with “≤” comparators (29-86%). Conclusions Comparators appear commonly in certain pathology tests in electronic health records. For most common affected tests, we expect there to be minimal implications for researchers for most use-cases. However, care should be taken around whether results falling exactly on clinical threshold values should be considered “normal” or “abnormal”. Results from GFR tests/calculations cannot reliably distinguish between mild kidney disease (stage G2, 60-<90) versus healthy kidney function (90+). More broadly, health data researchers using numeric test result values should consider the impact of comparators.
