Publications by authors named "David E Bruns"

85 Publications

Diagnosis of Gestational Diabetes Mellitus Will Be Flawed until We Can Measure Glucose.

Clin Chem 2020 02;66(2):265-267

Department of Laboratory Medicine, NIH Clinical Center, Bethesda, MD.

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http://dx.doi.org/10.1093/clinchem/hvz027DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7055661PMC
February 2020

Inadequate Reporting of Analytical Characteristics of Biomarkers Used in Clinical Research: A Threat to Interpretation and Replication of Study Findings.

Clin Chem 2019 12 31;65(12):1554-1562. Epub 2019 Oct 31.

Department of Laboratory Medicine, Clinical Center, NIH, Bethesda, MD;

Background: Analytical characteristics of methods to measure biomarkers determine how well the methods measure what they claim to measure. Transparent reporting of analytical characteristics allows readers to assess the validity and generalizability of clinical studies in which biomarkers are used. Our aims were to assess the reporting of analytical characteristics of biomarkers used in clinical research and to evaluate the extent of reported characterization procedures for assay precision.

Methods: We searched 5 medical journals (, , , , and ) over a 10-year period for the term "biomarker" in the full-text field. We included studies in which biomarkers were used for inclusion/exclusion of study participants, for patient classification, or as a study outcome. We tabulated the frequencies of reporting of 11 key analytical characteristics (such as analytical accuracy of test results) in the included studies.

Results: A total of 544 studies and 1299 biomarker uses met the inclusion criteria. No information on analytical characteristics was reported for 67% of the biomarkers. For 65 biomarkers (3%), ≥4 characteristics were reported (range, 4-8). The manufacturer of the measurement procedure could not be determined for 688 (53%) of the 1299 biomarkers. The extent of assessments of assay imprecision, when reported, did not meet expectations for clinical use of biomarkers.

Conclusions: Reporting of the analytical performance of biomarker measurements is variable and often absent from published clinical studies. We suggest that readers need fuller reporting of analytical characteristics to interpret study results, assess generalizability of conclusions, and compare results among clinical studies.
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http://dx.doi.org/10.1373/clinchem.2019.309575DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7055667PMC
December 2019

Streamlining Quality Review of Mass Spectrometry Data in the Clinical Laboratory by Use of Machine Learning.

Arch Pathol Lab Med 2019 08 20;143(8):990-998. Epub 2019 Feb 20.

From the Division of Laboratory Medicine, Department of Pathology, University of Virginia School of Medicine and Health System, Charlottesville. Dr Yu is currently located in the Department of Pathology and Laboratory Medicine, University of Kentucky Medical Center, Lexington.

Context.—: Turnaround time and productivity of clinical mass spectrometric (MS) testing are hampered by time-consuming manual review of the analytical quality of MS data before release of patient results.

Objective.—: To determine whether a classification model created by using standard machine learning algorithms can verify analytically acceptable MS results and thereby reduce manual review requirements.

Design.—: We obtained retrospective data from gas chromatography-MS analyses of 11-nor-9-carboxy-delta-9-tetrahydrocannabinol (THC-COOH) in 1267 urine samples. The data for each sample had been labeled previously as either analytically unacceptable or acceptable by manual review. The dataset was randomly split into training and test sets (848 and 419 samples, respectively), maintaining equal proportions of acceptable (90%) and unacceptable (10%) results in each set. We used stratified 10-fold cross-validation in assessing the abilities of 6 supervised machine learning algorithms to distinguish unacceptable from acceptable assay results in the training dataset. The classifier with the highest recall was used to build a final model, and its performance was evaluated against the test dataset.

Results.—: In comparison testing of the 6 classifiers, a model based on the Support Vector Machines algorithm yielded the highest recall and acceptable precision. After optimization, this model correctly identified all unacceptable results in the test dataset (100% recall) with a precision of 81%.

Conclusions.—: Automated data review identified all analytically unacceptable assays in the test dataset, while reducing the manual review requirement by about 87%. This automation strategy can focus manual review only on assays likely to be problematic, allowing improved throughput and turnaround time without reducing quality.
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http://dx.doi.org/10.5858/arpa.2018-0238-OADOI Listing
August 2019

Restricted IgG-Kappa and Free Alpha-Heavy-Chain Bands in an Asymptomatic 62-Year-Old Man.

Clin Chem 2018 02;64(2):265-268

Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, MN.

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http://dx.doi.org/10.1373/clinchem.2016.269050DOI Listing
February 2018

Recognition of rare hemoglobin variants by hemoglobin A measurement procedures.

Clin Chim Acta 2018 Jan 14;476:67-74. Epub 2017 Nov 14.

