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Many metabolomics studies of depression have been performed, but these have been limited by their scale. A comprehensive in silico analysis of global metabolite levels in large populations could provide robust insights into the pathological mechanisms underlying depression and candidate clinical biomarkers. Depression-associated metabolomics was studied in 2 datasets from the UK Biobank database: participants with lifetime depression (N = 123,459) and participants with current depression (N = 94,921). The Whitehall II cohort (N = 4744) was used for external validation. CatBoost machine learning was used for modeling, and Shapley additive explanations were used to interpret the model. Fivefold cross-validation was used to validate model performance, training the model on 3 of the 5 sets with the remaining 2 sets for validation and testing, respectively. Diagnostic performance was assessed using the area under the receiver operating characteristic curve. In the lifetime depression and current depression datasets and sex-specific analyses, 24 significantly associated metabolic biomarkers were identified, 12 of which overlapped in the 2 datasets. The addition of metabolic features slightly improved the performance of a diagnostic model using traditional (nonmetabolomics) risk factors alone (lifetime depression: area under the curve 0.655 vs. 0.658 with metabolomics; current depression: area under the curve 0.711 vs. 0.716 with metabolomics). The machine learning model identified 24 metabolic biomarkers associated with depression. If validated, metabolic biomarkers may have future clinical applications as supplementary information to guide early and population-based depression detection. [ABSTRACT FROM AUTHOR]

Summary: Complications are common following major surgery and are associated with increased use of healthcare resources, disability and mortality. Continued reliance on mortality estimates risks harming patients and health systems, but existing tools for predicting complications are unwieldy and inaccurate. We aimed to systematically construct an accurate pre‐operative model for predicting major postoperative complications; compare its performance against existing tools; and identify sources of inaccuracy in predictive models more generally. Complete patient records from the UK Peri‐operative Quality Improvement Programme dataset were analysed. Major complications were defined as Clavien–Dindo grade ≥ 2 for novel models. In a 75% train:25% test split cohort, we developed a pipeline of increasingly complex models, prioritising pre‐operative predictors using Least Absolute Shrinkage and Selection Operators (LASSO). We defined the best model in the training cohort by the lowest Akaike's information criterion, balancing accuracy and simplicity. Of the 24,983 included cases, 6389 (25.6%) patients developed major complications. Potentially modifiable risk factors (pain, reduced mobility and smoking) were retained. The best‐performing model was highly complex, specifying individual hospital complication rates and 11 patient covariates. This novel model showed substantially superior performance over generic and specific prediction models and scores. We have developed a novel complications model with good internal accuracy, re‐prioritised predictor variables and identified hospital‐level variation as an important, but overlooked, source of inaccuracy in existing tools. The complexity of the best‐performing model does, however, highlight the need for a step‐change in clinical risk prediction to automate the delivery of informative risk estimates in clinical systems. [ABSTRACT FROM AUTHOR]

Satellite observations have given us a clear idea of the changes in chlorophyll in the surface ocean on both a seasonal and interannual basis, but repeated observations at depth are much rarer. The permanently-stratified subtropical gyres in the Atlantic are highly oligotrophic, with most production centred on a deep chlorophyll maximum (DCM) just above the nitracline. This study explores the variations in this feature in the core of both gyres, considering both seasonal and interannual variations, and the linkages between changes at the surface and sub-surface. The in situ observations come from the Atlantic Meridional Transect (AMT), a long-running UK monitoring programme, and also from biogeochemical Argo floats. AMT provides measurements spanning more than 25 years directed through the centres of these gyres, but samples only 2 to 4 months per year and thus cannot resolve the seasonal variations, whereas the profiling floats give coverage throughout the year, but without the rigid spatial repeatability. These observational records are contrasted with representation of the centres of the gyres in two different biogeochemical models: MEDUSA and ERSEM, thus fulfilling one of AMT’s stated aims: the assessment of biogeochemical models. Whilst the four datasets show broadly the same seasonal patterns and that the DCM shallows when surface chlorophyll increases, the depth and peak concentration of the DCM differ among datasets. For most of the datasets the column-integrated chlorophyll for both gyres is around 19 mg m-2 (with the AMT fluorescence-derived values being much lower); however the MEDUSA model has a disparity between the northern and southern gyres that is not understood. Although the seasonal increase in surface chlorophyll is tied to a commensurate decrease in concentration at depth, on an interannual basis years with enhanced surface levels of chlorophyll correspond to increases at depth. Satellite-derived observations of surface chlorophyll concentration act as a good predictor of interannual changes in DCM depth for both gyres during their autumn season, but provide less skill in spring. [ABSTRACT FROM AUTHOR]

Context The adrenocorticotropin hormone stimulation test (AST) is used to diagnose adrenal insufficiency, and is often repeated in patients when monitoring recovery of the hypothalamo–pituitary–adrenal axis. Objective To develop and validate a prediction model that uses previous AST results with new baseline cortisol to predict the result of a new AST. Methods This was a retrospective, longitudinal cohort study in patients who had undergone at least 2 ASTs, using polynomial regression with backwards variable selection, at a Tertiary UK adult endocrinology center. Model was developed from 258 paired ASTs over 5 years in 175 adults (mean age 52.4 years, SD 16.4), then validated on data from 111 patients over 1 year (51.8, 17.5) from the same center, data collected after model development. Candidate prediction variables included previous test baseline adrenocorticotropin hormone (ACTH), previous test baseline and 30-minute cortisol, days between tests, and new baseline ACTH and cortisol used with calculated cortisol/ACTH ratios to assess 8 candidate predictors. The main outcome measure was a new test cortisol measured 30 minutes after Synacthen administration. Results Using 258 sequential ASTs from 175 patients for model development and 111 patient tests for model validation, previous baseline cortisol, previous 30-minute cortisol and new baseline cortisol were superior at predicting new 30-minute cortisol (R 2 = 0.71 [0.49-0.93], area under the curve [AUC] = 0.97 [0.94-1.0]) than new baseline cortisol alone (R 2 = 0.53 [0.22-0.84], AUC = 0.88 [0.81-0.95]). Conclusion Results of a previous AST can be objectively combined with new early-morning cortisol to predict the results of a new AST better than new early-morning cortisol alone. An online calculator is available at https://endocrinology.shinyapps.io/sheffield%5fsst%5fcalculator/ for external validation. [ABSTRACT FROM AUTHOR]

