bluuebunny/crossref_metadata_2025_split huggingface dataset:
A-314 Comparison of Calibrated Calculated Osmolality to Measured Osmolality by Different Equations: A Study using Pediatric Specimens
Authors: Garg U C , Wiebold A , Knoll M , McDonough R , Stickle D F , Ferguson A M 
Published: September 2023

Abstract

Background
American Diabetes Association recommends using serum osmolality to aid in differentiating between hyperosmolar hyperglycemia state (HHS) and Diabetic Ketoacidosis (DKA). Unfortunately, measured osmolality is unavailable in many small hospitals. Calculated osmolality is used commonly to evaluate the gap between calculated and measured osmolality. Using pediatric samples, we investigated whether calculated osmolality when calibrated against measured osmolality might serve as a surrogate for the measured osmolality.

Methods
Primary data were 441 samples having measured osmolality along with coincident measurements of Na, K, BUN and glucose. Calculated osmolality (α) was obtained using six equations from the literature: A: α = 2*Na+(Glu/18); B: α = 2*Na+(Glu/18)+(BUN/2.8); C: α = 2*(Na+K)+(Glu/18)+(BUN/2.8); D: α = 1.86*Na+(Glu/18)+(BUN/2.8)+9; E: α = 1.86*(Na+K)+(Glu/18)+(BUN/2.8)+10; F: α = 1.89*Na+1.38*K+(1.08*Glu/18)+(1.03*BUN/2.8)+7.45. For each equation, α was correlated with measured osmolality (M) by linear regression: α = slope*M+intercept. Calibrated values (β) were obtained as follows: β = (α-intercept)/slope. This procedure produces a linear regression for β vs M having slope = 1 and intercept = 0. The best calculated osmolality equation was the one for which the standard deviation (sd) of the distribution of errors for the calibrated data (the distribution of e = (β-M) across all points) was least.

Results
Measured osmolality (M) ranged from 232–462 mOsm/kg. The best relationship between β and M was for equation D. For D, calculated osmolality α = 0.77*M+49.2 mOsm/kg; r2 = 0.945. Calibrated β vs M is shown in Figure (r2 = 0.945). Errors (e) for β were normally distributed: e = 0 ± 8.9(1sd) mOsmol/kg. Thus, the 95% confidence interval (±2sd) for calibrated calculated osmolality using D was ±17.8 mOsm/kg. In our database, 18 specimens (4.1%) were outside of this boundary.

Conclusions
Calculated osmolality when calibrated to measured osmolality may be reasonable to report in pediatric hospitals when measured osmolality is not available, so long as physicians are fully informed and in agreement with what is being reported.
A-316 Suboptimal Compliance to Postpartum Glucose Monitoring in a High-risk Pregnancy Population - A Single Center Study
Authors: Olayinka L , Fox K , Aagaard K M , Devaraj S 
Published: September 2023

Abstract

Background
Gestational diabetes mellitus (GDM) is a predictive factor in developing type 2 diabetes mellitus (T2DM) in affected mothers and their offspring. Its prevalence is increasing proportionally with the obesity epidemic, affecting 10% of pregnant women and 50% of which are likely to develop T2DM. Current guidelines by the American Diabetes Association and the American College of Obstetricians and Gynecologists recommend screening at 4–12 weeks with a 2-hour postpartum glucose tolerance test (OGTT) in patients with a history of GDM. However, several studies have reported suboptimal test ordering and completion rates attributable to the busy postpartum period and provider role confusion between primary care physicians and obstetricians. Due to the complex maternal-fetal cases encountered at our institution, the aim of this study is to evaluate the ordering and test completion rates amongst care providers and patients to ensure compliance with stipulated guidelines.

Method
We conducted a retrospective audit of women diagnosed with GDM who delivered at our institution from Jan 2019–Oct 2022. We abstracted data from electronic health records. Gestational diabetes was defined by one or more of the following: abnormal 3hr OGTT or 1-hour OGTT of &gt;/=200 mg/dL at &gt;/= 24 weeks. We excluded patients with pre-existing diabetes defined by diagnosis code or by an abnormal 1st trimester 3hr GTT or HbA1C and patients who did not return for a postpartum visit. We compared the proportions of patients with postpartum glucose monitoring orders (glucose tolerance tests or Hemoglobin A1C) to those without orders. Factors such as race, GDM management, provider type and insurance carrier were assessed as potential barriers to non-compliance.

