bluuebunny/crossref_metadata_2025_split huggingface dataset:
A-205 Evaluating the Cost and Importance of Supply Chain Resilience in the Clinical Laboratory
Authors: Jurinic E 
Published: October 2024

Abstract

Background
Recent pandemics, epidemics and outbreaks continually underscore the critical importance of resilient supply chains in healthcare, particularly in clinical laboratories where timely access to supplies is essential for patient care. Supply chain disruptions, whether due to global crises or localized challenges, can significantly impact laboratory operations, leading to delayed diagnoses, compromised patient outcomes, and increased costs. It is estimated that 70%-80% of a patients’ EMR is clinical laboratory results. Despite this, supply chain resilience in clinical laboratories remains an under-addressed issue and remains a buzzword.

Methods
To evaluate the cost and importance of supply chain resilience in the clinical laboratory, we conducted a comprehensive review and interviews with 9 Healthcare Supply Chain experts analyzing the impact of supply chain disruptions on laboratory operations and patient care. Data was also collected from a diverse range of clinical laboratories, including hospitals, independent labs, and research institutions, spanning different geographic regions as well as a survey of 50 Laboratory experts with a 50% response rate. Key metrics assessed included: -Frequency and duration of supply chain disruptions. -Financial costs incurred due to disruptions, including expenses related to expedited shipping, alternative sourcing, and inventory management. -Impact on laboratory workflow, turnaround times, and patient care outcomes. -Strategies employed to mitigate supply chain risks and enhance resilience.

Results
Our review revealed findings regarding the cost and importance of supply chain resilience in the clinical laboratory: -Supply chain disruptions were frequent, with 85% of laboratories experiencing at least one significant disruption in the past year and 100% in experienced disruptions 2022. -The average duration of disruptions was 2-3 weeks, leading to substantial delays in test processing and patient care. -Financial costs associated with disruptions averaged $50,000 per laboratory per annum, including expenses for rush orders, and premium-priced alternatives. -Common strategies employed were increased inventory or safety stocks, multi-vendor sourcing, and supply chain mapping.

Conclusions
The findings of this study underscore the critical importance of supply chain resilience in the clinical laboratory. Supply chain disruptions are not just inconveniences; they pose significant risks to patient safety, operational efficiency, and financial sustainability. Investing in supply chain resilience is imperative for laboratories to fulfill their mission of delivering timely and accurate patient results. To continue to enhance supply chain resilience, laboratories must adopt a proactive approach, leveraging data-driven insights and collaborative partnerships. This includes diversifying supplier networks and fostering closer collaboration between laboratory and procurement teams.
A-210 Ldl-c to non-hdl-c ratio method: a simple way to determine bias for ldl-c methods on hypertriglyceridemic samples
Authors: Gcingca T A , Sampson M , Meeusen J W , Donato L J , Jaffe A S , Remaley A T 
Published: October 2024

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 (&gt;200 mg/dL) and low (&lt;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.
A-213 Comparison of low-density lipoprotein cholesterol (LDL-c) estimates by in-lab and point-of-care devices in the context of emerging LDL-c equations
Authors: Hubler Z , Roper S 
Published: October 2024

Abstract

Background
Low-density lipoprotein cholesterol (LDL-c) and lipid measurements are used for hyperlipidemia diagnosis, cardiovascular disease risk classification, and medication management. Millions of lipid panels are performed annually. Given the widespread need for lipid testing, there are two CLIA-waived point-of-care (POC) lipid testing devices. These devices were FDA-approved using the Friedewald equation to estimate LDL-c based on measurements of (triglycerides (TG), high-density lipoprotein cholesterol (HDL-c), and total cholesterol (TC). However, in laboratory-based testing, the Friedewald equation is being replaced by newer, more accurate equations such as the NIH-Sampson equation. These equations use the same lipid panel results to estimate LDL-c and only differ in the arithmetic of the estimation. The study objective is to evaluate how differences between the in-clinic POC machines and in-laboratory lipid measurements affect the estimated LDL-c results and are compounded by differences in the equations used for LDL-c estimation.

