Citations

Abstract Background Rapid testing for Neisseria gonorrhoeae, Chlamydia trachomatis, and Trichomonas vaginalis may be useful for antibiotic stewardship initiatives. Our institution recently implemented the second-generation Visby Medical Sexual Health Test (Visby Gen2) for rapid diagnosis of STIs in our emergency department. Here we (1) compared result concordance of the (Visby Gen2) to the first-generation Visby Medical Sexual Health Click Test (Visby Gen1), (2) compared assay failure rates of the Visby Gen1 to Gen2 tests, and (3) compared result turnaround time (TAT) for clinically ordered rapid STI tests to central laboratory STI results. Methods Accuracy was assessed using residual clinical samples in Aptima buffer submitted for routine molecular STI testing (C. trachomatis, N. gonorrhoeae, and T. vaginalis) in the central laboratory on the Panther platform (Hologic). Aptima samples were diluted 1:33 in Visby collection buffer prior to testing on the Visby cartridge. Clinically reported results were used as reference results. Diluted samples with discordant results were re-tested on the reference method and the result from the dilution was used in calculations. Assay failure rates were calculated as the number of failed assays compared to the total assays performed. TAT for rapid and central lab testing was calculated as time of sample received in laboratory to time verified in laboratory information system. Results For Visby Gen1 we observed negative concordance of 100% (41/41) for C. trachomatis, 97.5% (39/40) for N. gonorrhoeae, and 97.1% (34/35) for T. vaginalis. Positive concordance was 91.7% (11/12) for C. trachomatis, 100% (11/11) for N. gonorrhoeae, and 100% (17/17) for T. vaginalis. For Visby Gen1 we observed a 10.5% (12/114) cartridge failure rate. For Visby Gen2, we observed negative concordance of 100% (59/59) for C. trachomatis, 100% (63/63) for N. gonorrhoeae, and 100% (55/55) for T. vaginalis while positive concordance was 100% (10/10) for C. trachomatis, 100% (6/6) for N. gonorrhoeae, and 90.9% (10/11) for T. vaginalis. The Visby Gen2 assay displayed a 4.9% (4/81) cartridge failure rate. Six weeks after implementing the Visby Gen2 assay in our ED, rapid STI panel TAT was 0.48 hours on average (n=20). In the central laboratory the average TAT was 22.31 hours (n=7892). Conclusions Assay concordance to reference method was similar for Visby Gen1 and Gen2 assays. The cartridge failure rate improved by 46.7% with the Gen2 assay which should decrease the need to repeat testing due to assay failure. Result TAT decreased by 97.8% when comparing rapid Visby results to central laboratory. Therefore, Visby STI testing using the Gen2 assay can provide accuracy and rapid results likely to facilitate more rapid patient management.

Abstract Background Several studies have shown that the implementation of automation in microbiology laboratories may allow for increased volume growth without the need for additional staffing. One of the more recently available pieces of microbiology automation, the COPAN Colibri, automates the manual steps of colony spotting and preparation of targets for use on MALDI-TOF instruments for bacterial identification. This same instrument can also prepare a diluted bacterial inoculum for immediate use on automated susceptibility testing platforms. We wanted to evaluate the performance of both of these functions in real-time with clinical cultures, to determine if implementation would be beneficial over current workflows. Methods The COPAN Colibri was operated following the manufacturer’s recommendations with the exception of the identification of gram-positive organisms. The Colibri is FDA cleared for MALDI-TOF target spotting, however not with the use of formic acid, nor is it FDA cleared for susceptibility suspension preparation. Preliminary data suggested that addition of formic acid was necessary for accurate identification. A subset of cultures were digitally read in real-time as part of clinical workflows, selected for Colibri MALDI-TOF target spotting and Vitek2 suspension preparation using the COPAN WebApp software. Targets were run on the MALDI Bioptyper Sirius (Bruker) and suspensions were run on Vitek2 (BioMerieux) using the pre-dilution mode using GN-99 and GN-801 cards for appropriate organisms. All results were compared to standard manual workflows. Results A total of 145 gram-negative and 60 gram-positive organisms were picked for MALDI-target spotting on Colibri. Compared to standard testing, 144 (99%) gram-negative and 57 (95%) gram-positive Colibri spotted isolates matched expected species-level identifications. 97 Gram negative bacilli were run on Vitek 2 generating 95.52% categorical agreement across all drugs but with 13 major errors (MEs) and 2 very major errors (VMEs), which exceeded the acceptable percentage for certain drugs. The total number of failed runs was 13 (13.34% of total GNB isolates run), and all required complete resetting of MALDI-spotting and suspension preparation. Conclusions The Colibri performed well for MALDI-TOF target spotting with the addition of formic acid for gram-positive organisms. It did not perform within the acceptable range for susceptibility suspension preparation as it exceeded the recommended cutoffs for MEs and VMEs for certain drugs. The proportion of failed runs due to instrument errors may offset any advantages in labor savings due to the need for repeat set up.

