Machine learning to determine the main factors affecting creep rates in laser powder bed fusion.

There is an increasing need for the use of additive manufacturing (AM) to produce improved critical application engineering components. However, the materials manufactured using AM perform well below their traditionally manufactured counterparts, particularly for creep and fatigue. Research has shown that this difference in performance is due to the complex relationships between AM process parameters which affect the material microstructure and consequently the mechanical performance as well. Therefore, it is necessary to understand the impact of different AM build parameters on the mechanical performance of parts. Machine learning (ML) models are able to find hidden relationships in data using iterative statistical analyses and have the potential to develop process-structure-property-performance relationships for manufacturing processes, including AM. The aim of this work is to apply ML techniques to materials testing data in order to understand the effect of AM process parameters on the creep rate of additively built nickel-based superalloy and to predict the creep rate of the material from these process parameters. In this work, the predictive capabilities of ML and its ability to develop process-structure-property relationships are applied to the creep properties of laser powder bed fused alloy 718. The input data for the ML model included the Laser Powder Bed Fusion (LPBF) build parameters used-build orientation, scan strategy and number of lasers-and geometrical material descriptors which were extracted from optical microscope porosity images using image analysis techniques. The ML model was used to predict the minimum creep rate of the Laser Powder Bed Fused alloy 718 samples, which had been creep tested at 650 ∘ C and 600 MPa. The ML model was also used to identify the most relevant material descriptors affecting the minimum creep rate of the material (determined by using an ensemble feature importance framework). The creep rate was accurately predicted with a percentage error of 1.40 % in the best case. The most important material descriptors were found to be part density, number of pores, build orientation and scan strategy. These findings show the applicability and potential of using ML to determine and predict the mechanical properties of materials fabricated via different manufacturing processes, and to find process-structure-property relationships in AM. This increases the readiness of AM for use in critical applications.

QUADAS-C: A Tool for Assessing Risk of Bias in Comparative Diagnostic Accuracy Studies.

Comparative diagnostic test accuracy studies assess and compare the accuracy of 2 or more tests in the same study. Although these studies have the potential to yield reliable evidence regarding comparative accuracy, shortcomings in the design, conduct, and analysis may bias their results. The currently recommended quality assessment tool for diagnostic test accuracy studies, QUADAS-2 (Quality Assessment of Diagnostic Accuracy Studies-2), is not designed for the assessment of test comparisons. The QUADAS-C (Quality Assessment of Diagnostic Accuracy Studies-Comparative) tool was developed as an extension of QUADAS-2 to assess the risk of bias in comparative diagnostic test accuracy studies. Through a 4-round Delphi study involving 24 international experts in test evaluation and a face-to-face consensus meeting, an initial version of the tool was developed that was revised and finalized following a pilot study among potential users. The QUADAS-C tool retains the same 4-domain structure of QUADAS-2 (Patient Selection, Index Test, Reference Standard, and Flow and Timing) and comprises additional questions to each QUADAS-2 domain. A risk-of-bias judgment for comparative accuracy requires a risk-of-bias judgment for the accuracy of each test (resulting from QUADAS-2) and additional criteria specific to test comparisons. Examples of such additional criteria include whether participants either received all index tests or were randomly assigned to index tests, and whether index tests were interpreted with blinding to the results of other index tests. The QUADAS-C tool will be useful for systematic reviews of diagnostic test accuracy addressing comparative questions. Furthermore, researchers may use this tool to identify and avoid risk of bias when designing a comparative diagnostic test accuracy study.

Study designs for comparative diagnostic test accuracy: A methodological review and classification scheme.

OBJECTIVES: (1) To identify and classify comparative diagnostic test accuracy (DTA) study designs; (2) to describe study design labels used by authors of comparative DTA studies. METHODS: We performed a methodological review of 100 comparative DTA studies published between 2015 and 2017, randomly sampled from studies included in 238 comparative DTA systematic reviews indexed in MEDLINE in 2017. From each study report, we extracted six design elements characterizing participant flow and the labels used by authors. RESULTS: We identified a total of 46 unique combinations of study design features in our sample, based on six design elements characterizing participant flow. We classified the studies into five study design categories based on how participants were allocated to receive each index test: 'fully paired' (n=79), 'partially paired, random subset' (n=0), 'partially paired, nonrandom subset' (n=2), 'unpaired randomized' (n=1) and 'unpaired nonrandomized' (n=3). The allocation method used in 15 studies was unclear. Sixty-one studies reported, in total, 29 unique study design labels but only four labels referred to specific design features of comparative studies. CONCLUSION: Our classification scheme can help systematic review authors define study eligibility criteria, assess risk of bias, and communicate the strength of the evidence. A standardized labelling scheme could be developed to facilitate communication of specific design features.

