Citations

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 Turnaround time (TAT), defined as time of collection to time of result verification, is monitored monthly for cardiac Troponin (cTnT). Within our STAT laboratory, our TAT goal is 85% of cTnT results verified within 45 min. In October 2021, our lab began observing an increase in TAT. A quality improvement project was launched to identify a root cause and possible solutions. Methods cTnT TAT data from the Hospital Clinical Laboratory (STAT lab) at Mayo Clinic, Rochester, MN was extracted from our Laboratory Information System (LIS) using Tableau (Salesforce Inc., Seattle, WA). The data were categorized into three components: collection time, in lab processing and testing time, and time to result verification. Goal metrics for each component are: collection <10 min, in lab processing and testing <25 min, and result verification to LIS <5 min. The average time for each component and percentage not meeting TAT goals were calculated in Microsoft Excel. TAT while performing manual verification of results (January 2022) and after autoverification was implemented (December 2022) were compared. Results Prior to implementation of cTnT autoverification, total orders for January 2022 were 1408 with 82.0% meeting TAT of 45 min. Of the 18.0% not meeting TAT, 207 (14.7%) orders exceeded goals for one or more components (percentages do not total 100%): collection delays 131 (63.0%), in lab processing and testing delay 175 (85.0%), and delay in result verification to LIS 80 (39.0%). For those orders not meeting the 45 min TAT goal, mean times were 17.6, 40.3, and 8 min for collection time, in lab processing and testing, and result verification to LIS, respectively. Post implementation of autoverification, total orders for December 2022 were 1444 with 88.6% meeting TAT of 45 min. Of the 11.4% not meeting TAT, 93 (6.4%) orders were outside goals for multiple components: collection delays 51 (55.0%), in lab processing and testing delay 67 (72.0%), and result verification to LIS 0 (0.0%) orders. For those orders not meeting the 45 min TAT goal, mean times 16.6, 39.7, and 0 min for collection time, in lab processing and testing, and result verification to LIS, respectively. Conclusion Achieving TAT goals agreed upon by the laboratory and clinical practice is critical to quality patient care. An investigation into our delayed TAT for cTnT highlighted several opportunities for intervention within the control of the lab. We chose to implement autoverification of results to easily and consistently eliminate the time required for manual result verification. TAT was improved by 6.6% (82.0% to 88.6%) through autoverification of results, allowing technologists to focus on other clinical work.

Abstract Background Clinical laboratories are constantly changing with automated analyzers and total lab automation (TLA) implemented and replaced over time. Some benefits of newer technologies include decreased specimen volume requirements, less need for repeat testing, and greater throughput. Recent implementation of new analyzers and replacement of TLA in the Central Clinical Laboratory (CCL) at Mayo Clinic, Rochester, MN, prompted the laboratory to reassess blood collection volumes for commonly ordered tests. Objectives The aim of this study was to evaluate whether updating the blood collection volumes for clinical chemistry and immunoassay tests performed in the CCL could reduce the volume of blood collected from patients and the number of blood collection tubes utilized. Methods Nearly 100 different serum assays from the CCL test menu were reviewed including the basic and comprehensive metabolic, hepatic function, renal function, electrolyte, and lipid panels as well as thyroid function tests, enzymes, hormones, electrolytes, and iron status tests. Cortisol, ferritin, folate, and Vitamin B12 testing were performed on the Beckman DxI800; all other testing was on Roche Diagnostics Cobas 8000 chemistry (ISE, c701, c502) and immunoassay (e801) analyzers. Updated blood volumes for each assay were calculated utilizing manufacturer’s analytical volumes and “dead volume” requirements. Baseline data retrieved from the laboratory information system (LIS) included blood collection volumes and number of tubes collected for 128 patients with physician-orders for laboratory testing on 5/2/21. The collection logic was then updated in the test LIS environment using the new blood volumes requirements and applied to the same 128 orders. Blood volumes and number of tubes collected per order were compared before and after the volume updates. Results Baseline data from 128 unique orders showed that 414 total tubes were collected with a median(range) of 3 (2–6) tubes per order and 5 (1–18) tests per tube. The total volume of blood requested at baseline was 2065 mL. Post-intervention, 197 tubes were needed with a median(range) of 2 (1–3) tubes per order and 13 (1–30) tests per tube. The total volume of blood requested after updating volumes was 851 mL. Conclusions The practice of reviewing and updating specimen volume requirements when replacing or implementing new analyzers or TLA can greatly reduce the volume of blood collected from patients and the number of blood collection tubes per order. In this study of 128 physician orders for common laboratory tests, updating blood collection information in the LIS reduced the requested blood collection volumes by 59% and decreased the number of blood collection tubes required by 52%. This not only benefits patients because less blood is collected but also conserves blood collection tubes and reduces supply chain demands. Additionally, fewer tubes on the TLA system has the potential to improve throughput and test turnaround time.

