Prediction interval: A powerful statistical tool for monitoring patients and analytical systems

Graphical abstract Highlights • Prediction interval has a great potential to be used in laboratory medicine • It is a powerful tool for computing personalized reference interval and reference change value • It can be used to assess the stability of analytical systems • It can be used in monitoring the accuracy and reproducibility of analytical systems Monitoring is indispensable for assessing disease prognosis and evaluating the effectiveness of treatment strategies, both of which rely on serial measurements of patients’ data. It also plays a critical role in maintaining the stability of analytical systems, which is achieved through serial measurements of quality control samples. Accurate monitoring can be achieved through data collection, following a strict preanalytical and analytical protocol, and the application of a suitable statistical method. In a stable process, future observations can be predicted based on historical data collected during periods when the process was deemed reliable. This can be evaluated using the statistical prediction interval. Statistically, prediction interval gives an “interval” based on historical data where future measurement results can be located with a specified probability such as 95%. Prediction interval consists of two primary components: (i) the set point and (ii) the total variation around the set point which determines the upper and lower limits of the interval. Both can be calculated using the repeated measurement results obtained from the process during its steady-state. In this paper, (i) the theoretical bases of prediction intervals were outlined, and (ii) its practical application was explained through examples, aiming to facilitate the implementation of prediction intervals in laboratory medicine routine practice, as a robust tool for monitoring patients’ data and analytical systems

Six Sigma revisited: We need evidence to include a 1.5 SD shift in the extraanalytical phase of the total testing process

The Six Sigma methodology has been widely implemented in industry, healthcare, and laboratory medicine since the mid-1980s. The performance of a process is evaluated by the sigma metric (SM), and 6 sigma represents world class performance, which implies that only 3.4 or less defects (or errors) per million opportunities (DPMO) are expected to occur. However, statistically, 6 sigma corresponds to 0.002 DPMO rather than 3.4 DPMO. The reason for this difference is the introduction of a 1.5 standard deviation (SD) shift to account for the random variation of the process around its target. In contrast, a 1.5 SD shift should be taken into account for normally distributed data, such as the analytical phase of the total testing process; in practice, this shift has been included in all type of calculations related to SM including non-normally distributed data. This causes great deviation of the SM from the actual level. To ensure that the SM value accurately reflects process performance, we concluded that a 1.5 SD shift should be used where it is necessary and formally appropriate. Additionally, 1.5 SD shift should not be considered as a constant parameter automatically included in all calculations related to SM

Statistical distributions commonly used in measurement uncertainty in laboratory medicine

Uncertainty is an inseparable part of all types of measurement. Recently, the International Organization for Standardization (ISO) released a new standard (ISO 20914) on how to calculate measurement uncertainty (MU) in laboratory medicine. This standard can be regarded as the beginning of a new era in laboratory medicine. Measurement uncertainty comprises various components and is used to calculate the total uncertainty. All components must be expressed in standard deviation (SD) and then combined. However, the characteristics of these components are not the same; some are expressed as SD, while others are expressed as a ± b, such as the purity of the reagents. All non-SD variables must be transformed into SD, which requires a detailed knowledge of common statistical distributions used in the calculation of MU. Here, the main statistical distributions used in MU calculation are briefly summarized

Sigma metrics in laboratory medicine revisited: We are on the right road with the wrong map

Reliable procedures are needed to quantify the performance of instruments and methods in order to increase the quality in clinical laboratories. The Sigma metrics serves that purpose, and in the present study, the current methods for the calculation of the Sigma metrics are critically evaluated. Although the conventional model based on permissible (or allowable) total error is widely used, it has been shown to be flawed. An alternative method is proposed based on the within-subject biological variation. This model is conceptually similar to the model used in industry to quantify measurement performance, based on the concept of the number of distinct categories and consistent with the Six Sigma methodology. The quality of data produced in clinical laboratories is expected, however, to be higher than the quality of industrial products. It is concluded that this model is consistent with Six Sigma theory, original Sigma metrics equation and with the nature of patients’ samples. Therefore, it can be used easily to calculate the performance of measurement methods and instruments used in clinical laboratories

Personalized reference intervals - Statistical approaches and considerations

Under embargo until: 2022-12-13For many measurands, physicians depend on population-based reference intervals (popRI), when assessing laboratory test results. The availability of personalized reference intervals (prRI) may provide a means to improve the interpretation of laboratory test results for an individual. prRI can be calculated using estimates of biological and analytical variation and previous test results obtained in a steady-state situation. In this study, we aim to outline statistical approaches and considerations required when establishing and implementing prRI in clinical practice. Data quality assessment, including analysis for outliers and trends, is required prior to using previous test results to estimate the homeostatic set point. To calculate the prRI limits, two different statistical models based on ‘prediction intervals’ can be applied. The first model utilizes estimates of ‘within-person biological variation’ which are based on an individual’s own data. This model requires a minimum of five previous test results to generate the prRI. The second model is based on estimates of ‘within-subject biological variation’, which represents an average estimate for a population and can be found, for most measurands, in the EFLM Biological Variation Database. This model can be applied also when there are lower numbers of previous test results available. The prRI offers physicians the opportunity to improve interpretation of individuals’ test results, though studies are required to demonstrate if using prRI leads to better clinical outcomes. We recommend that both popRIs and prRIs are included in laboratory reports to aid in evaluating laboratory test results in the follow-up of patients.publishedVersio

