Search

Objectives: Quantitative human chorionic gonadotropin (hCG) measurements are used to manage women classified with a pregnancy of unknown location (PUL). Two point of care testing (POCT) devices that quantify hCG are commercially available. We verified the i-STAT 1 (Abbott) and the AQT 90 FLEX (Radiometer) prior to use in PUL triage., Methods: Tests for precision, external quality assurance (EQA), correlation, hook effect and recovery were undertaken alongside a POCT usability assessment during this prospective multi-center verification., Results: Coefficients of variation ranged between 4.0 and 5.1 % for the three i-STAT 1 internal quality control (IQC) solutions and between 6.8 and 7.3 % for the two AQT IQC solutions. Symmetric differences in POCT EQA results when compared with laboratory and EQA stock values ranged between 3.2 and 24.5 % for the i-STAT 1 and between 3.3 and 36.9 % for the AQT. Correlation coefficients (i-STAT 1: 0.96, AQT: 0.99) and goodness of fit curves (i-STAT 1: 0.92, AQT: 0.99) were excellent when using suitable whole blood samples. An hCG hook effect was noted with the i-STAT 1 between 572,194 and 799,089 IU/L, lower than the hook effect noted with the AQT, which was between 799,089 and 1,619,309 IU/L. When hematocrit concentration was considered in sample types validated for use with each device, hCG recovery was 108 % with the i-STAT 1 and 98 % with the AQT. The i-STAT 1 scored lower on usability overall (90/130) than the AQT (121/130, p<0.001, Mann-Whitney)., Conclusions: Both hCG POCT devices were verified for use in clinical practice. Practical factors must also be considered when choosing which device to use in each unit., (© 2023 the author(s), published by De Gruyter, Berlin/Boston.)

Objectives: According to international standards, clinical laboratories are required to verify the performance of assays prior to their implementation in routine practice. This typically involves the assessment of the assay's imprecision and trueness vs. appropriate targets. The analysis of these data is typically performed using frequentist statistical methods and often requires the use of closed source, proprietary software. The motivation for this paper was therefore to develop an open-source, freely available software capable of performing Bayesian analysis of verification data., Methods: The veRification application presented here was developed with the freely available R statistical computing environment, using the Shiny application framework. The codebase is fully open-source and is available as an R package on GitHub., Results: The developed application allows the user to analyze imprecision, trueness against external quality assurance, trueness against reference material, method comparison, and diagnostic performance data within a fully Bayesian framework (with frequentist methods also being available for some analyses)., Conclusions: Bayesian methods can have a steep learning curve and thus the work presented here aims to make Bayesian analyses of clinical laboratory data more accessible. Moreover, the development of the application and seeks to encourage the dissemination of open-source software within the community and provides a framework through which Shiny applications can be developed, shared, and iterated upon., (© 2023 the author(s), published by De Gruyter, Berlin/Boston.)

The downregulation of Pleckstrin Homology-Like Domain family A member 1 (PHLDA1) expression mediates resistance to targeted therapies in receptor tyrosine kinase-driven cancers. The restoration and maintenance of PHLDA1 levels in cancer cells thus constitutes a potential strategy to circumvent resistance to inhibitors of receptor tyrosine kinases. Through a pharmacological approach, we identify the inhibition of MAPK signalling as a crucial step in PHLDA1 downregulation. Further ChIP-qPCR analysis revealed that MEK1/2 inhibition produces significant epigenetic changes at the PHLDA1 locus, specifically a decrease in the activatory marks H3Kme3 and H3K27ac. In line with this, we show that treatment with the clinically relevant class I histone deacetylase (HDAC) inhibitor 4SC-202 restores PHLDA1 expression in lapatinib-resistant human epidermal growth factor receptor-2 (HER2) + breast cancer cells. Critically, we show that when given in combination, 4SC-202 and lapatinib exert synergistic effects on 2D cell proliferation and colony formation capacity. We therefore propose that co-treatment with 4SC-202 may prolong the clinical efficacy of lapatinib in HER2 + breast cancer patients.

