bluuebunny/crossref_metadata_2025_split huggingface dataset:
A-179 Improving specimen labeling errors in the pediatric emergency department at a tertiary care hospital
Authors: Wang L , Huang J , Wu Y , Chow S , Fernado J , Kim C 
Published: October 2024

Abstract

Background
Specimen labeling errors, such as unlabeled, mislabeled, or sample-requisition mismatches, pose risks to patient care. Correcting these errors, particularly for pediatric patients, is crucial to avoid unnecessary stress to the family and time spent on follow-up. In 2018, the BCCH Emergency Department (ED) reported the highest labeling error rates among all departments at our tertiary care hospital, accounting for approximately 30% of all errors.

Methods
To address this issue, a quality improvement project following the Model for Improvement framework was initiated in 2019 at the ED. An interdisciplinary team approach was adopted initially, and a positive patient identification system (PPID) was implemented later in February 2022. Patient Safety Learning System (PSLS) data from January 2019 to November 2023 were analyzed to evaluate the impact of these interventions.

Results
1763 (22.6%) of the 7802 PSLS events recorded during this period were sample labeling errors. Ward collections accounted for the majority of labeling errors compared to laboratory collections (20.3% vs 2.4%). In the ED, most of the labeling errors occurred with unspecified samples (25%), swabs (24%), urine (23%), and blood culture (19%). The interdisciplinary team intervention initially reduced labeling errors, but its impact was inconsistent. However, the implementation of PPID in 2022 led to a significant and consistent decrease in labeling errors in the ED (4.51 errors/month vs 1.14 errors/month; p = 0.01) (Figure. 1).

Conclusions
While the implementation of PPID has notably improved labeling errors in the ED, complete elimination remains challenging. In addition, more specific PSLS filing categories are needed to capture sources of labeling error from unspecified samples. Continued efforts are necessary to achieve the goal of zero labeling errors.
A-189 Six Sigma for Thyroid Stimulating Hormone (TSH)
Authors: Ramadan-Boscolo D R , Utiyama A H , de Andrade S S , Tufik S 
Published: October 2024

Abstract

Background
Effective quality control processes within clinical laboratories are crucial for monitoring and assessing the analytical performance in terms of precision and accuracy. The regular measurement of these parameters plays a pivotal role in ensuring patient safety and is often a prerequisite for accreditation by various organizations. Six Sigma metrics have emerged as one of the most efficient tools for evaluating the analytical performance of clinical laboratory systems. This approach integrates Total Allowable Error (TEa), encompassing both random and systematic errors into a single comprehensive index. This study details the establishment of Six Sigma in our laboratory for Thyroid Stimulating Hormone (TSH) for eight different modules of Siemens Atellica IM.

Methods
A one-year dataset was compiled from the assessment of internal quality control for evaluating the imprecision (expressed as coefficient of variation - CV%) and Bio Rad’s Unity Interlaboratory Program for inaccuracy (quantified as bias%) in TSH test processed across eight modules of Atellica IM (Siemens). The data was categorized into two groups based on QC levels. The TEa utilized in this study was set at 20% according to CLIA standards. Six Sigma metrics were subsequently calculated for each QC level and equipment based on TEa, CV%, and bias%, utilizing the formula Sigma=(TEa - bias%)/CV%.

Results
Six Sigma values calculated for the QC level 1 ranged from 4.92 to 6.20 across all equipment, while for the QC level 2 Six Sigma varied from 4.87 to 7.59. All values found were classified as Good, Excellent and World Class according to the normalized method decision chart. These results showed that analytical performance of all eight modules of Atellica IM were similar in terms of random and systematic error.

