Katzman, Brooke M.

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 Analytical outliers (fliers) occur with most troponin methods and can adversely affect patient management. We compared the analytical outlier rates for the Roche 5th generation Troponin T STAT (cTnT) reformulated (biotin-insensitive) reagent using a Cobas e601 analyzer (Indianapolis, IN) to rates observed in a previous study using the biotin-sensitive reagent on a Cobas e411 analyzer. Methods Plasma separator tubes (PST) were collected from hospital patients with orders for clinical cTnT testing. PST samples were centrifuged for 3 min at 4000g and analyzed using the biotin-insensitive cTnT Gen 5 assay on a Roche Cobas e601 immunoassay system. If samples displayed measurable cTnT Gen 5 (≥6 ng/L), samples were stored at 2–8 °C within 2 h of initial measurement (N = 2555). Within 24 h of initial analysis, samples were warmed to room temperature, aliquoted, re-centrifuged at 4000g for 3 min, and cTNT was re-measured on the same analyzer. Outlier rates were compared to a previous study (N = 1997) using nearly identical study design but using biotin-sensitive Roche 5th Gen cTnT reagent on an e411 analyzer. We defined analytical outliers by three methods: Repeat value (after aliquoting and re-centrifugation) that differed from the initial value for all samples with measurable cTnT by a critical difference (CD): where CD = z × √2 × SDanalytical, with a z-value of 3.719 and using SD from best fit line of precision vs cTnT concentration. Precision experiments were performed using four plasma pools in 5 × 5 precision experiment on the e411 analyzer. Using the same CD but considering only initial cTnT values <100 ng/L, those are most likely to be clinically significant or lead to misinterpretation. Using CD for initial values <100 ng/L, and a fixed 10% difference between replicates for initial values ≥100 ng/L, accounting for our practice of reporting the cTnT “delta” in percent (rather than absolute) for values ≥100 ng/L. Comparisons of outlier rates between the biotin-sensitive and biotin-insensitive reagents were performed using Pearson’s chi-squared test. Results Using the CD the total number of outliers that were observed for the biotin-insensitive reagent on the e601 was 208/2555 (8.1%), compared to 68/1997 (3.4%) on the biotin-sensitive reagent measured on the e411 (P < 0.0001). Outliers were observed in 138 (6.4%) of samples with initial values < 100 ng/L using the biotin-insensitive reagent on the e601 compared to 45 (2.6%) of such samples on the e411 (P < 0.0001). Using the Z score method to define outliers for initial values <100 ng/L and a fixed 10% to define outliers for initial values ≥100 ng/L, the outlier rate was 151 (5.9%) for the biotin-insensitive reagent on the e601 compared to 53 (2.7%) on the e411 (P < 0.0001). Conclusions Analytical outliers occurred more frequently using the reformulated (biotin-insensitive) reagent on the Roche Cobas e601 analyzer, compared to outlier rates observed for biotin-sensitive reagent on the Cobas e411.

Abstract Background Hemolysis is a pre-analytic issue that challenges clinical laboratories. When a specimen with significant hemolysis is obtained in our laboratory, tests where the hemolysis threshold is exceeded are not reported and blood is recollected. Unfortunately, in a subset of patients, the recollected specimen is also hemolyzed (termed “recurrent hemolysis”). In these cases, the second recollection utilizes special collection and handling protocols (i.e., hand-carried to lab, plasma collection, etc.) to mitigate preventable causes of hemolysis. Lipemia is also a known contributor to specimen hemolysis. Mayo Clinic Rochester experienced an increased rate of hemolysis as well as recurrent hemolysis during the height of the COVID-19 pandemic (January 2022). The objective of this study was to investigate the increased rate of recurrent hemolysis and compare to a rate prior to the COVID-19 pandemic (January 2019). Methods Retrospective data analysis was performed for January 2019 (negative control) and January 2022. The number of Mayo Clinic Rochester hospital admissions, admitting diagnosis, medications administered, and number of test collections performed were obtained using reporting functionality in our Electronic Health Record (EHR), Epic. The Laboratory Information System, SoftLab, was utilized to obtain recollection rates due to hemolysis. Data were normalized to total patient admissions for the specified period to account for inter-year variability. R/R Studio was utilized to analyze and visualize the data. Pearson’s Chi-squared test for independence was performed on categorical variables (significance defined as P < 0.05). Results A 28% decrease in hospital admissions (n = 3958:Jan-22 vs n = 5517:Jan-19) and a 23% decrease in total specimens collected on hospitalized patients (n = 40 557:Jan-22 vs n = 52 373:Jan-19) was observed between Jan-19 to Jan-22. However, the laboratory observed a 6% increase in hemolyzed specimens (n = 256:Jan-22 vs n = 241:Jan-19). Recurrent hemolysis occurred in 24% (n = 62/256) of Jan-22 and 17% (n = 43/241) of Jan-19 hemolyzed specimens and required special handling protocols. COVID positive patients comprised 48% (n = 30/62) of those requiring special handling protocols in Jan-22. Further examination revealed 34% (n = 21/62) of Jan-22 and 12% (n = 5/43) of Jan-19 cases of recurrent hemolysis were due to lipemia (P < 0.01). Chart review highlighted a potential source of lipemia from the administration of propofol, a lipophilic sedative emulsion. Of those COVID positive cases with recurrent hemolysis due to lipemia (n = 15/30), 93% (n = 14/15) were receiving propofol at the time of collection (P < 0.001). Conclusion We observed increased rates of specimen hemolysis during the COVID-19 pandemic. While most hemolysis resolves upon specimen recollection, patients with recurrently hemolyzed specimens often require special specimen collection and handling protocols to mitigate hemolysis. Approximately half of the recurrent hemolysis cases in Jan 2022 were COVID-19 positive patients. Lipemia-induced hemolysis was also more frequent in this patient population presumably due to the administration of propofol, a lipophilic emulsion sedative commonly used for COVID positive patients requiring sedation and ICU-level care. It is critical to understand the variety of factors which may contribute to recurrent hemolysis, as intervening with specialized collection and handling protocols to reduce observed pre-analytical hemolysis is required to provide accurate and timely laboratory results.