Objectives: To externally validate a published model predicting failure within 2 years after salvage focal ablation in men with localised radiorecurrent prostate cancer using a prospective, UK multicentre dataset. Patients and methods: Patients with biopsy‐confirmed ≤T3bN0M0 cancer after previous external beam radiotherapy or brachytherapy were included from the FOcal RECurrent Assessment and Salvage Treatment (FORECAST) trial (NCT01883128; 2014–2018; six centres), and from the high‐intensity focussed ultrasound (HIFU) Evaluation and Assessment of Treatment (HEAT) and International Cryotherapy Evaluation (ICE) UK‐based registries (2006–2022; nine centres). Eligible patients underwent either salvage focal HIFU or cryotherapy, with the choice based predominantly on anatomical factors. Per the original multivariable Cox regression model, the predicted outcome was a composite failure outcome. Model performance was assessed at 2 years post‐salvage with discrimination (concordance index [C‐index]), calibration (calibration curve and slope), and decision curve analysis. For the latter, two clinically‐reasonable risk threshold ranges of 0.14–0.52 and 0.26–0.36 were considered, corresponding to previously published pooled 2‐year recurrence‐free survival rates for salvage local treatments. Results: A total of 168 patients were included, of whom 84/168 (50%) experienced the primary outcome in all follow‐ups, and 72/168 (43%) within 2 years. The C‐index was 0.65 (95% confidence interval 0.58–0.71). On graphical inspection, there was close agreement between predicted and observed failure. The calibration slope was 1.01. In decision curve analysis, there was incremental net benefit vs a 'treat all' strategy at risk thresholds of ≥0.23. The net benefit was therefore higher across the majority of the 0.14–0.52 risk threshold range, and all of the 0.26–0.36 range. Conclusion: In external validation using prospective, multicentre data, this model demonstrated modest discrimination but good calibration and clinical utility for predicting failure of salvage focal ablation within 2 years. This model could be reasonably used to improve selection of appropriate treatment candidates for salvage focal ablation, and its use should be considered when discussing salvage options with patients. Further validation in larger, international cohorts with longer follow‐up is recommended. [ABSTRACT FROM AUTHOR]

Background: A prediction tool was developed and internally validated to aid the diagnosis of Cushing's syndrome in dogs attending UK primary‐care practices. External validation is an important part of model validation to assess model performance when used in different populations. Objectives: To assess the original prediction model's transportability, applicability, and diagnostic performance in a secondary‐care practice in the Netherlands. Animals: Two hundred thirty client‐owned dogs. Methods: Retrospective observational study. Medical records of dogs under investigation of Cushing's syndrome between 2011 and 2020 were reviewed. Dogs diagnosed with Cushing's syndrome by the attending internists and fulfilling ALIVE criteria were defined as cases, others as non‐cases. All dogs were scored using the aforementioned prediction tool. Dog characteristics and predictor‐outcome effects in development and validation data sets were compared to assess model transportability. Calibration and discrimination were examined to assess model performance. Results: Eighty of 230 dogs were defined as cases. Significant differences in dog characteristics were found between UK primary‐care and Dutch secondary‐care populations. Not all predictors from the original model were confirmed to be significant predictors in the validation sample. The model systematically overestimated the probability of having Cushing's syndrome (a = −1.10, P <.001). Calibration slope was 1.35 and discrimination proved excellent (area under the receiver operating curve = 0.83). Conclusions and Clinical Importance: The prediction model had moderate transportability, excellent discriminatory ability, and overall overestimated probability of having Cushing's syndrome. This study confirms its utility, though emphasizes that ongoing validation efforts of disease prediction tools are a worthwhile effort. [ABSTRACT FROM AUTHOR]

Background: Risk-based screening for lung cancer is currently being considered in several countries; however, the optimal approach to determine eligibility remains unclear. Ensemble machine learning could support the development of highly parsimonious prediction models that maintain the performance of more complex models while maximising simplicity and generalisability, supporting the widespread adoption of personalised screening. In this work, we aimed to develop and validate ensemble machine learning models to determine eligibility for risk-based lung cancer screening. Methods and findings: For model development, we used data from 216,714 ever-smokers recruited between 2006 and 2010 to the UK Biobank prospective cohort and 26,616 high-risk ever-smokers recruited between 2002 and 2004 to the control arm of the US National Lung Screening (NLST) randomised controlled trial. The NLST trial randomised high-risk smokers from 33 US centres with at least a 30 pack-year smoking history and fewer than 15 quit-years to annual CT or chest radiography screening for lung cancer. We externally validated our models among 49,593 participants in the chest radiography arm and all 80,659 ever-smoking participants in the US Prostate, Lung, Colorectal and Ovarian (PLCO) Screening Trial. The PLCO trial, recruiting from 1993 to 2001, analysed the impact of chest radiography or no chest radiography for lung cancer screening. We primarily validated in the PLCO chest radiography arm such that we could benchmark against comparator models developed within the PLCO control arm. Models were developed to predict the risk of 2 outcomes within 5 years from baseline: diagnosis of lung cancer and death from lung cancer. We assessed model discrimination (area under the receiver operating curve, AUC), calibration (calibration curves and expected/observed ratio), overall performance (Brier scores), and net benefit with decision curve analysis. Models predicting lung cancer death (UCL-D) and incidence (UCL-I) using 3 variables—age, smoking duration, and pack-years—achieved or exceeded parity in discrimination, overall performance, and net benefit with comparators currently in use, despite requiring only one-quarter of the predictors. In external validation in the PLCO trial, UCL-D had an AUC of 0.803 (95% CI: 0.783, 0.824) and was well calibrated with an expected/observed (E/O) ratio of 1.05 (95% CI: 0.95, 1.19). UCL-I had an AUC of 0.787 (95% CI: 0.771, 0.802), an E/O ratio of 1.0 (95% CI: 0.92, 1.07). The sensitivity of UCL-D was 85.5% and UCL-I was 83.9%, at 5-year risk thresholds of 0.68% and 1.17%, respectively, 7.9% and 6.2% higher than the USPSTF-2021 criteria at the same specificity. The main limitation of this study is that the models have not been validated outside of UK and US cohorts. Conclusions: We present parsimonious ensemble machine learning models to predict the risk of lung cancer in ever-smokers, demonstrating a novel approach that could simplify the implementation of risk-based lung cancer screening in multiple settings. Thomas Callender and colleagues present a development and validation study using parsimonious ensemble machine learning models to assess eligibility for lung cancer screening. Author summary: Why was this study done?: Screening and disease prevention programmes are increasingly bespoke; however, their simultaneous delivery at a population-scale presents considerable challenges. Lung cancer is the most common cause of cancer death worldwide, with poor survival in the absence of early detection. Screening for lung cancer among those at high-risk could reduce lung cancer-specific mortality by 20% to 24% among those screened, but the ideal way to determine if someone is high-risk remains uncertain and existing approaches are resource intensive. What did the researchers do and find?: We used data from the UK Biobank and US National Lung Screening Trial to develop novel, parsimonious models, to simplify the prediction of lung cancer risk and selection to lung cancer screening programmes. Using ensemble machine learning and 3 predictors—age, smoking duration, and pack-years—we found our models achieved or exceeded parity in performance with leading comparators despite requiring one-third of the variables. Our models were externally validated in the US Prostate, Lung, Colorectal, and Ovarian (PLCO) Cancer Screening Trial and benchmarked against models that are either in use or have performed strongly in previous analyses. What do these findings mean?: Risk assessment for lung cancer screening can be simplified without reducing performance, potentially improving the uptake and effectiveness of national lung cancer screening programmes, and therefore contributing to reducing deaths from lung cancer. Future research should focus on the application of this modelling approach to other conditions such as cardiovascular disease, diabetes, and chronic kidney disease to support the implementation at scale of multiple concurrent risk-stratified prevention and early detection programmes for major causes of morbidity and mortality. This study has key limitations as it is based on past data from the US and UK, so prospective evaluation in different countries and regions should be considered. [ABSTRACT FROM AUTHOR]