Department of Pathology, University of Virginia School of Medicine, Charlottesville, VA, United States. Electronic address:

Background: Unrecognized hemoglobinopathies can lead to measured hemoglobin A (Hb A) concentrations that are erroneous or misleading. We determined the effects of rare hemoglobin variants on capillary electrophoresis (CE) and HPLC methods for measurement of Hb A.

Methods: We prospectively investigated samples in which Hb A was measured by CE during a 14-month period. For samples in which the electropherograms suggested the presence of rare hemoglobinopathies, hemoglobin variants were identified by molecular analysis or by comparison with electropherograms of known variants. When sample volume permitted, Hb A was measured by 2 HPLC measurement procedures and by boronate affinity HPLC.

Results: Hb A was measured by CE in 33,859 samples from 26,850 patients. 15 patients (0.06%) were identified as having rare hemoglobinopathies: Hbs A2 prime, Agenogi, Fannin-Lubbock I, G Philadelphia, G San Jose, J Baltimore, La Desirade, N Baltimore, Nouakchott, and Roanne. Among 6 of these samples tested by 2 ion-exchange HPLC methods, the rare Hb was detected by both HPLC methods in only one sample, and none were detected by boronate affinity HPLC. The mean of the Hb A results of 2 HPLC methods differed from the result of the CE method by 0.7-2.2% Hb A in samples with variant hemoglobins versus <0.2% Hb A1c in samples without variants.

Conclusion: Measurement procedures differ in the ability to detect the presence of rare Hb variants and to quantify Hb A in patients who harbor such variants.
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http://dx.doi.org/10.1016/j.cca.2017.11.012DOI Listing
January 2018

Prevalence of Rare Hemoglobin Variants Identified During Measurements of Hb A by Capillary Electrophoresis.

Clin Chem 2017 12 13;63(12):1901-1902. Epub 2017 Sep 13.

Department of Pathology University of Virginia School of Medicine Charlottesville, VA

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http://dx.doi.org/10.1373/clinchem.2017.276857DOI Listing
December 2017

Air bubbles and hemolysis of blood samples during transport by pneumatic tube systems.

Clin Chim Acta 2017 Oct 10;473:9-13. Epub 2017 Aug 10.

Division of Laboratory Medicine, Department of Pathology, University of Virginia School of Medicine and Health Sciences Center, Charlottesville, VA, United States.

Background: Transport of blood samples through pneumatic tube systems (PTSs) generates air bubbles in transported blood samples and, with increasing duration of transport, the appearance of hemolysis. We investigated the role of air-bubble formation in PTS-induced hemolysis.

Methods: Air was introduced into blood samples for 0, 1, 3 or 5min to form air bubbles. Hemolysis in the blood was assessed by (H)-index, lactate dehydrogenase (LD) and potassium in plasma. In an effort to prevent PTS-induced hemolysis, blood sample tubes were completely filled, to prevent air bubble formation, and compared with partially filled samples after PTS transport. We also compared hemolysis in anticoagulated vs clotted blood subjected to PTS transport.

Results: As with transport through PTSs, the duration of air bubble formation in blood by a gentle stream of air predicted the extent of hemolysis as measured by H-index (p<0.01), LD (p<0.01), and potassium (p<0.02) in plasma. Removing air space in a blood sample prevented bubble formation and fully protected the blood from PTS-induced hemolysis (p<0.02 vs conventionally filled collection tube). Clotted blood developed less foaming during PTS transport and was partially protected from hemolysis vs anticoagulated blood as indicated by lower LD (p<0.03) in serum than in plasma after PTS sample transport.

Conclusions: Prevention of air bubble formation in blood samples during PTS transport protects samples from hemolysis.
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http://dx.doi.org/10.1016/j.cca.2017.08.008DOI Listing
October 2017

STARD 2015 guidelines for reporting diagnostic accuracy studies: explanation and elaboration.

BMJ Open 2016 11 14;6(11):e012799. Epub 2016 Nov 14.

Department of Clinical Epidemiology, Biostatistics and Bioinformatics, Academic Medical Centre, University of Amsterdam, Amsterdam, The Netherlands.

Diagnostic accuracy studies are, like other clinical studies, at risk of bias due to shortcomings in design and conduct, and the results of a diagnostic accuracy study may not apply to other patient groups and settings. Readers of study reports need to be informed about study design and conduct, in sufficient detail to judge the trustworthiness and applicability of the study findings. The STARD statement (Standards for Reporting of Diagnostic Accuracy Studies) was developed to improve the completeness and transparency of reports of diagnostic accuracy studies. STARD contains a list of essential items that can be used as a checklist, by authors, reviewers and other readers, to ensure that a report of a diagnostic accuracy study contains the necessary information. STARD was recently updated. All updated STARD materials, including the checklist, are available at http://www.equator-network.org/reporting-guidelines/stard Here, we present the STARD 2015 explanation and elaboration document. Through commented examples of appropriate reporting, we clarify the rationale for each of the 30 items on the STARD 2015 checklist, and describe what is expected from authors in developing sufficiently informative study reports.
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http://dx.doi.org/10.1136/bmjopen-2016-012799DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5128957PMC
November 2016

State of Harmonization of 24 Serum Albumin Measurement Procedures and Implications for Medical Decisions.