Sigma metric revisited: True known mistakes

Six Sigma methodology has been used successfully in industry since the mid-1980s. Unfortunately, the same success has not been achieved in laboratory medicine. In this case, although the multidisciplinary structure of laboratory medicine is an important factor, the concept and statistical principles of Six Sigma have not been transferred correctly from industry to laboratory medicine. Furthermore, the performance of instruments and methods used in laboratory medicine is calculated by a modified equation that produces a value lower than the actual level. This causes unnecessary, increasing pressure on manufacturers in the market. We concluded that accurate implementation of the sigma metric in laboratory medicine is essential to protect both manufacturers by calculating the actual performance level of instruments, and patients by calculating the actual error rates

Biological variations of ADAMTS13 and von Willebrand factor in human adults

Background: The ultra-large von Willebrand factor (vWF) multimers are very active and must be degraded by ADAMTS13 for optimal activity. A severe functional deficiency of ADAMTS13 has been associated with thrombotic thrombocytopenic purpura. The correct interpretation of patient vWF and ADAMTS13 plasma levels requires an understanding of the biological variation associated with these analytes. In the present paper, we aimed to determine the biological variation of ADAMTS13 and vWF in human adults. Materials and methods: Blood samples were collected weekly from 19 healthy subjects for 5 consecutive weeks. vWF activity and antigenicity were determined using aggregometric and immunoturbidimetric methods. ADAMTS13 antigenicity and activity were determined by ELISA. Results: The within-subject biological variations for vWF activity and antigenicity were 8.06% and 14.37%, respectively, while the between-subject biological variations were 18.5% and 22.59%, respectively. The index of individuality for vWF activity was 0.44, while vWF antigenicity was 0.64. Similarly, ADAMTS13 activity and antigenicity within-subject biological variations were 12.73% and 9.75%, respectively, while between-subject biological variations were 9.63% and 6.28%, respectively. The ADAMTS13 indexes of individuality were 1.32 and 1.55, respectively. Conclusion: We report high biological variation and individuality in vWF antigenicity and activity levels. However, ADAMTS13 antigenicity and activity displayed high biological variation, but low individuality. Thus, population-based reference intervals may be useful for monitoring ADAMTS13 antigenicity and activity, but not for vWF, which displays high individuality. These findings should be considered when determining the reference interval and other clinical variables associated with ADAMTS13 and vWF levels

The European Biological Variation Study (EuBIVAS):Biological variation data for testosterone, follicle stimulating hormone, prolactin, luteinizing hormone and dehydroepiandrosterone sulfate in men

BACKGROUND: Knowledge of biological variation (BV) of hormones is essential for interpretation of laboratory tests and for diagnostics of endocrinological and reproductive diseases. There is a lack of robust BV data for many hormones in men.METHODS: We used serum samples collected weekly over 10 weeks from the European Biological Variation Study (EuBIVAS) to determine BV of testosterone, follicle-stimulating hormone (FSH), prolactin, luteinizing hormone (LH) and dehydroepiandrosterone sulfate (DHEA-S) in 38 men. We derived within-subject (CVI) and between-subject (CVG) BV estimates by CV-ANOVA after trend, outlier, and homogeneity analysis and calculated reference change values, index of individuality (II), and analytical performance specifications.RESULTS: The CVI estimates were 10 % for testosterone, 8 % for FSH, 13 % for prolactin, 22 % for LH, and 9 % for DHEA-S, respectively. The IIs ranged between 0.14 for FSH to 0.66 for LH, indicating high individuality.CONCLUSIONS: In this study, we have used samples from the highly powered EuBIVAS study to derive BV estimates for testosterone, FSH, prolactin, LH and DHEA-S in men. Our data confirm previously published BV estimates of testosterone, FSH and LH. For prolactin and DHEA-S BV data for men are reported for the first time.</p

The European Biological Variation Study (EuBIVAS):Biological variation data for testosterone, follicle stimulating hormone, prolactin, luteinizing hormone and dehydroepiandrosterone sulfate in men

BACKGROUND: Knowledge of biological variation (BV) of hormones is essential for interpretation of laboratory tests and for diagnostics of endocrinological and reproductive diseases. There is a lack of robust BV data for many hormones in men.METHODS: We used serum samples collected weekly over 10 weeks from the European Biological Variation Study (EuBIVAS) to determine BV of testosterone, follicle-stimulating hormone (FSH), prolactin, luteinizing hormone (LH) and dehydroepiandrosterone sulfate (DHEA-S) in 38 men. We derived within-subject (CVI) and between-subject (CVG) BV estimates by CV-ANOVA after trend, outlier, and homogeneity analysis and calculated reference change values, index of individuality (II), and analytical performance specifications.RESULTS: The CVI estimates were 10 % for testosterone, 8 % for FSH, 13 % for prolactin, 22 % for LH, and 9 % for DHEA-S, respectively. The IIs ranged between 0.14 for FSH to 0.66 for LH, indicating high individuality.CONCLUSIONS: In this study, we have used samples from the highly powered EuBIVAS study to derive BV estimates for testosterone, FSH, prolactin, LH and DHEA-S in men. Our data confirm previously published BV estimates of testosterone, FSH and LH. For prolactin and DHEA-S BV data for men are reported for the first time.</p