High-accuracy protein model quality assessment using attention graph neural networks.

Great improvement has been brought to protein tertiary structure prediction through deep learning. It is important but very challenging to accurately rank and score decoy structures predicted by different models. CASP14 results show that existing quality assessment (QA) approaches lag behind the development of protein structure prediction methods, where almost all existing QA models degrade in accuracy when the target is a decoy of high quality. How to give an accurate assessment to high-accuracy decoys is particularly useful with the available of accurate structure prediction methods. Here we propose a fast and effective single-model QA method, QATEN, which can evaluate decoys only by their topological characteristics and atomic types. Our model uses graph neural networks and attention mechanisms to evaluate global and amino acid level scores, and uses specific loss functions to constrain the network to focus more on high-precision decoys and protein domains. On the CASP14 evaluation decoys, QATEN performs better than other QA models under all correlation coefficients when targeting average LDDT. QATEN shows promising performance when considering only high-accuracy decoys. Compared to the embedded evaluation modules of predicted ${C}_{\alpha^{-}} RMSD$ (pRMSD) in RosettaFold and predicted LDDT (pLDDT) in AlphaFold2, QATEN is complementary and capable of achieving better evaluation on some decoy structures generated by AlphaFold2 and RosettaFold. These results suggest that the new QATEN approach can be used as a reliable independent assessment algorithm for high-accuracy protein structure decoys.

Improved model quality assessment using sequence and structural information by enhanced deep neural networks.

Protein model quality assessment plays an important role in protein structure prediction, protein design and drug discovery. In this work, DeepUMQA2, a substantially improved version of DeepUMQA for protein model quality assessment, is proposed. First, sequence features containing protein co-evolution information and structural features reflecting family information are extracted to complement model-dependent features. Second, a novel backbone network based on triangular multiplication update and axial attention mechanism is designed to enhance information exchange between inter-residue pairs. On CASP13 and CASP14 datasets, the performance of DeepUMQA2 increases by 20.5 and 20.4% compared with DeepUMQA, respectively (measured by top 1 loss). Moreover, on the three-month CAMEO dataset (11 March to 04 June 2022), DeepUMQA2 outperforms DeepUMQA by 15.5% (measured by local AUC0,0.2) and ranks first among all competing server methods in CAMEO blind test. Experimental results show that DeepUMQA2 outperforms state-of-the-art model quality assessment methods, such as ProQ3D-LDDT, ModFOLD8, and DeepAccNet and DeepUMQA2 can select more suitable best models than state-of-the-art protein structure methods, such as AlphaFold2, RoseTTAFold and I-TASSER, provided themselves.

A joint brain extraction and image quality assessment framework for fetal brain MRI slices.

Brain extraction and image quality assessment are two fundamental steps in fetal brain magnetic resonance imaging (MRI) 3D reconstruction and quantification. However, the randomness of fetal position and orientation, the variability of fetal brain morphology, maternal organs around the fetus, and the scarcity of data samples, all add excessive noise and impose a great challenge to automated brain extraction and quality assessment of fetal MRI slices. Conventionally, brain extraction and quality assessment are typically performed independently. However, both of them focus on the brain image representation, so they can be jointly optimized to ensure the network learns more effective features and avoid overfitting. To this end, we propose a novel two-stage dual-task deep learning framework with a brain localization stage and a dual-task stage for joint brain extraction and quality assessment of fetal MRI slices. Specifically, the dual-task module compactly contains a feature extraction module, a quality assessment head and a segmentation head with feature fusion for simultaneous brain extraction and quality assessment. Besides, a transformer architecture is introduced into the feature extraction module and the segmentation head. We utilize a multi-step training strategy to guarantee a stable and successful training of all modules. Finally, we validate our method by a 5-fold cross-validation and ablation study on a dataset with fetal brain MRI slices in different qualities, and perform a cross-dataset validation in addition. Experiments show that the proposed framework achieves very promising performance.

The European Organisation of External Quality Assurance Providers in Laboratory Medicine (EQALM) Statement: guidelines for publishing about interlaboratory comparison studies (PubILC).