Abstract Background Patients at high risk for Atherosclerotic Cardiovascular Disease (ASCVD), who do not achieve adequate lowering of LDL-cholesterol (LDL-C) with maximally tolerated statins or who are statin-intolerant, are often treated with proprotein convertase subtilisin/kexin (type 9) serine protease inhibitor (PCSK9-inh) therapy. The LDL-C reference method, Beta-Quantitation (BQ), is not routinely performed in these patients. Because low values are measured less accurately with current methods, a variety of equations have been developed to calculate LDL-C from the standard lipid panel test results (total cholesterol (TC), triglycerides (TG), and high-density lipoprotein cholesterol (HDL-C)). We further modified the Sampson LDL-C (S-LDL-C) equation by a novel approach by combining logistic regression (LR) and linear regression to minimize errors when compared to BQ for low LDL-C. Methods A dataset of BQ results from Mayo Medical Labs was divided randomly into a training set (TG ≤ 1500 mg/dL, TC ≤ 1050 mg/dL, n=19,639) and a validation set (TG ≤ 800 mg/dL, BQ-LDL-C ≤ 130 mg/dL, n=12,910). The training dataset was analyzed by LR, using BQ-LDL-C to classify patients as being above or below the 70 mg/dL cut-point. Dependent variables in the LR model were the S-LDL-C, HDL-C, TC, TG and an interaction term (TG x NonHDL-C). The sum of the exponential terms in the trained LR equation for the dependent variables were then fitted by least squares linear regression to match BQ-LDL-C to develop the modified S-LDL-C equation (mS-LDL-C). We used the validation dataset to compare Friedewald (F-LDL-C), extended Martin (eM-LDL-C), Sampson (S-LDL-C) and modified Sampson (mS-LDL-C) equations by Cohen’s kappa coefficient for agreement with BQ-LDL-C and normalized Matthew’s correlation coefficient (MCC), which considers any imbalances in the number of false high versus false low results. Results The mS-LDL-C equation (mS-LDL-C = (TC/0.9423 - HDL-C/0.9582 - TG/9.397 - ((TGxNonHDL-C)/1305) + TG2/8569) - 5.688), primarily differs from the S-LDL-C equation in the value of the coefficients that include TG as a variable. For S-LDL-C ≤100 mg/dL, mS-LDL-C had the best concordance with BQ-LDL-C in terms of the total percent of patients misclassified as either high or low (mS-LDL-C: 21.4%, S-LDL-C: 24.4%, eM-LDL-C: 25.4% F-LDL-C: 30.8%). mS-LDL-C also had the best kappa score when compared to BQ (mS-LDL-C: 0.833, S-LDL-C: 0.810, eM-LDL-C: 0.789, F-LDL-C: 0.743). MCC was also the highest for mS-LDL-C (mS-LDL-C: 0.916, S-LDL-C: 0.906, eM-LDL-C: 0.894, F-LDL-C: 0.879). Finally, mS-LDL-C also showed the best concordance with BQ-LDL-C when an alternative lower LDL-C therapeutic decision cut-point of 55 mg/dL is used (kappa of 0.766 versus 0.724) but showed an increasingly poorer performance than S-LDL-C for higher LDL-C whenever it exceeded 150 mg/dL. Conclusions A strategy to maximize the overall accuracy of estimated LDL-C could provide an automatic reflex in a laboratory information system to the mS-LDL-C equation whenever S-LDL-C is <100 mg/dL. By doing so, the use of the more accurate mS-LDL-C equation for low LDL-C should improve PCSK9-inh therapeutic decision making over the other LDL-C equations.

