Performance of the Leicester Risk Assessment and Leicester Practice Risk Scores for assessing the risk of undiagnosed type 2 diabetes or prediabetes in diverse populations: protocol for a systematic review of published validations and updates

Performance of the Leicester Risk Assessment and Leicester Practice Risk Scores for assessing the risk of undiagnosed type 2 diabetes or prediabetes in diverse populations: protocol for a systematic review of published validations and updates | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF protocol Performance of the Leicester Risk Assessment and Leicester Practice Risk Scores for assessing the risk of undiagnosed type 2 diabetes or prediabetes in diverse populations: protocol for a systematic review of published validations and updates Louise Haddon, Joie Ensor, Kamlesh Khunti, Gray LJ This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7252002/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 15 Jan, 2026 Read the published version in Diagnostic and Prognostic Research → Version 1 posted 9 You are reading this latest preprint version Abstract Background Approximately one million adults in the UK are estimated to have undiagnosed type 2 diabetes mellitus (T2DM), with a further 5.1 million adults with nondiabetic hyperglycaemia (prediabetes) that does not meet the threshold for a diabetes diagnosis. As T2DM may by asymptomatic, diagnoses can be delayed. The Leicester Risk Assessment score (LRA) and Leicester Practice Risk score (LPR) are diagnostic risk prediction models that use a combination of patient characteristics to predict an individual’s risk of undiagnosed T2DM and prediabetes, developed for use in community and primary care settings respectively. This study will systematically review all applications of these models and any published updates to evaluate their performance in different populations. This review has been registered with PROSPERO (CRD420251005841). Methods We will implement a citation search strategy to search Scopus, Web of Science and Google Scholar, restricted to full text, English language papers. Eligible papers will validate, update or modify either model. Data will be extracted using a form based on the Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modelling Studies ( CHARMS) checklist; missing information will be sought from authors or estimated from other available information where possible. Meta-analysis of predictive performance measures will be completed if sufficient data exist. Subgroup and sensitivity analyses will be used to explore between-study heterogeneity and risk-of-bias impact. Discussion This review will identify studies that have implemented, modified or validated the LRA and LPR for the risk of undiagnosed T2DM and prediabetes in different populations. This will allow summary measures, including level of uncertainty, of model performance to be calculated, making this highly relevant to individuals and stakeholders who recommend and implement these models. Review conclusions will also inform the potential update and recalibration of the models. This will ultimately lead to improved outcomes through earlier diagnosis and management. Type 2 diabetes mellitus Prediction Prognosis Systematic Review Risk Study Protocol Diagnostic Background It is estimated that approximately one million adults in the UK have undiagnosed type 2 diabetes mellitus (T2DM) ( 1 ); prediabetes and non-diabetic hyperglycaemia are terms used to describe raised blood glucose levels which do not meet the criteria for diagnosis of T2DM ( 2 ), and there are a further 5.1 million adults in the UK with hyperglycaemia that does not meet the threshold for a diabetes diagnosis ( 1 ). Whilst type 1 diabetes classically presents with symptoms such as excessive thirst, frequent urination and weight loss, T2DM may be completely asymptomatic, or any symptoms may be much less overt ( 2 ). The lack of symptoms and the difficulty in determining an exact date of onset can mean that there is a prolonged period without a diagnosis where complications can occur. Despite the time to diagnosis reported to have reduced from 9–12 years ( 3 ) to 4–7 years ( 4 ), as many as 37% of individuals with a new T2DM diagnosis may present with chronic complications ( 5 ). Left untreated, T2DM can cause serious complications that place a significant burden on the individual, with reported life expectancy up to ten years less for an individual with a T2DM diagnosis, compared to individuals without a diabetes diagnosis ( 6 ). Where individuals do not progress to a diabetes diagnosis, prediabetes is still associated with an elevated long-term risk of complications including cardiovascular and renal disease ( 7 ). There is also significant burden on the wider health economy, with approximately 10% of total NHS expenditure spent on T2DM ( 8 ). The presence of these indicates a higher risk of developing T2DM in the future as well as an increased cardiovascular disease risk compared to those with glucose levels in the normal range ( 2 ). It is