Day, P.

Abstract Background Analytical and post-analytical errors are a persistent problem, which can be benchmarked using sigma metrics. Manual processes are associated with error rates of 3–4 sigma. Secondary review of manual reporting is meant to reduce/eliminate errors and is widely used, yet the true efficacy of manual secondary review is difficult to assess. Kidney stone analysis is a manually intensive process, requiring personnel to identify stone composition from spectra and to transcribe stone percent composition into an LIS system. Recently, we developed an AI program that allows for quantitative determination of kidney stone composition from spectra. This allows for a unique opportunity to estimate error rates associated with manual reporting and those following secondary review by means of subsequent AI quality assurance analysis. Methods Experienced technologists assessed stone composition and estimated the percent of each component (± 10%), based on Fourier transform infrared (FTIR) spectroscopy. These manual spectral composition assessments are secondarily reviewed by other experienced technologists for identity and percentage composition accuracy. The recently described Kidney stone AI spectral analysis (RokkStar) was implemented to assess reported stone composition identity and percent composition for agreement following secondary review in 159 334 stone reports. AI flagged discrepant reports were reviewed by personnel and reports were revised when appropriate. Secondary reviewer error detection rate was obtained by monitoring detection in a subset of 7440 kidney stones, and was that rate was extrapolated to establish secondary review error detection rate over the entire data set. The sum (total error) of estimated secondary reviewer error detection, and well as AI-mediated error detection, and all client reported errors were used to determine error rates in sigma following manual reporting, secondary review, and AI quality assurance. Results Manual entry led to an estimated 2060 errors in 159 334 stone reports, yielding an initial sigma value of 3.73. Secondary manual review resulted in an estimated 1909 (error detection rate of 92.5%) of these errors being detected over the entire report set, improving the sigma value to 4.61. Post-verification AI caught an additional 138 errors, leading to an improved post-AI sigma value of 5.27. Post-AI quality assurance, there were 13 remaining errors, 9 of which were manual reporting process deviations subsequently caught by laboratory staff, and 4 were reporting errors observed by client physicians. All 13 of these errors involved usual stone constituents or typographical errors that were not assessed by the current AI quality assurance version. Conclusion This study represents a unique large-scale estimate of efficacy of secondary result review by means of AI quality assurance review. While the presence of undetected errors cannot be completely ruled out, in this case the relative paucity of client reported errors, as well the alignment of the estimated rate of manual errors with previous reports may indicate that most errors were detected in this system. Importantly, manual secondary review improved errors rates by ∼0.9 sigma, and the use of AI further improved estimated overall sigma metric by ∼0.7, leading to a “world class” 5+ sigma level for this complex reporting process.

Abstract Background While clinical tests are available to aid in the diagnosis of Wilson’s Disease (WD), some such as total copper determination lack sensitivity and specificity, while others such as liver tissue copper assessment are costly and invasive. WD affects copper metabolism and is known to cause low serum copper concentrations. In addition, an elevated percentage of total serum copper exists as non-protein bound or labile bound copper (LBC). Previous studies have shown that measuring the ratio of labile bound copper to total serum copper may aid in the diagnosis of WD. To support the utility of a laboratory developed test using serum LBC ratios to aid in the diagnosis of WD, serum samples from healthy donors were used to establish a reference range for LBC and the ratio of LBC to total copper in adults. Methods Serum samples were collected in trace metal free tubes from 214 donors (n = 110 male, n = 104 female) between the ages of 19–80 years old, selected against pre-determined exclusion criteria including acute hepatitis, chronic hepatitis, renal failure, biliary cirrhosis, celiac disease, and leukemia. Prior to analysis, samples went through a centrifugation-based preparation process to isolate the LBC fraction using molecular weight size exclusion filters and EDTA chelation. Samples were then analyzed via ICP-MS in kinetic energy discrimination (KED) mode to directly measure the LBC and total copper concentrations. LBC ratio values were determined from the LBC/total copper concentrations. Results Preliminary reference ranges were developed for both LBC and the LBC ratio based on the 2.5th and 97.5th percentiles with 95% confidence intervals. Established sex specific reference intervals for total copper were deemed acceptable per CLSI guidelines in document EP28-A3c, with ≤10% of results from this sample set falling outside of the established intervals. No statistically significant relationship was found in the mid-95th percentile between LBC concentration or ratio with age or sex. The reference intervals for LBC concentration were calculated as 13.41–104.66 and 11.69–106.50 ng/mL for females and males respectively. The reference intervals for LBC ratio were calculated as 1.04–8.12 and 1.17–10.48 for females and males respectively. Conclusions A reference range of LBC and LBC ratio has now been determined for the healthy adult population. Further testing can now be done on disease state populations to determine the sensitivity and specificity of LBC ratio in the detection, monitoring, or diagnosis of WD.