Abstract Background Low-density lipoprotein-cholesterol (LDL-C) is the main univariate risk marker recommended for initial atherosclerotic cardiovascular disease (ASCVD) risk stratification. LDL, however, transports other constituents that can serve as pro-atherogenic risk markers. Like LDL-C, the triglyceride content of LDL (LDL-TG) can also be estimated from the lipid panel. Using data from UK Biobank (UKB), we compared LDL-C to LDL-TG for predicting future ASCVD events. Methods A UKB dataset (N=270,436) of primary prevention individuals not on lipid lowering therapy was used for analysis. LDL-C was calculated by the following equations: Friedewald, Extended Martin-Hopkins, Sampson-NIH, and Enhanced Sampson-NIH. LDL-TG was calculated by our newly devised equation (eLDL-TG = TG/38.46 + nonHDL-C/5.75 + (TG/nonHDL-C)/0.103 + (1/HDL-C)/0.0041 - 2.95)), which was derived from a dyslipidemic cohort (N=40,202) analyzed by the beta-quantification reference method. Results Although there was only a modest comparison between eLDL-TG and measured LDL-TG (R2=0.601, slope=0.611), eLDL-TG appeared to be a better risk marker than LDL-C in UKB. When the population was divided into quintiles and analyzed by survival curve analysis, eLDL-TG was better than LDL-C in segregating patients into low- and high-risk groups (Figure). Similar findings on the relative value of LDL-TG over other risk markers were also found when analyzed by ROC analysis (AUC: eLDL-TG>HDL-C>remnant-cholesterol>NonHDL-C>TG>apoB>Sampson-NIH-LDL-C). By Cox-proportional hazard analysis eLDL-TG showed alongside apoB and NonHDL-C the strongest association with future ASCVD events. Discordance analysis showed that when stratified as LDL-TG/LDL-C as High/Low there was an 26% increased risk for ASCVD, and when as High/High showed an 40% increased risk for ASCVD. Conclusions Like LDL-C, LDL-TG levels can also be estimated from the standard lipid panel. If our findings hold in other populations, the superiority of LDL-TG over LDL-C as a risk marker suggest that the reporting of calculated LDL-TG along with LDL-C could be a new strategy for improving ASCVD risk stratification.

