Bayes’ theorem, the ROC diagram and reference values: Definition and use in clinical diagnosis

Medicine is diagnosis, treatment and care. To diagnose is to consider the probability of the cause of discomfort experienced by the patient. The physician may face many options and all decisions are liable to uncertainty to some extent. The rational action is to perform selected tests and thereby increase the pre-test probability to reach a superior post-test probability of a particular option. To draw the right conclusions from a test, certain background information about the performance of the test is necessary. We set up a partially artificial dataset with measured results obtained from the laboratory information system and simulated diagnosis attached. The dataset is used to explore the use of contingency tables with a unique graphic design and software to establish and compare ROC graphs. The loss of information in the ROC curve is compensated by a cumulative data analysis (CDA) plot linked to a display of the efficiency and predictive values. A standard for the contingency table is suggested and the use of dynamic reference intervals discussed

Resolution of Students t-tests, ANOVA and analysis of variance components from intermediary data

Significance testing in comparisons is based on Student’s t-tests for pairs and analysis of variance (ANOVA) for simultaneous comparison of several procedures. Access to the average, standard deviation and number of observations is sufficient for calculating the significance of differences using the Student’s tests and the ANOVA. Once an ANOVA has been calculated, analysis of variance components from summary data becomes possible. Simple calculations based on summary data provide inference on significance testing. Examples are given from laboratory management and method comparisons. It is emphasized that the usual criteria of the underlying distribution of the raw data must be fulfilled

Analytical robustness of nine common assays: frequency of outliers and extreme differences identified by a large number of duplicate measurements

Introduction: Duplicate measurements can be used to describe the performance and analytical robustness of assays and to identify outliers. We performed about 235,000 duplicate measurements of nine routinely measured quantities and evaluated the observed differences between the replicates to develop new markers for analytical performance and robustness. Materials and methods: Catalytic activity concentrations of aspartate aminotransferase (AST), alanine aminotransferase (ALT), and concentrations of calcium, cholesterol, creatinine, C-reactive protein (CRP), lactate, triglycerides and thyroid-stimulating hormone (TSH) in 237,261 patient plasma samples were measured in replicates using routine methods. The performance of duplicate measurements was evaluated in scatterplots with a variable and symmetrical zone of acceptance (A-zone) around the equal line. Two quality markers were established: 1) AZ95: the width of an A-zone at which 95 % of all duplicate measurements were within this zone; and 2) OPM (outliers per mille): the relative number of outliers if an A-zone width of 5 % was applied. Results: The AZ95 ranges from 3.2% for calcium to 11.5% for CRP and the OPM from 5 (calcium) to 250 (creatinine). Calcium, TSH and cholesterol have an AZ95 of less than 5% and an OPM of less than 50. Conclusions: Duplicate measurements of a large number of patient samples identify even low frequencies of extreme differences and thereof defined outliers. We suggest two additional quality markers, AZ95 and OPM, to complement description of assay performance and robustness. This approach can aid the selection process of measurement procedures in view of clinical needs

Preanalytical Procedures in the Measurement of lonized Calcium in Serum and Plasma

Peer Reviewe

Repeatability imprecision from analysis of duplicates of patient samples and control materials

Measurement imprecision is usually calculated from measurement results of the same stabilized control material(s) obtained over time, and is therefore, principally, only valid at the concentration(s) of the selected control material(s). The resulting uncertainty has been obtained under reproducibility conditions and corresponds to the conventional analytical goals. Furthermore, the commutability of the control materials used determines whether the imprecision calculated from the control materials reflects the imprecision of measuring patient samples. Imprecision estimated by measurements of patient samples uses fully commutable samples, freely available in the laboratories. It is commonly performed by calculating the results of routine patient samples measured twice each. Since the duplicates are usually analysed throughout the entire concentration interval of the patient samples processed in the laboratory, the result will be a weighted average of the repeatability imprecision measured in the chosen measurement intervals or throughout the entire interval of concentrations encountered in patient care. In contrast, the uncertainty derived from many measurements of control materials over periods of weeks is usually made under reproducibility conditions. Consequently, the repeatability and reproducibility imprecision play different roles in the inference of results in clinical medicine. The purpose of the present review is to detail the properties of the imprecision calculated by duplicates of natural samples, to explain how it differs from imprecision calculated from single concentrations of control materials, and to elucidate what precautions need to be taken in case of bias, e.g. due to carry-over effects

An experimental study of methods for the analysis of variance components in the inference of laboratory information

Measurement uncertainty (MU) can be estimated and calculated by different procedures, representing different aspects and intended use. It is appropriate to distinguish between uncertainty determined under repeatability and reproducibility conditions, and to distinguish causes of variation using analysis of variance components. The intra-laboratory MU is frequently determined by repeated measurements of control material(s) of one or several concentrations during a prolonged period of time. We demonstrate, based on experimental results, how such results can be used to identify the repeatability, pure reproducibility and intra-laboratory variance as the sum of the two. Native patient material was used to establish repeatability using the Dahlberg formula for random differences between measurements and an expanded Dahlberg formula if a non-random difference, e.g. bias, was expected. Repeatability and reproducibility have different clinical relevance in intensive care compared to monitoring treatment of chronic diseases, comparison with reference intervals or screening.Funding Agencies|Karolinska university laboratory; County council of Ostergotland</p

Does eGFR improve the diagnostic capability of S-Creatinine concentration results? A retrospective population based study

The use of MDRD-eGFR to diagnose Chronic Kidney Disease (CKD) is based on the assumption that the algorithm will minimize the influence of age, gender and ethnicity that is observed in S-Creatinine concentration and thus allow a single cut-off at which further diagnostic and therapeutic actions should be considered. This hypothesis is tested in a retrospective analysis of outpatients (N=93,404) and hospitalised (N=35,572) patients in UK and Sweden, respectively. An algorithm based on the same model as the MDRD-eGFR algorithm was derived from simultaneously measured S-Creatinine concentrations and Iohexol GFR in a subset of 565 patients. The combined uncertainty of using this algorithm was estimated to about 15 % which is about three times that of the S-Creatinine concentration results. The diagnostic performance of S-Creatinine concentration was evaluated using the Iohexol clearance as the reference procedure. It was shown that the diagnostic capacity of MDRD-eGFR, as it stands, has no added value compared to S-Creatinine. The gender and age differences of the S-Creatinine concentrations in the dataset persist after applying the MDRD-eGFR algorithm. Thus, a general use of the MDRD-eGFR does not seem justified. Furthermore the claim that the eGFR is adjusted for body area is misleading; the algorithm does not include any body size marker. It is thus a dangerous marker for guiding drug administration.</p