Clin Chem 2017 03 10;63(3):770-779. Epub 2017 Jan 10.

Department of Pathology, Virginia Commonwealth University, Richmond, VA.

Background: Measurements of serum and plasma albumin are widely used in medicine, including as indicators of quality of patient care in renal dialysis centers.

Methods: Pools were prepared from residual patient serum (n = 50) and heparin plasma (n = 48) from patients without renal disease, and serum from patients with kidney failure before hemodialysis (n = 53). Albumin was measured in all samples and in ERM-DA470k/IFCC reference material (RM) by 3 immunochemical, 9 bromcresol green (BCG), and 12 bromcresol purple (BCP) methods.

Results: Two of 3 immunochemical procedures, 5 of 9 BCG, and 10 of 12 BCP methods recovered the RM value within its uncertainty. One immunochemical and 3 BCG methods were biased vs the RM value. Random error components were small for all measurement procedures. The Tina-quant immunochemical method was chosen as the reference measurement procedure based on recovery and results of error analyses. Mean biases for BCG vs Tina-quant were 1.5% to 13.9% and were larger at lower albumin concentrations. BCP methods' mean biases were -5.4% to 1.2% irrespective of albumin concentration. Biases for plasma samples were generally higher than for serum samples for all method types. For most measurement procedures, biases were lower for serum from patients on hemodialysis vs patients without kidney disease.

Conclusions: Significant differences among immunochemical, BCG, and BCP methods compromise interpretation of serum albumin results. Guidelines and calculations for clinical management of kidney and other diseases must consider the method used for albumin measurement until harmonization can be achieved.
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http://dx.doi.org/10.1373/clinchem.2016.262899DOI Listing
March 2017

Decrease of Serum IGF-I following Transsphenoidal Pituitary Surgery for Acromegaly.

Clin Chem 2017 Feb 14;63(2):486-494. Epub 2016 Dec 14.

Division of Endocrinology and Metabolism, Department of Medicine, University of Virginia School of Medicine and Health System, Charlottesville, VA.

Background: In the immediate postoperative period following resection of growth hormone (GH)-secreting pituitary tumors, serum concentrations of GH have limited ability to predict remission of acromegaly. Since many actions of GH actions are mediated by insulin-like growth factor-1 (IGF-I), we aimed to determine the rates of fall of IGF-I during 72 h after surgical resection of pituitary tumors.

Methods: We studied patients who were undergoing pituitary surgery for acromegaly. IGF-I was measured by LC-MS and GH by immunoassay. Remission was defined by the combination of serum GH <0.4 ng/mL during oral glucose tolerance testing performed 8 weeks after the surgical procedure and normal IGF-I at ≥8 weeks.

Results: During the first 72 h after surgery, the mean (SD) rate of decline of IGF-I was 185 (61) ng/mL per 24 h in those who achieved remission (n = 23), with a mean (SD) apparent half-life of 55 (19) h. IGF-I had decreased to <65% of the preoperative IGF-I on postoperative day 2 in 20 of 23 remission patients (87%) vs none of 5 patients who did not achieve remission. GH was <2.7 ng/mL on day 2 in 21 of 23 remission patients (91%), but in none of the nonremission patients. The combination of IGF-I and GH on day 2 separated the remission and nonremission groups of patients.

Conclusions: Rapid decline of serum IGF-I during the immediate postoperative period warrants further study as an analytically independent adjunct to GH measurement for early prediction of biochemical remission of acromegaly.
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http://dx.doi.org/10.1373/clinchem.2016.262592DOI Listing
February 2017

Smartphone monitoring of pneumatic tube system-induced sample hemolysis.

Clin Chim Acta 2016 Nov 20;462:1-5. Epub 2016 Aug 20.

Division of Laboratory Medicine, University of Virginia School of Medicine, Health Sciences Center, Charlottesville, VA, United States; Department of Pathology, University of Virginia School of Medicine, Health Sciences Center, Charlottesville, VA, United States.

Background: Pneumatic tube systems (PTSs) are convenient methods of patient sample transport in medical centers, but excessive acceleration force and time/distance traveled in the PTS have been correlated with increased blood-sample hemolysis. We investigated the utility of smartphones for monitoring of PTS-related variables.