Abstract Background Managing preanalytical variables is critical to providing quality laboratory test results. However, this can be a challenge for laboratory staff, particularly with manual detection of interfering substances and complicated workflows. For coagulation testing, we assess specimen integrity and acceptability by visual checks and quantification of interfering substance on a chemistry analyzer. To improve this subjective process, we evaluated the technical performance of a new model of coagulation analyzer with automated preanalytical specimen integrity detection. Our objective was to determine if we could reduce the number of specimens requiring visual inspection/off-analyzer interference testing in order to simplify our testing workflow. Methods Citrated plasma specimens received in the Hospital Clinical Laboratory at Mayo Clinic (Rochester, MN) were tested on the TOP ACL 500 or TOP ACL 550 Series Coagulation analyzers (Werfen, Boston, MA) according to manufacturer’s instruction. Specimens tested on the TOP 500 were visually inspected for hemolysis, icterus, and lipemia (HIL). If the technologist detected a possible interferent, an aliquot of plasma was analyzed on our cobas c501 chemistry analyzer (c501) (Roche Diagnostics, Indianapolis, IN) to obtain HIL values. Laboratory-established or manufacturer-defined interference thresholds were applied in determining specimen acceptability. For samples tested on the TOP 550, specimen integrity was assessed by the instrument’s automated interference detection module. The analyzer reported HIL as an “interference cloud” or estimated range, rather than a single value like the c501. HIL values were estimated by visual assessment of the low end of the “interference cloud”. Values were confirmed on the c501 only for specimens with an “interference cloud” at/exceeding the test-specific interference thresholds. We analyzed data from a 2-month period pre- (TOP 500) and post-implementation (TOP 550). The percent of specimens requiring quantitation/confirmation on the c501 was calculated. Statistical significance was calculated using the Chi-squared test where significance was defined as p<0.01. Correlation between c501 and TOP 550 estimated HIL values was evaluated. Results A total of 2.2% (171/7605) and 1.3% (113/8983) of specimens tested on the TOP 500 and TOP 550, respectively, required HIL measurements on the c501 (p<0.001). Of those samples, 8.2% (14/171) and 14.2% (16/113) from the TOP 500 and TOP 550, respectively, had HIL values above the interference thresholds and were rejected by the laboratory. Comparison of the estimated HIL values from the “interference cloud” and from the c501 resulted in slopes (Pearson correlation coefficients) of: H 1.56 (0.77), I 0.67 (0.97), and L 1.35 (0.99). Conclusions In comparison to our workflow with visual assessment of interference, use of the automated interference detection functionality on the TOP 550 instrument resulted in a lower percentage of specimens requiring HIL measurements on the chemistry analyzer. Agreement between interference measurements obtained on the c501 and the TOP 550 was moderate where TOP 550 overestimated the degree of hemolysis and lipemia and underestimated icterus. Implementation of automated interference detection on the TOP 550 analyzer improved our workflow by eliminating the need for tedious and subjective visual checks and reducing the percentage of specimens requiring additional HIL measurements.

Abstract Background Fibrinogen plays a critical role in hemostasis and is essential to clot formation. Changes in the quantity or activity of fibrinogen can lead to disorders affecting coagulation. Thus, it is essential to precisely quantify fibrinogen levels to differentiate between hyperfibrinogenemia in hypercoagulable disorders and dysfibrinogenemia, hypo/afibrinogenemia, and irregular bleeding. Current diagnostic tests that use antibodies to detect fibrinogen or activity-based measures are susceptible to interference from heparin contamination, hemolysis, and hyperbilirubinemia. Here, we evaluated the use of targeted proteomics for developing a quantitative assay for fibrinogen in plasma samples. Methods Four peptides each for alpha and gamma chains of fibrinogen were synthesized using FMOC chemistry on a Liberty Blue (CEM Corp) peptide synthesizer. A targeted multiple reaction monitoring (MRM) assay was developed and optimized using Dionex Ultimate 3000 HPLC connected to a TSQ Altis triple quadrupole mass spectrometer (Thermo Scientific). Evaluations were conducted using a range of standards, such as pure protein, WHO plasma and commercially available plasma with known fibrinogen amounts for the accurate quantitation of fibrinogen. Calibration curves were generated using different dilutions in various matrices. The repeatability and precision of the assay were evaluated across five different days. The plasma samples were processed and digested in a 96-well plate and subjected to C18 clean-up using a Bravo automated liquid handler (Agilent). All samples were spiked-in with corresponding heavy-labeled peptides prior to digestion with trypsin. Multiple reaction monitoring assays were performed on tryptic digests of plasma samples. Results Calibration curves using commercial plasma for all peptides showed linearity from 20 mg/dl to 1,308 mg/dl. This covers the entire clinical range including normal (200-350 mg/dl), deficient states (<100 mg/dl) and excess production (>350 mg/dl) that can be used for evaluation of bleeding disorders including consumption coagulopathy and hyperfibrinogenemia. Based on the overall performance in terms of linearity, limit of detection/quantitation, carryover and coefficient of variation (CV) across five different days, two peptides per chain were finally selected for developing the quantitative assay. The intra-assay variability was <5% and intra-day CV was ∼20%. This assay is currently being tested on a variety of clinical samples representing a range of fibrinogen levels. Conclusions Our study focused on assessing whether multiplexed MRM assay could be implemented to measure fibrinogen levels in plasma. Our data indicate the feasibility of MRM-based assays for determining fibrinogen levels as an alternative to existing non-mass spectrometry-based activity or antigen assays.