Methods
This retrospective observational study collected deidentified lipid results (TG, HDL-c, TC, LDL-c) and demographic data (age, gender) for all samples that had lipid panel results from in-lab and POC within twenty-four hours of each other in a single hospital system between 2014 and 2023. Three hundred twenty samples were identified; samples with non-numerical values or results outside of the reportable range for TG, HDL-c, or TC were not included in subsequent LDL-c calculations using the Friedewald and NIH-Sampson equations (69 results). Patients under the age of 18 were excluded (2 results). Two hundred and forty-nine samples met inclusion criteria; this cohort showed non-significant differences in lipid, age, and gender distributions relative to all POC results tested in the same time frame.

Results
Consistent with prior reports, we observed discordance between POC and the in-lab lipid measurements, particularly for TG (Bland-Altman: bias:-23.04 mg/dL, standard deviation:61.3 mg/dL). Regardless of the testing platform, LDL-c results are higher on an individual and population-based level with the NIH-Sampson equation. These analytical differences may not change clinical management if the cardiovascular risk classification using estimated LDL-c is unchanged. However, the risk classification based on the in-lab versus in-clinic testing differed for 20-30% of patients. The most pronounced difference in LDL-c estimation occurred when the POC machine used Friedewald and in-lab testing used NIH-Sampson, and resulted in 5% more patients differentially classified.

Conclusions
This study highlights the need for transparency in lipid panel reports regarding which LDL-c equation is used in both POC and in-lab testing formats, and the need for readily available tools for conversion between different LDL-c estimation equations.
A-216 Prevalence and status of Lipoprotein(a) in the Korean population visiting local clinics and hospitals
Authors: Jung S 
Published: October 2024

Abstract

Background
Lipoprotein a (Lp(a)) is a risk factor for cardiovascular disease (CVD) and has pro-atherogenic and pro-inflammatory effects. According to the Justification for the Use of Statins in Prevention: an Intervention Trial Evaluating Rosuvastatin (JUPITER) study, a sub-analysis was published showing that the risk of residual CVD after sufficiently lowering low-density lipoprotein cholesterol can be explained by Lp(a) concentration. The purpose of this study is to determine the prevalence of elevated Lp(a) and to analyze the characteristics of Lp(a) results by age, gender, and region in the Korean population visiting local clinics and hospitals.

Methods
We retrospectively reviewed Lp(a) test results from the Korean population who visited 117 local clinics and hospitals nationwide between February 2023 and January 2024. Serum levels of Lp(a) were measured by immunoturbidimetric assay. Lp(a) was analyzed to three age groups (&lt;20, 20-59, ≥60 years) according to Lp(a) values (&lt;30 or ≥30 mg/dl). One hundred seventeen local clinics and hospitals across the country were reclassified into 7 regions.

Results
A total of 15,523 patients (7,702 males and 7,821 females), aged 12-104 years, were included. The median (interquartile range, IQR) age of the population was 59 (49-69) years; the median (IQR) Lp(a) concentration was 9.7 (4.2-21.6) mg/dl. The prevalence of elevated Lp(a) levels (≥30 mg/dl) in this study was 16.7% (13.9% for males, 19.5% for females, p &lt;0.001). Prevalence of elevated Lp(a) levels by age groups were as follows: &lt;20 (8.9%, 5/56), 20-59 (13.9%, 1,071/7,724), ≥60 years (19.6%, 1,516/7,743). The correlation coefficient r between Lp(a) and age was 0.139 (p &lt;0.001). No significant relationship was found between Lp(a) and region. Among 15,523 patients, the number of patients with Lp (a) level ≥100 mg/dl was 174 (n=70 for males, n=104 for females, p=0.128), and median (IQR) Lp(a) concentration was 115.1 (106.8-131.6) mg/dl. Of them, four patients showed extremely high Lp(a) levels (&gt;180 mg/dL).

Conclusions
The prevalence of elevated Lp(a) in our population visiting local clinics and hospitals nationwide was 14.4% and differed by gender and age group. For successful management and establishment of the policy of CVD, it is important to identify the prevalence of elevated Lp(a). Future study is needed including the clinical information to confirm the present findings.
A-217 Estimation of the Prevalence and the Patterns of Dyslipidemia in the Korean population
Authors: Jung S , Hwang Y , Han G 
Published: October 2024

Abstract

Background
Dyslipidemia is one of the four major risk factors for the development of cardiovascular disease, along with hypertension, smoking, and diabetic mellitus. This countrywide study aimed to determine the prevalence and patterns of dyslipidemia and to describe their characteristics using data from the Korean population visiting local clinics.