Development, validation and effectiveness of diagnostic prediction tools for colorectal cancer in primary care: a systematic review.

BACKGROUND: Tools based on diagnostic prediction models are available to help general practitioners (GP) diagnose colorectal cancer. It is unclear how well they perform and whether they lead to increased or quicker diagnoses and ultimately impact on patient quality of life and/or survival. The aim of this systematic review is to evaluate the development, validation, effectiveness, and cost-effectiveness, of cancer diagnostic tools for colorectal cancer in primary care. METHODS: Electronic databases including Medline and Web of Science were searched in May 2017 (updated October 2019). Two reviewers independently screened titles, abstracts and full-texts. Studies were included if they reported the development, validation or accuracy of a prediction model, or assessed the effectiveness or cost-effectiveness of diagnostic tools based on prediction models to aid GP decision-making for symptomatic patients presenting with features potentially indicative of colorectal cancer. Data extraction and risk of bias were completed by one reviewer and checked by a second. A narrative synthesis was conducted. RESULTS: Eleven thousand one hundred thirteen records were screened and 23 studies met the inclusion criteria. Twenty-studies reported on the development, validation and/or accuracy of 13 prediction models: eight for colorectal cancer, five for cancer areas/types that include colorectal cancer. The Qcancer models were generally the best performing. Three impact studies met the inclusion criteria. Two (an RCT and a pre-post study) assessed tools based on the RAT prediction model. The third study looked at the impact of GP practices having access to RAT or Qcancer. Although the pre-post study reported a positive impact of the tools on outcomes, the results of the RCT and cross-sectional survey found no evidence that use of, or access to, the tools was associated with better outcomes. No study evaluated cost effectiveness. CONCLUSIONS: Many prediction models have been developed but none have been fully validated. Evidence demonstrating improved patient outcome of introducing the tools is the main deficiency and is essential given the imperfect classification achieved by all tools. This need is emphasised by the equivocal results of the small number of impact studies done so far.

Accuracy and precision analysis for a biophotonic assay of C-reactive protein.

A multiplexed biophotonic assay platform has been developed using the localised particle plasmon in gold nanoparticles assembled in an array and functionalised for two assays: total IgG and C-reactive protein (CRP). A protein A/G (PAG) assay, calibrated with a NIST reference material, shows a maximum surface coverage of θmax = 7.13 ± 0.19 mRIU, equivalent to 1.5 ng mm-2 of F(ab)-presenting antibody. The CRP capture antibody has an equivalent surface binding density of θmax = 2.95 ± 0.41 mRIU indicating a 41% capture antibody availability. Free PAG binding to the functionalised anti-CRP surface shows that only 47 ± 3% of CRP capture antibodies are correctly presenting Fab regions for antigen capture. The accuracy and precision of the CRP sensor assay was assessed with 54 blood samples containing spiked CRP in the range 2-160 mg L-1. The mean accuracy was 0.42 mg L-1 with Confidence Interval (CI) at 95% from -14.7 to 13.8 mg L-1 and the precision had a Coefficient of Variation (CV) of 10.6% with 95% CI 0.9%-20.2%. These biophotonic platform performance metrics indicate a CRP assay with 2-160 mg L-1 dynamic range, performed in 8 minutes from 5 µL of whole blood without sample preparation.

A rapid and quantitative technique for assessing IgG monomeric purity, calibrated with the NISTmAb reference material.