Abstract Background Thoroughly investigating single patient result errors may identify systemic, large-scale testing errors. The laboratory observed an event where a revision to a Total Bile Acid (TBA, Diazyme Laboratories, Inc.) patient result following a 22S quality control (QC) failure displayed a larger than expected difference (52 to 5mcmol/L). TBA is primarily used for diagnosis and monitoring of intrahepatic cholestasis of pregnancy with results >10mcmol/L considered elevated. Retrospective review of result revisions following QC failures (3/15/2017-5/30/2023) revealed that 50%(20/40) yielded result differences of >10mcmol/L(>20SD based on assay imprecision). The aim of this study was to apply a systematic approach to i) estimate the rate of TBA errors, ii) ensure accurate result reporting during investigation period, iii) perform root cause analysis (RCA), and iv) determine corrective and preventative action(s). Methods Residual samples from TBA testing (6/24-7/5/2023) were retested(n=158), differences >+/-3.0mcmol/L or 15% were confirmed on an alternate analyzer, and reports were revised. Automated repeat testing of TBA samples >10mcmol/L was operationalized 7/6/2023. Initial and repeat TBA results were compared(n=448) and results differing by >+/-20% were remeasured on an alternate analyzer. RCA was conducted using a fishbone diagram. NaOH reagent probe washes were implemented for TBA and the error rate was re-assessed. Assays run prior to TBA samples with errors (n=15) were identified. To assess reagent carryover a residual serum pool (TBA ∼5mcmol/L) was aliquoted into 5 tubes in a sample rack. The first sample in the rack was programmed to run an assay suspected of causing carryover followed by 4 TBA measurements. Mean±standard deviation(SD) TBA concentrations were calculated. TBA was also measured in the liquid reagent for amylase, lipase, acetylcholinesterase (ACE) and fructosamine. Results Initial TBA retesting yielded no errors when initial TBA was ≤10mcmol/L(n=51). For samples with TBA >10mcmol/L(n=107), 9(8.4%) had differences exceeding criteria with 8/9 being revised to ≤10mcmol/L. Analysis of automated patient repeat data showed a 3.8%(17/448) error rate when initial TBA >10mcmol/L. After NaOH reagent probe washes were implemented, the error rate decreased to 0.7%(3/448). Assays run directly before an erroneously high TBA result included: lactate, fructosamine, soluble transferrin receptor(STFR), lipase, and ACE. A serum TBA pool(mean±SD=5.1±0.4 mcmol/L,n=15) measured 37.6±1.2mcmol/L(n=3) and 6.7±0.7mcmol/L(n=3) after lipase and fructosamine, respectively. No other assays demonstrated carryover. TBA in lipase reagent compartment B and C was 653 and 649mcmol/L, respectively, and 653mcmol/L in fructosamine reagent compartment B. Conclusions A single patient TBA result revision was investigated and led to identification of reagent carryover causing erroneously high TBA results on the Roche c502 analyzer. Automatic, real-time, repeat testing was implemented to prevent reporting incorrect patient results until RCA could identify the cause. Occurrence rate of erroneous high TBA results decreased from 3.8% to 0.7% after implementation of NaOH washes on the instruments. Carryover experiments confirmed that lipase and fructosamine assays cause carryover when run prior to a TBA sample due to measurable TBA in the reagent. The lab is pursuing moving the fructosamine and lipase reagent to the Roche Cobas c701 to prevent the issue.