believed that up to 50% of T2DM diagnoses can be prevented or delayed ( 9 ). Age, family history and ethnicity are non-modifiable risk factors but there are risk factors for developing T2DM that can be addressed by the individual. There is evidence that offering people with prediabetes a lifestyle intervention programme is effective at both an individual ( 10 )( 11 )( 12 ) and at a population level ( 13 ) in reducing the rate of progression from prediabetes to T2DM. Therefore, early identification of individuals who are at high risk of developing or have undiagnosed T2DM benefits both the individual and the wider health economy. Current National Institute for Health and Care Excellence (NICE) guidelines for identification of adults at high risk of T2DM ( 14 ) recommend a two-stage strategy: individuals over 18 years old who receive a high-risk score from a validated risk-assessment tool or self-assessment questionnaire should be offered a fasting plasma glucose or HbA1c test ( 15 ). Thus, an individual’s risk of developing T2DM should first be evaluated with a simple clinical prediction model before a more invasive intervention is requested only for those with deemed to have an elevated risk. Clinical prediction models, such as risk assessment tools, are statistical equations that generate the outcome risk, or likelihood, that an individual will develop a health condition within a specified time-period, usually by combining several predictors. Predictors are often patient characteristics such as age, sex, ethnicity ( 16 ). Models can be broadly split into two types: diagnostic models estimate an individual’s risk (or probability) of a specific health condition currently being present, whilst prognostic models estimate the risk of developing a specific health outcome over a specified time-period ( 17 ). For example, a diagnostic prediction model could be used to estimate an individual’s risk of currently having undiagnosed (prevalent) T2DM and a prognostic prediction model would estimate the risk of an individual developing (incident) T2DM within a 10-year period. Prediction models can affect outcomes, for example one may lead to withholding a test or intervention that could be beneficial to a subset of the population and have a cost to implement. The recommended process of developing and validating a clinical prediction model has been well described in the literature ( 18 )( 19 )( 20 )( 21 ). A dataset representing the underlying or target population is used to develop a clinical prediction model; evaluating the performance of the model in the same dataset is internal validation. External validation evaluates the model in a new dataset to determine whether model predictions are accurate in another population or setting and may be referred to as generalizability or transportability ( 22 ). Validation of a clinical prediction model usually considers key performance measures: calibration, which examines the agreement between the observed outcome risk and the risk predicted by the model, and discrimination, which considers how well the model predictions separate those individuals who do and do not develop the outcome of interest ( 16 ). As populations and care pathways change over time, the performance of a clinical prediction model may also change and deteriorate ( 23 ) so it is recommended that model impact on both health outcomes and also cost effectiveness should be evaluated, although few model impact studies have been published for prediction models ( 24 ). There have been many clinical prediction models published that evaluate the risk of an individual having T2DM. In 2011, a systematic review found 39 studies reporting 43 different diagnostic and prognostic models for predicting the risk of incident or prevalent T2DM ( 25 ). More recently, Asgari et al. ( 26 ) found a further 19 models predicting the risk of undiagnosed (prevalent) T2DM and 24 models predicting the risk of developing (incident) T2DM had been published between 2011 and 2019, highlighting that new prediction models continue to be developed at a significant rate. This is despite recommendations to recalibrate, update or aggregate existing models; model updating is seen as a better option than continually discarding prediction models and developing new ones, where previous research and historical data is ignored ( 17 )( 23 ). Clinical prediction models are often not updated (with or without information from new predictors) or evaluated properly in specific settings ( 27 ). Without this type of model evaluation, it cannot be confirmed that models in current use remain relevant and accurate. This review will focus on two prediction models developed by Gray et al.