Methods
We retrospectively reviewed the lipid profiles (total cholesterol (TC), low-density lipoprotein cholesterol (LDLC), high-density lipoprotein cholesterol (HDLC), and triglycerides (TG)) and fasting plasma glucose (FPG) test results from Korean patients visiting local clinics during December 2023. Dyslipidemia was defined as having one or more of TC ≥200 mg/dl, LDLC ≥100 mg/dl, HDLC &lt;40 mg/dl for males and &lt;50 mg/dl for females, or TG ≥150 mg/dl (National Cholesterol Education Program, Adult Training Program III, 2002). Glucose levels are considered hyperglycemic if blood glucose ≥126 mg/dl. The subjects comprised 1,232 patients who visited 57 local clinics nationwide were included. Lipid profile tests (except LDLC) and FPG were performed on Hitachi008AS automatic biochemistry analyzer and the LDLC test was calculated by the Friedewald equation. Data for TG ≥400 mg/dL were excluded.

Results
The study subject population comprised 610 males and 622 females aged 2 to 91 years. The overall prevalence of dyslipidemia was 53.3% (657/1,232), with 49.3% (301/657) males and 57.2 (356/657) females (p&lt;0.001). TC, LDLC, and TG were high in 14.5% (95/657), 33.9% (184/657), and 23.6% (223/657) of patients, respectively; HDLC was low in 28.0% (155/657). The prevalence of dyslipidemia by age group was as follows: 2 to 19 years (36.4%, 4/11); 20 to 29 years (66.7%, 12/18); 30 to 39 years (52.5%, 52/99); 40 to 49 years (61.5%, 131/213); 50 to 59 years (57.0%, 167/293); 60 to 69 years (50.1%, 168/335); 70 to 79 years (48.4%, 91/188); 80 years or older, (42.7%, 32/75). There was a difference in the prevalence of dyslipidemia between the age group under 50 and the age group over 50 (p&lt;0.001). The prevalence of dyslipidemia among males and females was TC (72.6% vs 28.4%), HDLC (55.4% vs 44.6%), LDLC (53.8% vs 46.2%), TG (42.6% vs 57.4%), respectively (p&lt;0.001). Thirteen of the 1,232 patients had dyslipidemia accompanied by hyperglycemia. Seven of them had mixed dyslipidemia (including raised TC, LDLC, and TG (n=4), raised TG & Low HDLC (n=2), raised TC and LDLC (n=1)).

Conclusions
The prevalence of dyslipidemia in Korean patients visiting local clinics was 53.3% and differed by gender, age group, and lipid profile. Considering that the prevalence of dyslipidemia is higher in diabetic patients compared to patients without diabetes, additional studies targeting a large number of patients are needed to promote disease prevention and control.
A-222 Evaluation of the Analytical Performance of Apolipoprotein A-1, Apolipoprotein B, and Lipoprotein(a) Assays on the Atellica CI Analyzer
Authors: Huff J , Tyler C , Paudel P , Shalhoub V , Camacho J , Snyder J 
Published: October 2024

Abstract

Background
This study evaluated the analytical performance of the Atellica® CH Apolipoprotein A-1 (APO A1), Apolipoprotein B (APO B), and Lipoprotein(a) (Lp(a)) assays on the Atellica® CI Analyzer.

Methods
The Atellica CI assays use the same reagents and calibrators as the Atellica CH assays. Precision studies were performed according to CLSI EP05-A3 using human serum samples. Each sample was tested in duplicate in two runs per day for 20 days. MC studies were performed according to CLSI EP09-A3. Individual serum samples were analyzed using Atellica® CH and Atellica CI Analyzers.

Results
Across the three assays, repeatability CVs were &lt;1.3%, within-lab CVs were &lt;2.5%. Representative precision, and MC results observed for each assay across indicated sample ranges are presented in the table. Overall, slopes determined using the Deming linear regression model were approximately equal to 1.

