Donato, Leslie J.

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 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 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.

Abstract Background Beta-quantification (BQ) is the reference method for LDL-C but is not widely available. It is difficult, therefore, for clinical laboratories to independently assess the accuracy of their methods for either calculating or directly measuring LDL-C. Our goal was to investigate if an interrelationship between the tests in the standard lipid panel could be used to compare the performance of different LDL-C methods against BQ, specifically in samples with hypertriglyceridemia, a major cause of bias for LDL-C testing. Methods BQ results from Mayo Medical Labs (n=39,667) and the NIH (n=18,338) were used to identify a linear relationship by least-squares regression analysis between lipid panel test results and LDL-C to facilitate a statistical comparison between different LDL-C methods. Results It is well established that the number of VLDL particles increases as TG increases, because VLDL is the main carrier of TG. As a consequence, the ratio of LDL-C to non-HDL-C is inversely related to TG, which can be made linear by taking the square root of TG (Figure). Nearly identical regression equations were found for this relationship, using two different BQ datasets. When LDL-C was calculated by either Sampson equation, the LDL-C/non-HDL-C regression lines nearly overlapped with the one for BQ. In contrast, the Martin equation showed a positive bias with increasing TG, whereas the Friedewald equation showed a negative bias, consistent with past reports on their LDL-C biases with hypertriglyceridemia. In the UKBiobank, the Beckman direct LDL-C showed a positive bias for TG, which was confirmed in CAP proficiency test surveys. By simulation analysis, approximately 80 samples with high (>200 mg/dL) and low (<100 mg/dL) TG is statistically sufficient to assess if the LDL-C/non-HDL-C regressions lines of LDL-C methods differ from BQ. Conclusions The LDL-C/non-HDL-C ratio method is a simple way to examine LDL-C methods for bias on hypertriglyceridemic samples.

Abstract Background Calculated LDL cholesterol (LDL-C) is a standard component of the lipid panel and an important risk factor in managing atherosclerotic cardiovascular disease (ASCVD). Due to the assumptions in the derivation of the Friedwald formula, fasting (≥8 hours) and triglyceride levels ≤400mg/dL have long been recommended. More recent data have shown that in most cases fasting has minimal effect on results, leading to the endorsement of non-fasting lipid panels for routine evaluation of ASCVD risk in clinical practice guidelines. Furthermore, newer equations (e.g. Sampson-NIH) allow for more accurate estimation of LDL-C even in the presence of hypertriglyceridemia. Mayo Clinic has implemented the Sampson-NIH LDL-C calculation and removed fasting/non-fasting designation from lipid panel orders. We hypothesized that the change to fasting designation would allow more afternoon draws for routine lipid panels and that increased non-fasting collections may increase triglyceride levels, but that the Sampson-NIH equation would prevent bias in LDL-C estimation. In order to test these hypotheses, we analyzed patient data from 1 year prior and 1 year post-implementation. Methods Results of lipid panels performed for one year preceding (n=334,719) and one year after (n=381,804) removal of the fasting/non-fasting designation and implementation of Sampson-NIH equation were collected. Median, 10th, and 90th percentile of draw time, triglyceride, and LDL-C calculated by either Sampson-NIH or Friedwald formulas were compared by quantile regression. Results Draw times shifted post-implementation at the 10th percentile (7:06 vs. 7:10, p<0.0001), median (8:49 vs. 9:08, p<0.0001) and 90th (12:02 vs. 13:46, p<0.0001). No difference in median triglyceride measurement was observed between pre-and post-implementation (110mg/dL), but slight differences were observed in the 10th and 90th percentile values (60 vs 59mg/dL and 224 vs 229mg/dL, p<0.0001). No difference was observed in 10th, 50th, or 90th percentile LDL values when calculated by Sampson-NIH equation (56, 99, 153mg/dL); however, if the Friedwald formula is used, a slight shift is observed in the 10th and 50th percentile LDL values (54 vs. 53, 97 vs. 96; p<0.0001) but not the 90th (150mg/dL). Conclusions Changing to the Sampson/NIH formula for LDL-C and removing fasting vs. non-fasting designation from routine lipid panels had minimal effect on reported triglyceride and LDL-C values, while allowing increased flexibility in draw time and reporting of LDL-C on patients with triglycerides ≥400mg/dL. Patients with TG ≥400mg/dL represented 1.7% of the dataset overall, including 5,387 patients before the change in which LDL-C could not be reported and 6,697 patients in the year post-implementation for whom LDL-C was reported based on the Sampson-NIH formula. Using an equation that is robust in patients with elevated triglyceride levels and removing fasting requirements from routine lipid panels allows greater flexibility and convenience for patients while allowing for more even distribution of phlebotomy workload and reporting of LDL-C results for patients with hypertriglyceridemia without evidence of clinically significant bias in reported results.