Citations

Abstract Background The urgent need for rapid fentanyl detection at the point-of-care originates from its involvement in a high number of overdose deaths, primarily due to illicit use. Given fentanyl's potency compared to traditional opioids, accurate and sensitive detection is critical. Current lateral flow assays (LFA) cannot detect single-digit concentrations and may yield false negatives due to their lower sensitivity. Herein, we evaluate the clinical performance of a novel LFA (InstaStrip Fentanyl Rapid Test (urine)) with 1 ng/mL cutoff for fentanyl and no cross-reactivity for norfentanyl, offering enhanced sensitivity for effective overdose prevention at the ED. Methods A total of 172 urine samples submitted for UDS analysis were used for comparison studies. Fentanyl concentrations were categorized in 5 groups to cover various concentration categories around the cutoff of the LFA (1 ng/mL). These categories are: drug-free (<0.1 ng/mL, n=68), 0.1-0.5 ng/mL (n=14), 0.5-0.9 ng/mL (n=15), 1-1.5 ng/mL (n=9) and > 1.5 ng/mL (n= 66). Concentrations were determined by in-house LC-MS/MS methods validated according to CLIA standards, with fentanyl concentrations quantitated in the ABSciex software. For LFA testing, 150 μL of urine was added to a reagent tube containing dried rabbit anti-fentanyl antibody conjugated gold nanoparticles. After 20 sec of brief shaking, a test strip was inserted into the test tube. After 5 min, qualitative results were read as follow: both the control and test lines were visible in negative results, whereas only the control lines were visible in positive results. Method comparison study of the LFA was conducted by three operators who were blinded to the LC-MS/MS results. Result comparisons were made between the LFA strip and LC-MS/MS results, as well as among different operators for the same samples. Results Of the 75 urine samples with LC-MS/MS fentanyl concentrations ≥ 1 ng/mL, 74 produced consistently positive results using the InstaStrip Fentanyl Rapid Test. Of the 97 urine samples with LC-MS/MS fentanyl concentrations < 1 ng/mL, 96 produced consistently negative results using the InstaStrip Fentanyl Rapid Test. Using the LC-MS/MS as the gold standard method and 1 ng/mL as a cutoff, the clinical sensitivity of InstaStrip Fentanyl Rapid Test was calculated to be 98.67% (95% confidence interval (CI) 92.79-99.97%), and the clinical specificity was 98.97% (95% CI 94.39-99.97%). Consistent results among three operators were obtained for 100% of the “drug free”, “0.1- 0.5 ng/mL” and > 1.5 ng/mL groups, whereas 93.3 % (14 out of 15) and 88.9% (7 out of 8) were obtained for the “0.5-0.9 ng/mL” and “1-1.5 ng/mL” groups, respectively. The two samples with a singlet discrepant result (1 false positive and 1 false negative) in the triplicate experiments contained fentanyl concentrations of 0.9 and 1.0 ng/mL respectively, which are very close to the cutoff value. Conclusions In this study, the InstaStrip Fentanyl Rapid Test (urine) demonstrated high clinical sensitivity (98.67%), high specificity (98.97%) and high reproducibility showing that it may be useful in screening and identifying patients with fentanyl overdose in minutes in an emergency setting. This would facilitate appropriate patient triaging for optimal clinical care.