( 28 )( 29 ). The Leicester Risk Assessment score ( 28 )(LRA) is a validated non-invasive risk assessment tool that detects undiagnosed T2DM and prediabetes and includes age, sex, ethnicity, body mass index (BMI), waist circumference, family history of diabetes, hypertension as predictors. It was originally developed to be completed by hand as a simple scoring system and is recommended by NICE. The LRA is the only self-assessment tool listed by NHS England where an individual can find out whether they are at risk of developing T2DM ( 8 ). The majority of use of the LRA is as a web-based tool where the model is used on the Diabetes UK website( 30 ) and has been completed over 3 million times. In subsequent external validations, the LRA was shown to reliably identify individuals that may develop T2DM within the next 10 years ( 31 )( 32 ). Since the model was developed, it has been implemented and modified for use in different populations. The Leicester Practice Risk score ( 29 ) (LPR) is similar but was developed for use within a primary care setting, using predictor variables restricted to those routinely collected in health records including age, sex, ethnicity, BMI, family history of diabetes and hypertension; waist circumference was not as this is poorly recorded in primary care records ( 33 ). It has been included in electronic health records in primary care, where a GP practice population can be ranked according to risk so the highest risk group can be invited for targeted screening. This model differs from the LRA in how some of the predictors are treated; age and BMI are categorised in the LRA but are treated as continuous variables in the LPR. The aim of this systematic review is to identify and summarise all studies implementing, modifying or validating the LRA and/or LPR to assess model performance, in terms of discrimination, calibration and clinical utility, to predict the risk of undiagnosed T2DM and/or prediabetes in different populations. It will be the first to bring together all external validations of the models in different populations, including the addition of any new predictor variables. Thus, this review is important as it will yield a summary measure of both model’s discrimination and calibration in a wider population, including the level of uncertainty surrounding these, making this highly relevant to individuals and stakeholders who recommend and implement the models. The conclusions of the review will also inform a potential update and recalibration of the models. As the LRA is now increasingly used as a web-based tool, it provides the opportunity for a more complex model that can still be completed by a lay person. This will ultimately lead to improved outcomes for those with undiagnosed T2DM and prediabetes through earlier diagnosis and management. Methods The protocol is registered in PROSPERO (CRD420251005841) and reporting follows the Preferred Reporting Items for Systematic Reviews and Meta-Analyses Protocols (PRISMA-P) 2015 statement ( 34 ). Reporting of this review will adhere to Transparent reporting of multivariable prediction models for individual prognosis or diagnosis: checklist for systematic reviews and meta-analyses ( TRIPOD-SRMA) guidelines ( 35 ), and the guidelines provided by the Cochrane Prognosis Methods Group ( 36 ). Aims and objectives This review aims to a.) assess the performance of the LRA and LPR, in terms of discrimination, calibration and clinical utility, to predict an individual’s risk of undiagnosed T2DM and/or prediabetes by summarising available data in a meta-analysis to assess each model’s predictive performance, and b.) assess the effect of any additional predictor variables added to either model, including the strength of association for existing predictor variables. Gray et al. (2010) ( 28 ) did not publish the full LRA model equation, therefore model validations may have been based on the score generated by the model rather than the model itself. Thus, the review also aims to investigate whether validations based on the outcome of applying the model (the score) or the model itself affects conclusions on model performance. The review question has been outlined in Table 1 , according to the PICOTS format ( 37 ). To achieve these aims, we will conduct a systematic review to identify external validation studies published since August 2010, where the LRA or LPR has been implemented, with or without updating, and model impact studies that applied the LRA model and/or the LPR, or any modified version of, that was originally developed by Gray et al. (2010) and Gray et al. (2012). We will include all studies assessing the performance of the model, whether the primary objective of the study was to validate the LRA/LPR or to use the LRA/LPR as a comparator for other prognostic models. Table 1 Review question in PICOTS format Characteristics Details of what will be considered 1. Population Individuals without a confirmed diagnosis of T2DM and no confirmed diagnosis of non-diabetic hyperglycaemia/prediabetes 2. Index prediction model(s) Leicester Diabetes Risk score (LRA) published by Gray et al. 2010 Leicester Practice Risk score (LPR) published by Gray et al. 2012 3. Comparator prediction model(s) Comparing predictive accuracy of all applications of LRA or LRP, including those updated with additional predictors and recalibrated 4. Outcomes Diagnosis of type 2 diabetes or non-diabetic