Conclusions
Six Sigma serves as potent tool for monitoring the analytical performance of the TSH test. It is crucial to independently track different levels of QC in TSH testing for enhanced control over the analytical system. The performance of Siemens TSH test in our laboratory has proven to be very satisfactory.
A-194 Point of Care Glucose Test Time Interval Data Used as a Proxy Indicator for Glucose Meter Cleaning and Disinfection Adequacy Between Patient Testing Episodes
Authors: Reineks E Z , Carr M F 
Published: October 2024

Abstract

Background
At a community hospital within our health system (269 inpatient beds and 46 glucose meters), a condition level finding was cited for blood found on one meter during a routine regulatory survey. Point of Care (POC) test instrument portability is often an essential characteristic in choosing to perform a POC test at the bedside. Glucose meters are in close proximity to patients who are providing fingerstick blood samples for immediate testing. The process of obtaining the blood sample and applying it to the test strip may result in blood inadvertently getting on a caregiver’s gloves and the glucose meter itself. Per the testing protocol, when testing different patients consecutively, the caregiver safely discards the used test strip, changes gloves, and performs the cleaning and disinfecting (C&D) of the glucose meter to ensure the next patient is not exposed to blood from the previous test. The C&D procedure requires a minimum two minutes of drying time. We estimate that consecutive POC glucose tests should have about a three minute gap when the meter gets proper C&D between patients. We sought to determine whether the time interval between tests could be used an indicator of improved C&D compliance during a process improvement project.

Methods
As a patient safety concern, improved compliance with C&D steps was needed. Various stakeholder groups conducted a three month long campaign of education, training, and surveillance to improve C&D compliance. As a proxy for monitoring the C&D, data on test times from individual glucose meters was downloaded from the POC middleware system. Sample information, patient identifiers, and test results were not obtained. Time intervals between tests were calculated and the number of tests that occurred on the same instrument with short intervals (&lt; 3minutes) were counted.

Results
Data from the POC middleware system was downloaded from the month prior to and immediately following the project (July and November). In July, there were 733 instances of glucose tests being carried out within three minutes of a previous test on the same instrument, from a total of 6622 tests (11.1%). In November, there were 565 occurrences (out of 6445 tests, or 8.8%), a decrease of 23% from the beginning count, and a drop of 2.3% among all tests. A follow up survey found no instances of blood on meters.

Conclusions
Using the time between tests interval as a proxy for monitoring glucose meter C&D is straightforward when the data is readily available. In our application of this process, an improvement in the number of short intervals was found, but the number of short intervals was not completely eliminated. This is likely due to occurrences when a test was repeated on the same patient or when training or competency was taking place, circumstances in which C&D not required. To observe patient privacy, our data collection approach could not eliminate these potential confounders of our data.
A-199 Use of R programming for monthly quality control (QC) data review in a university hospital laboratory
Authors: Stickle D , Toohey J M 
Published: October 2024

Abstract

Background
Our laboratory information system (LIS) provides only cursory capability for monthly review of quality control measurements. We streamlined the major portion of this process using customized programming in R, as described below.

Methods
Month-long QC data were obtained using the LIS archive’s data retrieval program (QLIK). Data were read and processed using our R program (QC-R.R), for each analyte and QC level, to produce both Levey-Jennings (LJ) plots (for evaluation of possible temporal trends in QC data) as well as cumulative distribution (CD) plots (to evaluate whether QC data are normally distributed). Plots were sent to graphics files whereby they could be reviewed sequentially by a single forward-arrow click.

Results
Our laboratory utilizes two QC measurements for each of 157 analytes. Thus, monthly review involves evaluation of 314 datasets for QC results. With the R program outputs, results can be reviewed visually in rapid fashion, to determine whether there exist discrepancies between fixed limits and current data. An example CD plot is shown in Figure. In this example, QC data (points) were, appropriately, normally distributed (theoretical curved line based on data mean and SD). The currently operative fixed mean±2SD limits (vertical dashed lines) were, however, disparate from those of data (vertical solid lines). This set of results was thus marked for updating of mean and SD, with reference to a master list of results of calculations generated by the QC-R.R program. Time for review of monthly QC results was dramatically reduced, from more than 12 man-hours to less than 4 man-hours per month.