Data and results from interlaboratory comparison (ILC) studies, external quality assessment (EQA) and proficiency testing (PT) activities are important and valuable contributions both to the further development of all disciplines of medical laboratory diagnostics, and to the evaluation and comparison of in vitro diagnostic assays. So far, however, there are no recommendations as to which essential items should be addressed in publications on interlaboratory comparisons. The European Organization of External Quality Assurance Providers in Laboratory Medicine (EQALM) recognized the need for such recommendations, and these were developed by a group of experts. The result of this endeavor is the EQALM Statement on items recommended to be addressed in publications on interlaboratory comparison activities (PubILC), in conjunction with a user-friendly checklist. Once adopted by authors and journals, the EQALM Statement will ensure essential information and/or study-related facts are included within publications on EQA/PT activities.

Quality assessment of enzymatic methyl-seq library constructed using crude cell lysate.

Enzymatic methyl-seq (EM-seq), an enzyme-based method, identifies genome-wide DNA methylation, which enables us to obtain reliable methylome data from purified genomic DNA by avoiding bisulfite-induced DNA damage. However, the loss of DNA during purification hinders the methylome analysis of limited samples. The crude DNA extraction method is the quickest and minimal sample loss approach for obtaining useable DNA without requiring additional dissolution and purification. However, it remains unclear whether crude DNA can be used directly for EM-seq library construction. In this study, we aimed to assess the quality of EM-seq libraries prepared directly using crude DNA. The crude DNA-derived libraries provided appropriate fragment sizes and concentrations for sequencing similar to those of the purified DNA-derived libraries. However, the sequencing results of crude samples exhibited lower reference sequence mapping efficiencies than those of the purified samples. Additionally, the lower-input crude DNA-derived sample exhibited a marginally lower cytosine-to-thymine conversion efficiency and hypermethylated pattern around gene regulatory elements than the higher-input crude DNA- or purified DNA-derived samples. In contrast, the methylation profiles of the crude and purified samples exhibited a significant correlation. Our findings indicate that crude DNA can be used as a raw material for EM-seq library construction.

The "hidden noise" problem in MR image reconstruction.

PURPOSE: The performance of modern image reconstruction methods is commonly judged using quantitative error metrics like root mean squared-error and the structural similarity index, which are calculated by comparing reconstructed images against fully sampled reference data. In practice, the reference data will contain noise and is not a true gold standard. In this work, we demonstrate that the "hidden noise" present in reference data can substantially confound standard approaches for ranking different image reconstruction results. METHODS: Using both experimental and simulated k-space data and several different image reconstruction techniques, we examined whether there was correlation between performance metrics obtained with typical noisy reference data versus those obtained with higher-quality reference data. RESULTS: For conventional performance metrics, the reconstructions that matched best with the higher-quality reference data were substantially different from the reconstructions that matched best with typical noisy reference data. This leads to suboptimal reconstruction results if the performance with respect to noisy reference data is used to select which reconstruction methods/parameters to employ. These issues were reduced when employing alternative error metrics that better account for noise. CONCLUSION: Reference data containing hidden noise can substantially mislead the ranking of image reconstruction methods when using conventional error metrics, but this issue can be mitigated with alternative error metrics.

Inline automatic quality control of 2D phase-contrast flow MRI for subject-specific scan time adaptation.

PURPOSE: To develop an inline automatic quality control to achieve consistent diagnostic image quality with subject-specific scan time, and to demonstrate this method for 2D phase-contrast flow MRI to reach a predetermined SNR. METHODS: We designed a closed-loop feedback framework between image reconstruction and data acquisition to intermittently check SNR (every 20 s) and automatically stop the acquisition when a target SNR is achieved. A free-breathing 2D pseudo-golden-angle spiral phase-contrast sequence was modified to listen for image-quality messages from the reconstructions. Ten healthy volunteers and 1 patient were imaged at 0.55 T. Target SNR was selected based on retrospective analysis of cardiac output error, and performance of the automatic SNR-driven "stop" was assessed inline. RESULTS: SNR calculation and automated segmentation was feasible within 20 s with inline deployment. The SNR-driven acquisition time was 2 min 39 s ± 67 s (aorta) and 3 min ± 80 s (main pulmonary artery) with a min/max acquisition time of 1 min 43 s/4 min 52 s (aorta) and 1 min 43 s/5 min 50 s (main pulmonary artery) across 6 healthy volunteers, while ensuring a diagnostic measurement with relative absolute error in quantitative flow measurement lower than 2.1% (aorta) and 6.3% (main pulmonary artery). CONCLUSION: The inline quality control enables subject-specific optimized scan times while ensuring consistent diagnostic image quality. The distribution of automated stopping times across the population revealed the value of a subject-specific scan time.