Results
We identified 356 patients who were diagnosed with GDM during the study period. Of these, 158 patients were excluded who had pre-existing diabetes or did not return for postpartum care. The mean (SD) maternal age was 33.2 (6.0) years. Seventy-four percent (147/198) of patients had a postpartum glucose tolerance test order (Group A), and 26% (51/198) had no laboratory order for glucose monitoring (Group B). The proportion of patients with a completed test order was 24% (35/147), of which four results were abnormal (indicated for diabetes or pre-diabetes). There was no difference in parity, ethnicity, insurance type, diabetes management and provider type between the two groups. Poor compliance was observed in patients with glucose tolerance laboratory orders, while patients with an HbA1c order were 100% compliant.

Conclusion
Overall, this report demonstrates poor patient compliance with postpartum glucose tolerance testing. Although HbA1C is not recommended for postpartum glucose monitoring, patients were more likely to complete this test.
A-326 Argininosuccinic Acid - is it Routinely Detectable in the Urine of Healthy Individuals?
Authors: Karger K M , Ogilvie G , Tsai M Y 
Published: September 2023

Abstract

Background
Argininosuccinic aciduria is a genetic disorder that results in elevated levels of argininosuccinic acid (ASA) in urine and blood. While there is seemingly a consensus within the clinical literature that any detectable amount of ASA in blood/plasma is diagnostic for argininosuccinic aciduria, yet there is conflict as to whether ASA in urine can be found in unaffected individuals. Some literature states that ASA should not be found in the urine of a healthy individual, while other literature reports low levels of ASA in the urine of healthy individuals. Additionally, many laboratories do not have a normal range for ASA within urine, which implies that any level of ASA found within urine is indicative of argininosuccinic aciduria. This conflict may in part be due to differences in the methods used to determine ASA levels. Newer liquid chromatography tandem mass spectrometry (LCMSMS) methods have been shown to be more sensitive than ion-exchange chromatography (IEC) based amino acid methods for detection of ASA. Additionally, the detection of ASA via IEC requires the heating and acidification of the sample to convert ASA into a single peak for quantitation. Heating and acidification is not routinely done for urine amino acid analysis with IEC; with routine IEC urine amino acid analysis, ASA migrates as three small peaks that may go unnoticed. In our clinical laboratory, which utilizes the less sensitive IEC method, our procedure describes the presence of any ASA in urine as diagnostic for argininosuccinic aciduria. Given the conflicting literature, we set forth to determine whether low levels can be detected in unaffected individuals with an IEC method, and if so, establish a normal range of ASA levels in urine with IEC.

Methods
Twenty waste urine samples from patients with no prior diagnoses of argininosuccinic aciduria were evaluated. ASA levels within the samples were determined via high performance ion exchange chromatography for separation of compounds followed by derivatization with ninhydrin for spectrophotometric detection with a Hitachi Amino Acid analyzer. The samples were acidified and heated, which converts ASA into a single anhydride peak for accurate quantitation, and the results were normalized to urine creatinine.

Results
ASA was detected in all twenty urine samples, with an average ASA concentration of 27.2 μmol/g Cr and a range of 11.3–47.7 μmol/g Cr.

Conclusion
Given these findings, our clinical laboratory procedure has been updated to include the information that ASA can be present in the urine of healthy individuals up to a concentration of 50 μmol/g Cr. Other clinical laboratories that analyze urine to identify argininosuccinic aciduria should be aware of the misinformation in the literature that claims that ASA is not present in the urine of healthy individuals.
A-347 Performance Evaluation of Standard Q COVID/FLU Ag Combo for Detection of SARS-CoV-2 and Influenza A and B Antigens from the Patients with Suspected Respiratory Infections
Authors: Kim S , Choi W 
Published: September 2023

Abstract

Background
COVID-19 and influenza are highly contagious respiratory diseases and are sometimes fatal, making them a major concern worldwide. Hence, rapid and accurate detection and differentiation are required as preventive measures to control the further spread of these diseases.