Conclusions
Atellica CH APO A1, APO B, and Lp(a) Assays on the Atellica CI Analyzer demonstrated good precision and equivalent performance to the same assays on the Atellica CH Analyzer.
A-223 Direct LDL or not Direct-LDL, that is the question
Authors: Valaperta S , Apassiti Esposito S , Picciau A , Moioli V , Saiaci C , Marozzi R , Alessio M 
Published: October 2024

Abstract

Background
Management of Cardiovascular Disease (CVD) risk is dependent on Low Density Lipoprotein Cholesterol (LDL-C) levels; therefore LDL-C serves as the marker for the assessment of CVD risk and the focus of lipid lowering treatment. The gold standard reference method for measurement of LDL-C is beta-quantification. We compared calculated LDL-C with Friedewald (F-LDL-C), Martin-Hopkins (M-LDL-C) and Sampson (S-LDL-C) equations versus LDL-C directly measured (D-LDL-C). In CVD management also cholesterol ratios are evaluated. TC/HDL is associated with morbidity and mortality in the general population. Furthermore, several studies have stated that high triglycerides (TG) levels and low HDL-C (TG/HDL ratio) could function as an atherogenic index.

Methods
11399 samples were analyzed on Siemens Atellica Solution® for the lipid profile determination measuring Total Cholesterol (TC), D-LDL-C, HDL-C and TG. In the absence of additional clinical parameters, based on desirable levels for LDL-C recommended by guidelines, CVD risk was evaluated by assigning to LDL-C a cut-off of 100 mg/dL. In presence of misclassifications, we used two other parameters to assess LDL-C results: TG/HDL-C ratio and CT/HDL-C.

Results
Patients cohort had a mean age of 57.7 (±16.8) years of which 53% (age=58.6, ±15.9) were males and 47% (age=56.5, ±17.8 years) were females. Of these, the 57.7% were outpatients and 42.3% inpatients (42.1% of infectious diseases - 24.4% of the cardiovascular department). Data about LDL-C misclassification and ratio data parameters are shown in table 1. Discordant patients for D-LDL-C vs F-LDL-C are 8.5%, that of D-LDL-C vs M-LDL-C are 7.7% and D-LDL-C vs S-LDL-C are 7.2%.

Conclusions
Is known that LDL plays a central role in the pathogenesis of atherosclerosis. Accurate estimation of LDL-C is a fundamental point for the stratification of CVD risk and the achievement of the therapeutic goal; however, we are still uncertain about the best and accurate way to measure it.
A-224 Non HDL Cholesterol: could be e new cardiovascular disease risk stratification factor?
Authors: Valaperta S , Apassiti Esposito S , Moioli V , Picciau A , Saiaci C , Marozzi R , Alessio M 
Published: October 2024

Abstract

Background
A worldwide study demonstrated that among all modifiable risk factors of Cardiovascular Disease (CVD) to the abnormal cholesterol levels is attributable the highest risk of events. LDL-C is considered the most important lipoprotein risk factor. Some recent epidemiologic studies have suggested that non-HDL cholesterol (N-HDL-C) may be superior to LDL-C for identifying CVD risk. N-HDL-C is easily calculated from a lipid profile and represents all apolipoprotein B containing lipoproteins. Recent guidelines recommend LDL-C as a primary indicator and non-HDL-C as secondary. Moreover, cholesterol ratios are useful to stratification; the TC/HDL-C is associated with morbidity and mortality in the general population; in addition, several studies have stated that high triglycerides (TG) levels and low HDL-C are associated with increased risk and could function as atherogenic index.

Methods
11399 samples were analyzed on Atellica Solution for the determination of Total Cholesterol (TC), D-LDL-C, HDL-C and TG. In the absence of clinical parameters, based on the desirable levels recommended by the guidelines, CVD risk was evaluated by assigning a cut-off of 100 and 130 mg/dL for D-LDL-C and N-HDL-C respectively

Results
Patients cohort mean (±SD) for CT, D-LDL, HDL, TG and N-HDL-C are 180 mg/dL (±43.2), 113 mg/dL (± 38.5), 51.7 mg/dL (±15.6), 116 mg/dL (±73.8), 128 mg/dL (±39.5) respectively. Of the examined patients, the 57.7% were outpatients, 21.8% of infectious diseases and 12.6% of the cardiovascular department. Data about D-LDL-C and N-HDL-C comparison, evaluated on discordant patients, are shown in table 1.