Evaluation of quality and quantity of randomized controlled trials in hepatobiliary surgery: A scoping/mapping review.

AIM: To evaluate the quantity and quality of randomized controlled trials (RCTs) in hepatobiliary surgery and for identifying gaps in current evidences. METHODS: A systematic search was conducted in MEDLINE (via PubMed), Web of Science, and Cochrane Controlled Register of Trials (CENTRAL) for RCTs of hepatobiliary surgery published from inception until the end of 2023. The quality of each study was assessed using the Cochrane risk-of-bias (RoB) tool. The associations between risk of bias and the region and publication date were also assessed. Evidence mapping was performed to identify research gaps in the field. RESULTS: The study included 1187 records. The number and proportion of published randomized controlled trials (RCTs) in hepatobiliary surgery increased over time, from 13 RCTs (.0005% of publications) in 1970-1979 to 201 RCTs (.003% of publications) in 2020-2023. There was a significant increase in the number of studies with a low risk of bias in RoB domains (p < .01). The proportion of RCTs with low risk of bias improved significantly after the introduction of CONSORT guidelines (p < .001). The evidence mapping revealed a significant research focus on major and minor hepatectomy and cholecystectomy. However, gaps were identified in liver cyst surgery and hepatobiliary vascular surgery. Additionally, there are gaps in the field of perioperative management and nutrition intervention. CONCLUSION: The quantity and quality of RCTs in hepatobiliary surgery have increased over time, but there is still room for improvement. We have identified gaps in current research that can be addressed in future studies.

Emerging trends in sperm selection: enhancing success rates in assisted reproduction.

This comprehensive review explores the evolving landscape of sperm selection techniques within the realm of Assisted Reproductive Technology (ART). Our analysis delves into a range of methods from traditional approaches like density gradient centrifugation to advanced techniques such as Magnetic-Activated Cell Sorting (MACS) and Intracytoplasmic Morphologically Selected Sperm Injection (IMSI). We critically assess the efficacy of these methods in terms of sperm motility, morphology, DNA integrity, and other functional attributes, providing a detailed comparison of their clinical outcomes. We highlight the transition from conventional sperm selection methods, which primarily focus on physical characteristics, to more sophisticated techniques that offer a comprehensive evaluation of sperm molecular properties. This shift not only promises enhanced prediction of fertilization success but also has significant implications for improving embryo quality and increasing the chances of live birth. By synthesizing various studies and research papers, we present an in-depth analysis of the predictability of different sperm selection procedures in ART. The review also discusses the clinical applicability of these methods, emphasizing their potential in shaping the future of assisted reproduction. Our findings suggest that the integration of advanced sperm selection strategies in ART could lead to more cost-effective treatments with reduced duration and higher success rates. This review aims to provide clinicians and researchers in reproductive medicine with comprehensive insights into the current state and future prospects of sperm selection technologies in ART.

Analyses of quality of life in cancer drug trials - a review of measurements and analytical choices in post-reimbursement studies.