Objective: Heart failure (HF) often develops in patients with diabetes and is recognized for its role in increased cardiovascular morbidity and mortality in this population. Most existing models predict risk in patients with prevalent rather than incident diabetes and fail to account for sex differences in HF risk factors. We derived sex-specific models in Ontario, Canada to predict HF at diabetes onset and externally validated these models in the U.K.Research Design and Methods: Retrospective cohort study using international population-based data. Our derivation cohort comprised all Ontario residents aged ≥18 years who were diagnosed with diabetes between 2009 and 2018. Our validation cohort comprised U.K. patients aged ≥35 years who were diagnosed with diabetes between 2007 and 2017. Primary outcome was incident HF. Sex-stratified multivariable Fine and Gray subdistribution hazard models were constructed, with death as a competing event.Results: A total of 348,027 Ontarians (45% women) and 54,483 U.K. residents (45% women) were included. At 1, 5, and 9 years, respectively, in the external validation cohort, the C-statistics were 0.81 (95% CI 0.79-0.84), 0.79 (0.77-0.80), and 0.78 (0.76-0.79) for the female-specific model; and 0.78 (0.75-0.80), 0.77 (0.76-0.79), and 0.77 (0.75-0.79) for the male-specific model. The models were well-calibrated. Age, rurality, hypertension duration, hemoglobin, HbA1c, and cardiovascular diseases were common predictors in both sexes. Additionally, mood disorder and alcoholism (heavy drinker) were female-specific predictors, while income and liver disease were male-specific predictors.Conclusions: Our findings highlight the importance of developing sex-specific models and represent an important step toward personalized lifestyle and pharmacologic prevention of future HF development. [ABSTRACT FROM AUTHOR]

The G-ROP model was proposed to improve the retinopathy of prematurity (ROP) screening efficiency. It is based on gestational age, birth weight and postnatal weight gain. The current study aimed to validate the G-ROP model's ability to predict ROP in cohorts of premature infants from Egypt and the United Kingdom (UK). We retrospectively reviewed the records of preterm infants born between 1st of January and 30th of June 2018 with a known outcome for ROP screening and regular weight measurements until day 39 after birth. We applied the G-ROP model to the study group and calculated the sensitivity of the model for detecting Early Treatment of ROP (ETROP) study type 1 ROP and for any ROP and calculated the reduction of the number of infants requiring ROP screening by the model application. We applied the G-ROP model on 605 infants (504 from Egypt and 101 from the UK). The model successfully predicted all type 1 ROP cases (100% sensitivity) in both cohorts (95% confidence interval [CI], 91.1–100% in the Egyptian cohort and 65.5–100% in the UK cohort). The model reduced the number of infants requiring screening by 14.1% in the Egyptian cohort and 21.8% in the UK cohort. The G-ROP model was successfully validated for detecting type 1 ROP and in both cohorts from Egypt and the UK. [ABSTRACT FROM AUTHOR]

Significant geoelectric fields are produced by the interaction of rapidly varying magnetic fields with the conductive Earth, particularly during intense geomagnetic activity. Though usually harmless, large or sustained geoelectric fields can damage grounded infrastructure such as high-voltage transformers and pipelines via geomagnetically induced currents (GICs). A key aspect of understanding the effects of space weather on grounded infrastructure is through the spatial and temporal variation of the geoelectric field. Globally, there are few long-term monitoring sites of the geoelectric field, so in 2012 measurements of the horizontal surface field were started at Lerwick, Eskdalemuir and Hartland observatories in the UK. Between 2012 and 2020, the maximum value of the geoelectric field observed was around 1 V/km in Lerwick, 0.5 V/km in Eskdalemuir and 0.1 V/km in Hartland during the March 2015 storm. These long-term observations also allow comparisons with models of the geoelectric field to be made. We use the measurements to compute magnetotelluric impedance transfer functions at each observatory for periods from 20 to 30,000 s. These are then used to predict the geoelectric field at the observatory sites during selected storm times that match the recorded fields very well (correlation around 0.9). We also compute geoelectric field values from a thin-sheet model of Britain, accounting for the diverse geological and bathymetric island setting. We find the thin-sheet model captures the peak and phase of the band-passed geoelectric field reasonably well, with linear correlation of around 0.4 in general. From these two modelling approaches, we generate geoelectric field values for historic storms (March 1989 and October 2003) and find the estimates of past peak geoelectric fields of up to 1.75 V/km in Eskdalemuir. However, evidence from high voltage transformer GIC measurements during these storms suggests these estimates are likely to represent an underestimate of the true value. [ABSTRACT FROM AUTHOR]