The fraction of intact monomer in a sample (moles/moles), the monomeric purity, is measured as a quality control in therapeutic monoclonal antibodies but is often unknown in research samples and remains a major source of variation in quantitative antibody-based techniques such as immunoassay development. Here, we describe a novel multiplex technique for estimating the monomeric purity and antigen affinity of research grade antibody samples. Light scattering was used to simultaneously observe the mass of antibody binding to biosensor surfaces functionalised with antigen (revealing Fab binding kinetics) or protein A/G (PAG). Initial estimates of monomeric purity in 7 antibody samples including a therapeutic infliximab biosimilar were estimated by observing a mass deficit on the PAG surface compared to the NISTmAb standard of high monomeric purity. Monomeric purity estimates were improved in a second step by observing the mass of antigen binding to the mass of antibody on the PAG surface. The NISTmAb and infliximab biosimilar displayed tightly controlled stoichiometries for antigen binding of 1.31 ± 0.57 and 1.71 ± 0.16 (95% confidence interval)-within the theoretical limit of 1-2 antigens per antibody depending on avidity. The other antibodies in the panel displayed antigen binding stoichiometries in the range 0.06-1.15, attributed to lower monomeric purity. The monomeric purity estimates were verified by electrospray ionization mass spectrometry (ESI), the gold standard technique for structural characterization of antibodies. ESI data indicated that the NISTmAb and infliximab biosimilar samples had monomeric purity values of 93.5% and 94.7%, respectively, whilst the research grade samples were significantly lower (54-89%). Our results demonstrate rapid quality control testing for monomeric purity of antibody samples (< 15 min) which could improve the reproducibility of antibody-based experiments.

Preferred Reporting Items for a Systematic Review and Meta-analysis of Diagnostic Test Accuracy Studies: The PRISMA-DTA Statement.

Importance: Systematic reviews of diagnostic test accuracy synthesize data from primary diagnostic studies that have evaluated the accuracy of 1 or more index tests against a reference standard, provide estimates of test performance, allow comparisons of the accuracy of different tests, and facilitate the identification of sources of variability in test accuracy. Objective: To develop the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) diagnostic test accuracy guideline as a stand-alone extension of the PRISMA statement. Modifications to the PRISMA statement reflect the specific requirements for reporting of systematic reviews and meta-analyses of diagnostic test accuracy studies and the abstracts for these reviews. Design: Established standards from the Enhancing the Quality and Transparency of Health Research (EQUATOR) Network were followed for the development of the guideline. The original PRISMA statement was used as a framework on which to modify and add items. A group of 24 multidisciplinary experts used a systematic review of articles on existing reporting guidelines and methods, a 3-round Delphi process, a consensus meeting, pilot testing, and iterative refinement to develop the PRISMA diagnostic test accuracy guideline. The final version of the PRISMA diagnostic test accuracy guideline checklist was approved by the group. Findings: The systematic review (produced 64 items) and the Delphi process (provided feedback on 7 proposed items; 1 item was later split into 2 items) identified 71 potentially relevant items for consideration. The Delphi process reduced these to 60 items that were discussed at the consensus meeting. Following the meeting, pilot testing and iterative feedback were used to generate the 27-item PRISMA diagnostic test accuracy checklist. To reflect specific or optimal contemporary systematic review methods for diagnostic test accuracy, 8 of the 27 original PRISMA items were left unchanged, 17 were modified, 2 were added, and 2 were omitted. Conclusions and Relevance: The 27-item PRISMA diagnostic test accuracy checklist provides specific guidance for reporting of systematic reviews. The PRISMA diagnostic test accuracy guideline can facilitate the transparent reporting of reviews, and may assist in the evaluation of validity and applicability, enhance replicability of reviews, and make the results from systematic reviews of diagnostic test accuracy studies more useful.

The comparative diagnostic accuracy of the Mini Mental State Examination (MMSE) and the General Practitioner assessment of Cognition (GPCOG) for identifying dementia in primary care: a systematic review protocol.