OBJECTIVES: For drugs reimbursed with limited evidence of patient benefits, confirmatory evidence of overall survival (OS) and quality of life (QoL) benefits is important. For QoL data to serve as valuable input to patients and decision-makers, it must be measured and analyzed using appropriate methods. We aimed to assess the measurement and analyses of post-reimbursement QoL data for cancer drugs introduced in Swedish healthcare with limited evidence at the time of reimbursement. METHODS: We reviewed any published post-reimbursement trial data on QoL for cancer drugs reimbursed in Sweden between 2010 and 2020 with limited evidence of improvement in QoL and OS benefits at the time of reimbursement. We extracted information on the instruments used, frequency of measurement, extent of missing data, statistical approaches, and the use of pre-registration and study protocols. RESULTS: Out of 22 drugs satisfying our inclusion criteria, we identified published QoL data for 12 drugs in 22 studies covering multiple cancer types. The most frequently used QoL instruments were EORTC QLQ-C30 and EQ-5D-3/5L. We identified three areas needing improvement in QoL measurement and analysis: (i) motivation for the frequency of measurements, (ii) handling of the substantial missing data problem, and (iii) inclusion and adherence to QoL analyses in clinical trial pre-registration and study protocols. CONCLUSIONS: Our review shows that the measurements and analysis of QoL data in our sample of cancer trials covering drugs initially reimbursed without any confirmed QoL or OS evidence have significant room for improvement. The increasing use of QoL assessments must be accompanied by a stricter adherence to best-practice guidelines to provide valuable input to patients and decision-makers.

Motion Robust MR Fingerprinting Scan to Image Neonates With Prenatal Opioid Exposure.

PURPOSE: To explore whether MR fingerprinting (MRF) scans provide motion-robust and quantitative brain tissue measurements for non-sedated infants with prenatal opioid exposure (POE). STUDY TYPE: Prospective. POPULATION: 13 infants with POE (3 male; 12 newborns (age 7-65 days) and 1 infant aged 9-months). FIELD STRENGTH/SEQUENCE: 3T, 3D T1-weighted MPRAGE, 3D T2-weighted TSE and MRF sequences. ASSESSMENT: The image quality of MRF and MRI was assessed in a fully crossed, multiple-reader, multiple-case study. Sixteen image quality features in three types-image artifacts, structure and myelination visualization-were ranked by four neuroradiologists (8, 7, 5, and 8 years of experience respectively), using a 3-point scale. MRF T1 and T2 values in 8 white matter brain regions were compared between babies younger than 1 month and babies between 1 and 2 months. STATISTICAL TESTS: Generalized estimating equations model to test the significance of differences of regional T1 and T2 values of babies under 1 month and those older. MRI and MRF image quality was assessed using Gwet's second order auto-correlation coefficient (AC2) with confidence levels. The Cochran-Mantel-Haenszel test was used to assess the difference in proportions between MRF and MRI for all features and stratified by the type of features. A P value <0.05 was considered statistically significant. RESULTS: The MRF of two infants were excluded in T1 and T2 value analysis due to severe motion artifact but were included in the image quality assessment. In infants under 1 month of age (N = 6), the T1 and T2 values were significantly higher compared to those between 1 and 2 months of age (N = 4). MRF images showed significantly higher image quality ratings in all three feature types compared to MRI images. CONCLUSIONS: MR Fingerprinting scans have potential to be a motion-robust and efficient method for nonsedated infants. LEVEL OF EVIDENCE: 2 TECHNICAL EFFICACY STAGE: 1.

Elevating nanomaterial optical sensor arrays through the integration of advanced machine learning techniques for enhancing visual inspection of food quality and safety.

Food quality and safety problems caused by inefficient control in the food chain have significant implications for human health, social stability, and economic progress and optical sensor arrays (OSAs) can effectively address these challenges. This review aims to summarize the recent applications of nanomaterials-based OSA for food quality and safety visual monitoring, including colourimetric sensor array (CSA) and fluorescent sensor array (FSA). First, the fundamental properties of various advanced nanomaterials, mainly including metal nanoparticles (MNPs) and nanoclusters (MNCs), quantum dots (QDs), upconversion nanoparticles (UCNPs), and others, were described. Besides, the diverse machine learning (ML) and deep learning (DL) methods of high-dimensional data obtained from the responses between different sensing elements and analytes were presented. Moreover, the recent and representative applications in pesticide residues, heavy metal ions, bacterial contamination, antioxidants, flavor matters, and food freshness detection were comprehensively summarized. Finally, the challenges and future perspectives for nanomaterials-based OSAs are discussed. It is believed that with the advancements in artificial intelligence (AI) techniques and integrated technology, nanomaterials-based OSAs are expected to be an intelligent, effective, and rapid tool for food quality assessment and safety control.

Diagnostic performance of mpox virus (MPXV) real-time PCR assays: multicenter assessment and extended sensitivity analysis.