Conclusions
The data obtained from evaluation would seem to confirm hypothesis according to which non-HDL cholesterol would have a greater power in the identification of CVD risk because of cholesterol ratios are suggestive of increased risk for patients with an LDL-C&lt;100 and N-LDL-C&gt;130 mg/dL. Further studies assessing CVD risk, not only through laboratory data, are necessary to confirm this supposition.
A-230 The Effect of Fasting on Serum Asymmetric Dimethylarginine (ADMA) and Symmetric Dimethylarginine (SDMA): A Report from a Large Reference Laboratory
Authors: Albeiroti S , Winchester T 
Published: October 2024

Abstract

Background
Asymmetric dimethylarginine (ADMA) and symmetric dimethylarginine (SDMA) are metabolites of L-arginine that interfere with the production of nitric oxide (NO). NO deficiency can lead to endothelial dysfunction, which promotes atherosclerosis. High levels of ADMA are associated with increased risk for cardiovascular disease, while elevated SDMA levels correlate with reduced renal function. It is unknown whether fasting status influences ADMA and SDMA blood levels, and previously published studies are collectively inconclusive.

Methods
We performed retrospective analysis of previously reported serum ADMA and SDMA, measured by liquid chromatography tandem mass spectrometry (LC-MS/MS). Results from self-reported fasting and non-fasting patients were compared. Fasting status was collected at the time of blood collection. Statistical significance was determined using unpaired Student's t-test.

Results
A total of 46,781 fasting and 3,136 non-fasting results were included in the analysis. The average of fasting ADMA results was 101.3 ± 17.0 ng/mL (mean ± SD) with a median of 100.0 ng/mL, while the average of non-fasting ADMA results was 100.3 ± 16.9 ng/mL with a median of 99.0 ng/mL. For SDMA, fasting and non-fasting patient averages were 107.9 ± 30.6 ng/mL and 106.6 ± 33.4 ng/mL, respectively, while fasting and non-fasting patient medians were 102.0 ng/mL and 101.0 ng/mL, respectively. Mean non-fasting ADMA and SDMA results were lower than fasting results (ADMA: %difference = -0.9% and p-value of &lt;0.01; SDMA: %difference = -1.2% and p-value of &lt;0.05).

Conclusions
The small difference between fasting and non-fasting ADMA and SDMA results, while statistically significant, is within analytical variation of the test, and is considered clinically insignificant. Using large sets of real patient results, we were able to demonstrate that fasting is not required for the determination of serum ADMA and SDMA. This finding simplifies ordering ADMA and SDMA along with other cardiovascular disease risk tests such as the lipid panel, which does not require fasting.
A-237 Analytical Performance Evaluation of Cytomegalovirus IgG and Syphilis Assays on the Atellica CI Analyzer
Authors: Quintanilla M , Valdivia B , Halik L , Arrode-Bruses G , Leipold H 
Published: October 2024

Abstract

Background
The Atellica® CI Analyzer is an automated, high-throughput integrated chemistry and immunoassay analyzer utilizing both Atellica® CH and Atellica® IM Assays. This study evaluated the analytical performance of the Atellica IM Cytomegalovirus IgG (CMV IgG) and Syphilis (Syph) Assays on the Atellica CI Analyzer.

Methods
Precision studies were performed according to CLSI EP05-A3 using native and contrived human serum samples. CMV IgG and Syph Assays were evaluated with one reagent lot on two Atellica CI analyzers. One aliquot of each sample was tested in duplicate in two runs per day ≥2 hours apart on each analyzer for ≥20 days. Method comparison studies were performed according to CLSI EP12-A2. Individual native human serum samples were analyzed using the Atellica IM CMV IgG and Syph Assays on the Atellica IM and Atellica CI Analyzers. The results were assessed based on Index values distinguishing reactive (Index ≥cut-off value) and nonreactive (Index &lt;cut-off value) specimens.

Results
As shown in table below, repeatability and within-lab %CVs for the two assays presented were &lt;3.7% and &lt;7.0%. Negative and positive agreement were 100% for the 111 nonreactive CMV IgG samples and the 169 CMV IgG reactive samples tested. Negative and positive agreement were 100% for the 103 Syph reactive samples and the 126 nonreactive Syph samples tested. Overall clinical agreement between each of the presented assays on the Atellica CI Analyzer and Atellica IM Analyzer was 100%.

Conclusions
Evaluation of the Atellica IM CMV IgG and Syph Assays using the Atellica CI Analyzer demonstrated acceptable precision and equivalent performance compared to the same assays on the Atellica IM Analyzer.