In travel demand modelling, trip distance distributions or trip time distributions are used to evaluate how well a model fits with observed sample data. Therefore, the comparison of distributions is an essential part in the model validation process. Despite its importance, the common modelling guidelines from the UK, the USA or Austria provide little information about the correct structure and handling of such distributions. Likewise, common statistical methods are not practicable for the validation of transport models. This lack of rules leads to individual solutions, which complicate a model validation and the comparison of models. For example, when comparing two distributions the quality indicator strongly depends on the number of classes. Therefore, guidelines for model validation need to suggest an appropriate way to determine the number of classes. The paper suggests a method for evaluating trip distance distributions and trip time distributions within the model validation process of a travel demand model. It proposes (a) indicators for a classification which consider mode-specific trip distances and trip times (b) a generic classification method based on an equiquantile class width, quality indicators for comparing two distributions and (c) to use relative frequencies instead of absolute frequencies for the calculation of the quality indicators. [ABSTRACT FROM AUTHOR]

Exploring and combining results from more than one real‐world data (RWD) source might be necessary in order to explore variability and demonstrate generalizability of the results or for regulatory requirements. However, the heterogeneous nature of RWD poses challenges when working with more than one source, some of which can be solved by analyzing databases converted into a common data model (CDM). The main objective of the study was to evaluate the implementation of the Observational Medical Outcome Partnership (OMOP) CDM on IQVIA Medical Research Data (IMRD)‐UK data. A drug utilization study describing the prescribing of codeine for pain in children was used as a case study to be replicated in IMRD‐UK and its corresponding OMOP CDM transformation. Differences between IMRD‐UK source and OMOP CDM were identified and investigated. In IMRD‐UK updated to May 2017, results were similar between source and transformed data with few discrepancies. These were the result of different conventions applied during the transformation regarding the date of birth for children younger than 15 years and the start of the observation period, and of a misclassification of two drug treatments. After the initial analysis and feedback provided, a rerun of the analysis in IMRD‐UK updated to September 2018 showed almost identical results for all the measures analyzed. For this study, the conversion to OMOP CDM was adequate. Although some decisions and mapping could be improved, these impacted on the absolute results but not on the study inferences. This validation study supports six recommendations for good practice in transforming to CDMs. [ABSTRACT FROM AUTHOR]

Objective: To develop enhanced prediction models to update the QUiPP App prototype, a tool providing individualized risk of spontaneous preterm birth (sPTB), for use in women with symptoms of threatened preterm labor (TPTL), incorporating risk factors, transvaginal ultrasound assessment of cervical length (CL) and cervicovaginal fluid quantitative fetal fibronectin (qfFN) test results.Methods: Participants were pregnant women between 23 + 0 and 34 + 6 weeks' gestation with symptoms of TPTL, recruited as part of four prospective cohort studies carried out at 16 UK hospitals between October 2010 and October 2017. The training set comprised all women whose outcomes were known in May 2017 (n = 1032). The validation set comprised women whose outcomes were gathered between June 2017 and March 2018 (n = 506). Parametric survival models were developed for three combinations of predictors: risk factors plus qfFN test results alone, risk factors plus CL alone, and risk factors plus both qfFN and CL. The best models were selected using the Akaike and Bayesian information criteria. The estimated probability of sPTB < 30, < 34 or < 37 weeks' gestation and within 1 or 2 weeks of testing was calculated and receiver-operating-characteristics (ROC) curves were created to demonstrate the diagnostic ability of the prediction models.Results: Predictive statistics were similar between the training and the validation sets at most outcome time points and for each combination of predictors. Areas under the ROC curves (AUC) demonstrated that all three algorithms had good accuracy for the prediction of sPTB at < 30, < 34 and < 37 weeks' gestation and within 1 and 2 weeks' post-testing in the validation set, particularly the model combining risk factors plus qfFN alone (AUC: 0.96 at < 30 weeks; 0.85 at < 34 weeks; 0.77 at < 37 weeks; 0.91 at < 1 week from testing; and 0.92 at < 2 weeks from testing).Conclusions: Validation of the new prediction models suggests that the QUiPP App v.2 can reliably calculate risk of sPTB in women with TPTL. Use of the QUiPP App in practice could lead to better targeting of intervention, while providing reassurance and avoiding unnecessary intervention in women at low risk. Copyright © 2019 ISUOG. Published by John Wiley & Sons Ltd. [ABSTRACT FROM AUTHOR]

Objectives: Accurate mid-pregnancy prediction of spontaneous preterm birth (sPTB) is essential to ensure appropriate surveillance of high-risk women. Advancing the QUiPP App prototype, QUiPP App v.2 aimed to provide individualized risk of delivery based on cervical length (CL), quantitative fetal fibronectin (qfFN) or both tests combined, taking into account further risk factors, such as multiple pregnancy. Here we report development of the QUiPP App v.2 predictive models for use in asymptomatic high-risk women, and validation using a distinct dataset in order to confirm the accuracy and transportability of the QUiPP App, overall and within specific clinically relevant time frames.Methods: This was a prospective secondary analysis of data of asymptomatic women at high risk of sPTB recruited in 13 UK preterm birth clinics. Women were offered longitudinal qfFN testing every 2-4 weeks and/or transvaginal ultrasound CL measurement between 18 + 0 and 36 + 6 weeks' gestation. A total of 1803 women (3878 visits) were included in the training set and 904 women (1400 visits) in the validation set. Prediction models were created based on the training set for use in three groups: patients with risk factors for sPTB and CL measurement alone, with risk factors for sPTB and qfFN measurement alone, and those with risk factors for sPTB and both CL and qfFN measurements. Survival analysis was used to identify the significant predictors of sPTB, and parametric structures for survival models were compared and the best selected. The estimated overall probability of delivery before six clinically important time points (< 30, < 34 and < 37 weeks' gestation and within 1, 2 and 4 weeks after testing) was calculated for each woman and analyzed as a predictive test for the actual occurrence of each event. This allowed receiver-operating-characteristics curves to be plotted, and areas under the curve (AUC) to be calculated. Calibration was performed to measure the agreement between expected and observed outcomes.Results: All three algorithms demonstrated high accuracy for the prediction of sPTB at < 30, < 34 and < 37 weeks' gestation and within 1, 2 and 4 weeks of testing, with AUCs between 0.75 and 0.90 for the use of qfFN and CL combined, between 0.68 and 0.90 for qfFN alone, and between 0.71 and 0.87 for CL alone. The differences between the three algorithms were not statistically significant. Calibration confirmed no significant differences between expected and observed rates of sPTB within 4 weeks and a slight overestimation of risk with the use of CL measurement between 22 + 0 and 25 + 6 weeks' gestation.Conclusions: The QUiPP App v.2 is a highly accurate prediction tool for sPTB that is based on a unique combination of biomarkers, symptoms and statistical algorithms. It can be used reliably in the context of communicating to patients the risk of sPTB. Whilst further work is required to determine its role in identifying women requiring prophylactic interventions, it is a reliable and convenient screening tool for planning follow-up or hospitalization for high-risk women. Copyright © 2019 ISUOG. Published by John Wiley & Sons Ltd. [ABSTRACT FROM AUTHOR]