PURPOSE: To comprehensively investigate the diagnostic performance of routinely used assays in MPXV testing, the National Center of Clinical Laboratories in China conducted a nationwide external quality assessment (EQA) scheme and an evaluated nine assays used by ≥ 5 laboratories in the EQA. METHODS: MPXV virus-like particles with 2700, 900 and 300 copies/mL were distributed to 195 EQA laboratories. For extended analysis, triple-diluted samples from 9000 to 4.12 copies/mL were repeated 20 times using the assays employed by ≥ 5 laboratories. The diagnostic performance was assessed by analyzing EQA data and calculating the limits of detection (LODs). RESULTS: The performance was competent in 87.69% (171/195) of the participants and 87.94% (175/199) of the datasets. The positive percentage agreements (PPAs) were greater than 99% for samples at 2700 and 900 copies/mL, and 95.60% (761/796) for samples at 300 copies/mL. The calculated LODs for the two clades ranged from 228.44 to 924.31 copies/mL and were greater than the LODs specified by the respective kits. EasyDiagnosis had the lowest calculated LODs and showed superior performance in EQA, whereas BioGerm and Sansure, with higher calculated LODs, did not perform well in EQA. CONCLUSION: This study provides valuable information from the EQA data and evaluation of the diagnostic performance of MPXV detection assays. It also provided insights into reagent optimization and enabled prompt public health interventions for the outbreak.

The development and implementation of a proficiency testing program for SARS-CoV-2 using dried tube specimens in resource-limited countries.

INTRODUCTION: When COVID-19 hit the world in 2019, an enhanced focus on diagnostic testing for SARS-CoV-2 was essential for a successful pandemic response. Testing laboratories stretched their capabilities for the new coronavirus by adopting different test methods. The necessity of having external quality assurance (EQA) mechanisms was even more critical due to this rapid expansion. However, there was a lack of experience in providing the necessary SARS-CoV-2 EQA materials, especially in locations with constrained resources. OBJECTIVE: We aimed to create a PT (Proficiency testing) programme based on the Dried Tube Specimens (DTS) method that would be a practical option for molecular based SARS-CoV-2 EQA in Low- and Middle-Income Countries. METHODS: Based on previous ISO/IEC 17043:2010 accreditation experiences and with assistance from the US Centers for Disease Control and Prevention, The Supranational Reference Laboratory of Uganda (adapted the DTS sample preparation method and completed a pilot EQA program between 2020 and 2021. Stability and panel validation testing was conducted on the designed materials before shipping to pilot participants in six African countries. Participants received a panel containing five SARS-CoV-2 DTS samples, transported at ambient conditions. Results submitted by participants were compared to validation results. Participants were graded as satisfactory (≥ 80%) or unsatisfactory (< 80%) and performance reports disseminated. RESULTS: Our SARS-CoV-2 stability experiments showed that SARS-CoV-2 RNA was stable (-15 to -25 °C, 4 to 8 °C, (18 to 28 °C) room temperature and 35 to 38 °C) as well as DTS panels (4 to 8 °C, 18 to 28 °C, 35 to 38 °C and 45 °C) for a period of 4 weeks. The SARS-CoV-2 DTS panels were successfully piloted in 35 test sites from Zambia, Malawi, Mozambique, Nigeria, and Seychelles. The pilot results of the participants showed good accuracy, with an average of 86% (30/35) concordance with the original SARS CoV-2 expectations. CONCLUSION: The SARS-CoV-2 DTS PT panel is reliable, stable at ambient temperature, simple to prepare and requires minimal resources.

OILVEQ: an Italian external quality control scheme for cannabinoids analysis in galenic preparations of cannabis oil.