Abstract Background Serum aspartate aminotransferase (AST) is a commonly ordered test useful for diagnosing and monitoring hepatocellular damage. The hemolysis (H) index interference threshold for the Roche Cobas AST reagent is H=20, which is the lowest of all tests on Roche chemistry platforms (Cobas c701, c502, and e801). Previous optimization studies supported the implementation of an AST concentration-specific tiered hemolysis interference threshold schema: AST<100 U/L (H=50), AST=100-200 U/L (H=100), AST=201-300 U/L (H=200) and AST>301 U/L (H=500). This schema significantly reduced redraws; however, a significant number of rejected samples were noted to have AST results within the reference interval (RI). The aim of this study was to establish an H-index threshold for samples with AST results within the RI. Methods Twelve pools of residual serum samples were created with AST concentrations ranging from 17-66 U/L. Each pool was split into 2 aliquots, receiving either 10% of a lab-produced hemolysate solution generating H-indices from 62-508 or 10% of a 0.9% saline control. Aliquots were mixed in equal portions to create a series of 5 samples with increasing hemolysis according to CLSI guideline EP-07 and analyzed in EP Evaluator. Median(interquartile range (IQR)) H-index thresholds were calculated for error limits beginning at +5 U/L and increasing incrementally (+1 U/L) to +10 U/L. Clinical cases with an H-index slightly above the hemolysis interference threshold (50≤H≤70) and results within the RIs (Male:8-48U/L; Female:8-43U/L) were reviewed by the Chemistry Resident/Fellow on-call (n=68) over a period of 8 months. Results were released with a comment if risk of clinical misinterpretation due to a falsely elevated result was low. Previous, non-hemolyzed AST results were compared within 45 days, if available, by paired-Wilcoxon test (Graphpad Prism). Results The median(range) of initial AST results in the serum pools was 20(17-66) U/L. The calculated H-index thresholds (median(IQR)) using error limits of +5, +6, +7, +8, +9, +10 U/L were 49(43-53), 56(52-63), 66(61-78), 78(72-81), 91(84-91) and 101 (96-107), respectively. All slightly hemolyzed AST results (50≤H≤70) within the RI were released with a comment upon chart review. Previously reported AST values (n=23) were compared to current (hemolyzed) concentrations and found to be statistically significantly increased relative to previous results from the same patient (median(range) difference: 3.7(-3.6-11)U/L, p=0.009). However, the median difference was <5U/L or 10%, the laboratory’s criteria used to define a clinically significant difference. Conclusions A median H-index limit of up to 100 can be applied to samples when AST is within the RI. The risk of misinterpretation for results with mild-moderate hemolysis was low in the cases reviewed. Sample rejection rates and reporting delays can be diminished by implementing this practice. Interpretation can be further enhanced by appending the following comment to hemolyzed samples: “Specimen was received with hemolysis slightly above the accepted threshold. This degree of hemolysis is associated with a false increase of approximately 5-10 U/L. Interpret in conjunction with other laboratory and clinical findings.”