BACKGROUND: Improved dementia identification is a global health priority, and general practitioners (GPs) are often the first point of contact for people with concerns about their cognition. However, GPs often express uncertainty in using assessment tools and the evidence based on which tests are most accurate in identifying dementia is unclear. In particular, there is little certainty around how the accuracy of available brief cognitive assessments compares within a clinical family practice setting.Grounded in existing brief cognitive assessment evidence, we will compare the diagnostic test accuracy of the Mini Mental State Examination (MMSE) to the General Practitioner Assessment of Cognition (GPCOG) against the best available reference standard when used within a family practice setting. METHODS: We will employ robust systematic review methods to assess studies of diagnostic accuracy where both the MMSE and GPCOG have been evaluated as direct comparisons, i.e. within the same study population. This approach will enable us to minimise between-study heterogeneity, to eliminate the risk of bias due to confounding and increase the opportunity to make clinically useful and useable comparisons of diagnostic accuracy across both the MMSE and GPCOG. This systematic review will be conducted using a pragmatic search strategy, refining searches that build upon studies identified as part of our overview of systematic reviews of the diagnostic accuracy of brief cognitive assessments for identifying dementia in primary care. DISCUSSION: Through this systematic review, we aim to improve existing evidence on how the diagnostic accuracy of MMSE and GPCOG compares when used to identify dementia within the family practice setting. We also aim to make clinical practice recommendations based upon the variations in diagnostic accuracy identified between the MMSE and GPCOG.

Markers of ß-Cell Failure Predict Poor Glycemic Response to GLP-1 Receptor Agonist Therapy in Type 2 Diabetes.

OBJECTIVE: To assess whether clinical characteristics and simple biomarkers of ß-cell failure are associated with individual variation in glycemic response to GLP-1 receptor agonist (GLP-1RA) therapy in patients with type 2 diabetes. RESEARCH DESIGN AND METHODS: We prospectively studied 620 participants with type 2 diabetes and HbA1c ≥58 mmol/mol (7.5%) commencing GLP-1RA therapy as part of their usual diabetes care and assessed response to therapy over 6 months. We assessed the association between baseline clinical measurements associated with ß-cell failure and glycemic response (primary outcome HbA1c change 0-6 months) with change in weight (0-6 months) as a secondary outcome using linear regression and ANOVA with adjustment for baseline HbA1c and cotreatment change. RESULTS: Reduced glycemic response to GLP-1RAs was associated with longer duration of diabetes, insulin cotreatment, lower fasting C-peptide, lower postmeal urine C-peptide-to-creatinine ratio, and positive GAD or IA2 islet autoantibodies (P ≤ 0.01 for all). Participants with positive autoantibodies or severe insulin deficiency (fasting C-peptide ≤0.25 nmol/L) had markedly reduced glycemic response to GLP-1RA therapy (autoantibodies, mean HbA1c change -5.2 vs. -15.2 mmol/mol [-0.5 vs. -1.4%], P = 0.005; C-peptide <0.25 nmol/L, mean change -2.1 vs. -15.3 mmol/mol [-0.2 vs. -1.4%], P = 0.002). These markers were predominantly present in insulin-treated participants and were not associated with weight change. CONCLUSIONS: Clinical markers of low ß-cell function are associated with reduced glycemic response to GLP-1RA therapy. C-peptide and islet autoantibodies represent potential biomarkers for the stratification of GLP-1RA therapy in insulin-treated diabetes.

Can clinical features be used to differentiate type 1 from type 2 diabetes? A systematic review of the literature.

OBJECTIVE: Clinicians predominantly use clinical features to differentiate type 1 from type 2 diabetes yet there are no evidence-based clinical criteria to aid classification of patients. Misclassification of diabetes is widespread (7-15% of cases), resulting in patients receiving inappropriate treatment. We sought to identify which clinical criteria could be used to discriminate type 1 and type 2 diabetes. DESIGN: Systematic review of all diagnostic accuracy studies published since 1979 using clinical criteria to predict insulin deficiency (measured by C-peptide). DATA SOURCES: 14 databases including: MEDLINE, MEDLINE in Process and EMBASE. The search strategy took the form of: (terms for diabetes) AND (terms for C-Peptide). ELIGIBILITY CRITERIA: Diagnostic accuracy studies of any routinely available clinical predictors against a reference standard of insulin deficiency defined by cut-offs of C-peptide concentrations. No restrictions on race, age, language or country of origin. RESULTS: 10,917 abstracts were screened, and 231 full texts reviewed. 11 studies met inclusion criteria, but varied by age, race, year and proportion of participants who were C-peptide negative. Age at diagnosis was the most discriminatory feature in 7/9 studies where it was assessed, with optimal cut-offs (>70% mean sensitivity and specificity) across studies being <30 years or <40 years. Use of/time to insulin treatment and body mass index (BMI) were also discriminatory. When combining features, BMI added little over age at diagnosis and/or time to insulin (<1% improvement in classification). CONCLUSIONS: Despite finding only 11 studies, and considerable heterogeneity between studies, age at diagnosis and time to insulin were consistently the most discriminatory criteria. BMI, despite being widely used in clinical practice, adds little to these two criteria. The criteria identified are similar to the Royal College of General Practitioners National Health Service (RCGP/NHS) Diabetes classification guidelines, which use age at diagnosis <35 years and time to insulin <6 m. Until further studies are carried out, these guidelines represent a suitable classification scheme. SYSTEMATIC REVIEW REGISTRATION: PROSPERO reference CRD42012001736.