Conclusions
Limitations of our LIS for monthly QC review were bypassed using R-based programming to automate a significant fraction of the process. This led to a significant savings in labor attending to this important task. The use of CD plots is helpful for rapid data interpretation.
A-206 Exploring the Surge in Clinical Laboratory Inflation: A Comprehensive Review
Authors: Jurinic E 
Published: October 2024

Abstract

Background
The healthcare industry has been experiencing significant inflationary trends, particularly in clinical laboratory services. This surge in inflation is attributed to various factors, including the increased demand for laboratory services, changes in the practice of clinical laboratory science due to technological advances, and vacancy rates that exceed the number of Medical Laboratory Science (MLS) graduates. Additionally, supply chain shortages, increased operational costs, and staffing budget increases, further exacerbating the inflationary pressures on clinical laboratory services.

Methods
The review of the factors behind the surge in clinical laboratory inflation involved a comprehensive review of industry data and insights from supply chain leaders, and recent surveys conducted by the author sent to 50 laboratory leaders with a response rate of 50%, Data from the Bureau of Labor Statistics (BLS) and industry interviews were integrated to provide an understanding of the inflationary trends affecting the healthcare industry, with a specific focus on clinical laboratory services.

Results
The review revealed that domestically produced healthcare inputs, labor, diagnostics, and lab equipment have seen dramatic price increases. For instance, prices manufacturers charge for diagnostic reagents for testing have increased by 9.2%, while lab equipment prices have risen by 7.1%. Some product lines have experienced increases of 15-20%. Moreover, the post pandemic has led to attrition rates climbing from 10%-37% to the current rate of 35%-61%, resulting in 30%-50% staffing budget increases. 36% of lab leaders feel Group Purchasing Organizations (GPO’s) are incentivized to allow increases, it’s estimated that as much as 80% of GPO revenue is generated by fees paid by vendors. 28% feel the vendors are passing along but not sharing in the inflationary pressures as reimbursements decline. 20% feel it’s an excuse for vendors to pad margins. 16% feel that it’s due to lack resources or lab specific procurement expertise in supply chain departments.

Conclusions
These factors have contributed to the significant surge in clinical laboratory inflation. The surge in is a complex phenomenon influenced by multiple interconnected factors. Addressing this inflationary trend will require a collaborative approach that considers the impact of supply chain shortages, increased operational costs, and staffing. Proactive measures to mitigate the adverse effects of inflation on clinical laboratory services, such as ensuring reliable data in negotiations, addressing the workforce shortage and strong negotiations with vendors will be essential to sustain the cost, quality, and accessibility of these critical healthcare services.
A-212 Comparison of formulas for measuring LDL in cancer patients with hypertriglyceridemia
Authors: Sánchez -Jacinto B , Arroyo- Huanaco O , Pizarro-Robles R 
Published: October 2024

Abstract

Background
Treatments in recent years in cancer patients have been very favorable. However, some data suggest that dyslipidemia plays a role in carcinogenesis through the deregulation of the lipid signal, and this possibly leads to the development of cardiovascular diseases. A marker for the diagnosis and treatment of dyslipidemias is low density lipoproteins (LDL), and currently, the primary care level still calculates LDL due to the high costs of the direct method; Friedewald has been used since 1972, and in recent years, new equations have appeared such as Sampson and Martin - Hopkins (MH). These formulas use total cholesterol (TC), high-density lipoproteins (HDL), and triglycerides (TG). Objective was to compare the formulas for measuring LDL in cancer patients with hypertriglyceridemia.

Methods
It´s an observational, cross-sectional - and analytical study. The sample size was 880 lipid profiles processed in an automated dry chemistry analyzer between January and March 2023 at an Oncology Institute in Lima, Peru. The population was divided into several groups of triglyceride levels (&lt;200 mg/dl, 200- 399 mg/dl, ≥ 400 mg/dl), and LDL was stratified according to ATP III to evaluate the classification of patients with respect to LDL formulas. The statistical analysis was performed by descriptive analysis of the categorical and numerical variables; The correlation was performed using Pearson, and a comparison of the LDL formulas was used by Bland Altman to determine the bias and percentage bias. To clinical relevance, it was compared with the biological variation of the Westgard table. A p-value &lt;0.05 was considered significant, and the data were processed in MedCalc v20.027.