Abstract Objectives To evaluate the analytical and clinical performance characteristics of the fifth-generation troponin T reagent. Methods Troponin T was measured in 2,332 paired serum and plasma samples from emergency department and hospital patients using the fourth- and fifth-generation reagents. Testing was repeated after recentrifugation to determine the frequency of analytical outliers and percentage of patients with elevated values for each assay. We conducted separate experiments to determine the effects of biotin and hemolysis interference, as well as measure interinstrument variability, for fifth-generation troponin T. Results Analytic outliers occurred more frequently using the fifth-generation reagent (3.4%) compared with the fourth-generation reagent (1.0%). The frequency of elevated troponin T above the 99th percentile upper reference limit was 26% for the fourth-generation reagent and 52% for the fifth-generation reagent. Clinically significant assay interference by biotin was observed at 20 ng/mL, but hemolysis interference was not observed until an H index of 150. Instrument-to-instrument variability between e411 and e601/602 instrument platforms is predicted to confound clinical interpretation of troponin changes. Conclusions Analytical outliers and instrument-to-instrument variability are the two analytical variables most likely to confound interpretation of changes in fifth-generation troponin T results over time.

Abstract Background It is important for clinical laboratories to have protocols for investigating suspected biotin interference in patient samples. VeraPrep Biotin™ is a commercial product used to rapidly deplete biotin from serum/plasma samples. The objectives of this study were to verify that VeraPrep Biotin™: (a) does not impact immunoassay analyte recovery in control samples and (b) can effectively deplete biotin from samples (both biotin-spiked and samples from donors who ingested biotin supplements). Methods De-identified residual waste serum/plasma samples were combined to create 9 pools for each immunoassay. Plasma/serum samples (n = 23) were obtained from 6 healthy donors at varying times following ingestion of biotin (20 mg, 100 mg, or 200 mg). Nine Elecsys immunoassays were evaluated using the e 602 (Roche Diagnostics Inc.). Control, biotin-spiked (n = 10, ∼400 ng/mL), and donor samples were assayed pre- and post-VeraPrep treatment. Percentage analyte recovery [(posttreatment/pretreatment) × 100] was calculated for control samples. A laboratory-developed LC–MS/MS method was used to quantify biotin. Results In control samples (n = 81), 90–110% analyte recovery was observed post-VeraPrep treatment in over 95% of samples (77/81). The pre- and post-VeraPrep treatment biotin concentration [mean ± standard deviation (SD)] for specimens spiked with up to 500 ng/mL biotin was 357 ± 47 ng/mL and 1.0 ± 0.6 ng/mL, respectively. The mean (range) biotin concentration for the donor samples pre- and post-treatment was 166 (15–1029) ng/mL and 0.2 (<0.1–3) ng/mL, respectively (P = 0.004). Conclusions These data demonstrate that treatment with VeraPrep Biotin™ does not affect analyte recovery in biotin-negative samples and effectively depletes both spiked and endogenous biotin in serum/plasma.