Abstract Background Tuberculosis (TB) is an infectious disease that induces a complex response from the host immune system. Most carriers of the bacteria experience the asymptomatic, non-infectious phase, latent tuberculosis infection (LTBI), but have the potential to reactivate to active, contagious TB infection. Current diagnostics for TB are not sufficient for LTBI and are focused on using one biomarker, interferon-gamma (IFN-g). To build better diagnostics for the complex disease, multi-biomarker approaches to profile the immune system, coupled with machine learning algorithms, are necessary to diagnose LTBI and determine patients' risks of reactivation to active TB. We aim to use a multiplexed silicon photonic microring resonator sensing platform to profile thirteen biomarkers in patients with LTBI infection and use machine learning data analysis to identify biomarkers relevant for diagnosing LTBI and predicting reactivation risk. Methods We employed microring resonators coupled with a sandwich-style detection format to simultaneously detect thirteen cytokines and chemokines in plasma in under 40 minutes. Chip-integrated silicon photonic microrings are small in size and easily multiplexed with up to sixteen different capture agents. Biomolecular binding events are measured by monitoring the resonant wavelength within the sensing cavity, which shifts as binding events occur. Quantitation of each biomarker is possible with calibration curves that correlate standard protein concentrations to wavelength shifts. The multiplexed assay has been optimized for each individual biomarker, tested for cross-reactivity, and calibrated in the plasma matrix of interest. The LODs vary, depending on biomarker and matrix dilution, but range from 4.9 pg/mL to 691 pg/mL. The patient plasma samples are left over from the TB screening IFN-g release assay, QuantiFERON-TB Gold Plus (QFT), putting this method within the current clinical workflow. We use precision normalization approaches from the four QFT stimulations to account for immunologic differences among the subjects. The biomarker concentrations are evaluated using a random forest machine learning model to identify which biomarkers are important for identifying patients with LTBI and predicting their reactivation risk. Results We used this sensor method to profile 42 patients, 24 LTBI negative controls and 18 LTBI positive controls, with 13 being considered at high risk of reactivation. Using a precision normalization approach, a combination of nine normalized conditions using five of the thirteen biomarkers (CCL4, CCL8, IP-10, IL-2, IL-17) discriminated between LTBI positive and negative subjects with a ROC AUC of 0.90. Additionally, eight normalized conditions using four of the same biomarkers discriminated between high and low-risk subjects with an AUC of 0.83. Currently, we have expanded the sample cohort to include an additional 72 subjects, 25 LTBI positive and 47 LTBI negative. Preliminary analysis shows CCL8, IL-2, and IP-10 alone are able to distinguish between LTBI status (P values <0.01) using raw target concentrations prior to precision normalization. Conclusion Overall, we show the importance of multi-biomarker approaches to develop diagnostic and prognostic tools for a complex disease, such as TB. Further work to use the initial machine learning algorithm with the expanded cohort to validate biomarker importance is underway.