Methods
We evaluated performance of the Standard Q COVID/FLU Ag Combo test (Q Ag combo test) (SD Biosensor, Korea) for detection of SARS-CoV-2, influenza A, or influenza B using a single point-of-care device compared with real-time PCR. A total of 408 individuals: 55 positives of SARS-CoV-2, 90 influenza A, 68 influenza B, and 195 negatives from all viruses investigated.

Results
The Q Ag combo test demonstrated a high sensitivity of up to 92.73% and specificity of up to 99.49% for the detection of SARS-CoV-2. Similarly, a sensitivity of 92.22% and specificity of 100% for influenza A, and a sensitivity of 91.18%, and a specificity of 99.49% for influenza B were observed. When the days from symptom onset (DSO) were restricted to 0&lt;DSO≤6, the sensitivity of the Q Ag combo test to detect SARS-CoV-2 was up to 100%, and when the Ct value of RdRp is ≤ 20, the sensitivity to detect SARS-CoV-2 was up to 93.10%.

Conclusion
The Q Ag combo might be useful to detect and differentiate SARS-CoV-2, and influenza A/B efficiently and rapidly.
A-352 Investigation of the Immunochromatographic Method using 150 nm Gold Colloids (1)-Comparison of Different Types of Gold Colloids
Authors: Hirano A , Asahi M , Otsuka M , Kubota R 
Published: September 2023

Abstract

Background
Gold colloids, which have been extensively used in immunochromatography, are typically 40 nm in particle size. Recently, 150 nm gold colloidal nanoparticles, which are prepared by gold coating a silica nucleus up to a diameter of 150 nm, have attracted considerable attention. These nanoparticles enable high sensitivity in immunochromatography. The purpose of this study is to compare the antibodies labeled with 40 and 150 nm gold colloids using the physisorption and covalent bonding methods, which are applied to the immunochromatography method developed in our laboratory.

Methods
1) For the physisorption method, 40 nm gold colloids (gold colloid AuH2, Morinaga Institute of Biochemistry) and 150 nm gold nanoshells (nanoComposix) were used to physically adsorb the antibody. The covalent gold conjugation kit (nanoComposix) was used to prepare 40 nm gold colloid-labeled antibodies by covalent bonding, and the high sensitivity gold conjugation kit was used to perform covalent bonding of the antibody and gold colloid after buffer displacement of the antibody.2) Anti-human albumin monoclonal antibody (abcam) was applied as the solid-phase antibody onto a nitrocellulose membrane (HF075, Merck Millipore) and dried. A human serum albumin sample (Fujifilm Wako Pure Chemicals) was diluted to a concentration of 1 mg/mL using phosphate-buffered saline (PBS). The sample was mixed with the gold colloid-labeled antibodies and a blocking solution (EZBlock Chemi, ATTO). Subsequently, 90 μL of the mixture was added dropwise and allowed to expand for 15 min before analysis.

Results
1) Comparison of detection linesA red line was detected in the judgment zone for the 40 nm gold colloid, whereas a black line was detected in the judgment zone for the 150 nm gold colloid. The background of the detection area for the physisorption method was higher than that for the covalent bonding method. 2) Comparison of detection sensitivitiesUsing the physisorption method, the 40 nm gold colloid detected up to 10 ng/mL. In contrast, using the covalent bonding method, the 40 nm gold colloid detected up to 100 ng/mL. Using the physisorption method, the 150 nm gold colloid detected up to 3 ng/mL when tested at 100, 10, and 3 ng/mL. In contrast, using the covalent bonding method, the 150 nm gold colloid detected up to 3 ng/mL.

Conclusion
Currently, 40 nm gold colloids are extensively used as a labeling material in immunochromatography. However, the use of 150 nm gold colloids, which have better detection sensitivity, may increase sensitivity and lead to more accurate detection.
B-015 Magnesium Fractionation using Ultrafiltration System
Authors: Badr H , Anthony O O 
Published: September 2023

Abstract

Background
Total magnesium (tMg) comprises 3 fractions: ionized (iMg), protein-bound (pbMg), and complexed magnesium (cMg). Serum iMg can be measured directly using ion-selective electrode method, but determination of pbMg and cMg has not been addressed. The aim of this study is to examine the efficiency of a newly introduced ultrafiltration system to fractionate tMg and validate proposed its reference intervals for Mg fractions.