Methods: Smartphones were sent through the PTS from several hospital locations. Each smartphone used 2 apps as data-loggers to record force of acceleration vs time. To relate the smartphone data to sample integrity, blood samples were collected from 5 volunteers, and hemolysis of the samples was analyzed after they were transported by hand or via 1 of 2 PTS routes. Increased sample hemolysis as measured by plasma lactate dehydrogenase (LD) was also related to the amount of transport in the PTS.

Results: The smartphones showed higher duration of forceful acceleration during transport through 1 of the 2 PTS routes, and the increased duration correlated with significant increases in hemolysis (H)-index and plasma LD. In addition, plasma LD showed a positive linear relationship with number of shock forces experienced during transport through the PTS.

Conclusions: Smartphones can monitor PTS variables that cause sample hemolysis. This provides an accessible method for investigating specific PTS routes in medical centers.
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http://dx.doi.org/10.1016/j.cca.2016.08.011DOI Listing
November 2016

Undetectable Urine Calcium in a Gastric Bypass Patient.

Clin Chem 2016 08;62(8):1161

Department of Pathology and.

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http://dx.doi.org/10.1373/clinchem.2015.251868DOI Listing
August 2016

Updating standards for reporting diagnostic accuracy: the development of STARD 2015.

Res Integr Peer Rev 2016 7;1. Epub 2016 Jun 7.

1Department of Clinical Epidemiology, Biostatistics and Bioinformatics, Academic Medical Centre, University of Amsterdam, Amsterdam, The Netherlands.

Background: Although the number of reporting guidelines has grown rapidly, few have gone through an updating process. The STARD statement (Standards for Reporting Diagnostic Accuracy), published in 2003 to help improve the transparency and completeness of reporting of diagnostic accuracy studies, was recently updated in a systematic way. Here, we describe the steps taken and a justification for the changes made.

Results: A 4-member Project Team coordinated the updating process; a 14-member Steering Committee was regularly solicited by the Project Team when making critical decisions. First, a review of the literature was performed to identify topics and items potentially relevant to the STARD updating process. After this, the 85 members of the STARD Group were invited to participate in two online surveys to identify items that needed to be modified, removed from, or added to the STARD checklist. Based on the results of the literature review process, 33 items were presented to the STARD Group in the online survey: 25 original items and 8 new items; 73 STARD Group members (86 %) completed the first survey, and 79 STARD Group members (93 %) completed the second survey.Then, an in-person consensus meeting was organized among the members of the Project Team and Steering Committee to develop a consensual draft version of STARD 2015. This version was piloted in three rounds among a total of 32 expert and non-expert users. Piloting mostly led to rewording of items. After this, the update was finalized. The updated STARD 2015 list now consists of 30 items. Compared to the previous version of STARD, three original items were each converted into two new items, four original items were incorporated into other items, and seven new items were added.

Conclusions: After a systematic updating process, STARD 2015 provides an updated list of 30 essential items for reporting diagnostic accuracy studies.
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http://dx.doi.org/10.1186/s41073-016-0014-7DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5803584PMC
June 2016

Smartphones Can Monitor Medical Center Pneumatic Tube Systems.

Clin Chem 2016 06 21;62(6):891-3. Epub 2016 Apr 21.

Division of Laboratory Medicine Department of Pathology.

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http://dx.doi.org/10.1373/clinchem.2016.257063DOI Listing
June 2016

Undetectable Alanine Aminotransferase during Hospitalization.

Clin Chem 2016 Mar;62(3):535-6

Department of Pathology, University of Virginia School of Medicine and Health Sciences Center, Charlottesville, VA.

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http://dx.doi.org/10.1373/clinchem.2015.247692DOI Listing
March 2016

STARD 2015: an updated list of essential items for reporting diagnostic accuracy studies.

BMJ 2015 Oct 28;351:h5527. Epub 2015 Oct 28.

Department of Clinical Epidemiology, Biostatistics and Bioinformatics, Academic Medical Centre, University of Amsterdam, Amsterdam, the Netherlands INSERM UMR 1153 and Department of Pediatrics, Necker Hospital, AP-HP, Paris Descartes University, Paris, France.

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http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4623764PMC
http://dx.doi.org/10.1136/bmj.h5527DOI Listing
October 2015

Persistence of Infliximab in Circulation for 7 Years?

Clin Chem 2015 Nov;61(11):1420-1

Department of Pathology and

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http://dx.doi.org/10.1373/clinchem.2015.242024DOI Listing
November 2015

STARD 2015: An Updated List of Essential Items for Reporting Diagnostic Accuracy Studies.

Clin Chem 2015 Dec 28;61(12):1446-52. Epub 2015 Oct 28.