OBJECTIVES: Italy legalized cannabis oil for specific medical conditions (neuropathic pain, refractory epilepsy and other established pathologies) in 2015, but mandates titration of principal cannabinoids before marketing each batch using iphenated techniques coupled with mass spectrometry. To assess reliability of laboratories from the Italian National Health Service in charge of titrating the batches, the Italian National Institute of Health set up an quality control program on determination of Δ9-tetrahydrocannabinol l (THC), cannabidiol (CBD), Δ9-tetrahydrocannabinolic acid A (THCA-A) and cannabidiolic acid (CBDA) in cannabis oil preparations. METHODS: Two rounds of exercises have been carried out since 2019, involving sixteen Italian laboratories. Five different cannabis oil samples (19-1A and 19-1B for the first round and 22-1A, 22-1B and 22-1C for the second one were prepared and 1 mL amount of each sample was sent to the laboratories. The quantitative performance of each laboratory was assessed calculating the z-score value, a statistical measurement for value's relationship to the mean of a group of values. RESULTS: In the first round, eight out of fourteen laboratories employed an LC-MS while the remaining six used GC-MS. Differently, in the second round, six out of eleven laboratories employed a GC-MS while the remaining five used LC-MS. In the first round, only 28.6 % laboratories achieved an acceptable performance (z-score±2), and all of them used LC-MS as analytical method. In the second round, none of the laboratories achieved an acceptable performance. Satisfactory results, based on z-scores, were generally low (0.0-75.0 %), with only one exception of 100 % for THCA-A determination in sample 22-1B. In the second round, three false negatives (two THC and one CBD by GC-MS determination) were reported while no false positives were described in the blank sample. The two rounds yielded a mean ERR% of 42 % approximately and a mean CV% around 70 % in GC-MS determination. When applying LC-MS determination, the two rounds yielded a mean ERR% of 36 % approximately and a mean CV% around 33 %. CONCLUSIONS: The obtained results underline the need for a clear and consistent protocol to be adopted by all laboratories intending to include the titration of oily cannabis-based products into their routinely analytical techniques. This emphasis on methodology standardization and participation to quality control schemes is essential for ensuring reliable and accurate measurements, ultimately enhancing the overall effectiveness and reliability of medical cannabis treatments.

Assessment of accuracy of laboratory testing results, relative to peer group consensus values in external quality control, by bivariate z-score analysis: the example of D-Dimer.

OBJECTIVES: The aim of this study is to develop a practical method for bivariate z-score analysis which can be applied to the survey of an external quality assessment programme. METHODS: To develop the bivariate z-score analysis, the results of four surveys of the international D-Dimer external quality assessment programme of 2022 of the ECAT Foundation were used. The proposed methodology starts by identifying the bivariate outliers, using a Supervised Sequential Hotelling T2 control chart. The outlying data are removed, and all the remaining data are used to provide robust estimates of the parameters of the assumed underlying bivariate normal distribution. Based on these estimates two nested homocentric ellipses are drawn, corresponding to confidence levels of 95 and 99.7 %. The bivariate z-score plot described provides the laboratory with an indication of both systematic and random deviations from zero z-score values. The bivariate z-score analysis was examined within survey 2022-D4 across the three most frequently used methods. RESULTS: The number of z-score pairs included varied between 830 and 857 and the number of bivariate outliers varied between 20 and 28. The correlation between the z-score pairs varied between 0.431 and 0.647. The correlation between the z-score pairs for the three most frequently used varied between 0.208 and 0.636. CONCLUSIONS: The use of the bivariate z-score analysis is of major importance when multiple samples are distributed around in the same survey and dependency of the results is likely. Important lessons can be drawn from the shape of the ellipse with respect to random and systematic deviations, while individual laboratories have been informed about their position in the state-of-the-art distribution and whether they have to deal with systematic and/or random deviations.

Understanding the limitations of your assay using EQA data with serum creatinine as an example.

OBJECTIVES: Laboratories need to take into consideration the specificity and imprecision of assays not only in verification, but also of quality assessment. This study investigates the composition of serum used in EQA materials by comparing material from a single and multiple donors (pooled material), across multiple methods, using creatinine as an example. METHODS: Sixteen different serum matrices were distributed as 36 specimens through the UK NEQAS for Acute and Chronic Kidney Disease Scheme from March 2022 to March 2023. Male-only and female-only serum was used as single donations, pooled donations, unmanipulated or with added exogenous creatinine. Specimens were distributed to primarily UK participants (approximately n=500) for creatinine analysis. Data has been reviewed by method compared to the enzymatic creatinine method principle mean. RESULTS: From the 16 different matrices, only the enzymatic creatinine assay systems from Roche Cobas and Siemens Atellica met the minimum acceptable bias goal, from biological data, of 5.6 %, in all specimens. Pooled material showed less variation in bias across all methods. CONCLUSIONS: Since Laboratories invest a lot of time and money in quality management, they need to know the limitations of their assays so that they are not investigating 'apparent' EQA/IQC problems which are purely due to non-specific, imprecise assay, rather than an analytical issue in their laboratory. When large numbers of individual donations are combined, interferents are essentially diluted out. Therefore, if EQA material is of this type it will be very difficult to determine the actual assay's bias and variability.