Abstract Background The racial, ethnic and sex compositions of a study population may impact the clinical performance of a biomarker. In a prospective clinical study to evaluate biomarkers for asthmatic and allergic conditions in a large medical center in the upper Midwest, a largely White-alone patient population was encountered. Using several outreach strategies, potential eligible patients were contacted to enroll in a biomarker clinical study and to provide blood specimens. The patient demographics between the eligible candidates and the enrolled participants were compared. The analysis was carried out to understand the likelihood of enrollment in different race, ethnicity, and sex groups; and to identify factors that contribute to selection biases in enrolled cohorts. Methods Patients with eligible asthma and allergic conditions were selected based on relevant diagnostic codes in their electronic medical records, verified by clinical research coordinator. In accordance with Institutional Review Board approval, eligible patients were contacted via their electronic healthcare portal or with a phone call. The study information was also posted in an internal classified ad as an additional outreach strategy. Interested participants were then contacted a second time to complete consent. Upon consent, participants were considered as “enrolled”. Demographics information including sex, race, ethnicity, and age were de-identified for comparison analysis. Results Among all eligible patients, a disproportionally low representation of Blacks, Asians, and Hispanics (“non-White” groups) was observed, relative to regional demographics (22.2% non-White groups, 2021 census) and higher asthma and allergy prevalence in the non-White groups. In eligible adult patients (>18 years old) with asthma, only 13.4% were from non-White racial and ethnic groups. In eligible pediatric asthma patients, a larger proportion at 24.1% were from American Indian, Asian, Black, or Hispanic groups. In patients with allergic conditions, 10.3% of the adult patients were from non-White only groups, compared to 21.9% in pediatric patients. Among all eligible patients, White-alone patients were more likely to enroll in the study compared to their counterparts from non-White groups, further skewing the racial and ethnic distribution in the enrolled cohorts. As a result, less than 10% of enrolled adult patients were from non-White background in both allergy and asthma cohorts. Among the enrolled pediatric asthma and allergy patients, 16.9% and 10.3% respectively were from non-White groups. We also identified sex and age dependent enrollment behavior across different study cohorts. In adult groups, there were more female eligible patients (allergy: 66.7% female; asthma: 66.7% female) than male patients. Female adult patients were significantly more likely to participate, thus constituting 79.6% and 75.8% of the enrolled allergy and asthma cohorts. In the pediatric groups, male patients represented a larger share of eligible patients (allergy: 58.6%; asthma 64.7%). Female pediatric patients were still more likely to participate in the study. Hence, male and female enrolled patients were closer to equal distribution (allergy: 50.0% male; asthma 59.7% male). Conclusion The non-even distribution of demographics in eligible patients contributed to biased distribution in enrolled patients. Existing outreach approaches were not effective in reversing enrollment rates in underrepresented racial and ethnic groups.

Abstract Background The legality and use of marijuana is a controversial topic. With increased prevalence of usage, cannabis detection for legal, medical, and workforce drug testing is on the rise. LC-MS/MS quantification of delta 9-carboxy tetrahydrocannabinol (delta 9-THC-COOH) in urine is accepted as the standard to detect marijuana use. One challenge faced by testing laboratories has been identifying adulterated samples. Many products/reagents can be used to manipulate the urine to avoid positive drug testing results. In this study, we developed a dilute-and-shoot strategy to separate and quantify both the delta 8- and 9-THC-COOH in urine samples and tested the effects of adulteration using a combination of nitrite and/or acids which can be used to mask cannabinoid use. Methods Five authentic delta 8- and 9-THC-COOH positive specimens, and one spiked sample were treated with strong acids (1N HCL or 10% TCA), 500 µg/mL nitrite, or a combination. After hydrolysis with sodium hydroxide, the analytes were quantified using a 6495 Mass Spectrometer coupled to a StreamSelect LC system (Agilent). LC separation was performed with a CORTECS T3 column utilizing a shallow gradient. In addition, twelve urine specimens with varying concentrations of delta 8- and 9-THC-COOH were treated with nitrite and screened using a delta 9-THC-COOH immunoassay on the Roche Cobas C501 and analyzed by LC-MS/MS. Results Nitrite did not change the physical appearance or the pH of the urine samples. Strong acid treatment also did not alter the appearance but did drop the urine pH ≤2. Pre-treated specimens with 1N HCL, 10% TCA, or 500 µg/mL of nitrite did not affect the delta 8- and 9-THC-COOH concentrations up to 6 days (refrigerated); however, specimens treated with strong acid combined with 500 µg/mL of nitrite for 30 min made both analytes undetectable. Additional experiments confirmed that 0.5 and 1 mol/L of nitrite lowered the concentration of all analytes, including internal standard, to undetectable levels in 48 h. Twelve additional specimens were collected, 3 with delta 8-THC-COOH, 3 with delta 9-THC-COOH, and 6 with both. All 12 samples screened positive using the Roche immunoassay and were quantified using the LC-MS/MS method. After treatment with 0.5 mol/L of nitrite, 10 of the 12 samples were negative using the immunoassay, but 2 samples remained positive. However, all nitrite treated specimens were negative (<LLOQ) using the LC-MS/MS method and the internal standard was eliminated. Later experiments confirmed that the delta 8- and 9-THC-COOH could be eliminated with as little as 0.1 mol/L nitrite for 30 min. Conclusion The unique dilute-and-shoot method can separate and quantify delta 8- and 9-THC-COOH. In addition, high concentrations of nitrite were shown to degrade both analytes along with the isotopically labeled internal standard. As a result, labs can distinguish true negative delta 8- and 9-THC-COOH (no analyte peaks) from nitrite adulterated specimens since the adulterated samples have no peaks for both the analytes and internal standard. A limitation of the study is that only sodium nitrite was tested. Other adulterants, such as potassium nitrite, pyridinium chlorochromate, peroxide etc. require further investigation.