Quantifying tumor-infiltrating lymphocytes (TILs) in breast cancer tumors is a challenging task for pathologists. With the advent of whole slide imaging that digitizes glass slides, it is possible to apply computational models to quantify TILs for pathologists. Development of computational models requires significant time, expertise, consensus, and investment. To reduce this burden, we are preparing a dataset for developers to validate their models and a proposal to the Medical Device Development Tool (MDDT) program in the Center for Devices and Radiological Health of the U.S. Food and Drug Administration (FDA). If the FDA qualifies the dataset for its submitted context of use, model developers can use it in a regulatory submission within the qualified context of use without additional documentation. Our dataset aims at reducing the regulatory burden placed on developers of models that estimate the density of TILs and will allow head-to-head comparison of multiple computational models on the same data. In this paper, we discuss the MDDT preparation and submission process, including the feedback we received from our initial interactions with the FDA and propose how a qualified MDDT validation dataset could be a mechanism for open, fair, and consistent measures of computational model performance. Our experiences will help the community understand what the FDA considers relevant and appropriate (from the perspective of the submitter), at the early stages of the MDDT submission process, for validating stromal TIL density estimation models and other potential computational models. © 2023 The Authors. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of The Pathological Society of Great Britain and Ireland. This article has been contributed to by U.S. Government employees and their work is in the public domain in the USA., (© 2023 The Authors. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of The Pathological Society of Great Britain and Ireland. This article has been contributed to by U.S. Government employees and their work is in the public domain in the USA.)

The behaviour of the CRI-MECH chemistry scheme was investigated over North-West Europe for the first week of each month for the year of 2015 using the mesoscale non-hydrostatic 3-D meteorological model, WRF-Chem. The model simulations of the distribution of important trace gas species (e.g. ozone, CO, NO, NO 2 and SO 2) were compared with ground-based measurements of selected rural and urban areas in UK. The model validation using the UK Defra Automatic Urban and Rural Network (AURN) measured data from sites across the UK showed a reasonable agreement between model-measurement for ozone (R = 0.45 for rural sites and R = 0.34 for urban sites), for NO 2 (R = 0.65 for rural sites and R = 0.45 for urban sites) and for NO (R = 0.35 for rural sites and R = 0.22 for urban sites). However, a poor correlation between model-measurement is found for CO (R = 0.15 for urban sites) and for SO 2 (R = 0.1 for rural sites and R = 0.27 for urban sites). Further investigations showed a poorer performance of the model ozone, CO, NO and NO 2 during summer and autumn months compared with winter months. The differences in model performance were most noticeable for ozone with the other species having more variance between sites and months. The possible causes for the deviations between the modelled and the measured data are discussed. • Investigating the behavior of the CRI-MECH over North-West Europe for ozone, NOx, CO and SO 2 for different seasons. • Validating the model using UK Defra Automatic Urban and Rural Network measured data across the UK. • A better model performance is found in rural sites compared to urban sites and during winter compared to summer and autumn. [ABSTRACT FROM AUTHOR]

Background: Atrial fibrillation (AF) is caused by different mechanisms but current treatment strategies do not target these mechanisms. Stratified therapy based on mechanistic drivers and biomarkers of AF have the potential to improve AF prevention and management outcomes. We will integrate mechanistic insights with known pathophysiological drivers of AF in models predicting recurrent AF and prevalent AF to test hypotheses related to AF mechanisms and response to rhythm control therapy.Methods: We will harmonise and combine baseline and outcome data from 12 studies collected by six centres from the United Kingdom, Germany, France, Spain, and the Netherlands which assess prevalent AF or recurrent AF. A Delphi process and statistical selection will be used to identify candidate clinical predictors. Prediction models will be developed in patients with AF for AF recurrence and AF-related outcomes, and in patients with or without AF at baseline for prevalent AF. Models will be used to test mechanistic hypotheses and investigate the predictive value of plasma biomarkers.Discussion: This retrospective, harmonised, individual patient data analysis will use information from 12 datasets collected in five European countries. It is envisioned that the outcome of this analysis would provide a greater understanding of the factors associated with recurrent and prevalent AF, potentially allowing development of stratified approaches to prevention and therapy management. [ABSTRACT FROM AUTHOR]

Background: Seizures are the main cause of maternal death in women with epilepsy, but there are no tools for predicting seizures in pregnancy. We set out to develop and validate a prognostic model, using information collected during the antenatal booking visit, to predict seizure risk at any time in pregnancy and until 6 weeks postpartum in women with epilepsy on antiepileptic drugs.Methods and Findings: We used datasets of a prospective cohort study (EMPiRE) of 527 pregnant women with epilepsy on medication recruited from 50 hospitals in the UK (4 November 2011-17 August 2014). The model development cohort comprised 399 women whose antiepileptic drug doses were adjusted based on clinical features only; the validation cohort comprised 128 women whose drug dose adjustments were informed by serum drug levels. The outcome was epileptic (non-eclamptic) seizure captured using diary records. We fitted the model using LASSO (least absolute shrinkage and selection operator) regression, and reported the performance using C-statistic (scale 0-1, values > 0.5 show discrimination) and calibration slope (scale 0-1, values near 1 show accuracy) with 95% confidence intervals (CIs). We determined the net benefit (a weighted sum of true positive and false positive classifications) of using the model, with various probability thresholds, to aid clinicians in making individualised decisions regarding, for example, referral to tertiary care, frequency and intensity of monitoring, and changes in antiepileptic medication. Seizures occurred in 183 women (46%, 183/399) in the model development cohort and in 57 women (45%, 57/128) in the validation cohort. The model included age at first seizure, baseline seizure classification, history of mental health disorder or learning difficulty, occurrence of tonic-clonic and non-tonic-clonic seizures in the 3 months before pregnancy, previous admission to hospital for seizures during pregnancy, and baseline dose of lamotrigine and levetiracetam. The C-statistic was 0.79 (95% CI 0.75, 0.84). On external validation, the model showed good performance (C-statistic 0.76, 95% CI 0.66, 0.85; calibration slope 0.93, 95% CI 0.44, 1.41) but with imprecise estimates. The EMPiRE model showed the highest net proportional benefit for predicted probability thresholds between 12% and 99%. Limitations of this study include the varied gestational ages of women at recruitment, retrospective patient recall of seizure history, potential variations in seizure classification, the small number of events in the validation cohort, and the clinical utility restricted to decision-making thresholds above 12%. The model findings may not be generalisable to low- and middle-income countries, or when information on all predictors is not available.Conclusions: The EMPiRE model showed good performance in predicting the risk of seizures in pregnant women with epilepsy who are prescribed antiepileptic drugs. Integration of the tool within the antenatal booking visit, deployed as a simple nomogram, can help to optimise care in women with epilepsy. [ABSTRACT FROM AUTHOR]