Methods
Leftover whole blood samples were collected from 40 subjects. iMg and tMG were measured in whole blood using Stat Profile Prime® ES, and ultrafiltrate using VITROS 5600 Analyzer, respectively. Serum fractionation was done by ultrafiltration system (Centrifree®, Sigma-Aldrich, MA, USA). pbMg and cMG were calculated by subtracting tMg ultrafiltrate from tMg in whole blood, and subtracting iMg from tMg concentrations in the ultrafiltrate, respectively.

Results
Reference intervals of iMg, pbMg, and cMg were proposed as mean± 2 SD of 0.93–1.61 mg/dL, 0–0.85 mg/dL, and 0.07–0.57 mg/dl, respectively. iMg, pbMg, and cMg represented 65%, 20%, and 16% of the tMg concentration in normo-magnesemic subjects with slight variation in hypo- and hyper-magnesemic subjects. iMg showed weak positive correlation with tMg, (r = 0.5, 0.03, 0.005), in normo-magnesemic, hypo- and hyper-magnesemic subjects, respectively. iMg showed weak positive correlation with cMg (r = 0.11) and strongly with pbMg (r = 0.41) in hypermagnesemic subjects. iMg was not dependent on protein concentration or pH, however it correlated positively with blood sugar (r = 0.5 and 0.2) and serum creatinine levels (r = 0.3 and 0.5) in hypo- & hyper-magnesemic subjects, respectively.

Conclusions
The ultrafiltration system was a promising means to assess cMg and pbMg in critical care patients. iMg measurement provides better discrimination in ill patients and was associated with lifestyle factors including alcohol consumption, hypertension and diabetes. Stat Profile Prime® ES point-of-care analyzer provides rapid and precise direct measurement comparable to current tMg standard method. Due to its rapid turnaround time, it can help to efficiently manage workload of high complexity laboratories, and alleviate the burden on the critical care team.
B-037 Comparison of Performance between Siemens Clinitek + Status Analyzer to ARKRAY AUTION MAX AX-4030 for Suspected Urinary Tract Infections
Authors: Ali M , Yang L , Forbush K , Rohr D 
Published: September 2023

Abstract

Background
The purpose of the study is to evaluate the performance and agreement of the rapid point-of-care (POCT) urine dipstick testing in the emergency department (ED) to a laboratory-automated platform.

Methods
A total of 30 urine specimens were collected from the emergency department at our hospital and were simultaneously tested for urinalysis (UA) on the POCT Siemens Clinitek Status+ analyzer using the Multistix 10SG strip. In the laboratory, we performed the tests on the automated ARKRAY Aution MAXTM instrument, using the Aution Sticks 9EB strips. Samples with more than a trace of leukocyte esterase and/or nitrates are considered positive for a urinalysis and further, the samples were reflexed to urine culture. The performance of both platforms was analyzed using EP Evaluator (Data Innovations LLC.).

Results
A total of 30 urine specimens were analyzed for UA using both the POCT and automated laboratory analyzers. The agreement (95% CI) between leukocyte esterase, nitrites, and blood are as follows: 53.3% (36.1 to 69.8%); 96.7% (83.3 to 99.4%), and 70.0% (52.1 to 83.3%) (see Table 1). Furthermore, out of the 30 specimens that were tested on both platforms, there was prominent discordance between the POCT and lab UA test. The 7 urine samples that were negative on POCT were positive on the laboratory UA platform. All 7 were sent for urine culture, out of 7 only 1 sample was positive for culture which was abnormal: &gt;100 000 CFU/ml Escherichia coli. Out of 30 urine samples specimens that were tested on both platforms, 23 urine samples were in concordance with both platforms out of which 8 urine samples tested positive for culture (5 were E. coli, 1 was E. coli combined with Klebsiella pneumoniae, 1 was solely Klebsiella pneumoniae, 1 was Staphylococcus saprophyticus, and 1 was Streptococcus agalactiae). The remaining 15 urine samples resulted in no significant growth of bacteria or suggesting contamination. The sensitivity of POCT UA and laboratory-performed UA was 89% and 100% respectively. Furthermore, the specificity of POCT UA and laboratory-performed UA was 36% and 7% respectively.