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.

Abstract Background We took advantage of our verification of a new biotin-resistant 25-hydroxyvitamin D (vitamin D) immunoassay formulation to compare analytical performance of the outgoing formulation and assays from Roche, Diasorin and Beckman. Liquid chromatography with tandem mass spectrometry (LCMS) was an independent arbiter of vitamin D concentration and provided quantification of vitamin D2 and vitamin D3. Methods Twenty-five specimens from patients on high-dose vitamin D2 supplementation (50 000 IU/week) and fifty additional specimens spanning our measurable range were identified by the Dartmouth Hitchcock Analytics Institute. Aliquots were tested by the Roche cobas Vitamin D Total II and Vitamin D Total III assays, deidentified, split, and shipped to other study sites for measurement by LCMS, the Diasorin Liaison 25 OH Vitamin D Total assay, and the Beckman Coulter Access 25(OH) Vitamin D Total assay. Estimated vitamin D2 was calculated by subtracting the LCMS-measured vitamin D3 result from the immunoassay total vitamin D result. Bias calculations assume LCMS measurements as the true concentration. Statistical analyses were performed in R. This study was approved by the Dartmouth Hitchcock Medical Center Institutional Review Board. Results No significant differences were observed between platforms in absolute or relative bias in total vitamin D or estimated vitamin D2. Clinical classification was unanimous in 62/75 specimens. For thirteen specimens in which at least one disagreement was observed, immunoassay results for discrepant classification were no more than 10.2 ng/mL from the classification cutoff (median 2 ng/mL). Two specimens with 68 and 70 ng/mL vitamin D were classified as toxic (>100 ng/mL) by one immunoassay each. Conclusion Current vitamin D immunoassays continue to improve over their predecessors and offer good agreement across platforms. Vitamin D2 recovery no longer represents a clinically-concerning obstacle. These immunoassays display positive bias for total vitamin D relative to LCMS but generally result in correct clinical classification.