The stress concentration factors (SCFs) in tubular T-joints reinforced with external ring under in-plane bending (IPB) moment are investigated. After validation of the FE model with experimental results, a set of 156 3D FE models was generated and analyzed to parametrically investigate the effect of the ring and joint geometry on the SCFs and SCF ratios. Results showed that the SCF of the T-joints with external ring can be down to 71% of the SCF of the corresponding un-reinforced joint. Also, the reducing effect of the ring width and thickness on the SCFs becomes more significant when one of these parameters is big. Despite the significant effect of the ring on the decrease of the SCFs, no study is conducted on the SCFs in any joints with external ring. Consequently, the FE results were used to propose an accurate formula for determining the SCFs. Also, the proposed equation was checked according to the UK DoE acceptance standard. • The stress concentration factors (SCFs) in tubular T-joints reinforced with external ring subjected to IPB moment are evaluated. • 156 FE models are produced and analyzed for the parametric investigation. • The effect of the external ring geometry (width and thickness) and the joint geometry (τ , γ , and β) in T-joints are investigated. • A design formula is proposed for determining the SCFs in T-joints with external ring under IPB. [ABSTRACT FROM AUTHOR]

Background: Chronic kidney disease (CKD) is a major and increasing constituent of disease burdens worldwide. Early identification of patients at increased risk of developing CKD can guide interventions to slow disease progression, initiate timely referral to appropriate kidney care services, and support targeting of care resources. Risk prediction models can extend laboratory-based CKD screening to earlier stages of disease; however, to date, only a few of them have been externally validated or directly compared outside development populations. Our objective was to validate published CKD prediction models applicable in primary care.Methods: We synthesised two recent systematic reviews of CKD risk prediction models and externally validated selected models for a 5-year horizon of disease onset. We used linked, anonymised, structured (coded) primary and secondary care data from patients resident in Salford (population ~234 k), UK. All adult patients with at least one record in 2009 were followed-up until the end of 2014, death, or CKD onset (n = 178,399). CKD onset was defined as repeated impaired eGFR measures over a period of at least 3 months, or physician diagnosis of CKD Stage 3-5. For each model, we assessed discrimination, calibration, and decision curve analysis.Results: Seven relevant CKD risk prediction models were identified. Five models also had an associated simplified scoring system. All models discriminated well between patients developing CKD or not, with c-statistics around 0.90. Most of the models were poorly calibrated to our population, substantially over-predicting risk. The two models that did not require recalibration were also the ones that had the best performance in the decision curve analysis.Conclusions: Included CKD prediction models showed good discriminative ability but over-predicted the actual 5-year CKD risk in English primary care patients. QKidney, the only UK-developed model, outperformed the others. Clinical prediction models should be (re)calibrated for their intended uses. [ABSTRACT FROM AUTHOR]

Flood simulation models and hazard maps are only as good as the underlying data against which they are calibrated and tested. However, extreme flood events are by definition rare, so the observational data of flood inundation extent are limited in both quality and quantity. The relative importance of these observational uncertainties has increased now that computing power and accurate lidar scans make it possible to run high-resolution 2D models to simulate floods in urban areas. However, the value of these simulations is limited by the uncertainty in the true extent of the flood. This paper addresses that challenge by analyzing a point dataset of maximum water extent from a flood event on the River Eden at Carlisle, United Kingdom, in January 2005. The observation dataset is based on a collection of wrack and water marks from two postevent surveys. A smoothing algorithm for identifying, quantifying, and reducing localized inconsistencies in the dataset is proposed and evaluated showing positive results. The proposed smoothing algorithm can be applied in order to improve flood inundation modeling assessment and the determination of risk zones on the floodplain. [ABSTRACT FROM AUTHOR]

We investigate how well the variability of extreme daily precipitation events across the United Kingdom is represented in a set of regional climate models and the E-OBS gridded data set. Instead of simply evaluating the climatologies of extreme precipitation measures, we develop an approach to validate the representation of physical mechanisms controlling extreme precipitation variability. In part I of this study we applied a statistical model to investigate the influence of the synoptic scale atmospheric circulation on extreme precipitation using observational rain gauge data. More specifically, airflow strength, direction and vorticity are used as predictors for the parameters of the generalised extreme value (GEV) distribution of local precipitation extremes. Here we employ this statistical model for our validation study. In a first step, the statistical model is calibrated against a gridded precipitation data set provided by the UK Met Office. In a second step, the same statistical model is calibrated against 14 ERA40 driven 25 km resolution RCMs from the ENSEMBLES project and the E-OBS gridded data set. Validation indices describing relevant physical mechanisms are derived from the statistical models for observations and RCMs and are compared using pattern standard deviation, pattern correlation and centered pattern root mean squared error as validation measures. The results for the different RCMs and E-OBS are visualised using Taylor diagrams. We show that the RCMs adequately simulate moderately extreme precipitation and the influence of airflow strength and vorticity on precipitation extremes, but show deficits in representing the influence of airflow direction. Also very rare extremes are misrepresented, but this result is afflicted with a high uncertainty. E-OBS shows considerable biases, in particular in regions of sparse data. The proposed approach might be used to validate other physical relationships in regional as well as global climate models. [ABSTRACT FROM AUTHOR]