Department of Clinical Epidemiology, Biostatistics and Bioinformatics, Academic Medical Centre, University of Amsterdam, Amsterdam, the Netherlands; INSERM UMR 1153 and Department of Pediatrics, Necker Hospital, AP-HP, Paris Descartes University, Paris, France.

Incomplete reporting has been identified as a major source of avoidable waste in biomedical research. Essential information is often not provided in study reports, impeding the identification, critical appraisal, and replication of studies. To improve the quality of reporting of diagnostic accuracy studies, the Standards for Reporting of Diagnostic Accuracy Studies (STARD) statement was developed. Here we present STARD 2015, an updated list of 30 essential items that should be included in every report of a diagnostic accuracy study. This update incorporates recent evidence about sources of bias and variability in diagnostic accuracy and is intended to facilitate the use of STARD. As such, STARD 2015 may help to improve completeness and transparency in reporting of diagnostic accuracy studies.
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http://dx.doi.org/10.1373/clinchem.2015.246280DOI Listing
December 2015

STARD 2015: An Updated List of Essential Items for Reporting Diagnostic Accuracy Studies.

Radiology 2015 Dec 28;277(3):826-32. Epub 2015 Oct 28.

From the Department of Clinical Epidemiology, Biostatistics and Bioinformatics, Academic Medical Centre, University of Amsterdam, PO Box 22700, 1100 DE Amsterdam, the Netherlands (P.M.B., D.A.K.); Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, University of Utrecht, Utrecht, the Netherlands (J.B.R.); Department of Pathology, University of Virginia School of Medicine, Charlottesville, Va (D.E.B.); Center for Statistical Sciences, Brown University School of Public Health, Providence, RI (C.A.G.); Centre for Research in Evidence-Based Practice, Faculty of Health Sciences and Medicine, Bond University, Gold Coast, Queensland, Australia (P.P.G.); Screening and Diagnostic Test Evaluation Program, School of Public Health, University of Sydney, Sydney, New South Wales, Australia (L.I.); Department of Psychiatry, Onze Lieve Vrouwe Gasthuis, Amsterdam, the Netherlands (J.G.L.); Clinical Epidemiology Program, Ottawa Hospital Research Institute, Ottawa, Canada (D.M.); School of Epidemiology, Public Health and Preventive Medicine, University of Ottawa, Ottawa, Canada (D.M.); Peer Review Congress, Chicago, Ill (D.R.); Philip R. Lee Institute for Health Policy Studies, University of California, San Francisco, Calif (D.R.); Department of Epidemiology and Biostatistics, EMGO Institute for Health and Care Research, VU University Medical Center, Amsterdam, the Netherlands (H.C.W.d.V.); Department of Radiology, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, Mass (H.Y.K.); Radiology Editorial Office, Boston, Mass (H.Y.K.); Department of Laboratory Medicine, Boston Children's Hospital, Harvard Medical School, Boston, Mass (N.R.); Clinical Chemistry Editorial Office, Washington, DC (N.R.); Division of General Internal Medicine and Geriatrics and Department of Preventive Medicine, Northwestern University Feinberg School of Medicine, Chicago, Ill (R.M.G.); JAMA Editorial Office, Chicago, Ill (R.M.G.); Centre for Statistics in Me

Incomplete reporting has been identified as a major source of avoidable waste in biomedical research. Essential information is often not provided in study reports, impeding the identification, critical appraisal, and replication of studies. To improve the quality of reporting of diagnostic accuracy studies, the Standards for Reporting of Diagnostic Accuracy Studies (STARD) statement was developed. Here we present STARD 2015, an updated list of 30 essential items that should be included in every report of a diagnostic accuracy study. This update incorporates recent evidence about sources of bias and variability in diagnostic accuracy and is intended to facilitate the use of STARD. As such, STARD 2015 may help to improve completeness and transparency in reporting of diagnostic accuracy studies.
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http://dx.doi.org/10.1148/radiol.2015151516DOI Listing
December 2015

Membrane associated cancer-oocyte neoantigen SAS1B/ovastacin is a candidate immunotherapeutic target for uterine tumors.

Oncotarget 2015 Oct;6(30):30194-211

Department of Cell Biology at The School of Medicine, University of Virginia, Charlottesville, Virginia, USA.