Results
The average age of the patients was 55.02 ± 13.92 years, and the frequency of female sex was 77.39% (n = 681). The average measured TC, HDL, and LDL were 208.95 ± 54.84 mg/dl, 41.53 ± 13.83 mg/dl, and 115.70 ±45.53 mg/dl, respectively, while the calculated average LDL in Friedewald was 120.40 ± 48.57, MH was 130.49 ± 44.15 mg/dl and Sampson was 125.97 ± 45.53 mg/dl. The correlations of measured versus calculated LDL were strong (r &gt; 0.89) at different TG levels; Statistical (p&lt;0.05) and clinical relevance was observed in the Friedewald formula when TG ≥ 400 mg/dl, while the MH and Sampson formulas presented clinical and significant relevance at all triglyceride levels. Patients with an LDL level between 130 and 159 mg/dl presented a more substantial classification error according to the Friedewald, MH, and Sampson formulas.

Conclusions
Friedewald formula presented a clinically acceptable bias up to 400 mg/dl, unlike the MH and Sampson formulas, which were affected by triglycerides ≥ 150 mg/dl.
A-220 Method Intercomparison Study between Atellica Solution® and CI® Analyzers for HDL and LDL Methods
Authors: Ruiz-Alvarez M , Lapeña-Garcia S , Garcia-Valdecasas S , Costales-Lucia T , Sanchez-Fuertes J , Barrionuevo-Gonzalez M 
Published: October 2024

Abstract

Background
After validating a new Siemens equipment, the Atellica CI®, it is essential to ensure result interchangeability in our laboratory accredited by ISO 15189:2012.

Objectives
verify if the results of the HDL and LDL methods measured by Atellica Solution® and CI® analyzers are interchangeable and could be used interchangeably on both instruments

Methods
40 patient samples were selected for each assay, with results distributed across the entire measurement range and analyzed by both instruments. Result evaluation was conducted using the statistical program Method Validator v.19. To determine the degree of agreement between the instruments, a Bland-Altman mean difference analysis and a Passing-Bablok regression were performed. Statistics are expressed with their 95% confidence intervals (CI).

Results
HDL: After analyzing the Bland-Altman differences, a systematic error was found as the mean difference between the results obtained by Atellica Solution® and CI® was 0.686, with higher results in CI 1900®. Regarding the Passing Bablok analysis, constant systematic differences were observed as the confidence interval of the intercept does not include the zero value, and statistically significant proportional differences based on the slope whose 95% CI does not include the value 1.LDL: After analyzing the Bland-Altman differences, a systematic error was found as the mean difference between the results obtained by Atellica and CI® was -3.16, with higher results in Atellica Solution®. Regarding the Passing Bablok analysis, there were no systematic differences of constant type as the confidence interval of the intercept includes the zero value, but there were proportional differences based on the slope whose 95% CI does not include the value 1.

Conclusions
After evaluating the results, it is concluded that Atellica Solution® and CI® analyzers are interchangeable for HDL and LDL assays despite the presence of a systematic error, as the observed difference is not clinically significant.
A-226 Validation of Calculated Glycerol Corrected Triglycerides
Authors: Scott R , Meeusen J W , Jaffe A S , Vasile V C , Donato L J 
Published: October 2024

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&gt;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.
A-228 Evaluation of an Equation for Low-Density Lipoproteins-Triglycerides in Predicting ASCVD Risk in the UK Biobank Population
Authors: Wolska A , Sampson M , Zubiran R , Meeusen J W , Donato L J , Jaffe A S , Remaley A T 
Published: October 2024

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&gt;HDL-C&gt;remnant-cholesterol&gt;NonHDL-C&gt;TG&gt;apoB&gt;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.