Abstract Background Professional medical societies recommend that carrier screening for 113 different genetic disorders be offered during pregnancy or preconception to all individuals, regardless of ancestry, in order to provide more equitable opportunities for patients to ascertain their reproductive risks. In our clinical laboratory experience, however, the vast majority of healthcare providers continue to order carrier screening for only cystic fibrosis (CF), which is disproportionately common in white individuals of Northern European descent. Our highest volume CF screening test is performed on >10,500 diverse patients per year and utilizes a targeted genotyping array that detects >500 variants in CFTR and >1200 variants in >150 other pertinent genes. Here we report the inequities in diagnostic yield among our first 8,000 patients for whom only limited testing was ordered and examine the barriers to widespread adoption of more inclusive carrier screening. Methods Three carrier screening options (CF, CF+spinal muscular atrophy [SMA], CF+SMA+fragile X syndrome [FXS]+hemoglobinopathies) were ordered clinically and performed on DNA extracted from 8,000 whole blood samples from unique individuals utilizing a custom SNP array (Thermo Fisher GeneTitanTM platform) designed to detect >1700 pathogenic variants in 218 different genes. Data was retrospectively reviewed to determine overall detection rates for carriers of all recommended conditions vs. only the condition(s) included in the originally ordered service. Finally, reproductive health providers at nine different ordering sites across the U.S. were interviewed regarding their current carrier screening practices. Results Patients were referred for testing by 119 different clinical facilities. 98.1% (7847/8000) had CF-only testing ordered, 1.2% (99/8000) had CF+SMA testing ordered, and 0.68% (54/8000) had testing for CF+SMA+FXS+hemoglobinopathies ordered. 7.1% (568/8000) of patients had reportable variants in CFTR. Had CF+SMA testing been ordered on all 8,000 patients, however, the diagnostic yield would have been 8.9%. Of the patients who had CF-only or CF+SMA testing ordered, 11.9% (943/7946) were found to be carriers of hemoglobinopathies. Finally, had all 8000 patients had testing for all currently recommended targets on the panel, the diagnostic yield would have increased to 55% (4396/8000). Ordering providers at 9/50 different contacted sites agreed to interview requests. 100% (9/9) of respondents indicated that their patients experience significant disparities in access to carrier screening, and that the primary cause of these inequities was insufficient insurance reimbursement. 67% (6/9) of respondents indicated that >50% of their patient population identified as non-white and would have “very strongly” benefitted from “testing for other disorders beyond CF.” Conclusions Despite clinical practice guidelines urging for more population-neutral carrier screening, major gaps exist between what is recommended versus ultimately ordered. This significantly limits the clinical utility of testing, especially in patients from underrepresented backgrounds. Improved insurance coverage for more inclusive carrier screening is needed to help reduce such disparities.

Abstract Background Multiple sclerosis (MS) is an immune-mediated central nervous system (CNS) demyelinating disease which is diagnosed based on CNS lesions' spatial and temporal dissemination. The 2017 revised McDonald Criteria include cerebrospinal fluid (CSF) specific oligoclonal bands (OCBs) to indicate dissemination in time, but their assessment is labor-intensive and requires a paired serum sample. Kappa immunoglobulin free light chain in CSF (KCSF) has emerged as a more sensitive but less specific MS biomarker, measured from a single CSF sample. Our institution's MS testing panel uses KCSF initially to rule out MS, with reflex to OCB for positive confirmation if KCSF is borderline positive (≥0.06 mg/dL). However, manual correlation of laboratory and clinical data hinders the direct comparison of the diagnostic performance between these markers. Herein we report a retrospective assessment of KCSF and OCB diagnostic performance using automated chart review. Methods Between January 1st, 2021 and December 31st, 2022, there were 1,237 patients with simultaneously measured OCB and KCSF. CSF-specific OCBs were measured by separating CSF and serum proteins using an agarose isoelectric focusing gel with a pH gradient from 3-10 (Sebia). After transfer to nitrocellulose paper, antisera were applied to visualize IgG specific bands and quantify CSF-specific OCBs. KCSF was quantitated using nephelometry by applying free light chain antisera (Binding Site) to CSF samples using a Siemens BNII analyzer. An automated chart review process utilizing keyword matching was implemented to identify patients with MS. An initial filter phase required presence of the term “multiple sclerosis”, while a second required the presence of any of the following terms: “primary progressive,” “secondary progressive,” “relapsing remitting,” “progressive relapsing,” or “tumefactive” to identify patients with diagnosed MS. The automated method’s performance was then compared to a manual chart review of 200 randomly selected patients. Subsequently, the automated process was used to determine the MS diagnosis status of all 1,237 patients. Results Of the 200 manually reviewed patients, 44 MS diagnoses were found. The automated process correctly identified 41 of these with a resulting sensitivity and specificity of 93.18% and 95.51%, respectively. Applied to the entire cohort, the automated review identified 275 MS diagnoses. For the entire cohort, the area under the curve (AUC) for the receiver operating characteristic (ROC) curves for OCB and KCSF were 0.824 (95% confidence interval: 0.794, 0.854) and 0.808 (0.777, 0.839) respectively. Our institution’s diagnostic cutoff of ≥2 for OCB yielded a sensitivity and specificity of 74.2% (68.3%, 79.1%) and 84.9% (73.8%, 89.5%), respectively. The best combination for sensitivity and specificity for KCSF with the Youden index yielded a cutoff ≥0.0654 mg/dL (sensitivity of 79.6% [73.1%, 84.0%] and specificity of 72.4% [62.8%, 76.8%]), which is similar to previous publications. This supports the use of automated chart review and suggests the KCSF cutoff has been stable over time. Conclusions KCSF exhibits comparable MS diagnostic performance to OCB yet only requires one CSF sample and can be measured rapidly. When validated, mining unstructured data allows for test performance characterization in large patient cohorts without the need for manual chart review.