The metalloproteinase SAS1B [ovastacin, ASTL, astacin-like] was immunolocalized on the oolemma of ovulated human oocytes and in normal ovaries within the pool of growing oocytes where SAS1B protein was restricted to follicular stages spanning the primary-secondary follicle transition through ovulation. Gene-specific PCR and immunohistochemical studies revealed ASTL messages and SAS1B protein in both endometrioid [74%] and malignant mixed Mullerian tumors (MMMT) [87%] of the uterus. A MMMT-derived cell line, SNU539, expressed cell surface SAS1B that, after binding polyclonal antibodies, internalized into EEA1/LAMP1-positive early and late endosomes. Treatment of SNU539 cells with anti-SAS1B polyclonal antibodies caused growth arrest in the presence of active complement. A saporin-immunotoxin directed to SAS1B induced growth arrest and cell death. The oocyte restricted expression pattern of SAS1B among adult organs, cell-surface accessibility, internalization into the endocytic pathway, and tumor cell growth arrest induced by antibody-toxin conjugates suggest therapeutic approaches that would selectively target tumors while limiting adverse drug effects in healthy cells. The SAS1B metalloproteinase is proposed as a prototype cancer-oocyte tumor surface neoantigen for development of targeted immunotherapeutics with limited on-target/off tumor effects predicted to be restricted to the population of growing oocytes.
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http://dx.doi.org/10.18632/oncotarget.4734DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4745790PMC
October 2015

The impact of measurement frequency on the domains of glycemic control in the critically ill--a Monte Carlo simulation.

J Diabetes Sci Technol 2015 Mar 6;9(2):237-45. Epub 2015 Jan 6.

Department of Pathology, University of Virginia, Charlottesville VA, USA.

The role of blood glucose (BG) measurement frequency on the domains of glycemic control is not well defined. This Monte Carlo mathematical simulation of glycemic control in a cohort of critically ill patients modeled sets of 100 patients with simulated BG-measuring devices having 5 levels of measurement imprecision, using 2 published insulin infusion protocols, for 200 hours, with 3 different BG-measurement intervals-15 minutes (Q15'), 1 hour (Q1h), and 2 hours (Q2h)-resulting in 1,100,000 BG measurements for 3000 simulated patients. The model varied insulin sensitivity, initial BG value and rate of gluconeogenesis. The primary outcomes included rates of hyperglycemia (BG > 180 mg/dL), hypoglycemia (BG < 70 and 40 mg/dL), proportion of patients with elevated glucose variability (within-patient coefficient of variation [CV] > 20%), and time in range (BG ranges 80-150 mg/dL and 80-180 mg/dL). Percentages of hyperglycemia, hypoglycemia at both thresholds, and patients with elevated glucose variability as well as time outside glycemic targets were substantially higher in simulations with measurement interval Q2h compared to those with measurement interval Q1h and moderately higher in simulations with Q1h than in those with Q15'. Higher measurement frequency mitigated the deleterious effect of high measurement imprecision, defined as CV ≥ 15%. This Monte Carlo simulation suggests that glycemic control in critically ill patients is more optimal with a BG measurement interval no longer than 1h, with further benefit obtained with use of measurement interval of 15'. These findings have important implications for the development of glycemic control standards.
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http://dx.doi.org/10.1177/1932296814566507DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4604588PMC
March 2015

Variability of ethics education in laboratory medicine training programs: results of an international survey.

Clin Chim Acta 2015 Mar 29;442:115-8. Epub 2014 Nov 29.

Department of Biochemistry and Molecular Biology, Faculty of Medicine, University of Iceland, Reykjavík Iceland; Department of Genetics and Molecular Medicine, Landspitali - National University Hospital of Iceland, Reykjavík Iceland. Electronic address:

Background: Ethical considerations are increasingly important in medicine. We aimed to determine the mode and extent of teaching of ethics in training programs in clinical chemistry and laboratory medicine.

Methods: We developed an on-line survey of teaching in areas of ethics relevant to laboratory medicine. Reponses were invited from directors of training programs who were recruited via email to leaders of national organizations.

Results: The survey was completed by 80 directors from 24 countries who directed 113 programs. The largest numbers of respondents directed postdoctoral training of scientists (42%) or physicians (33%), post-masters degree programs (33%), and PhD programs (29%). Most programs (82%) were 2years or longer in duration. Formal training was offered in research ethics by 39%, medical ethics by 31%, professional ethics by 24% and business ethics by 9%. The number of reported hours of formal training varied widely, e.g., from 0 to >15h/year for research ethics and from 0 to >15h for medical ethics. Ethics training was required and/or tested in 75% of programs that offered training. A majority (54%) of respondents reported plans to add or enhance training in ethics; many indicated a desire for online resources related to ethics, especially resources with self-assessment tools.

Conclusion: Formal teaching of ethics is absent from many training programs in clinical chemistry and laboratory medicine, with heterogeneity in the extent and methods of ethics training among the programs that provide the training. A perceived need exists for online training tools, especially tools with self-assessment components.
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http://dx.doi.org/10.1016/j.cca.2014.11.023DOI Listing
March 2015

Performance requirements for glucose assays in intensive care units.

