The relationship between measurement uncertainty and reporting interval

Background Measurement uncertainty (MU) estimates can be used by clinicians in result interpretation for diagnosis and monitoring and by laboratories in assessing assay fitness for use and analytical troubleshooting. However, MU is not routinely used to assess the appropriateness of the analyte reporting interval. We describe the relationship between MU and the analyte reporting interval. Methods and results The reporting interval R is the smallest unit of measurement chosen for clinical reporting. When choosing the appropriate value for R, it is necessary that the reference change values and expanded MU values can be meaningfully calculated. Expanded MU provides the tighter criterion for defining an upper limit for R. This limit can be determined as R ≤  k·SDa/1.9, where SDa is the analytical standard deviation and k is the coverage factor (usually 2). Conclusion Using MU estimates to determine the reporting interval for quantitative laboratory results ensures that reporting practices match local analytical performance and recognizes the inherent error of the measurement process. </jats:sec

Reference interval studies:What is the maximum number of samples recommended?

Abstract Background: Little attention has been paid to the maximum number of specimens for reference interval calculation, i.e., the number of specimens beyond which there is no further benefit in reference interval calculation. We present a model for the estimation of the maximum number of specimens for reference interval studies based on setting the 90% confidence interval of the reference limits to be equal to the analyte reporting interval. Methods: Equations describing the bounds on the upper and lower 90% confidence intervals for logarithmically transformed and untransformed data were derived and applied to determine the maximum number of specimens required to calculate a reference interval for 12 common chemistry and hematology analytes. Results: Maximum sample sizes ranged from 126 to 18,171 and depended on the standard deviation of the population, any transformation involved and on the chosen reporting interval. Conclusions: This paper demonstrates the importance of the influence of reporting interval on reference intervals. Using this technique can reduce the cost of determining a reference interval by identifying the maximum number of specimens required.</jats:p

Current methods of haemolysis detection and reporting as a source of risk to patient safety : a narrative review

Aim: Haemolysis has a major impact on patient safety as the need for a replacement specimen increases the risk of injury and infection, delays test results and extends the duration of hospital stays. Consistency of haemolysis detection and reporting can facilitate the generation of benchmark data used to develop quality practices to monitor and reduce this leading cause of pre-analytical laboratory error. This review aims to investigate current methods of haemolysis detection and reporting. Method: Due to known heterogeneity and immaturity of the research field, a scoping search was conducted using PUBMED, Embase, Medline and CINAHL. Articles published between 2000 and 2014 that reported haemolysis rates in specimens from the general population were included. Results: Of the 50 studies that met the inclusion criteria, 20 detected haemolysis using the Haemolysis Index (HI), 19 by visual inspection and 13 by undefined methods. There was large intra-study variation in the plasma free haemoglobin level used to establish haemolysis (HI: mean±SD 846±795 mg/L, range 150-3000 mg/L; Visual: 850±436 mg/L, 500-3000 mg/L). Sixteen studies reported the analyte of interest, with only three studies reporting a haemoglobin level at which the specimen would be rejected. Conclusion: Despite haemolysis being a frequent and costly problem with a negative impact on patient care, there is poor consistency in haemolysis detection and reporting between studies. Improved consistency would facilitate the generation of benchmark data used to create quality practices to monitor and reduce this leading cause of pre-analytical laboratory error.9 page(s