Abstract Background THES is a rare autosomal recessive syndromic enteropathy caused by pathogenic variants in two genes, the Tetratricopeptide Repeat Domain 37 (TTC37) and Ski2 Like RNA Helicase (SKIV2L), that encode components of the human SKI complex involved in RNA degradation. This disease is characterized by several clinical features with variable penetrance including intractable congenital diarrhea, wooly hair abnormalities, intrauterine growth restriction, facial dysmorphism, immune dysfunction, liver abnormalities, and skin hyperpigmentation. Hypogammaglobulinemia with low IgG and normal to low IgM levels have been described in these cases which may require intravenous immunoglobulin (IVIG) replacement therapy. Some cases have a reduced population of switched memory B cells, T cells with reduced IFN-γ secretion, and hyporesponsive NK cells. Our patient is a 4-year-old female reported to have a homozygous TTC37 mutation with clinical manifestations of TPN dependence, liver fibrosis with portal hypertension, splenomegaly, developmental delay, thrombocytopenia and anemia requiring transfusion, and a history of hypogammaglobinemia requiring IVIG replacement. Patient would go on to develop elevated immunoglobin levels leading to additional testing described below. Methods Serum total protein analysis performed using the using Siemens Atellica CH Total Protein II test (Weichselbaum method using biuret reagent). Serum immunoglobin (IgA, IgG and IgG), IgG subclass, and kappa/lambda free light chain level analysis performed by turbidimetric photometry assay using a Binding Site Optilite analyzer. Serum protein electrophoresis and immunotyping performed using CAPILLARYS 2 capillary zone electrophoresis and immunosubtraction with antisera. Serum protein immunofixation performed using Sebia Hydrasys 2 with Hydragel 9 IF Maxi kit. Confirmation and final determination of serum immunoproteins performed at our reference laboratory using MASS-FIX (MALDI-TOF MS) matrix-assisted laser desorption/ionization-time of flight mass spectrometry after patient serum applied to 5 separate immunoaffinity resins specific to immunoglobulin G, A, M, K, and L for purification. Results Initial testing showed a broadly migrating beta-gamma region IgG kappa monotypic restriction measuring 1.7 g/dL, with predominantly kappa subtraction and trivial lambda subtraction by immunosubtraction; serum free kappa/lambda light chain ratio was increased at 22.94. Serum immunoglobins and free light chains measured (mg/dL): IgA 738 (15 - 160), IgM 48 (50 - 240), IgG 2,805 (405 - 1,160), free kappa 20.42 (0.33 - 1.94), and free lambda 0.89 (0.57 - 2.63). IgG subclasses measured (mg/dL): IgG1 533 (320 - 950), IgG2 174 (50 - 340), IgG3 16.9 (10.0 - 120.0), IgG4 1,221.1 (2.0 - 110.0). Additional serum protein values included (g/dL): total protein 8.10 (6.00 - 8.30), albumin 2.97 (3.43 - 4.84), alpha-1 0.32 (0.21 - 0.44), alpha-2 0.52 (0.54 - 0.97), beta globulin 3.42 (0.65 - 1.03), and gamma globulin 0.87 (0.70 - 2.30). The polyclonal nature of the IgG4 spike was later demonstrated by the MASS-FIX mass spectrograph. Conclusions MASS-FIX is a useful addition to the toolset used in the diagnosis of possible monoclonal gammopathies and protein disorders, especially where broad monotypic restrictions are observed or in patients with abnormal presentations for monoclonal gammopathies. Using current standard techniques, the above case could have been incorrectly classified as a monoclonal process resulting in unnecessary cost and incorrect treatment.

