Transformation of Sequential Hospital and Outpatient Laboratory Data into Between-Day Reference Change Values.

BACKGROUND: Serial differences between intrapatient consecutive measurements can be transformed into Taylor series of variation vs time with the intersection at time = 0 (y0) equal to the total variation (analytical + biological + preanalytical). With small preanalytical variation, y0, expressed as a percentage of the mean, is equal to the variable component of the reference change value (RCV) calculation: (CVA2 + CVI2)1/2. METHODS: We determined the between-day RCV of patient data for 17 analytes and compared them to healthy participants' RCVs. We analyzed 653 consecutive days of Dartmouth-Hitchcock Roche Modular general chemistry data (4.2 million results: 60% inpatient, 40% outpatient). The serial patient values of 17 analytes were transformed into 95% 2-sided RCV (RCVAlternate), and 3 sets of RCVhealthy were calculated from 3 Roche Modular analyzers' quality control summaries and CVI derived from biological variation (BV) studies using healthy participants. RESULTS: The RCVAlternate values are similar to RCVhealthy derived from known components of variation. For sodium, chloride, bicarbonate calcium, magnesium, phosphate, alanine aminotransferase, albumin, and total protein, the RCVs are equivalent. As expected, increased variation was found for glucose, aspartate aminotransferase, creatinine, and potassium. Direct bilirubin and urea demonstrated lower variation. CONCLUSIONS: Our RCVAlternate values integrate known and unknown components of analytic, biologic, and preanalytic variation, and depict the variations observed by clinical teams that make medical decisions based on the test values. The RCVAlternate values are similar to the RCVhealthy values derived from known components of variation and suggest further studies to better understand the results being generated on actual patients tested in typical laboratory environments.

The use of serial patient blood gas, electrolyte and glucose results to derive biologic variation: a new tool to assess the acceptability of intensive care unit testing.

BACKGROUND: Most estimates of biologic variation (s(b)) are based on periodically acquiring and storing specimens, followed by analysis within a single analytic run. We demonstrate for many intensive care unit (ICU) tests, only patient results need be statistically analyzed to provide reliable estimates of s(b). METHODS: Over 11 months, approximately 28,000 blood gas measurements (including electrolyte panels and glucose) were performed on one of two Radiometer ABL800 FLEX analyzers (Radiometer, Copenhagen, Denmark) from 1676 ICU patients. We tabulated the measurements of paired intra-patient blood samples drawn within 24 h of each other. After removal of outliers, we calculated the standard deviations of duplicates (SDD) of the intra-patient pairs grouped in 2-h intervals: 0-2 h, 2-4 h, 4-6 h, … 20-22 h and 22-24 h. The SDDs were then regressed against the time intervals of 2-14 h; extrapolation to zero time represents the sum of s(b) and short-term analytic variation (s(a)). RESULTS: Substitution of experimentally derived analytic error permitted the calculation of coefficient of variation (biologic) (CV(b)) (100 s(b)/mean): pH, 0.3%; pCO(2), 5.7%; pO(2), 13%; Na(+), 0.6%; K(+), 4.8%; Cl(-), 0.8%; HCO(3)(-), 3.2%; iCa(++), 2.4%; and glucose, 10.3%. The CV(b) of the electrolytes very closely matches the lowest estimates obtained in the usual manner. CONCLUSIONS: Derivation of the ratio of biologic to analytic variation indicates that the ABL800 is extremely suitable for ICU testing. This analysis should be extended to other point of care instrument systems.

Variation in the frequency of hemoglobin A1c (HbA1c) testing: population studies used to assess compliance with clinical practice guidelines and use of HbA1c to screen for diabetes.

BACKGROUND: The volume of hemoglobin A1c (HbA1c) testing has increased dramatically over the past decade and few studies have attempted to determine how the test is used. The goals of this study were to evaluate the frequency of HbA1c testing in regional populations to assess the extent of screening for diabetes and to determine if the HbA1c testing intervals of known diabetic patients were consistent with clinical practice guidelines. METHODS: Two years of HbA1c results were extracted from laboratory information systems in four regions of the province of Alberta that represent urban, mixed urban-rural, and rural populations. HbA1c testing frequencies and the proportions of nondiabetic patients undergoing HbA1c tests were derived. RESULTS: Approximately 60% of HbA1c tests in each region were done on patients who had only a single test during the 2-year interval. Testing of nondiabetic patients accounted for 24% of HbA1c tests and varied by region. While the cumulative frequency distributions of HbA1c test intervals resembled each other, detailed analyses of the frequency distributions depicted broad multimodal peaks and regional variations that suggest a great deal of heterogeneity among practices. The most common HbA1c testing interval was 3 months +/- 3 weeks in each region and is consistent with the 3-month test interval target in a clinical practice guideline. CONCLUSIONS: HbA1c testing is being performed on a substantial proportion of nondiabetic patients. On average, patients with diabetes in Alberta receive 1.5 HbA1c tests per year. However, we observed regional differences in the frequency of testing and variation in compliance with clinical practice guidelines.

