A Machine Learning Model for the Routine Detection of "Wrong Blood in Complete Blood Count Tube" Errors.

Background: Current laboratory procedures may fail to detect wrong blood in tube (WBIT) errors. Machine learning models have the potential to improve WBIT error detection, as demonstrated by proof-of-concept studies. The models developed so far, however, are not appropriate for routine use because they are unable to handle missing values and have low positive predictive value (PPV).

Machine learning models outperform manual result review for the identification of wrong blood in tube errors in complete blood count results.

Introduction: Wrong blood in tube (WBIT) errors are a significant patient-safety issue encountered by clinical laboratories. This study assessed the performance of machine learning models for the identification of WBIT errors affecting complete blood count (CBC) results against the benchmark of manual review of results by laboratory staff.

Methods: De-identified current and previous (within seven days) CBC results were used in the computer simulation of WBIT errors.

Decision support or autonomous artificial intelligence? The case of wrong blood in tube errors.

Objectives: Artificial intelligence (AI) models are increasingly being developed for clinical chemistry applications, however, it is not understood whether human interaction with the models, which may occur once they are implemented, improves or worsens their performance. This study examined the effect of human supervision on an artificial neural network trained to identify wrong blood in tube (WBIT) errors.

Methods: De-identified patient data for current and previous (within seven days) electrolytes, urea and creatinine (EUC) results were used in the computer simulation of WBIT errors at a rate of 50%.

Identifying mislabelled samples: Machine learning models exceed human performance.

Background: It is difficult for clinical laboratories to identify samples that are labelled with the details of an incorrect patient. Many laboratories screen for these errors with delta checks, with final decision-making based on manual review of results by laboratory staff. Machine learning models have been shown to outperform delta checks for identifying these errors.

Indirect Reference Intervals: Harnessing the Power of Stored Laboratory Data.

Reference intervals are relied upon by clinicians when interpreting their patients' test results. Therefore, laboratorians directly contribute to patient care when they report accurate reference intervals. The traditional approach to establishing reference intervals is to perform a study on healthy volunteers.

Parathyroid hormone: Data mining for age-related reference intervals in adults.

Objective: Age-related changes in parathyroid hormone (PTH) have been previously documented in adults. However, because of the limitations of traditional approaches to establishing reference intervals, age-related reference intervals have not been defined. We sought to use a data mining approach to derive age-related PTH reference intervals.

Assessment of vitamin D status - a changing landscape.

In recent years it has been shown that vitamin D deficiency is associated with an increased incidence as well as the progression of a broad range of diseases including osteoporosis, rickets, cardiovascular disease, autoimmune disease, multiple sclerosis and cancer. Consequently, requests for the assessment of vitamin D status have increased dramatically. Despite significant progress in the analysis of vitamin D metabolites and an expansion of our pathophysiological knowledge of vitamin D, the assessment of vitamin D status remains a challenging and partially unresolved issue.

Serum indices: managing assay interference.

Clinical laboratories frequently encounter samples showing significant haemolysis, icterus or lipaemia. Technical advances, utilizing spectrophotometric measurements on automated chemistry analysers, allow rapid and accurate identification of such samples. However, accurate quantification of haemolysis, icterus and lipaemia interference is of limited value if laboratories do not set rational alert limits, based on sound interference testing experiments.

The role of telomeres and vitamin D in cellular aging and age-related diseases.

Aging is a complex biological process characterized by a progressive decline of organ functions leading to an increased risk of age-associated diseases and death. Decades of intensive research have identified a range of molecular and biochemical pathways contributing to aging. However, many aspects regarding the regulation and interplay of these pathways are insufficiently understood.

Impact of assay design on test performance: lessons learned from 25-hydroxyvitamin D.

Background: Current automated immunoassays vary significantly in many aspects of their design. This study sought to establish if the theoretical advantages and disadvantages associated with different design formats of automated 25-hydroxyvitamin D (25-OHD) assays are translated into variations in assay performance in practice.

Methods: 25-OHD was measured in 1236 samples using automated assays from Abbott, DiaSorin, Roche and Siemens.

Determination of vitamin D and its metabolites.

The demand for analysis of 25-hydroxyvitamin D has increased dramatically throughout the world over the past decade. As a consequence, a number of new automated assays have been introduced for 25-hydroxyvitamin D measurement. Automated assays have shown variable ability to meet the technical challenges associated with 25-hydroxyvitamin D measurement.

Red cell or serum folate: what to do in clinical practice?

Folate deficiency has been linked to diverse clinical manifestations and despite the importance of accurate assessment of folate status, the best test for routine use is uncertain. Both serum and red cell folate assays are widely available in clinical laboratories; however, red cell folate is the more time-consuming and costly test. This review sought to evaluate whether the red cell assay demonstrated superior performance characteristics to justify these disadvantages.

State-of-the-art vitamin D assays: a comparison of automated immunoassays with liquid chromatography-tandem mass spectrometry methods.

Background: Vitamin D testing is increasing worldwide. Recently several diagnostic manufacturers including Abbott and Siemens have launched automated 25-hydroxy vitamin D (25OH-D) immunoassays. Furthermore, preexisting assays from DiaSorin and Roche have recently been modified.