George S. Cembrowski

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.

Abstract Background: Most estimates of biologic variation (sb) 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 sb. 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 sb and short-term analytic variation (sa). Results: Substitution of experimentally derived analytic error permitted the calculation of coefficient of variation (biologic) (CVb) (100 sb/mean): pH, 0.3%; pCO2, 5.7%; pO2, 13%; Na+, 0.6%; K+, 4.8%; Cl–, 0.8%; HCO3–, 3.2%; iCa++, 2.4%; and glucose, 10.3%. The CVb 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. Clin Chem Lab Med 2010;48:1447–54.

Could Susceptibility to Low Hematocrit Interference Have Compromised the Results of the NICE-SUGAR Trial?

The recently published findings of the Normoglycemia in Intensive Care Evaluation and Survival Using Glucose Algorithm Regulation (NICE-SUGAR)1 trial have dramatically changed clinician attitudes toward the achievement of euglycemia in intensive care unit (ICU) patients (1). In defending the proof-of-concept studies that validated the efficacy of normalizing blood glucose in the ICU, Van den Berghe et al. pointed out numerous variances between their original studies and those of the NICE-SUGAR trial(2). They included differences in blood glucose targets, insulin administration, blood sampling, nutritional strategies, clinician expertise, and the relative accuracy of the glucose measurement devices. Recently, Clinical Chemistry presented a very interesting Q&A on the use of blood glucose meters to achieve tight glucose control in patients in the ICU(3). Because one of our ICUs participated in the NICE-SUGAR trial, we report here some interesting and relevant data that shed more light on the NICE-SUGAR trial, data that yield more questions than answers. In our 30-bed general systems ICU at the University of Alberta Hospital, point-of-care glucose concentrations can be measured in 2 different ways: respiratory therapists measure arterial blood gases, hemoglobin, electrolytes, and glucose values with the Radiometer 800 blood gas system (BGA) and nurses measure arterial blood and …

Plasma fibronectin concentration in patients with acquired consumptive coagulopathies.

Plasma fibronectin was assayed in 179 hospitalized patients referred for workup of possible acquired coagulopathy. Based on laboratory results and chart review, these patients were classified as having no coagulopathy (N = 36), defibrination syndrome (N = 31), compensated defibrination syndrome (N = 100), microangiopathic thrombocytopenia (N = 7), and primary fibrinolysis (N = 5). Compared to patients with no coagulopathy, fibronectin concentration was reduced in patients with defibrination syndrome (p less than 0.005) and compensated defibrination syndrome (p less than 0.10). Fibronectin concentration was not reduced in patients with microangiopathic thrombocytopenia and primary fibrinolysis. In patients with defibrination syndrome, the reduction of fibronectin was correlated to the degree of liver impairment. This finding is consistent with the liver being the primary site of synthesis of plasma fibronectin. Fibronectin was significantly correlated to plasminogen and antithrombin III. The sensitivity of fibronectin for the diagnosis of coagulopathy is low except for patients with defibrination syndrome.

Performance Characteristics of Several Rules for Self-interpretation of Proficiency Testing Data

CONTEXT Proficiency testing (PT) participants can interpret their results to detect errors even when their performance is acceptable according to the limits set by the PT provider. OBJECTIVE To determine which rules for interpreting PT data provide optimal performance for PT with 5 samples per event. DESIGN We used Monte Carlo computer simulation techniques to study the performance of several rules, relating their error detection capabilities to (1) the analytic quality of the method, (2) the probability of failing PT, and (3) the ratio of the peer group SD to the mean intralaboratory SD. Analytic quality is indicated by the ratio of the PT allowable error to the intralaboratory SD. Failure of PT was defined (Clinical Laboratory Improvement Amendments of 1988) as an event when 2 or more results out of 5 exceeded acceptable limits. We investigated rules with limits based on the SD index, the mean SD index, and percentages of allowable error. RESULTS No single rule performs optimally across the range of method quality. CONCLUSIONS We recommend further investigation when PT data cause rejection by any of the following 3 rules: any result exceeds 75% of allowable error, the difference between any 2 results exceeds 4 times the peer group SD, or the mean SD index of all 5 results exceeds 1.5. As method quality increases from marginal to high, false rejections range from 16% to nearly zero, and the probability of detecting a shift equal to 2 times the intralaboratory SD ranges from 94% to 69%.

Towards more complete specifications for acceptable analytical performance - a plea for error grid analysis.