Statistical analyses form a fundamental part of causal inference in the experimental sciences. The statistical paradigm most commonly taught to science students around the world is that of frequentism, with a particular emphasis on the null hypothesis significance testing borne by the work of Neyman and Pearson in the early 20th century. This paradigm is often lauded as being the most objective of methods and remains commonplace in scientific journals. Despite its widespread use-and, indeed, requirement for publication in some journals-this paradigm has received substantial criticism in recent decades, and its impact on scientific publishing has been subjected to deeper scrutiny in response to the replication crisis in the psychological and medical sciences. It has been posited that the increasing use of the Bayesian statistical paradigm, made more accessible through technological advances in the last few decades, may have an important role to play in rendering research and statistical inference more robust, transparent, and reproducible. These methods can have a steep learning curve, and thus this paper seeks to introduce those working within clinical laboratories to the Bayesian paradigm of statistical analysis and provides worked examples of the Bayesian analysis of data commonly encountered in laboratory medicine using freely available, open source tools., (© American Association for Clinical Chemistry 2022.)

Objectives: The purpose of this study was to establish whether an artificially intelligent (AI) system can be developed to automate stress echocardiography analysis and support clinician interpretation., Background: Coronary artery disease is the leading global cause of mortality and morbidity and stress echocardiography remains one of the most commonly used diagnostic imaging tests., Methods: An automated image processing pipeline was developed to extract novel geometric and kinematic features from stress echocardiograms collected as part of a large, United Kingdom-based prospective, multicenter, multivendor study. An ensemble machine learning classifier was trained, using the extracted features, to identify patients with severe coronary artery disease on invasive coronary angiography. The model was tested in an independent U.S., Study: How availability of an AI classification might impact clinical interpretation of stress echocardiograms was evaluated in a randomized crossover reader study., Results: Acceptable classification accuracy for identification of patients with severe coronary artery disease in the training data set was achieved on cross-fold validation based on 31 unique geometric and kinematic features, with a specificity of 92.7% and a sensitivity of 84.4%. This accuracy was maintained in the independent validation data set. The use of the AI classification tool by clinicians increased inter-reader agreement and confidence as well as sensitivity for detection of disease by 10% to achieve an area under the receiver-operating characteristic curve of 0.93., Conclusions: Automated analysis of stress echocardiograms is possible using AI and provision of automated classifications to clinicians when reading stress echocardiograms could improve accuracy, inter-reader agreement, and reader confidence., Competing Interests: Funding Support and Author Disclosures Data and images used in the training data set were collected with the support of a National Institute of Health Research (NIHR) Health Education England (HEE) Healthcare Science Research Fellowship (NIHR-HCS-P13-04-001); Cardiovascular Clinical Research Facility, University of Oxford; Ultromics Ltd; Lantheus Medical Imaging Inc; NIHR Oxford Biomedical Research Centre; University of Oxford; and Oxford British Heart Foundation Centre for Research Excellence. Dr Lamata holds a Wellcome Trust Senior Research Fellowship (209450/Z/17/Z). Dr Upton is the CEO, co-founder, and a shareholder of Ultromics Ltd, which develops artificial intelligence echocardiography software. Dr Leeson is a co-founder and a shareholder of Ultromics Ltd; has previously consulted for Intelligent Ultrasound; and has held research grants from Lantheus Medical Imaging. Drs Upton, Leeson, Markham, and Wilkes are inventors on filed patents in the field of echocardiography. Drs Mumith, Beqiri, Parker, Hawkes, Gao, Porumb, Markham, O'Driscoll, Hassanali, Groves, and Woodward, Ms Marques, and Mr Kenworthy are employees of Ultromics Ltd. All other authors have reported that they have no relationships relevant to the contents of this paper to disclose., (Copyright © 2022 The Authors. Published by Elsevier Inc. All rights reserved.)