hyperglycaemia / prediabetes 5. Timing (two elements) Baseline: no T2DM or persistent hyperglycaemia diagnosis Outcome: risk of having either undiagnosed T2DM or prediabetes or developing T2DM over any time period 6. Setting To fully assess model performance in all populations it has been implemented, all studies regardless of care setting and geographical location will be included. Study eligibility criteria Studies will be eligible for inclusion in this review if they meet the following criteria: full-text publications in English, assessment of the performance of the LRA or LPR including diagnostic, calibration, discrimination and cost utility performance measures. Studies available in abstract form only will not be included as it is unlikely that enough information to adequately describe the target population and assess study quality and model performance would be included. Studies involving participants with an existing T2DM or prediabetes diagnosis will be excluded. No restrictions will be placed on the target population in order to fully assess model performance in all settings. Search strategy A forwards citation search strategy focused on the LRA and LPR will be used. Key publications will be identified as the primary papers (seed references) a priori . Titles and abstracts of published papers that have cited any of the key publications will be collected; relevant retrieved articles will then be used as new key references for a further iteration of citation searching. The process will repeat until no further eligible studies are identified ( 38 ). The number of iterations will be reported. It is acknowledged that citation searching is only an effective strategy if all eligible studies cite relevant early work ( 39 ). Key references will include the original model development papers and three subsequent validations of one or both models completed by or with the original authors to maximise the likelihood of finding all relevant papers, included in appendix 1. For each iteration of citation search, abstracts will be screened and grouped by model into one of three categories by two independent reviewers: papers that validate or implement the original clinical prediction model; papers that modify/amend the original prediction model (e.g. by amending or adding predictor variables); papers that are not relevant to the focus of this review. Information sources Citations and references from the key publications will be identified using the following databases to maximise the possible number of citations: Scopus Web of Science Google Scholar How the model is described (name and keywords) within included papers will be extracted and then a keyword search in the PubMed database using these key descriptors will be performed to ensure the search strategy is as comprehensive as possible. In addition to the electronic searches, reference and citation lists of the retrieved articles will be hand-searched for further papers of interest. Data management Screening will be performed using Covidence systematic review software ( 40 ) and selected articles, including PDF files, will be managed using Zotero ( 41 ). Selection process Two reviewers will independently screen titles and abstracts for eligibility, followed by full text assessment. Disagreements will be resolved by discussion; in cases of no consensus, final resolution will be achieved by involving a third reviewer. Study selection will be documented in a flow chart (PRISMA) ( 34 ). Data collection process Data will be extracted independently by two reviewers, in a piloted data extraction form based on the Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modelling Studies ( CHARMS) checklist ( 34 , 42 ); the applicability and appropriateness of the extraction form will be tested using the first five included studies. Any disagreements that cannot be resolved by discussion will be referred to a third reviewer. Data items Data extraction will include: Study information: author, title, publication date, date of validation, source, country Source of data: e.g. registry data, existing cohort Which model is being assessed (LRA, LPR or both, whether the model or score is being used) Study design characteristics (prospective or retrospective, type) Patient characteristics including mean age, % male, family history of diabetes, mean BMI, % history of hypertension, ethnicity Predictors included in the model including continuous or categorised, methods of measurement, thresholds for continuous predictors Completeness of data (missing data, losses to follow up) and how missing data are dealt with (complete case or type of imputation) Model validation: total sample size, total number of events, target population, setting, whether model was recalibrated, predictor effects adjusted Outcome measures used for diagnosis including HbA1c, fasting plasma glucose, random plasma glucose, oral glucose tolerance test and cut-off points used Timespan of prediction will be recorded to assess how the model is being used in practice. Model performance