What is the test's accuracy in my practice population? Tailored meta-analysis provides a plausible estimate.

OBJECTIVES: Diagnostic test accuracy studies and meta-analyses may, in some cases, provide estimates that are highly improbable in practice; tailored meta-analysis provides a potential solution. To investigate the utility of tailored meta-analysis in synthesizing estimates of a test's accuracy compared with conventional meta-analysis for three case examples. STUDY DESIGN AND SETTING: MEDLINE, Embase, and CINAHL were searched for relevant studies, and routine data were collected on the test positive rate and disease prevalence from the case settings to define an applicable region for each setting. Three cases were evaluated: mammography in the NHS Breast Screening Programme, Patient Health Questionnaire-9 to screen for depression in general practice, and Centor's criteria used to diagnose group A ß-hemolytic streptococcus in general practice. For conventional meta-analysis, studies were selected using standard systematic review methods; for tailored meta-analysis, this selection was refined to those with results compatible with the applicable region for the setting. RESULTS: In each example, studies were excluded as a result of incorporating an applicable region for the setting. Comparing tailored with conventional meta-analysis, the positive likelihood ratios (with 95% confidence intervals in brackets) were 36.5 (23.0, 57.9) and 19.8 (12.8, 30.9), respectively, for mammography and 4.89 (2.02, 11.8) and 2.35 (1.51, 3.67), respectively, for Centor's criteria. This had the effect of increasing the positive predictive value from 17% to 27% for mammography and 23% to 38% for Centor's criteria. CONCLUSION: Tailored meta-analysis has the potential to provide a plausible estimate for a test's accuracy, which is specific to the practice setting. When compared with conventional meta-analysis, the difference may, in some cases, be sufficient to lead to different decisions on patient management.

Identifying good responders to glucose lowering therapy in type 2 diabetes: implications for stratified medicine.

AIMS: Defining responders to glucose lowering therapy can be important for both clinical care and for the development of a stratified approach to diabetes management. Response is commonly defined by either HbA1c change after treatment or whether a target HbA1c is achieved. We aimed to determine the extent to which the individuals identified as responders and non-responders to glucose lowering therapy, and their characteristics, depend on the response definition chosen. METHODS: We prospectively studied 230 participants commencing GLP-1 agonist therapy. We assessed participant characteristics at baseline and repeated HbA1c after 3 months treatment. We defined responders (best quartile of response) based on HbA1c change or HbA1c achieved. We assessed the extent to which these methods identified the same individuals and how this affected the baseline characteristics associated with treatment response. RESULTS: Different definitions of response identified different participants. Only 39% of responders by one definition were also good responders by the other. Characteristics associated with good response depend on the response definition chosen: good response by HbA1c achieved was associated with low baseline HbA1c (p<0.001), high C-peptide (p<0.001) and shorter diabetes duration (p = 0.01) whereas response defined by HbA1c change was associated with high HbA1c (p<0.001) only. We describe a simple novel method of defining treatment response based on a combination of HbA1c change and HbA1c achieved that defines response groups with similar baseline glycaemia. CONCLUSIONS: The outcome of studies aiming to identify predictors of treatment response to glucose lowering therapy may depend on how response is defined. Alternative definitions of response should be considered which minimise influence of baseline glycaemia.

Estimating a test's accuracy using tailored meta-analysis-How setting-specific data may aid study selection.