Use of serial patient hemoglobin A1c differences to determine long-term imprecision of immunoassay and high-performance liquid chromatography analyzers.

BACKGROUND: The quality of the HbA1c assay is inversely proportional to the variation of the assay. Most published measures of HbA1c variation are limited by the data collection period, the statistical treatment of outliers, and even the noncommutability of the products used to generate the variation measurements. We have used an alternate approach to derive HbA1c variation, using serial patient data. METHODS: HbA1c measurements of outpatient blood sample pairs drawn within 30 days of each other were made on three different immunoassay systems: the Roche INTEGRA 700, the Roche INTEGRA 400, and the Dade Dimension RxL; and two high-performance liquid chromatography assays: the Tosoh G7 and the Tosoh 2.2+. The standard deviation of duplicates was calculated for the following time intervals: 1 to 3 days, 4 to 6 days, 7 to 9 days,.., 28 to 30 days. These intra-individual variations were then plotted; extrapolation to time zero yields the long term total random error which consists of both analytic and pre-analytic error. Data collection periods were usually 2 years. RESULTS: At the mean HbA1cs of 7.08%, 7.14%, 7.20%, 6.96%, and 7.51% for populations tested on the Roche INTEGRA 700, Roche INTEGRA 400, Dade Dimension RxL, Tosoh 2.2+, and Tosoh G7, respectively, the total analytic imprecisions (coefficient of variation) were 2.56%, 2.29%, 2.25%, 1.66%, and 1.14%, respectively. CONCLUSION: Assessment of the HbA1c long term total imprecisions shows that while the three immunoassay systems are acceptable, the Tosoh HbA1c analyzers demonstrate superior analytic performance.

Application of 3-D Delta check graphs to HbA1c quality control and HbA1c utilization.

Delta checking is a laboratory information system (LIS)-based tool that detects patient and laboratory quality control errors. By using hemoglobin A1c (HbA1c) data, we developed a novel approach to summarizing and presenting patient Delta values to address limitations of current Delta check algorithms. Delta values were calculated from intrapatient pairs of HbA1c (n = 55,327) measured during 2 years in a single referral or a university hospital laboratory. Three-dimensional Delta-time (DeltaT) and percentile limit graphs were constructed. Cumulative distribution function analysis was used to explore clinical utilization. The DeltaT graphs showed that HbA1c Delta values increase asymmetrically over time. Although the 2.5 to 97.5 and 5.0 to 95.0 percentile Delta check limits were similar for both sites, the referral laboratory's 0.5 to 99.5 percentile limits were wider. For acute patient care environments, we recommend limits of -3.5% and 1.8% for measurements between 0 and 60 days and -4.0% and 2.0% for measurements between 60 and 120 days. For the outpatient environment, we recommend limits of -4.2% and 2.1% and 5.0% and 2.5% for measurements between 0 and 60 days and 60 and 120 days, respectively.Delta checking can be significantly improved with customization of limits set by population and interobservation period. Because LIS systems are incapable of these customizations, customers must become advocates for these modifications.

Unique approach to derivation of random error in laboratory assays: application to glycohemoglobin testing demonstrates poor clinical performance for immunochemistry assay.

The measurement of glycohemoglobin is the best measure of mean glucose within a 3-4 month range. As it is used for patient education, counseling, feedback control, and ultimately for patient motivation, its measurement should be optimally accurate and precise. Duplicate hemoglobin A1c readings were used to determine physiological (changes over time between measurements) and analytic variation of two widely used laboratory assays: Bio-Rad Variant II's high-performance liquid chromatography (HPLC) system and Roche's immunoassay. The average variation of grouped duplicates was calculated and graphed against corresponding time intervals. Regression to the y-intercept (0 day separation between readings) was used to determine the analytic variation. Analytic coefficients of variation (CVs) for the HPLC and immunoassay were determined as 2.6% and 5.1%, respectively. The CV of the immunoassay method exceeds physiologically established limits of 2-3% and those of the National Glycohemoglobin Standardization Program (3-4%). The Bio-Rad HPLC system produces a CV within these limits.