Abstract Background Reflex testing algorithms are effective tools to reduce unnecessary laboratory testing. Direct (conjugated) bilirubin (DB) and total bilirubin (TB) are often ordered together at our institution. Therefore, the objective of our study was to evaluate the potential impact of performing reflex testing for DB when TB is elevated. Methods We performed a retrospective review of test orders (patients ≥18 years of age) for DB, TB, or for both DB and TB on the same accession number received in our stat laboratory from January through April 2017. The orders were binned into 4 categories depending on the results from each individual test: (a) DB normal and TB normal, (b) DB normal and TB high, (c) DB high and TB normal, and (d) DB high and TB high. The percentage of orders and median (range) test result for each category was calculated. Results During the months evaluated, a total of 4828 stat orders were placed for DB, TB, or both DB and TB. A total of 4296 stat orders (89%) were placed with both DB and TB on the same accession number for 4158 unique patients. Of those orders, the vast majority of tests (87.3%) contained normal results for both analytes; only 12.7% of orders contained ≥1 abnormal result. Conclusions The majority of all bilirubin tests ordered stat for emergency department and hospitalized patients have values within the reference interval. Consequently, if reflex testing were executed on elevated TB, a large number of DB tests could be avoided.

AbstractBackgroundThe necessity of individual tests within the most commonly used disease-oriented test panels has not been well established. We evaluated test-ordering practices for total calcium, both before and after implementation of American Medical Association (AMA)–approved panels (basic metabolic panel [BMP] and comprehensive metabolic panel [CMP]) in our electronic ordering system.MethodsWe performed a retrospective review of all total calcium orders placed during April and June 2018, before and after implementation of the panels. Orders from inpatient, outpatient, and emergency department (ED) care units were totaled, and the percentage of abnormal test results was calculated. We then queried institutional databases to determine the number of unique patients with calcium-related diagnoses and compared the rates from a 5-month period both before and after implementation of the panels.ResultsTotal test volumes and tests per unique patient increased by more than 3-fold after implementation of calcium-containing AMA-approved panels, with the majority of those orders coming from BMPs and CMPs. The rate of low calcium values increased because of the shift toward more inpatient testing; however, the percentage of abnormal results within each patient population (inpatient, outpatient, ED) decreased. The prevalence of hypo- and hypercalcemia-related diagnoses among patients in the 5 months after implementation did not change significantly (1.29% before implementation vs 1.27% after implementation).ConclusionsImplementation of BMPs and CMPs dramatically increased total calcium testing volumes without changing the rate of calcium-related diagnoses. The results suggest that the increase in total calcium orders associated with panel-based testing largely constitutes excess or unnecessary testing.

The use of high-dose vitamin C in cancer care has offered promising results for some patients. However, the intravenous (IV) doses used for these patients can reach concentrations that interfere with some strip-based glucose meters. We characterized the impact of vitamin C interference, from standard to the very high doses used for some cancer protocols, using three different hospital-use glucose meters. For two of the three devices tested, increasing concentrations of ascorbic acid caused false elevations in the glucose measurements. The third glucose meter did not provide inaccurate results, regardless of the vitamin C concentration present. Rather, above a certain threshold, the device generated error messages and no results could be obtained.

Abstract Introduction Analytical outliers occur with most troponin assays and can adversely affect patient management. We developed a system to detect analytical outliers for the Roche Troponin T (cTnT) Gen 5 assay using repeat analysis of samples manifesting changing values over 2 to 6 hours. Methods In our ED practice, troponins are collected at baseline, 2 hours, and at times at 6 hours in plasma separator tubes and processed on the Roche Cobas e411 (Indianapolis, IN). When troponin values change by ≥10 ng/L or ≤10 ng/L in the 2- or 6-hour sample compared to baseline, the sample that produced the result is repeated. If initial and repeat values differ by > ±5 ng/L (cTnT <100 ng/L) or ±5% (cTnT ≥100 ng/L), the initial value is deemed to be an analytical outlier. Outliers are confirmed by a third measurement. Results Since March 2018, 19 of 17,154 (0.11%) troponin samples analyzed have been labeled outliers. Outliers were falsely elevated in 14 cases and falsely decreased in 5. Mean (range) of initial cTnT values for outliers was 39 ng/L (18-621 ng/L), with only one initial value >100 ng/L. All changes could affect analysis of change over time. Eleven of 19 outliers were outside gender-specific reference interval on both samples and thus did not cause a change from abnormal to normal. Repeat measurements on 8 of 19 outliers were within the URL, which may have greater clinical consequences. Conclusions Repeat analysis upon observation of changing values allows detection of analytical outliers in real time, prior to reporting results. These outliers are almost always clinically significant in terms of interpretation of changing values and at times change patients from above to below the URL. If we tested more than just changing samples, we might detect still more outliers.