Price of High-Throughput 25-Hydroxyvitamin D Immunoassays: Frequency of Inaccurate Results.

BACKGROUND: With a 10-year sustained increase in 25-hydroxyvitamin D [25(OH)D] testing, laboratories have swapped their LC-MS/MS methods for high-throughput automated immunoassays. Although it is generally well-known that immunoassays have poor recoveries for 25-hydroxyvitamin D2 [25(OH)D2], the frequency and extent to which this impacts total 25(OH)D have not been previously demonstrated. We evaluated 3 automated immunoassays against the first FDA-cleared CDC/NIST-traceable LC-MS/MS method. METHODS: Method comparison was performed for the Siemens ADVIA Centaur, Roche Elecsys Cobas, and Abbott Architect 25(OH)D immunoassay methods in real patient samples (n = 105). We calculated the mean bias in samples containing >20 ng/mL 25(OH)D2 and estimated the percent 25(OH)D2 cross-reactivities. We determined the prevalence of appreciable concentrations of 25(OH)D2 in our patient population through random sampling (n = 120) and projected the frequency of inaccurate 25(OH)D immunoassay results. RESULTS: Linear regression for 25(OH)D was y = 1.09x - 4.44 (Centaur), y = 0.84 + 0.43 (Cobas), and y = 0.83x - 0.48 (Architect). The mean biases of 25(OH)D concentrations were 5.6 (11.0) ng/mL (Centaur), -17.5 (7.2) ng/mL (Cobas), and -20.3 (9.8) ng/mL (Architect) in samples containing >20 ng/mL 25(OH)D2. The observed percent cross-reactivities for 25(OH)D2 were 115% (Centaur), 52% (Cobas), and 44% (Architect). We estimate that 8% of our population has >20 ng/mL 25(OH)D2, thereby compromising the accuracy of 25(OH)D results in >3000 samples annually. CONCLUSIONS: We demonstrate that immunoassay manufacturer package inserts indicate much better recoveries of 25(OH)D2 than what is observed in unadulterated real patient samples. We estimate the frequency of inaccurate total 25(OH)D determination by these immunoassay methods to be largely dependent on the concentration of 25(OH)D2 in each sample.

Assessment of Intensive Care Unit Laboratory Values That Differ From Reference Ranges and Association With Patient Mortality and Length of Stay.

Importance: Laboratory data are frequently collected throughout the care of critically ill patients. Currently, these data are interpreted by comparison with values from healthy outpatient volunteers. Whether this is the most useful comparison has yet to be demonstrated. Objectives: To understand how the distribution of intensive care unit (ICU) laboratory values differs from the reference range, and how these distributions are related to patient outcomes. Design, Setting, and Participants: Cross-sectional study of a large critical care database, the Medical Information Mart for Intensive Care database, from January 1, 2001, to October 31, 2012. The database is collected from ICU data from a large tertiary medical center in Boston, Massachusetts. The data are collected from medical, cardiac, neurologic, and surgical ICUs. All patients in the database from all ICUs for 2001 to 2012 were included. Common laboratory measurements over the time window of interest were sampled. The analysis was conducted from March to June 2017. Main Outcomes and Measures: The overlapping coefficient and Cohen standardized mean difference between distributions were calculated, and kernel density estimate visualizations for the association between laboratory values and the probability of death or quartile of ICU length of stay were created. Results: Among 38 605 patients in the ICU (21 852 [56.6%] male; mean [SD] age, 74.5 [55.1] years), 8878 (23%) had the best outcome (ICU survival, shortest quartile length of stay) and 3090 (8%) had the worst outcome (ICU nonsurvival). Distribution curves based on ICU data differed significantly from the hospital standard range (mean [SD] overlapping coefficient, 0.51 [0.32-0.69]). All laboratory values for the best outcome group differed significantly from those in the worst outcome group. Both the best and worst outcome group curves revealed little overlap with and marked divergence from the reference range. Conclusions and Relevance: The standard reference ranges obtained from healthy volunteers differ from the analogous range generated from data from patients in intensive care. Laboratory data interpretation may benefit from greater consideration of clinically contextual and outcomes-related factors.

Clinical Laboratory Quality Practices When Hemolysis Occurs.

