Guidelines and Recommendations for Laboratory Analysis in the Diagnosis and Management of Diabetes Mellitus.

BACKGROUND: Numerous laboratory tests are used in the diagnosis and management of diabetes mellitus. The quality of the scientific evidence supporting the use of these assays varies substantially. APPROACH: An expert committee compiled evidence-based recommendations for laboratory analysis in screening, diagnosis, or monitoring of diabetes. The overall quality of the evidence and the strength of the recommendations were evaluated. The draft consensus recommendations were evaluated by invited reviewers and presented for public comment. Suggestions were incorporated as deemed appropriate by the authors (see Acknowledgments). The guidelines were reviewed by the Evidence Based Laboratory Medicine Committee and the Board of Directors of the American Association of Clinical Chemistry and by the Professional Practice Committee of the American Diabetes Association. CONTENT: Diabetes can be diagnosed by demonstrating increased concentrations of glucose in venous plasma or increased hemoglobin A1c (Hb A1c) in the blood. Glycemic control is monitored by the people with diabetes measuring their own blood glucose with meters and/or with continuous interstitial glucose monitoring (CGM) devices and also by laboratory analysis of Hb A1c. The potential roles of noninvasive glucose monitoring, genetic testing, and measurement of ketones, autoantibodies, urine albumin, insulin, proinsulin, and C-peptide are addressed. SUMMARY: The guidelines provide specific recommendations based on published data or derived from expert consensus. Several analytes are found to have minimal clinical value at the present time, and measurement of them is not recommended.

Executive Summary: Guidelines and Recommendations for Laboratory Analysis in the Diagnosis and Management of Diabetes Mellitus.

BACKGROUND: Numerous laboratory tests are used in the diagnosis and management of patients with diabetes mellitus. The quality of the scientific evidence supporting the use of these assays varies substantially. An expert committee compiled evidence-based recommendations for laboratory analysis in patients with diabetes. The overall quality of the evidence and the strength of the recommendations were evaluated. The draft consensus recommendations were evaluated by invited reviewers and presented for public comment. Suggestions were incorporated as deemed appropriate by the authors (see Acknowledgments in the full version of the guideline). The guidelines were reviewed by the Evidence Based Laboratory Medicine Committee and the Board of Directors of the American Association of Clinical Chemistry and by the Professional Practice Committee of the American Diabetes Association. CONTENT: Diabetes can be diagnosed by demonstrating increased concentrations of glucose in venous plasma or increased hemoglobin A1c (Hb A1c) in the blood. Glycemic control is monitored by the patients measuring their own blood glucose with meters and/or with continuous interstitial glucose monitoring devices and also by laboratory analysis of Hb A1c. The potential roles of noninvasive glucose monitoring; genetic testing; and measurement of ketones, autoantibodies, urine albumin, insulin, proinsulin, and C-peptide are addressed. SUMMARY: The guidelines provide specific recommendations based on published data or derived from expert consensus. Several analytes are found to have minimal clinical value at the present time, and measurement of them is not recommended.

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

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 (Annals of Internal Medicine, JAMA: The Journal of the American Medical Association, The Lancet, The New England Journal of Medicine, and PLOS Medicine) 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.

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

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.

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

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.

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

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.

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

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.

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

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.

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

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.

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

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.

Evaluation of high resolution gel ß(2)-transferrin for detection of cerebrospinal fluid leak.

BACKGROUND: Cerebrospinal fluid (CSF) leaks are potentially life-threatening conditions that can be diagnosed by detection of ß(2)-transferrin using protein electrophoresis. Another less commonly available test is ß-trace protein quantitation using immunoassay. The objectives of this study were to evaluate a new immunofixation-based ß(2)-transferrin test for detection of CSF leaks and to compare it to an existing agarose gel electrophoresis test and ß-trace protein immunoassay. METHODS: For method comparison, 63 consecutive samples from physician-ordered ß(2)-transferrin tests were analyzed using two different electrophoresis methods, agarose gel fractionation followed by acid-violet staining, and high resolution agarose gel electrophoresis followed by ß(2)-transferrin immunofixation. A subset of samples (16/63) were analyzed for ß-trace protein. Results were compared against patient chart data for the presence of a CSF leak. Additional studies were performed to assess the stability, detection limit, and analytical specificity of the ß(2)-transferrin immunofixation test. RESULTS: The ß(2)-transferrin immunofixation test had a sensitivity of 100 % (40/40) and specificity of 71 % (12/17) for detection of CSF leaks. By comparison, the agarose gel test had a sensitivity of 87 % (35/40) and specificity of 94 % (16/17). ß-trace protein had a sensitivity of 100 % (10/10) and specificity of 86 % (5/6). Serum and saliva could be differentiated from CSF by the ß(2)-transferrin immunofixation test based on their migration patterns. However, whole blood samples appeared positive for ß(2)-transferrin at a threshold of ~ 4 g/L hemoglobin. At a cut-off of 3 mg/L, ß-trace protein was increased in 10/10 cases with documented CSF leak and in 1/6 patients without CSF leak. CONCLUSIONS: Both the new immunofixation test for ß(2)-transferrin and the ß-trace protein were effective at detecting CSF leaks. Users of the ß(2)-transferrin immunofixation test should be cautioned against interpreting samples with blood contamination.