Abstract We examine limitations of common analytical performance specifications for quantitative assays. Specifications can be either clinical or regulatory. Problems with current specifications include specifying limits for only 95% of the results, having only one set of limits that demarcate no harm from minor harm, using incomplete models for total error, not accounting for the potential of user error, and not supplying sufficient protocol requirements. Error grids are recommended to address these problems as error grids account for 100% of the data and stratify errors into different severity categories. Total error estimation from a method comparison can be used to estimate the inner region of an error grid, but the outer region needs to be addressed using risk management techniques. The risk management steps, foreign to many in laboratory medicine, are outlined.

Tighter precision target required for lactate testing in patients with lactic acidosis

Abstract Background: Allowable analytic errors are generally based on biologic variation in normal, healthy subjects. Some analytes like blood lactate have low concentrations in healthy individuals and resultant allowable variation is large when expressed as a coefficient of variation (CV). In Ricós’ compendium of biologic variation, the relative pooled intra-individual lactate variation (si) averages 27% and the desirable imprecision becomes 13.5%. We derived biologic variability (sb) from consecutive patient data and demonstrate that sb of lactate is significantly lower. Methods: A data repository provided lactate results measured over 18 months in the General Systems intensive care unit (ICU) at the University of Alberta Hospital in Edmonton, Canada. In total 54,000 lactate measurements were made on two point-of-care Radiometer 800 blood gas systems operated by Respiratory Therapy. The standard deviations of duplicates (SDD) were tabulated for the intra-patient lactates that were separated by 0–1, 1–2…up to 16 h. The graphs of SDD vs. time interval were approximately linear; the y-intercept provided by the linear regression represents the sum of sb and short-term analytic variation (sa):y0=(sa2+s)b212. \(({{\rm{s}}_{\rm{a}}}):{{\rm{y}}_{\rm{0}}} = {({\rm{s}}_{\rm{a}}^{\rm{2}} + {\rm{s}}{}_{\rm{b}}^{\rm{2}})^{{1 \over 2}}}.\) The short-term sa was determined from imprecisions provided by Radiometer and confirmed with onsite controls. The derivation of sb was performed for multiple patient ranges of lactate. Results: The relative desirable lactate imprecision for patients with lactic acidosis is about half that of normal individuals. Conclusions: As such, evaluations of lactate measurements must use tighter allowable error limits.

POLAC, A problem oriented language for analytical chemistry

Abstract POLAC, Problem Oriented Language for Analytical Chemistry, is a special purpose, user-oriented language specifically designed to control automated equipment for routine and research-oriented analytical determinations. POLAC has been designed so that laboratory personnel, totally unfamiliar with computers and their languages, can write meaningful control and data acquisition programs after short familiarization. The POLAC system was originally used for spectrophotometric endpoint and kinetic analyses in the clinical laboratory. This system has also been applied to the automation of radioimmunoassay. Modular system design readily permits the incorporation of new measurement transducers, e.g. pH and other ion-selective electrodes, voltammetric electrodes, and fluorimeters. A specially built interface facilitates the control of external equipment. POLAC differs from most interpretive languages in that it first translates (assembles) the instructions into a compact binary representation. Program execution is an instruction-by-instruction interpretation of this representation.

Within-individual mean corpuscular volume variation.

To the Editor.—We read with interest ‘‘Biological Variations of Hematologic Parameters Determined by UniCel DxH 800 Hematology Analyzer.’’ Most of the estimates of biologic variation of the common analytes reported by Zhang et al were close to previously reported literature values. We are puzzled, however, about the broad intraindividual variation for mean corpuscular volume (MCV; 1.12%). Although compilations of biologic variation published before 2000 indicated intraindividual MCV coefficients of variation (CVs) could exceed 1%, 2 recent studies determined the CV was much lower, either 0.18% or 0.34%. As a result, the Zhang et al reference change values for MCV are much higher than our intuitive reference change values (perhaps, 2 fL) when we examine sequential patient MCVs measured by analyzers that are much older than the analyzer used by Zhang et al. The larger intraindividual MCV CVs reported before 2000 were likely associated with the use of more-imprecise instrumentation. Today, our analytic variations in MCV measurements are miniscule. Reasons for a tripling or quadrupling of the estimated intraindividual CV might include the incorporation of some outlying data, analyzer noise, or even some unusual individual-specific variation. We are wondering if Zhang et al would reexamine their MCV data and correlate any available quality control results with those data. Could this variation have been due to some sporadic instrument or individual source?