Transformation of Sequential Hospital and Outpatient Laboratory Data into Between-Day Reference Change Values. (8th February 2022) - Cembrowski, George S; Lyon, Andrew W; McCudden, Christopher; Qiu, Yuelin; Xu, Qian; Mei, Junyi; Tran, David V; Sadrzadeh, S M Hossein; Cervinski, Mark A
Abstract: Background: Serial differences between intrapatient consecutive measurements can be transformed into Taylor series of variation vs time with the intersection at time = 0 ( y 0 ) equal to the total variation (analytical + biological + preanalytical). With small preanalytical variation, y 0, expressed as a percentage of the mean, is equal to the variable component of the reference change value (RCV) calculation: (CV A 2 + CV I 2 ) 1/2 . Methods: We determined the between-day RCV of patient data for 17 analytes and compared them to healthy participants' RCVs. We analyzed 653 consecutive days of Dartmouth-Hitchcock Roche Modular general chemistry data (4.2 million results: 60% inpatient, 40% outpatient). The serial patient values of 17 analytes were transformed into 95% 2-sided RCV (RCVAlternate ), and 3 sets of RCVhealthy were calculated from 3 Roche Modular analyzers' quality control summaries and CVI derived from biological variation (BV) studies using healthy participants. Results: The RCVAlternate values are similar to RCVhealthy derived from known components of variation. For sodium, chloride, bicarbonate calcium, magnesium, phosphate, alanine aminotransferase, albumin, and total protein, the RCVs are equivalent. As expected, increased variation was found for glucose, aspartate aminotransferase, creatinine, and potassium. Direct bilirubin and urea demonstrated lower variation. Conclusions: Our RCVAlternate values integrate known and unknown components of analytic, biologic, and preanalytic variation, and depict the variations observed by clinical teams that make medical decisions based on the test values. The RCVAlternate values are similar to the RCVhealthy values derived from known components of variation and suggest further studies to better understand the results being generated on actual patients tested in typical laboratory environments.