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

Changes in error rates in the Australian key incident monitoring and management system program

Introduction: The Key incident monitoring and management system program (KIMMS) program collects data for 19 quality indicators (QIs) from Australian medical laboratories. This paper aims to review the data submitted to see whether the number of errors with a higher risk priority number (RPN) have been reduced in preference to those with a lower RPN, and to calculate the cost of these errors. Materials and methods: Data for QIs from 60 laboratories collected through the KIMMS program from 2015 until 2018 were retrospectively reviewed. The results for each QI were averaged for the four-year average and coefficient of variation. To review the changes in QI frequency, the yearly averages for 2015 and 2018 were compared. By dividing the total RPN by 4 and multiplying that number by the cost of recollection of 30 AUD, it was possible to assign the risk cost of these errors. Results: The analysis showed a drop in the overall frequency of incidents (6.5%), but a larger drop in risk (9.4%) over the period investigated. Recollections per year in Australia cost the healthcare industry 27 million AUD. If the RPN data is used, this cost increases to 66 million AUD per year. Conclusions: Errors with a higher RPN have fallen more than those with lower RPN. The data shows that the errors associated with phlebotomy are the ones that have most improved. Further improvements require a better understanding of the root cause of the errors and to achieve this, work is required in the collection of the data to establish best-practice guidelines

Clinical chemistry in higher dimensions: machine-learning and enhanced prediction from routine clinical chemistry data

Big Data is having an impact on many areas of research, not the least of which is biomedical science. In this review paper, big data and machine learning are defined in terms accessible to the clinical chemistry community. Seven myths associated with machine learning and big data are then presented, with the aim of managing expectation of machine learning amongst clinical chemists. The myths are illustrated with four examples investigating the relationship between biomarkers in liver function tests, enhanced laboratory prediction of hepatitis virus infection, the relationship between bilirubin and white cell count, and the relationship between red cell distribution width and laboratory prediction of anaemia.This work was supported by the Quality Use of Pathology Programme (QUPP), The Commonwealth Department of Health

Immediate and delayed hypersensitivity reactions to a single dose of oxaliplatin

Hypersensitivity reaction (HSR), occurring during or immediately after oxaliplatin infusions are well documented and occur more commonly after multiple courses of therapy.A 69-year-old white woman with Stage Ill colon cancer commenced adjuvant chemotherapy with oxaliplatin and capecitabine. Ten minutes after the completion of the oxaliplatin infusion she experienced a severe hypersensitivity reaction. Symptoms resolved after treatment With corticosteroids, antihistamines, and bronchodilators. Nineteen hours later, a similar reaction Occurred.A review of the literature found 2 similar cases of delayed reactions to oxaliplatin occurring 20 and 10 hours after infusion, respectively. The first case occurred after the initial dose and again with cycle 2. The second case happened after the sixth infusion.This is the first reported case, to our knowledge, of immediate and delayed hypersensitivity reactions occurring after the initial dose of oxaliplatin

Patient-based quality control for glucometers: using the moving sum of positive patient results and moving average

Introduction: The capability of glucometer internal quality control (QC) in detecting varying magnitude of systematic error (bias), and the potential use of moving sum of positive results (MovSum) and moving average (MA) techniques as potential alternatives were evaluated. Materials and methods: The probability of error detection using routine QC and manufacturer’s control limits were investigated using historical data. Moving sum of positive results and MA algorithms were developed and optimized before being evaluated through numerical simulation for false positive rate and probability of error detection. Results: When the manufacturer’s default control limits (that are multiple times higher than the running standard deviation (SD) of the glucometer) was used, they had 0-75% probability of detecting small errors up to 0.8 mmol/L. However, the error detection capability improved to 20-100% when the running SD of the glucometer was used. At a binarization threshold of 6.2 mmol/L and block sizes of 200 to 400, MovSum has a 100% probability of detecting a bias that is greater than 0.5 mmol/L. Compared to MovSum, the MA technique had lower probability of bias detection, especially for smaller bias magnitudes; MA also had higher false positive rates. Conclusions: The MovSum technique is suited for detecting small, but clinically significant biases. Point of care QC should follow conventional practice by setting the control limits according to the running mean and SD to allow proper error detection. The glucometer manufacturers have an active role to play in liberalizing QC settings and also enhancing the middleware to facility patient-based QC practices

Impact of combining data from multiple instruments on performance of patient-based real-time quality control

It is unclear what is the best strategy for applying patient-based real-time quality control (PBRTQC) algorithm in the presence of multiple instruments. This simulation study compared the error detection capability of applying PBRTQC algorithms for instruments individually and in combination using serum sodium as an example. Four sets of random serum sodium measurements were generated with differing means and standard deviations to represent four simulated instruments. Moving median with winsorization was selected as the PBRTQC algorithm. The PBRTQC parameters (block size and control limits) were optimized and applied to the four simulated laboratory data sets individually and in combination. When the PBRTQC algorithm were individually optimized and applied to the data of the individual simulated instruments, it was able to detect bias several folds faster than when they were combined. Similarly, the individually applied algorithms had perfect error detection rates across different magnitudes of bias, whereas the error detection rates of the algorithm applied on the combined data missed smaller biases. The performance of the individually applied PBRTQC algorithm performed more consistently among the simulated instruments compared to when the data were combined. While combining data from different instruments can increase the data stream and hence, increase the speed of error detection, it may widen the control limits and compromising the probability of error detection. The presence of multiple instruments in the data stream may dilute the effect of the error when it only affects a selected instrument