Abstract Background Total protein is measured in serum and body fluids as part of routine evaluations of general nutritional status and differentiating exudative and transudative effusions. Spectral interference thresholds for bilirubin (e.g., icterus) is set by assay manufacturers to prevent inaccurate results from being reported. The Roche Cobas serum total protein assay package insert states no significant interference from bilirubin up to an icterus (I) index of 20. Body fluids tend to have lower total protein concentrations; therefore, thresholds were derived during prior validation studies and set to I = 10, a threshold that is exceeded routinely. Laboratories can mitigate some interferences by recollecting (e.g., hemolysis) or ultracentrifuging (e.g., lipemia) specimens. Serial dilutions are another option used; however, it is imperative that the laboratory understands the mechanism of interference and can demonstrate that it is mitigated by sample dilution. The aim of this study was to investigate whether bilirubin interference can be mitigated by sample dilution in pleural fluid, peritoneal fluid, and serum specimens. Methods Residual clinically ordered pleural fluid (n = 3), peritoneal fluid (n = 3), and serum (n = 3) samples submitted for protein testing were split into two equal volume aliquots with concentrations ranging 2.7 to 4.3 g/dL in pleural fluids, 3.9 to 5.4 g/dL in peritoneal fluids, and 3.8 to 11.0 g/dL in serum. One aliquot was spiked (<10% by volume) with a bilirubin conjugate solution (∼1000 mg/dL) to surpass the icterus index threshold (spiked). The second aliquot was spiked with an equal volume of 0.9% saline (control). Total protein was measured on the Roche Cobas c701 instrument in both samples to calculate difference (Spiked-Control). The mean(SD) difference was calculated for each sample type. Each spiked sample was serially diluted (2-fold, 4-fold and 8-fold) with 0.9% saline. Total protein was measured for each diluted sample. Mean(SD) difference was calculated (DilutedX-Control), where X = 2-fold, 4-fold, 8-fold. Linear regression analysis was performed by plotting the measured (x-axis) vs expected (y-axis) total protein concentration. Slope, y-intercept, and R2 were determined from serially diluted samples where the expected concentrations were derived using the spiked concentration to replicate the practice of diluting an icteric sample. Results Bilirubin spiking (average icterus index = 33.5) into pleural fluid, peritoneal fluid, and serum, respectively demonstrated mean(SD) bias (g/dL) = −1.0(0.2), −0.9(0.0), and −0.7(0.0). Subsequent 2-fold dilution demonstrated mean(SD) bias (g/dL) = −1.0(0.2), −1.0(0.0), and −0.8(0.1), 4-fold dilution with mean(SD) bias (g/dL) = −1.1(0.2), −1.0(0.0), and -1.0(0.1), and 8-fold dilution with mean(SD) bias (g/dL) = −1.3(0.2), −1.0(0.0), and −1.4(0.1). Diluting pleural, peritoneal, and serum samples, respectively spiked with bilirubin resulted in slope = 0.99, 1.00, and 0.99; y-intercept = 0.04, 0.01, and 0.07; R2 = 0.99, 0.99, 0.99. Conclusion The dilutions exhibited a linear dilution response, however the dilution results never returned to baseline (non-spiked) concentration. Laboratories should exercise caution when conducting serial dilutions to diminish bilirubin interference in samples when measuring total protein as the mechanism of interference does not appear to be reversible by dilution. This behavior was demonstrated in both body fluids and serum.

Abstract Introduction The development of endoscopic techniques has made endoscopic endonasal approaches (EEAs) to the anterior cranial fossa (ACF) increasingly popular. Still, the steps and nuances involved in the approach may be difficult to understand for trainees. Thus, we aim to didactically describe the EEAs to the ACF in an anatomically based, step-by-step manner with supplementary clinical cases. Methods Six cadaveric head specimens were dissected. Endoscopic endonasal Draf I, IIA, IIB, and III frontal sinusotomies, endoscopic endonasal superior ethmoidectomy, and endoscopic endonasal transcribriform and transplanum approaches were modularly performed. The specimens were photodocumented with endoscopic techniques. Results Draf I frontal sinusotomy started with the complete removal of the anteromedial portion of the agger nasi cell, exposing the medial orbital wall, cranial base, and anterior cribriform plate. Draf II frontal sinusotomy proceeded with the removal of the floor of the frontal sinus between the lamina papyracea and the middle turbinate (IIa), and the nasal septum (IIb) until the first olfactory filaments were exposed. Draf III proceeded by creating a superior septal window just below the floor of the frontal sinus. The bone of the ACF bounded by the limbus sphenoidale posteriorly, frontal sinus anteriorly, and the medial orbital walls bilaterally was removed; the cribriform plate was removed; and the crista galli was dissected free from the dural leaflets of the falx cerebri and removed. Conclusion We provide a step-by-step dissection describing basic surgical steps and anatomy of the EEAs to the ACF to facilitate the learning process for skull base surgery trainees.