Conclusion
Our data shows although POCT UA is rapid, a prominent disparity is observed between the POCT UA and automated lab testing. The lab-performed UA has greater sensitivity and specificity compared to the POCT UA. Clinicians should interpret the urine dipstick test results in a clinical context that includes the patient’s history and symptoms, especially when using POCT in the ED.
B-042 Evaluation of the Analytical Performance of Three Prostate-specific Antigen Immunoassays on the Atellica CI 1900 Analyzer
Authors: Quintanilla M , Thakur K , Clark E , Smith M , Arrode-Bruses G , Rhea-McManus J , Leipold H 
Published: September 2023

Abstract

Background
The Atellica® CI 1900 Analyzer is an automated, mid-throughput, integrated chemistry and immunoassay analyzer employing both Atellica CH and Atellica IM assays. This study was designed to evaluate the analytical performance of the Atellica IM Prostate-Specific Antigen (PSA)*, Complexed Prostate-Specific Antigen (cPSA)*, and Free Prostate-Specific Antigen (fPSA)* assays on the Atellica CI 1900 Analyzer.

Methods
The Atellica CI 1900 PSA, cPSA, and fPSA assays use the same reagents and calibrators as the comparable Atellica IM assays. Limit of quantitation (LoQ), precision, and method comparison (MC) were used as performance indicators for the Atellica CI 1900 Analyzer. LoQ studies were performed in accordance with CLSI EP17-A2 using native and contrived human serum samples. Precision studies were performed according to CLSI EP05-A3 using contrived human serum samples. One aliquot of each sample pool was tested in duplicate in two runs per day &gt;2 hours apart on each analyzer for &gt;20 days. MC studies were performed according to CLSI EP09-A3. Individual native human serum samples were analyzed using the Atellica IM assays on both the Atellica IM and Atellica CI 1900 Analyzers.

Results
LoQ is defined as the lowest amount of analyte in a sample at which the within-laboratory %CV is ≤20%. Representative precision and MC results for each assay across indicated sample ranges are listed in the table. Over the three assays tested, repeatability and within-lab %CVs were ≤8.0% and ≤15.5%, respectively. Slopes determined by the Deming linear regression model were approximately equal to 1.

Conclusion
Evaluation of the Atellica IM PSA, cPSA, and fPSA assays using the Atellica CI 1900 Analyzer demonstrated good precision and equivalent performance compared to the same assays on the Atellica IM Analyzer.
*The products/features mentioned here are not commercially available in all countries. Their future availability cannot be guaranteed. fPSA assay is not available for sale in US.
B-046 Curious Thing, an Artificial Intelligence (AI) Based Conversational Agents in COVID-19 Patient Management
Authors: Chow J S 
Published: September 2023

Abstract

Background
There are no clear guidelines or validated models for artificial intelligence (AI) based approaches in monitoring of COVID-19 patients who were isolated in the community, in order to identify early deterioration of their health symptoms.

Methods
Developed in partnership with Curious Thing (CT), Sydney-based AI conversational technology, a new care robot technology was introduced in South Western Sydney (SWS) in September 2021 to manage the large numbers of low to medium risk patients with a COVID-19 diagnosis and isolated at home. The CT interface made contact with patients via their mobile phone, following a locally produced script to obtain information recording physical condition, wellness and support.

Results
Between 2nd September to 14th December 2021, there were 32 001 conversations (every phone call that resulted in a conversation with a patient) conducted with 6323 unique patients (patients included in call lists identified by phone number) engaged. Over half of the completed calls (n = 13 143, 53.7%) identified that individuals needed no further support (freeing up valuable clinician time). 4.24% (n = 1077) identified as needing urgent support (getting to them faster than a standard clinician dialling pattern). The AI assisted phone calls effectively identified the patients requiring further support, saved clinician time by monitoring less ailing patients remotely and enabled them to spend more time on critically ill patients, thus ensured that service and supply resources could be directed to those at greatest need.

Conclusion
Based on the severity of the COVID-19, different approaches have been adopted by the healthcare professionals for handling the patients in the most effective manner. This paper describes the AI assisted conversational agent which can act as an alternate approach to identify the deteriorating patients requiring further support while they are isolated at home. The findings are intended to inform policy makers and health professionals about the implications of the use of these AI-based technologies for the management of current or any future pandemic.