OBJECTIVES: To determine a plausible estimate for a test's performance in a specific setting using a new method for selecting studies. STUDY DESIGN AND SETTING: It is shown how routine data from practice may be used to define an "applicable region" for studies in receiver operating characteristic space. After qualitative appraisal, studies are selected based on the probability that their study accuracy estimates arose from parameters lying in this applicable region. Three methods for calculating these probabilities are developed and used to tailor the selection of studies for meta-analysis. The Pap test applied to the UK National Health Service (NHS) Cervical Screening Programme provides a case example. RESULTS: The meta-analysis for the Pap test included 68 studies, but at most 17 studies were considered applicable to the NHS. For conventional meta-analysis, the sensitivity and specificity (with 95% confidence intervals) were estimated to be 72.8% (65.8, 78.8) and 75.4% (68.1, 81.5) compared with 50.9% (35.8, 66.0) and 98.0% (95.4, 99.1) from tailored meta-analysis using a binomial method for selection. Thus, for a cervical intraepithelial neoplasia (CIN) 1 prevalence of 2.2%, the post-test probability for CIN 1 would increase from 6.2% to 36.6% between the two methods of meta-analysis. CONCLUSION: Tailored meta-analysis provides a method for augmenting study selection based on the study's applicability to a setting. As such, the summary estimate is more likely to be plausible for a setting and could improve diagnostic prediction in practice.

Psychological consequences of false-positive screening mammograms in the UK.

OBJECTIVES: To identify the psychological effects of false-positive screening mammograms in the UK. METHODS: Systematic review of all controlled studies and qualitative studies of women with a false-positive screening mammogram. The control group participants had normal mammograms. All psychological outcomes including returning for routine screening were permitted. All studies had a narrative synthesis. RESULTS: The searches returned seven includable studies (7/4423). Heterogeneity was such that meta-analysis was not possible. Studies using disease-specific measures found that, compared to normal results, there could be enduring psychological distress that lasted up to 3 years; the level of distress was related to the degree of invasiveness of the assessment. At 3 years the relative risks were, further mammography, 1.28 (95% CI 0.82 to 2.00), fine needle aspiration 1.80 (95% CI 1.17 to 2.77), biopsy 2.07 (95% CI 1.22 to 3.52) and early recall 1.82 (95% CI 1.22 to 2.72). Studies that used generic measures of anxiety and depression found no such impact up to 3 months after screening. Evidence suggests that women with false-positive mammograms have an increased likelihood of failing to reattend for routine screening, relative risk 0.97 (95% CI 0.96 to 0.98) compared with women with normal mammograms. CONCLUSIONS: Having a false-positive screening mammogram can cause breast cancer-specific distress for up to 3 years. The degree of distress is related to the invasiveness of the assessment. Women with false-positive mammograms are less likely to return for routine assessment than those with normal ones.

Route of delivery and baseline left ventricular ejection fraction, key factors of bone-marrow-derived cell therapy for ischaemic heart disease.

AIMS: Previous evaluation of autologous bone-marrow stem-cell (BMSC) therapy following acute myocardial infarction (AMI) suggests that cell dose and timing of stem-cell administration post-MI are important factors in the efficacy of cellular therapy. This study aimed to assess whether route of delivery and baseline left ventricular ejection fraction (LVEF) of the participants may also affect the outcome of BMSC treatment in patients with AMI and ischaemic heart disease (IHD). METHODS AND RESULTS: Randomized controlled trials of BMSCs as treatment for AMI and IHD were identified by searching MEDLINE, EMBASE, the Cochrane Library, and the Current Controlled Trials Register through to November 2008. Twenty-one trials (25 comparisons) with a total of 1091 participants were eligible. Data were analysed using a random-effects model. Improvement in LVEF in favour of the control was observed when BMSC were administered by intracoronary infusion [-0.19% (95% CI, -0.24 to -0.14; P < 0.00001)] in IHD patients. However, the effect on LVEF was statistically significant and in favour of BMSC when cells were delivered by intra-myocardial injection [5.85% (95% CI, 2.50-9.19; P = 0.0006)]. The significant improvement in LVEF observed in AMI patients was independent from the baseline LVEF of the participants. However, in patients suffering from chronic IHD, increase in LVEF was significant only in the group with lower LVEF at baseline [4.42% (CI, 1.87-6.96; P = 0.0007)]. CONCLUSION: Clinical evidence suggests that route of delivery and baseline LVEF influence the effect of BMSC therapy in treating AMI and chronic IHD.