measures, including validation performance statistics for calibration, discrimination and overall performance measures including 95% confidence intervals or standard errors For calibration: calibration slope, intercepts, goodness-of-fit test or observed/expected (O/E) outcome ratios if reported. The number of observed events and the number of expected events will be extracted or derived from plots. As a binary outcome, Brier score will be extracted if available 38 . For discrimination: the c-statistic, including uncertainty measures (confidence interval or standard error.). As the c-statistic is identical to the area under the receiver operating curver (AUROC), this will also be used. Mean values and standard deviations of the LRA, LPR and their predictors will be extracted to explore the effect of patient characteristics / case-mix on LRA/LPR performance. Sensitivity and specificity (also reported as true positive fraction and false positive fraction respectively), defined as the probability of correct classification in individuals with and without the target condition, are used to assess accuracy of a diagnostic test and are recommended for meta-analysis ( 43 ) Positive and negative predictive values, the probability of the health outcome given a positive test result and the probability of not having the health outcome given a negative test result for threshold values are described as more interpretable and clinically relevant than sensitivity and specificity ( 43 ) The distribution of the linear predictor to determine how different the study sample is from the original develop sample (this can inform how we interpret the performance at validation). Statistical methods used, including if/how any new predictors were included and any recalibration Any clinical utility measures reported e.g. net benefit or decision curve analysis Missing data If performance measures are not explicitly stated, methods outlined by Debray et al. (2017 and 2018) ( 37 )( 44 ) will be used to estimate these; missing performance measures and confidence intervals or standard errors will be calculated if the required information is reported. Missing total O:E ratio and standard error will be estimated using statistical equations using the total number of observed and expected events and the total sample size. Calibration-in-the-large will be estimated using the linear predictor and regression co-efficients of the model. Variance or standard deviation of key patient characteristics can be estimated from reported ranges or histograms. Missing positive and negative predictive values will be generated from sensitivity and specificity ( 43 ). Where relevant information is not reported, efforts will be made to contact the study authors to request this. It is possible that some applications of the model may include additional predictor variables or exclude some key parameters. The effect of this on model performance will be examined in subgroup and sensitivity analyses. Estimates will be obtained using methods implemented in Stata package ( 45 ). If five or more studies are available, the same package will be used to meta-analyse model performance measures. If meta-analysis is not possible, results will be presented as a narrative. Risk-of-bias assessment Risk of bias of included studies will be assessed using the PROBAST (Prediction study of Risk of Bias Assessment Tool) ( 46 ) by two independent reviewers according to the following PROBAST domains: participants, predictors, outcome, analysis. Based on answers to the signalling questions within each domain, studies will be rated as low, high or unclear risk of bias. If information required to assess risk of bias is missing, study authors will be contacted to request additional information. Assessment of heterogeneity Possible causes of heterogeneity may be differences in study characteristic or quality, difference in baseline risk of the prevalence of T2DM across different populations, performance estimates may be based on validations of a modified model with adjusted parameters ( 44 ) or if a predictor variable included in the model is not measured or not included in one or more studies (systematically missing predictor variables) ( 47 ). It is possible that proxy predictor variables may have been used in some studies, for example if waist circumference is not recorded. Assessment of reporting deficiencies Whilst it is recommended that all prediction models report calibration and discrimination 30 , it has been reported that calibration and discrimination measures are often poorly reported or not reported at all ( 48 ). Therefore, this review will evaluate reporting deficiencies in included studies. Funnel plots will be used to visually assess for any evidence of publication bias ( 49 ). Data analysis and synthesis All following analyses will be performed for the LRA and LPR separately so that each individual model is evaluated. Performance measures will be summarized in tables and illustrated graphically. As the LRA and LPR are diagnostic models, they can be evaluated