Clin Chem 2014 Dec 20;60(12):1463-5. Epub 2014 Oct 20.

Department of Pathology, University of Virginia School of Medicine, Charlottesville, VA.

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http://dx.doi.org/10.1373/clinchem.2014.231258DOI Listing
December 2014

Falsely increased chloride and missed anion gap elevation during treatment with sodium thiosulfate.

Clin Chim Acta 2014 Apr 7;431:77-9. Epub 2014 Feb 7.

Department of Pathology, University of Virginia School of Medicine and Health System, Charlottesville, VA, United States.

Background: Sodium thiosulfate (STS) is used to treat calciphylaxis and cyanide poisoning, but can lead to a serious anion-gap acidosis. We suspected that the calculated anion gap in a patient treated with STS for calciphylaxis was decreased to normal by a falsely increased chloride, and we hypothesized that STS directly interfered with chloride measurements.

Methods: Plasma pools were prepared with 12 concentrations of STS from 0 to 20 mmol/l. Chloride was measured in each sample on 9 analyzers: Architect 16200, StatProfile pHOx Plus, RapidLab 1265®, Vitros 350®, Advia 1800, Roche Modular, iSTAT1, RAPIDpoint 500, and Radiometer ABL735.

Results: Statistically significant, dose-dependent increases in reported chloride concentrations were seen with all analyzers except the RAPIDpoint 500 and Vitros. The increases ranged from 5 to 75 mmol/l at the peak thiosulfate concentrations (33 mmol/l) expected in treated patients. The CLIA-allowable error of 5% was exceeded by 4 analyzers (Architect 16200, iSTAT1, StatProfile pHOx Plus, and Radiometer ABL735). The RAPIDpoint 500 showed a 3-mmol/l decrease in measured chloride over the tested range. The Vitros analyzer showed no interference.

Conclusions: Interference of STS in chloride measurement in several common analyzers may lead to erroneous anion-gap calculations and confound the diagnosis of STS-induced anion-gap acidosis.
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http://dx.doi.org/10.1016/j.cca.2014.01.020DOI Listing
April 2014

Effects of measurement frequency on analytical quality required for glucose measurements in intensive care units: assessments by simulation models.

Clin Chem 2014 Apr 15;60(4):644-50. Epub 2014 Jan 15.

Department of Pathology, University of Virginia, Charlottesville, VA.

Background: Total error allowances have been proposed for glucose meters used in tight-glucose-control (TGC) protocols. It is unclear whether these proposed quality specifications are appropriate for continuous glucose monitoring (CGM).

Methods: We performed Monte Carlo simulations of patients on TGC protocols. To simulate use of glucose meters, measurements were made hourly. To simulate CGM, glucose measurements were made every 5 min. Glucose was measured with defined bias (varied from -20% to 20%) and imprecision (0% to 20% CV). The measured glucose concentrations were used to alter insulin infusion rates according to established treatment protocols. Changes in true glucose were calculated hourly on the basis of the insulin infusion rate, the modeled patient's insulin sensitivity, and a model of glucose homeostasis. We modeled 18 000 patients, equally divided between the hourly and every-5-min measurement schemas and distributed among 45 combinations of bias and imprecision and 2 treatment protocols.

Results: With both treatment protocols and both measurement frequencies, higher measurement imprecision increased the rates of hypoglycemia and hyperglycemia and increased glycemic variability (SD). These adverse effects of measurement imprecision were lower at the higher measurement frequency. The rate of hypoglycemia at an imprecision (CV) of 5% with hourly measurements was similar to the rate of hypoglycemia at 10% CV when measurements were made every 5 min. With measurements every 5 min, imprecision up to 10% had minimal effects on hyperglycemia or glycemic variability. Effects of simulated analytical bias on glycemia were unaffected by measurement frequency.

Conclusions: Quality specifications for imprecision of glucose meters are not transferable to CGM.
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http://dx.doi.org/10.1373/clinchem.2013.216366DOI Listing
April 2014

State of the art for measurement of urine albumin: comparison of routine measurement procedures to isotope dilution tandem mass spectrometry.

Clin Chem 2014 Mar 26;60(3):471-80. Epub 2013 Nov 26.

Department of Pathology, Virginia Commonwealth University, Richmond, VA;

Background: Urine albumin is the primary biomarker for detection and monitoring of kidney damage. Because fixed decision criteria are used to identify patients with increased values, we investigated if commonly used routine measurement procedures gave comparable results.

Methods: Results from 17 commercially available urine albumin measurement procedures were investigated vs an isotope dilution mass spectrometry (IDMS) procedure. Nonfrozen aliquots of freshly collected urine from 332 patients with chronic kidney disease, diabetes, cardiovascular disease, and hypertension were distributed to manufacturers to perform urine albumin measurements according to the respective instructions for use for each procedure. Frozen aliquots were used for measurements by the IDMS procedure. An error model was used to determine imprecision and bias components.

