Erdahl, Sarah A

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.

AbstractPurposeDental amalgam contains mercury and is commonly used in dental restorations. The impact of MRI on mercury excretion from dental amalgam is not well understood across clinical field strengths, especially 7T. We investigated the effects of MRI exposure on mercury excretion using fresh, lab‐created dental amalgam restorations and in extracted teeth with old, pre‐existing restorations.MethodsDonated, unfilled human teeth (n = 120) were restored with amalgam before being stored in saline, artificial saliva, or a dry box prior to MRI scanning. The teeth were placed in individual tubes of fresh artificial saliva and scanned at 1.5T, 3T, or 7T or left unscanned as controls. Mercury concentrations were measured 24–30 h later. Donated teeth with pre‐existing restorations (n = 40) were stored in artificial saliva, scanned at 7T or left unscanned as controls, and mercury concentration tested.ResultsFor teeth extracted and restored in a laboratory, no significant difference was found (F = 2.42, P = 0.072) between mean mercury concentrations of unscanned teeth (13.72 μg/L) and teeth scanned at 1.5T (10.88 μg/L), 3T (12.65 μg/L), or 7T (8.88 μg/L). For teeth extracted with previously placed restorations, no significant difference (P = 0.288) was found between unscanned controls (4.28 μg/L) and teeth scanned at 7T (6.63 μg/L).ConclusionMRI of dental amalgam does not significantly increase mercury excretion at 1.5T, 3T, or 7T compared to unscanned teeth. This holds true for controlled laboratory restorations as well as for those placed and lived with prior to extraction and scanning, demonstrating no added risk to the clinical patient or research subject.

Abstract Background Arsenic is a naturally occurring element with varying species and levels of toxicity. Inorganic arsenic (e.g., arsenite (AsIII) and arsenate (AsV)) are toxic, while its metabolites (e.g., monomethylarsonic acid (MMA) and dimethylarsinic acid (DMA)) are less toxic). Symptoms of exposure can include headaches, confusion, diarrhea, and drowsiness. As these symptoms overlap with many other conditions, arsenic exposure can often be overlooked as a cause. Arsenic toxicity may be treated with chelation and/or electrolyte replacement therapy. However, treatment is not without risks and is unnecessary for exposure to organic (nontoxic) forms of arsenic. This makes screening and differentiation of arsenic important for clinical testing. Method An IC-ICP-MS method was developed using a Dionex 5000 with ion exchange chromatography for separation and iCAP Q for detection. Nontoxic species are arsenobetaine and arsenocholine, and toxic species are AsIII, DMA, MMA, and AsV. Results Precision, linearity, and specificity studies produced acceptable results. For accuracy, proficiency testing and method comparison samples were analyzed and produced acceptable results. Carryover studies demonstrated single species carryover from the diluter at levels of 500 µg/L, which can be avoided by analysis rules in the standard operating procedure. Limit of detection studies yielded a lower limit of quantitation of 1 µg/L per species. Conclusions Here, we present a rapid and reliable method for quantifying and differentiating toxic and nontoxic forms of arsenic to allow for swift and appropriate management of patients with exposure.

Background Gadolinium-based contrast agents (GBCAs) have been used in magnetic resonance imaging for the past 30 years, where they have significantly improved the effectiveness of imaging studies. However, the increased usage of gadolinium in the medical community has also resulted in unexpected interferences in other laboratory assays. This has been particularly the case in clinical elemental analysis using inductively coupled plasma mass spectrometry (ICP-MS). Methods By conducting ICP-MS interference experiments, we describe how gadolinium interferes with elemental analysis by space charge effect, double charge effect and the creation of polyatomic interferences. Additionally, by reviewing more than a year of reference laboratory data from our laboratory information system, we determined the number of elemental tests cancelled due to gadolinium interference. Results Interference experiments show that gadolinium normally found in GBCAs can interfere with heavy metals, platinum and selenium analysis of biological fluids using ICP-MS. Within one year, our institution’s metals laboratory had to cancel 42 selenium serum tests and 19 heavy metal urine panels due to potential interference caused by gadolinium. Conclusions GBCAs will continue to be utilized in hospitals around the world. However, clinical laboratories should be wary of potential interferences caused by GBCAs. Relevant interferences include space charge effect, double charge interference, and the formation of polyatomic interferences caused by gadolinium. These interferences can negatively affect patient care by resulting in cancelled laboratory tests and causing patients to have blood redrawn and analysed at a later date leading to delays in their diagnosis/treatment.