CONTEXT: Hemolyzed specimens delay clinical laboratory results, proliferate unnecessary testing, complicate physician decisions, injure patients indirectly, and increase health care costs. OBJECTIVE: To determine quality improvement practices when hemolysis occurs. DESIGN: We used the College of American Pathologists (CAP) Survey Program to distribute a Q-Probes-type questionnaire about hemolysis practices to CAP Chemistry Survey participants. RESULTS: Of 3495 participants sent the questionnaire, 846 (24%) responded. Although 85%, 69%, and 55% of participants had written hemolysis policies for potassium, lactate dehydrogenase, and glucose, respectively, only a few (46%, 40%, and 40%) had standardized hemolysis reports between their primary and secondary chemistry analyzers for these 3 analytes. Most participants (70%) had not attempted to validate the manufacturers' hemolysis data for these 3 analytes; however, essentially all who tried, succeeded. Forty-nine percent of participants had taken corrective action to reduce hemolysis during the past year and used, on average, 2.4 different actions, with collection and distribution of hemolysis data to administrative leadership (57%), troubleshooting outliers (55%), retraining phlebotomist (53%), and establishment of quality improvement teams among the laboratory and at problem locations (37%) being the most common actions. When asked to assess their progress in reducing hemolysis, 70% noted slow to no progress, and 2% gave up on improvement. Upon measuring potassium, lactate dehydrogenase, and glucose, approximately 60% of participants used the same specimen flag for hemolysis as for lipemia and icterus. CONCLUSIONS: Hemolysis decreases the quality and increases the cost of health care. Practices for measuring, reporting, and decreasing hemolysis rates need improvement.

Utility, charge, and cost of inpatient and emergency department serum folate testing.

BACKGROUND: Serum folate levels are commonly ordered for multiple indications in the inpatient and emergency department settings. Since mandatory folic acid fortification in 1998, there has been a decreasing prevalence of folate deficiency in the United States. OBJECTIVE: Our objective was to determine the indications, rate of deficiency, charge and cost per deficient result, and change in management per deficient result in serum folate testing in inpatients and emergency department patients. DESIGN: Retrospective analysis of all inpatient and emergency department serum folate tests. METHODS: We analyzed all inpatient and emergency department serum folate tests performed over a 12-month period. We reviewed the charts of 250 patients and all low-normal or deficient serum folate levels to determine indications, comorbidities, and change in management based on result. Charge and cost analyses were performed. SETTING/PATIENTS: All inpatient and emergency department patients with a serum folate test performed at a major medical center in Boston, Massachusetts. RESULTS: A total of 2093 serum folate tests were performed in 1944 patients with 2 deficient levels. The most common indications were anemia without macrocytosis and anemia with macrocytosis. The amount charged per deficient result was $158,022. The cost to the hospital per deficient result was less than $2093. CONCLUSIONS: In folic acid fortified countries, serum folate testing has low utility and poor cost effectiveness for all indications in inpatients and emergency department patients.

Proficiency testing/external quality assessment: current challenges and future directions.

BACKGROUND: Proficiency testing (PT), or external quality assessment (EQA), is intended to verify on a recurring basis that laboratory results conform to expectations for the quality required for patient care. CONTENT: Key factors for interpreting PT/EQA results are knowledge of the commutability of the samples used and the process used for target value assignment. A commutable PT/EQA sample demonstrates the same numeric relationship between different measurement procedures as that expected for patients' samples. Noncommutable PT/EQA samples frequently have a matrix-related bias of unknown magnitude that limits interpretation of results. PT/EQA results for commutable samples can be used to assess accuracy against a reference measurement procedure or a designated comparison method. In addition, the agreement of the results between different measurement procedures for commutable samples reflects that which would be seen for patients' samples. PT/EQA results for noncommutable samples must be compared to a peer group mean/median of results from participants who use measurement procedures that are expected to have the same or very similar matrix-related bias. Peer group evaluation is used to asses whether a laboratory is using a measurement procedure in conformance to the manufacturer's specifications and/or in conformance to other laboratories using the same technology. A noncommutable PT/EQA sample does not give meaningful information about the relationship of results for patients' samples between different measurement procedures. SUMMARY: PT/EQA provides substantial value to the practice of laboratory medicine by assessing the performance of individual laboratories and, when commutable samples are used, the status of standardization or harmonization among different measurement procedures.