Results: Median differences between the largest positive and negative biases vs IDMS were 45%, 37%, and 42% in the concentration intervals of 12-30 mg/L, 31-200 mg/L, and 201-1064 mg/L, respectively. Biases varied with concentration for most procedures and exceeded ± 10% over the concentration interval for 14 of 16 quantitative procedures. Mean biases ranged from -35% to 34% at 15 mg/L. Dilution of samples with high concentrations introduced bias for 4 procedures. The combined CV was >10% for 5 procedures. It was not possible to estimate total error due to dependence of bias on concentration. CVs for sample-specific influences were 0% to 15.2%.

Conclusions: Bias was the dominant source of disagreement among routine measurement procedures. Consequently, standardization efforts will improve agreement among results. Variation of bias with concentration needs to be addressed by manufacturers.
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http://dx.doi.org/10.1373/clinchem.2013.210302DOI Listing
March 2014

Analytic evaluation of a new glucose meter system in 15 different critical care settings.

J Diabetes Sci Technol 2013 Sep 1;7(5):1282-7. Epub 2013 Sep 1.

Washington University, St. Louis, Missouri.

Background: Maintaining appropriate glycemic control in critically ill patients reduces morbidity and mortality. The use of point-of-care (POC) glucose devices is necessary to obtain rapid results at the patient's bedside. However, the devices should be thoroughly tested in the intended population before implementation. The use of POC glucose meters in critically ill patients has been questioned both in the literature and by regulatory agencies. The aim of this study was to determine if the ACCU-CHEK® Inform II system (Roche Diagnostics) POC glucose meter demonstrated the desired accuracy and precision, as defined by Clinical and Laboratory Standards Institute guideline POCT12-A3, in a large number of critically ill patients from multiple intensive care settings at two academic medical centers.

Methods: A total of 1200 whole blood meter results from 600 patients were compared with central laboratory plasma values. Whole blood aliquots from venous samples were used to obtain duplicate meter results with the remaining sample being processed to obtain plasma for central laboratory testing within 5 min of meter testing.

Results: A total of 1185 (98.8%) of the new meter's glucose values were within ± 12.5% (± 12 mg/dl for values ≥ 100 mg/dl) of the comparative laboratory glucose values, and 1198 (99.8%) were within ± 20% (± 20 mg/dl for values <100 mg/dl).

Conclusions: Considering the large number of patients from numerous critical care units examined, the new glucose meter system appears to have sufficient analytic accuracy for use in critically ill patients.
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http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3876372PMC
http://dx.doi.org/10.1177/193229681300700518DOI Listing
September 2013

Failure of current laboratory protocols to detect lot-to-lot reagent differences: findings and possible solutions.

Clin Chem 2013 Aug 16;59(8):1187-94. Epub 2013 Apr 16.

Division of Clinical Biochemistry and Immunology, Department of Laboratory, Medicine and Pathology, Mayo Clinic, Rochester, MN, USA.

Background: Maintaining consistency of results over time is a challenge in laboratory medicine. Lot-to-lot reagent changes are a major threat to consistency of results.

Methods: For the period October 2007 through July 2012, we reviewed lot validation data for each new lot of insulin-like growth factor 1 (IGF-1) reagents (Siemens Healthcare Diagnostics) at Mayo Clinic, Rochester, MN, and the University of Virginia, Charlottesville, VA. Analyses of discarded patient samples were used for comparison of lots. For the same period, we determined the distributions of reported patient results for each lot of reagents at the 2 institutions.

Results: Lot-to-lot validation studies identified no reagent lot as significantly different from the preceding lot. By contrast, significant lot-to-lot changes were seen in the means and medians of 105 668 reported patient IGF-I results during the period. The frequency of increased results increased nearly 2-fold to a high of 17%, without detectable changes in the underlying patient demographics. Retrospective statistical analysis indicated that lot-to-lot comparison protocols were underpowered and that validation studies for this assay required testing >100 samples to achieve 90% power to detect reagent lots that would significantly alter the distributions of patient results.

Conclusions: The number of test samples required for adequate lot-to-lot validation protocols is high and may be prohibitively large, especially for low-volume or complex assays. Monitoring of the distributions of patient results has the potential to detect lot-to-lot inconsistencies relatively quickly. We recommend that manufacturers implement remote monitoring of patient results from analyzers in multiple institutions to allow rapid identification of between-lot result inconsistency.
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http://dx.doi.org/10.1373/clinchem.2013.205070DOI Listing
August 2013