Biomedical subjects
Publications and source records attributed to A F Krieg.
During the past 20 years, laboratory systems have evolved to a high degree of complexity. In some cases, continuing addition of new features has adversely affected functionality. There is increasing recognition that the number of features listed on a request for proposal does not necessarily provide an accurate guide to functionality in day to day operations.
Complete evaluations of test performance require data on many test results over many clinical states, not restricted to the traditional 2 X 2 table of two possible test results and two possible clinical states. Published reports on test performance often include dot diagrams, depicting many test results over many clinical states. From such dot diagrams, several methods may be used to obtain numerical data for quantitative evaluations of test performance. Dot diagrams, long used to depict multiple test results over multiple clinical states, can serve as source documents for quantitative evaluations of test performance.
We suggest that evaluations of diagnostic tests start with dot plots that depict multiple test results over multiple clinical states. From this starting point we can calculate posttest probabilities for multiple clinical states at multiple test results. Also, we can project one subset of clinical states as "disease positive" and a second subset as "disease negative" to provide standard analyses such as likelihood ratios, relative operating characteristic curves, posttest/pretest probability plots, sensitivity, specificity, and predictive value. Finally, this starting point provides an excellent basis for comparing multiple studies of diagnostic performance. The advantages of dot plots are illustrated with data on serum ferritin levels over multiple clinical states.
We present a general spreadsheet model for evaluating diagnostic performance of clinical tests. Our model depicts test results as an r X c matrix, with r possible test results and c possible clinical states. Analysis of this matrix is based on the Ri/Cj ratio, calculated as a number of subjects having a specified result Ri within a given clinical state Cj, divided by total subjects within this clinical state. From this model, we can identify three special cases: (1) a 2 X c matrix, with two possible test results of T+ or T-, over c possible clinical states; (2) an r X 2 matrix, with r possible test results, over two possible clinical states of D+ or D-; and (3) a 2 X 2 matrix, with two possible test results over two possible clinical states. Application of the Ri/Cj ratio to the r X c matrix provides a useful approach to graphic analysis of multiple test results over multiple clinical states. The Ri/Cj ratio also provides a general approach to Bayesian analysis, in which likelihood ratio, relative operating characteristic analysis, sensitivity, and specificity represent special cases or special applications.
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The question "When is a diagnostic test result positive?" can be addressed by clinical decision analysis. We developed two simple decision tree models for selecting appropriate cutoff levels: a net utility model and a threshold model. These models have been incorporated in a software program for desktop computers. We believe it is important for investigators to provide raw data on test performance, for three reasons. First, these data can be used in simple decision tree models to identify "appropriate" cutoff levels. Second, they can be used to evaluate empiric cutoff levels or decision rules. Third, they can be used to evaluate optimal cutoff levels for detailed decision trees depicting specific clinical problems.
We developed a microcomputer program that provides a Bayesian model of diagnostic performance and a simple decision tree model of clinical utility. We have used this program to review diagnostic performance and clinical utility for proposed new services at our 360-bed university hospital. We believe that significant benefits can be achieved if medical journals report complete data on test performance. First, this allows physicians to perform their own evaluations of diagnostic performance. Second, this allows physicians to evaluate clinical utility using either standard decision trees or decision trees that reflect specific clinical problems.
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1. The EMIT-AED procedures for phenobarbital and diphenylhydantoin have been adapted for use on the ABA-100. 2. A basic language computer program has been developed for data reduction and quality control. 3. Within-run and run-to-run coefficients of variations were, in general, less than 8% across the concentration range tested. 4. Performance data on agreement of duplicates and recovery from "spiked" samples were aceptable. 5. The automated procedure provides a two-thirds savings in reagents.
The authors have developed a system that provides information about costs related to specific laboratory tests. These costs are derived from: (1) labor and material actually used for specific tests; (2) allocation of costs for labor and material shared by several different tests; (3) allocation of supervisory and management costs. The "cost" of a specific procedure is a theoretical construct that is significantly affected by the method chosen for allocation. We know of no cost-analysis system generally accepted by all clinical laboratories. Since third-party payers may base future reimbursement on costs rather than charges, accurate systems for clinical laboratory cost analysis may have some practical value.
A mathematical model depicting operation of a blood-gas workstation was developed by two systems analysts working closely with two clinical pathologists. This model was used to provide estimates of average as well as maximum turnaround times under various conditions of workload, specimen types (capillary vs. syringe), methodology (use of IL 513 vs. IL 313 for capillary samples), and reporting procedures (report each sample as analyzed vs. report after analysis of all samples in batch). These estimates have been validated against actual experience in our laboratory. Such an objective mathematical model can be used to plan optimal service.
A new thyroid hormone index, based on measurements of thyroxine (T4), triiodothyronine (T3) and resin uptake (RT3U), is proposed. In contrast to previous thyroid hormone indices, it incorporates both T3 and T4 measurements. This combined thyroid hormone index appears to have superior predictive value in terms of clinical correlation with thyroid disease.
This article describes a simple method for summarizing total activity from the worksheets used at each workstation in a clinical laboratory. Monthly reports display day-to-day trends in total workload, as well as changes in the ratio between charged and total tests. Cumulative summaries of the monthly reports display month-to-month trends of total workload, as well as the ratio between charged and total tests. These reports also provide: (1) a simple method to calculate "lost revenue" (difference between total "chargeable" work and credited revenue); (2) more rapid and more uniform calculation of CAP workload units. In the author's laboratory, time required to maintain this data base and calculate CAP workload units has been less than the time previously required for calculation of CAP workload units alone.
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An improved micromethod for plasma fibrinogen, based on the Ellis-Stransky thrombin-clot density procedure, is presented. Modifications includes: (1) addition of Polybrene eliminates heparin interference; (2) measurement of absorbance at 340 nm provides improved sensitivity at low fibrinogen levels as well as with small volumes of plasma (40 mul.); (3) higher concentration of thrombin reduces the incubation time. Plasma fibrinogen by the proposed method compares well with a reference procedure based on total clottable protein (coefficient of correlation 0.99).
We describe a single-reagent, enzymatic procedure for triglycerides in serum, which may be performed in 5 min per sample with use of an Abbott ABA-100 analyzer system. This procedure is compared to the semi-automated enzymatic procedure with the ABA-100, which requires 30 min per sample.