Executive Summary: Guidelines and Recommendations for Laboratory Analysis in the Diagnosis and Management of Diabetes Mellitus.

BACKGROUND: Numerous laboratory tests are used in the diagnosis and management of patients with diabetes mellitus. The quality of the scientific evidence supporting the use of these assays varies substantially. An expert committee compiled evidence-based recommendations for laboratory analysis in patients with diabetes. The overall quality of the evidence and the strength of the recommendations were evaluated. The draft consensus recommendations were evaluated by invited reviewers and presented for public comment. Suggestions were incorporated as deemed appropriate by the authors (see Acknowledgments in the full version of the guideline). The guidelines were reviewed by the Evidence Based Laboratory Medicine Committee and the Board of Directors of the American Association for Clinical Chemistry and by the Professional Practice Committee of the American Diabetes Association. CONTENT: Diabetes can be diagnosed by demonstrating increased concentrations of glucose in venous plasma or increased hemoglobin A1c (HbA1c) in the blood. Glycemic control is monitored by the patients measuring their own blood glucose with meters and/or with continuous interstitial glucose monitoring devices and also by laboratory analysis of HbA1c. The potential roles of noninvasive glucose monitoring; genetic testing; and measurement of ketones, autoantibodies, urine albumin, insulin, proinsulin, and C-peptide are addressed. SUMMARY: The guidelines provide specific recommendations based on published data or derived from expert consensus. Several analytes are found to have minimal clinical value at the present time, and measurement of them is not recommended.

Guidelines and Recommendations for Laboratory Analysis in the Diagnosis and Management of Diabetes Mellitus.

BACKGROUND: Numerous laboratory tests are used in the diagnosis and management of diabetes mellitus. The quality of the scientific evidence supporting the use of these assays varies substantially. APPROACH: An expert committee compiled evidence-based recommendations for laboratory analysis in screening, diagnosis, or monitoring of diabetes. The overall quality of the evidence and the strength of the recommendations were evaluated. The draft consensus recommendations were evaluated by invited reviewers and presented for public comment. Suggestions were incorporated as deemed appropriate by the authors (see Acknowledgments). The guidelines were reviewed by the Evidence Based Laboratory Medicine Committee and the Board of Directors of the American Association for Clinical Chemistry and by the Professional Practice Committee of the American Diabetes Association. CONTENT: Diabetes can be diagnosed by demonstrating increased concentrations of glucose in venous plasma or increased hemoglobin A1c (HbA1c) in the blood. Glycemic control is monitored by the people with diabetes measuring their own blood glucose with meters and/or with continuous interstitial glucose monitoring (CGM) devices and also by laboratory analysis of HbA1c. The potential roles of noninvasive glucose monitoring, genetic testing, and measurement of ketones, autoantibodies, urine albumin, insulin, proinsulin, and C-peptide are addressed. SUMMARY: The guidelines provide specific recommendations based on published data or derived from expert consensus. Several analytes are found to have minimal clinical value at the present time, and measurement of them is not recommended.

Guidelines and recommendations for laboratory analysis in the diagnosis and management of diabetes mellitus.

BACKGROUND: Multiple laboratory tests are used to diagnose and manage patients with diabetes mellitus. The quality of the scientific evidence supporting the use of these tests varies substantially. APPROACH: An expert committee compiled evidence-based recommendations for the use of laboratory testing for patients with diabetes. A new system was developed to grade the overall quality of the evidence and the strength of the recommendations. Draft guidelines were posted on the Internet and presented at the 2007 Arnold O. Beckman Conference. The document was modified in response to oral and written comments, and a revised draft was posted in 2010 and again modified in response to written comments. The National Academy of Clinical Biochemistry and the Evidence Based Laboratory Medicine Committee of the AACC jointly reviewed the guidelines, which were accepted after revisions by the Professional Practice Committee and subsequently approved by the Executive Committee of the American Diabetes Association. CONTENT: In addition to long-standing criteria based on measurement of plasma glucose, diabetes can be diagnosed by demonstrating increased blood hemoglobin A(1c) (Hb A(1c)) concentrations. Monitoring of glycemic control is performed by self-monitoring of plasma or blood glucose with meters and by laboratory analysis of Hb A(1c). The potential roles of noninvasive glucose monitoring, genetic testing, and measurement of autoantibodies, urine albumin, insulin, proinsulin, C-peptide, and other analytes are addressed. SUMMARY: The guidelines provide specific recommendations that are based on published data or derived from expert consensus. Several analytes have minimal clinical value at present, and their measurement is not recommended.