Informative observation in health data: Association of past level and trend with time to next measurement

In routine health data, risk factors and biomarkers are typically measured irregularly in time, with the frequency of their measurement depending on a range of factors – for example, sicker patients are measured more often. This is termed informative observation. Failure to account for this in subsequent modelling can lead to bias. Here, we illustrate this issue using body mass index measurements taken on patients with type 2 diabetes in Salford, UK. We modelled the observation process (time to next measurement) as a recurrent event Cox model, and studied whether previous measurements in BMI, and trends in the BMI, were associated with changes in the frequency of measurement. Interestingly, we found that increasing BMI led to a lower propensity for future measurements. More broadly, this illustrates the need and opportunity to develop and apply models that account for, and exploit, informative observation

Type 2 diabetes: a cohort study of treatment, ethnic and social group influences on glycated haemoglobin

OBJECTIVES: To assess whether in people with poorly controlled type 2 diabetes (HbA1c>7.5%) improvement in HbA1c varies by ethnic and social group. DESIGN: Prospective 2-year cohort of type 2 diabetes treated in general practice. SETTING AND PARTICIPANTS: All patients with type 2 diabetes in 100 of the 101 general practices in two London boroughs. The sample consisted of an ethnically diverse group with uncontrolled type 2 diabetes aged 37–71 years in 2007 and with HbA1c recording in 2008–2009. OUTCOME MEASURE: Change from baseline HbA1c in 2007 and achievement of HbA1c control in 2008 and 2009 were estimated for each ethnic, social and treatment group using multilevel modelling. RESULTS: The sample consisted of 6104 people; 18% were white, 63% south Asian, 16% black African/Caribbean and 3% other ethnic groups. HbA1c was lower after 1 and 2 years in all ethnic groups but south Asian people received significantly less benefit from each diabetes treatment. After adjustment, south Asian people were found to have 0.14% less reduction in HbA1c compared to white people (95% CI 0.04% to 0.24%) and white people were 1.6 (95% CI 1.2 to 2.0) times more likely to achieve HbA1c controlled to 7.5% or less relative to south Asian people. HbA1c reduction and control in black African/Caribbean and white people did not differ significantly. There was no evidence that social deprivation influenced HbA1c reduction or control in this cohort. CONCLUSIONS: In all treatment groups, south Asian people with poorly controlled diabetes are less likely to achieve controlled HbA1c, with less reduction in mean HbA1c than white or black African/Caribbean people

The importance of low level QC for high sensitivity troponin assays

BACKGROUND With the advent of the new high-sensitivity troponin assays, it is becoming critical to measure troponin accurately to low concentrations. To ensure assay performance is acceptable, appropriate QC must be run. METHODS In addition to the routine use of commercial QC material, we prepared pools of human QC material with low troponin concentrations close to the limit of quantitation, and ran these regularly on our laboratory analysers. RESULTS Over 3 years we found no drift or shift in our hs-cTnI assay. We found that only the very low concentration human QC material gave warning of precision problems with the hs-cTnI assay. At the time of the documented poor assay precision, the higher concentration QC material indicated satisfactory performance. CONCLUSIONS Choice of QC material with an appropriate concentration is important for any assay. For hs-cTn assays, it is of particular importance to use control material with a concentration near to the limit of quantitation

Best Practice Pathology Collection in Australia

Objectives: The specific objectives of the study were to (a) identify current best practice in pathology specimen collection and assess the extent to which Australian pathology services currently satisfy best practice standards; and (b) identify training and other strategies that would mitigate any gaps between current and best practice. Methods: A total of 22 case studies were undertaken with pathology collector employers from public, not for profit and private pathology organisations andacross urban and rural locations and eight focus groups with pathology collection services consumers were conducted in December 2012 in four different cities. Results: The preferred minimum qualification of the majority of case study employers for pathology collectors is the nationally recognised Certificate III in Pathology. This qualification maps well to an accepted international best practice guideline for pathology collection competency standards but has some noted deficiencies identified which need to be rectified. These particularly include competencies related to communicating with consumers. The preferred way of training for this qualification is largely through structured and supervised on the job learning&nbsp;experiences supported by theoretical classroom instruction delivered in-house or in off the job settings. The study found a need to ensure a greater proportion of the pathology collection workforce is appropriately qualified. Conclusion: The most effective pathway to best practice pathology collection requires strong policies that define how pathology samples are to be collected, stored and transported and a pathology collection workforce that is competent and presents to consumers with a credible qualification and in a professional manner. Abbreviations: CHF – Consumer Health Forum of Australia; KIMMS – Key Incident Monitoring and Management Systems; NAACLS – National Accrediting Agency for Clinical Laboratory Sciences; NACCHO – National Aboriginal Community Controlled Health Organisation; NPAAC – National Pathology Accreditation Advisory Council; RCPA – Royal College of Pathology Australasia; RTO – Registered Training Organisation