Abstract: Objectives: To compare four UK models evaluating the cost-effectiveness of interventions in coronary heart disease (CHD), exploring the relative importance of structure and inputs in accounting for differences, and the scope for consensus on structure and data. Methods: We compared published cost-effectiveness results (incremental cost, quality-adjusted life year, and cost-effectiveness ratio) of three models conforming to the National Institute for Health and Clinical Excellence guidelines dealing with three interventions (statins, percutaneous coronary intervention, and clopidogrel) with a model developed in Southampton. Comparisons were made using three separate stages: 1) comparison of published results; 2) comparison of the results using the same data inputs wherever possible; and 3) an in-depth exploration of reasons for differences and the potential for consensus. Results: Although published results differed by up to 73% (for statins), standardization of inputs (stage 2) narrowed these gaps. Greater understanding of the reasons for differences was achieved, but a consensus on preferred values for all data inputs was not reached. Conclusions: We found that published guidance on methods was important to reduce variation in important model inputs. Although the comparison of models did not lead to consensus for all model inputs, it provided a better understanding of the reasons for these differences, and enhanced the transparency and credibility of all models. Similar comparisons would be aided by fuller publication of models, perhaps through detailed web appendices. [Copyright &y& Elsevier]

We develop a vector generalised linear model to describe the influence of the atmospheric circulation on extreme daily precipitation across the UK. The atmospheric circulation is represented by three covariates, namely synoptic scale airflow strength, direction and vorticity; the extremes are represented by the monthly maxima of daily precipitation, modelled by the generalised extreme value distribution (GEV). The model parameters for data from 689 rain gauges across the UK are estimated using a maximum likelihood estimator. Within the framework of vector generalised linear models, various plausible models exist to describe the influence of the individual covariates, possible nonlinearities in the covariates and seasonality. We selected the final model based on the Akaike information criterion (AIC), and evaluated the predictive power of individual covariates by means of quantile verification scores and leave-one-out cross validation. The final model conditions the location and scale parameter of the GEV on all three covariates; the shape parameter is modelled as a constant. The relationships between strength and vorticity on the one hand, and the GEV location and scale parameters on the other hand are modelled as natural cubic splines with two degrees of freedom. The influence of direction is parameterised as a sine with amplitude and phase. The final model has a common parameterisation for the whole year. Seasonality is partly captured by the covariates themselves, but mostly by an additional annual cycle that is parameterised as a phase-shifted sine and accounts for physical influences that we have not attempted to explicitly model, such as humidity. [ABSTRACT FROM AUTHOR]

The article presents a study on the analysis of the guidelines that will be used to evaluate the hydrodynamic and sediment transport models of estuary in Great Britain. The numerical hydrodynamic and sediment transport models have been anticipated to provide a better understanding of the estuarine process. The study further suggests the need to evaluate the sophisticated and computationally-intensive model with the use of the objective functions to prevent poor performance.

Summary: Flood inundation modelling on urbanised floodplains has become more feasible due to the increased availability of high resolution digital terrain data and computer power. However, studies that validate simulations with spatially distributed measurements are rare, especially for large events. This paper reports one of the most comprehensive validation data sets available to date on an urban flood, collected in January 2005 after a major event in the city of Carlisle, UK. For the first time this case study collated distributed urban maximum water level and extent measurements with gauged hydrographs, LiDAR elevation data and digital Mastermap® data. These data were used to build and calibrate two 2D diffusion wave models at the whole city scale, based on digital elevation data with and without buildings. RMSE between measured and simulated maximum water level was 0.32m and 0.28m for the models with and without buildings, respectively, the latter being more accurate due to blockages on the floodplain when building heights were included in the topography. The magnitude of simulation errors compared well with other studies in the literature, and considering potential errors in the measurement data indicates that the diffusion wave approach was adequate to capture the first-order physics relevant here, although a global channel roughness parameter was unable to capture the full spatially-varying dynamics of the flood event. [Copyright &y& Elsevier]

The article presents a study on the use of gamma test (GT) in the development of nonlinear modelling technique for daily solar radiation estimation in Great Britain. It says that useful information for input data selection and subsequent model development has been provided by GT. Moreover, the study suggests further exploration of the technique for informed data improvement and model selection and wider implications on various hydrological modelling practices. It also directs to a resolution about the relative importance of input variables and determine the optimum number of data points in smooth model construction.

In the present study, the stress concentration factors (SCFs) in circular hollow section X-connections retrofitted with fiber reinforced polymer (FRP) under compressive load are evaluated. After validation of the FE model with several available experimental data, a set of 279 FE models was created to parametrically investigates the efficacy of the FRP and connection geometry on the SCFs and SCF ratios. In the FE models, the contact between the FRP layers and steel members (chord, weld, and braces) was modeled. Results showed that the rise of the FRP sheet number causes a considerable drop in the SCFs. Moreover, the use of FRP causes the propagation of stresses in steel and FRP. Moreover, the rise of the elastic modulus of FRP along the fibers causes the more drop in the SCFs. Despite the notable efficacy of the FRP on the drop of the SCFs in the X-joints, there is not any study or formula in X-connections with FRP. Hence, the FE results were used to propose two precise formulas for quantifying the SCFs in X-connections with FRP layers at the saddle and crown points. Also, the proposed equations were checked according to the UK DoE acceptance standard. • The stress concentration factors (SCFs) in tubular X-joints retrofitted with the FRP are investigated. • 279 3-D FE models are created and analyzed for the parametric study. • The effect of the FRP (layer number, orientation, and material) and the joint geometry (β , γ , and τ) in X-joints are investigated. • Two parametric formulas for determining the SCFs in tubular X-joints retrofitted with FRP are proposed. [ABSTRACT FROM AUTHOR]