Abstract Background Automated plating instruments have been shown to improve quality in Microbiology laboratories in a number of ways including consistent streaking patterns that provide better isolation of bacterial colonies compared to manual methods. The time per culture set up and automated camera imaging is dependent upon the amount of media set, such that more individual plates slow down turn around time. Conversely, smaller media surface area and the use of non-selective media may result in mixed colonies that require sub-culture for isolation before identification and susceptibilities can be performed. Based upon these findings, we attempted to reduce our media usage per culture with the implementation of automation, with the goal of using as few plates as possible while at the same time not negatively impacting the turn around time of the culture via the need to subculture poorly isolated colonies. We conducted a pre-and post-implementation study evaluating the costs per culture and turn around time delays related to subculturing in a subset. Methods In the pre-implementation period, from April-January 2023, three individual media plates (sheep blood agar, chocolate agar and CNA/MacConkey bi-plate) were routinely set for general aerobic bacterial culture sources including swabs and body fluids. In the post implementation period, from April-January 2024, a single sheep blood/chocolate agar bi-plate was set unless the gram stain showed evidence of mixed organisms (defined as more than one morphology) at which time a secondary selective media (CNA/MacConkey) was manually set. For urine cultures, in the pre-implementation period 2 plates were set (sheep blood and CNA/MacConkey bi-plate) and in the post-implementation period a single chromogenic agar/sheep blood bi-plate was set. Total numbers of cultures in both periods were collected as well as the number of cultures for which additional media were set based on gram stain results. Orders of subculture media in the laboratory information system were compared per positive cultures. Statistical analysis was performed in GraphPad Prism. Results In the pre- versus post-implementation periods, a total of 16,454 vs 17,782 general aerobic cultures and 50,680 vs 53,916 urine cultures were set, respectively. Only 3.6% of general cultures required additional media set based on gram stain. The difference in cost between pre- and post-implementation was approximately a two-thirds reduction in cost for general cultures and a 25% reduction for urine cultures, with a total savings of approximately $34,238 in media avoidance in the post-implementation period. The frequency of positive cultures requiring subculture was not significantly different between the pre- (average 36% of positive growth) and post-implementation (average 22% of positive growth) periods for both culture types and may actually have been reduced due to automation. Conclusions The implementation of reduced media concomitant with streaking via automation resulted in significant cost savings while not negatively impacting isolation, which in turn impacts culture turn around times. The automation may have reduced need for subculture, perhaps due to better isolation, thus subsequent analyses will explore this additional finding.

Abstract Background Bicarbonate stability in serum depends on time, exposure to air, temperature, tube size and sample volume. With implementation of Cobas Connection Module (CCM) automation, the workflow for bicarbonate measurement needed to be re-evaluated. The aim of this study was to determine bicarbonate stability in filled 3.5 mL and 5 mL serum-separator tubes (SSTs) and capped and uncapped aliquot tubes on the CCM automation. Methods Bicarbonate was measured on a Cobas c701 (Roche Diagnostics) at various time points. SSTs (n=8) were centrifuged 6 min at 1649g ∼30 min post-collection. Serum was aliquoted (1 mL) into capped or uncapped tubes and kept ambient (15-25°C) on the CCM track for 0, 60, 90, 150, 180, 240 and 300 minutes (n=2 per time point). Primary SSTs (5mL n=4, and 3.5mL n=5) were uncapped on the CCM track and bicarbonate measured at baseline and 30 minute intervals through 6.5 hours. An absolute difference of +/- 4 mmol/L from baseline was considered acceptable. Results Bicarbonate concentrations decrease with time at rates dependent on whether the aliquot tube is capped or uncapped or the SST is 3.5 mL or 5 mL (Figure 1).Figure 1.Average decrease in bicarbonate concentration with time for capped aliquot tubes (black square), uncapped aliquot tubes (red circle), 3.5 mL serum separator tube (blue up triangle), 5 mL serum separator tube (green down triangle). Bars represent +/- 1SD. Conclusions Bicarbonate in serum is stable for testing for 2.5 hours in uncapped 1 mL aliquot tubes, 3.5 hours in capped 1 mL aliquot tubes, 3.5 hours in uncapped full 3.5 mL SST tubes and 6 hours in uncapped full 5 mL SST tubes at ambient temperatures (15-25°C). Bicarbonate testing can be accommodated by total laboratory automation but processes such as add-ons and retesting must be done with caution.

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.