Targeted ultra performance liquid chromatography tandem mass spectrometry procedures for the diagnosis of inborn errors of metabolism: validation through ERNDIM external quality assessment schemes.

OBJECTIVES: Early diagnosis of inborn errors of metabolism (IEM) is crucial to ensure early detection of conditions which are treatable. This study reports on targeted metabolomic procedures for the diagnosis of IEM of amino acids, acylcarnitines, creatine/guanidinoacetate, purines/pyrimidines and oligosaccharides, and describes its validation through external quality assessment schemes (EQA). METHODS: Analysis was performed on a Waters ACQUITY UPLC H-class system coupled to a Waters Xevo triple-quadrupole (TQD) mass spectrometer, operating in both positive and negative electrospray ionization mode. Chromatographic separation was performed on a CORTECS C18 column (2.1 × 150, 1.6 µm). Data were collected by multiple reaction monitoring. RESULTS: The internal and EQA results were generally adequate, with a few exceptions. We calculated the relative measurement error (RME) and only a few metabolites displayed a RME higher than 30 % (asparagine and some acylcarnitine species). For oligosaccharides, semi-quantitative analysis of an educational panel clearly identified the 8 different diseases included. CONCLUSIONS: Overall, we have validated our analytical system through an external quality control assessment. This validation will contribute to harmonization between laboratories, thus improving identification and management of patients with IEM.

Analytical performance specifications for trace elements in biological fluids derived from six countries federated external quality assessment schemes over 10 years.

OBJECTIVES: This article defines analytical performance specifications (APS) for evaluating laboratory proficiency through an external quality assessment scheme. METHODS: Standard deviations for proficiency assessment were derived from Thompson's characteristic function applied to robust data calculated from participants' submissions in the Occupational and Environmental Laboratory Medicine (OELM) external quality assurance scheme for trace elements in serum, whole blood and urine. Characteristic function was based on two parameters: (1) ß - the average coefficient of variation (CV) at high sample concentrations; (2) α - the average standard deviation (SD) at low sample concentrations. APSs were defined as 1.65 standard deviations calculated by Thompson's approach. Comparison between OELM robust data and characteristic function were used to validate the model. RESULTS: Application of the characteristic function allowed calculated APS for 18 elements across three matrices. Some limitations were noted, particularly for elements (1) with no sample concentrations near analytical technique limit of detection; (2) exhibiting high robust CV at high concentration; (3) exhibiting high analytical variability such as whole blood Tl and urine Pb; (4) with an unbalanced number of robust SD above and under the characteristic function such as whole blood Mn and serum Al and Zn. CONCLUSIONS: The characteristic function was a useful means of deriving APS for trace elements in biological fluids where biological variation data or outcome studies were not available. However, OELM external quality assurance scheme data suggests that the characteristic functions are not appropriate for all elements.

Performance measurement part I: Foundational knowledge for measure development.

As medicine is moving toward performance and outcome-based payment and is transitioning away from productivity-based systems, value is now being appraised in healthcare through "performance measures." Over the past few decades, assessment of clinical performance in health care has been essential in ensuring safe and cost-effective patient care. The Centers for Medicare & Medicaid Services is further driving this change with measurable, outcomes-based national payer incentive payment systems. With the continually evolving requirements in health care reform focused on value-based care, there is a growing concern that clinicians, particularly dermatologists, may not understand the scientific rationale of health care quality measurement. As such, in order to help dermatologists understand the health care measurement science landscape to empower them to engage in the performance measure development and implementation process, the first article in this 2-part continuing medical education series reviews the value equation, historic and evolving policy issues, and the American Academy of Dermatology's approach to performance measurement development to provide the required foundational knowledge for performance measure developers.