Background: Pre-eclampsia is a leading cause of maternal and perinatal mortality and morbidity. Early identification of women at risk during pregnancy is required to plan management. Although there are many published prediction models for pre-eclampsia, few have been validated in external data. Our objective was to externally validate published prediction models for pre-eclampsia using individual participant data (IPD) from UK studies, to evaluate whether any of the models can accurately predict the condition when used within the UK healthcare setting.Methods: IPD from 11 UK cohort studies (217,415 pregnant women) within the International Prediction of Pregnancy Complications (IPPIC) pre-eclampsia network contributed to external validation of published prediction models, identified by systematic review. Cohorts that measured all predictor variables in at least one of the identified models and reported pre-eclampsia as an outcome were included for validation. We reported the model predictive performance as discrimination (C-statistic), calibration (calibration plots, calibration slope, calibration-in-the-large), and net benefit. Performance measures were estimated separately in each available study and then, where possible, combined across studies in a random-effects meta-analysis.Results: Of 131 published models, 67 provided the full model equation and 24 could be validated in 11 UK cohorts. Most of the models showed modest discrimination with summary C-statistics between 0.6 and 0.7. The calibration of the predicted compared to observed risk was generally poor for most models with observed calibration slopes less than 1, indicating that predictions were generally too extreme, although confidence intervals were wide. There was large between-study heterogeneity in each model's calibration-in-the-large, suggesting poor calibration of the predicted overall risk across populations. In a subset of models, the net benefit of using the models to inform clinical decisions appeared small and limited to probability thresholds between 5 and 7%.Conclusions: The evaluated models had modest predictive performance, with key limitations such as poor calibration (likely due to overfitting in the original development datasets), substantial heterogeneity, and small net benefit across settings. The evidence to support the use of these prediction models for pre-eclampsia in clinical decision-making is limited. Any models that we could not validate should be examined in terms of their predictive performance, net benefit, and heterogeneity across multiple UK settings before consideration for use in practice.Trial Registration: PROSPERO ID: CRD42015029349 . [ABSTRACT FROM AUTHOR]

Background: The current severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) outbreak is a public health emergency and the case fatality rate in the United Kingdom is significant. Although there appear to be several early predictors of outcome, there are no currently validated prognostic models or scoring systems applicable specifically to patients with confirmed SARS-CoV-2.Objective: We aim to create a point-of-admission mortality risk scoring system using an artificial neural network (ANN).Methods: We present an ANN that can provide a patient-specific, point-of-admission mortality risk prediction to inform clinical management decisions at the earliest opportunity. The ANN analyzes a set of patient features including demographics, comorbidities, smoking history, and presenting symptoms and predicts patient-specific mortality risk during the current hospital admission. The model was trained and validated on data extracted from 398 patients admitted to hospital with a positive real-time reverse transcription polymerase chain reaction (RT-PCR) test for SARS-CoV-2.Results: Patient-specific mortality was predicted with 86.25% accuracy, with a sensitivity of 87.50% (95% CI 61.65%-98.45%) and specificity of 85.94% (95% CI 74.98%-93.36%). The positive predictive value was 60.87% (95% CI 45.23%-74.56%), and the negative predictive value was 96.49% (95% CI 88.23%-99.02%). The area under the receiver operating characteristic curve was 90.12%.Conclusions: This analysis demonstrates an adaptive ANN trained on data at a single site, which demonstrates the early utility of deep learning approaches in a rapidly evolving pandemic with no established or validated prognostic scoring systems. [ABSTRACT FROM AUTHOR]

A clinical prediction model is a tool for predicting healthcare outcomes, usually within a specific population and context. A common approach is to develop a new clinical prediction model for each population and context; however, this wastes potentially useful historical information. A better approach is to update or incorporate the existing clinical prediction models already developed for use in similar contexts or populations. In addition, clinical prediction models commonly become miscalibrated over time, and need replacing or updating. In this article, we review a range of approaches for re-using and updating clinical prediction models; these fall in into three main categories: simple coefficient updating, combining multiple previous clinical prediction models in a meta-model and dynamic updating of models. We evaluated the performance (discrimination and calibration) of the different strategies using data on mortality following cardiac surgery in the United Kingdom: We found that no single strategy performed sufficiently well to be used to the exclusion of the others. In conclusion, useful tools exist for updating existing clinical prediction models to a new population or context, and these should be implemented rather than developing a new clinical prediction model from scratch, using a breadth of complementary statistical methods.

The 2nd conference on 'Algal Modelling: Processes and Management' was held at The University of Reading on Monday July 6th 1998. A full capacity of 50 delegates attended, including visitors from overseas and the U.K. water industry. Twelve papers were delivered in two sessions chaired by Prof. Colin Reynolds (Institute of Freshwater Ecology &Visiting Prof. at the University of Reading) and Professor Paul Whitehead (Director of the Aquatic Environments Research Centre at the University of Reading). A key-note address was given by Prof. Steve Chapra (University of Colorado and University of Reading). Selected papers from the conference have been produced and refereed and now form part of this special issue of Hydrobiologia. The conference followed the 1st event held in 1996 and published in Hydrobiologia (Howard, 1997a) and aimed to provide an up-to-date review of the state-of-the-artof algal modelling and its application to the management of problem algal blooms. The problem of toxic cyanobacteria provided the basis for the conference and for the papers presented though attention was given to a wide range of hydrological and biological processes. In recent years, there has been widespread media coverage of the cyanobacterial bloom problem prompted by the rapid deaths of many animals afterimmersion in water containing toxic cyanobacteria and, in 1996, the apparent fatal poisoning of 45 dialysis patients in Brazil after receiving water supplied by a bloom-affected river. Public concern about the potential threat of toxic cyanobacteria is usually justified. Theapparent increase in the frequency of problem blooms may reflect increased monitoring, but is also a symptom of the global eutrophicationproblem. In England and Wales, the organisation responsible for monitoring and protecting the water environment, the National Rivers Authority (now part of the Environment Agency) established a Toxic Algae Task Group in 1989. At the first conference, the group's Secretary, Dr Fer [ABSTRACT FROM AUTHOR]

A street canyon model has been formulated based on work published byHertel and Berkowicz. An outline is given of the theoretical approach used, followed by a modelling of nitrogen oxides and carbon monoxide measurements from sites at Cromwell Road, Central London and Stratfard Road, Birmingham. Modelled concentrations were compared with observed mixing ratios for both sites. At Cromwell Road, good agreement was achieved for one month but which was not reproduced as well for the other two months tested. There is uncertainty as to the effect of one of the side streets and whether the general flow is altered duringperiods of marked solar heating. Also emissions from vehicles may vary from those assumed. The interpretation of the Stratford Road site's results was less straightforward with complications concerning background pollutant levels and changes in emissions from interrupted traffic flow. [ABSTRACT FROM AUTHOR]