Abstract Background Autoimmunity targeting GQ1b is associated with group of disorders that includes Miller-Fisher Syndrome (MFS), Bickerstaff Brainstem Encephalitis (BBE) and classic Guillain Barre Syndrome (GBS) with ophthalmoplegia. Collectively these are referred to as GQ1B-IgG related syndromes. The prevalence of GQ1b-IgG in this population of patients is high and has been reported to be >80% in well-defined clinical cohorts. Clinical phenotypic presentation is diverse and requires highly accurate antibody testing. The objective of this study was to evaluate a newly developed custom GQ1b-IgG assay, using a semi-quantitative ELISA-based method. Methods A GQ1b-IgG ELISA was developed and validated. Patient sera along with assay controls and a single-point calibrator are diluted and incubated in duplicate wells in a 96-well plate coated with GQ1b antigen. The plate is washed and incubated with anti-human-IgG secondary antibody conjugated with HRP. After a colorimetric reaction, optical densities (OD) are read. The OD of patient samples are compared against the calibrator, generating a cut-off index (COI). Samples ≥1.0 COI are considered positive. During the validation phase, analytical and clinical performance characteristics were evaluated. Studies included assessment of imprecision at multiple concentrations of GQ1b-IgG, accuracy using method comparison, analytical sensitivity and specificity, reference range, common interferences, plate drift, and clinical performance in relevant disease and control cohorts. Results Intra-assay imprecision (coefficient of variation; %CV) ranged from 1.0 - 16% across all concentrations of GQ1b-IgG. Inter-assay imprecision (%CV) ranged from 12.4 - 44.5% but was <20% at or above the assay cut-off. We found perfect qualitative agreement with an independent ELISA reference method (20 positive and 20 negative samples selected based on reference method results [reference interval <1:100]). Limit of detection (LOD) was calculated at a COI of 0.22. To assess analytical specificity, 40 samples with high concentrations of immunoglobulins (hypergammaglobulinemia [N=15], IgG M-protein [N=15] and systemic lupus erythematosus [N=10] were tested and all were negative. A reference interval study was performed on 140 healthy donor serums, all were negative. Interference studies were conducted on 2 positive and 2 negative serum samples, spiked for highly hemolytic (1000 mg/dL), lipemic (2000 mg/dL) and icteric (60 mg/dL) conditions. There was no impact on the assay even with high levels of a given interferent. Plate drift on 2 distinct patient pools tested across the plate (COI range 0.7 - 1.3) had overall plate CV’s ≤10%. ROC analysis was performed on a cohort of 278 patient samples (15 MFS cases and 263 controls [suspected demyelinating neuropathy or disease mimics]). A cut-off value of 1.0 (COI) was selected based on the Youden index. At this cut-off, clinical sensitivity for MFS was 87% while clinical specificity was 99.2%. Conclusions The observed performance of the GQ1b-IgG ELISA was acceptable for clinical laboratory implementation. A COI of ≥ 1.0 was selected for clinical use.

Abstract Background Patients with elevated free glycerol can have falsely elevated triglyceride concentrations known as pseudohypertriglyceridemia. Most clinical laboratory triglycerides methods measure the concentration of glycerol released following the enzymatic hydrolysis of triglycerides to quantitate triglycerides. Thus, increased free glycerol can lead to falsely increased triglyceride concentration. Identifying and measuring the interference is necessary to effectively manage patient care interventions. A glycerol-corrected triglyceride assay was discontinued from Roche Diagnostics in 2021. We created a process to determine glycerol-corrected triglycerides by measuring glycerol directly using an assay from Randox Laboratories. Methods Glycerol (Randox Laboratories) and nonglycerol-blanked triglycerides (Roche Diagnostics) were measured in serum using the Roche Cobas c501. Stability (n=10) and expected values (n=206) were determined using serum from healthy volunteers. Linearity (n=5) and precision (5 replicates on 2 runs over 5 days) were determined using residual clinical serum spiked with exogenous glycerol as necessary to span the analytical measuring range. Glycerol corrected triglyceride concentrations were calculated by subtracting glycerol (mmol/L) from triglyceride (mmol/L) and converting to mg/dL (x88.59). Samples previously measured using the Triglyceride/Glycerol Blanked Reagent (n=53, Roche Diagnostics) were used to assess accuracy. (Figure 1) Results Glycerol measures were stable 3 days refrigerated and 30 days frozen. Precision coefficient of variation was 1.0% within run and 7.1% within lab. Expected values were 10 - 109 mcmol/L, and the method was linear between 2 - 3,971 mcmol/L. The average bias between methods was 11.0% with slope of 1.02 and R2>0.97 (Figure 1). Conclusions Glycerol can be accurately measured using the Randox method and the result can be used to generate an accurate glycerol-corrected triglyceride concentration.