following methods described for meta-analysis of accuracy of a diagnostic test for use in NICE decision models. This uses a Bayesian approach, allowing more complex analyses considering multiple thresholds i.e. specific thresholds for risk grouping, fitting a random effects model for sensitivity and specificity ( 43 ). A random-effects meta-analysis of calibration (O:E ratio, calibration slope) and discrimination (c-statistic or AUROC) ( 43 , 50 ) will be performed if more than five studies are sufficiently similar ( 51 ). The c-statistics will be transformed with a logit transformation of the c-statistic, using statistical package Stata ( 45 ). If performance measures are not reported, they will be estimated according to methods outlined by Debray et al. ( 37 ) ( 44 ). Where appropriate, meta-analyses will be performed using a random-effects model to account for heterogeneity; between-study variance ( \(\:{\tau\:}^{2}\) ) and the percentage total variation in the study estimate due to between-study heterogeneity ( \(\:{I}^{2}\) ) will be calculated. An approximate 95% prediction interval for discrimination (C-statistic) and calibration (O:E ratio) measures will be calculated ( 37 ). Although no Grading of Recommendations, Assessment, Development, and Evaluations (GRADE) guidance currently exists for rating certainty of prediction model performance, a GRADE approach to assessing certainty in model calibration (assessed by the O:E ratio) ( 52 ) and proposing different methods to choose a threshold for rating certainty of evidence when assessing discrimination ( 53 ) has recently been published. Subgroup analysis and investigation of heterogeneity Meta-regression and subgroup analyses will be used to evaluate model performance if the number of identified studies supports this. Foroutan et al. (2021) ( 52 ) suggest reporting calibration separately for different risk categories or clinically meaningful subgroups as an overall O:E ratio may appear adequate despite a model underestimating the risk in half of the population and overestimating the risk in the other half. To identify potential sources of heterogeneity, we plan to compare the model performance in the whole study population with that in subgroups of studies that included participants with the following characteristics: By country By method/criteria used to diagnose diabetes Validation based on score or model It is expected that most studies will report aggregate data. Meta regression to investigate the association between patient-level characteristics and intervention effect can lead to ecological bias when using aggregate data ( 54 ) ( 55 ) and so characteristics in the target populations will be summarised and explored in subgroup analyses if little overlap exists, unless individual patient-level data is available. Sensitivity Analysis Sensitivity analysis will be used to test the effects of: estimation of any missing performance measures, if this method is used risk-of-bias rating, if a sufficient number of studies within each rating is found effect of estimating 95% prediction intervals and 95% confidence intervals on levels of certainty by exploring any changes between reported and estimated Discussion The results of this review will identify all studies that have implemented, modified or validated the LRA and LPR for the risk of undiagnosed T2DM and/or prediabetes in different populations. This will allow summary measures, including level of uncertainty, of model performance to be calculated, making this highly relevant to individuals and stakeholders who recommend and implement these models. The conclusions of the review will also inform the potential update and recalibration of the models. As the LRA is now increasingly used as a web-based tool, it provides the opportunity for a more complex model that can still be completed by a lay person. This will ultimately lead to improved outcomes for those with undiagnosed T2DM and prediabetes through earlier diagnosis and management. NICE guidelines (PH38) for the prevention of T2DM in people at high-risk list three examples of risk assessment tools: the LRA, LRP and QDiabetes risk calculator ( 56 ). This review focuses on the LRA and LRP models as these were developed for use in the UK to identify undiagnosed (prevalent) T2DM and/or prediabetes, allowing interventions such as the NHS Diabetes Prevention Programme to be targeted to individuals at high-risk. The QDiabetes risk calculator is a prognostic model to identify the (incident) risk of developing diabetes over the next 10 years ( 57 ) and so was developed for a different outcome, explaining the decision not to include this model in the review. As this review focuses on implementation and validation of two specific clinical prediction models, the research team deemed there to be a high probability that the original papers describing the model developments would be referenced in relevant studies. Therefore, it is expected that a high percentage of titles from the searches will be included, given the citation search strategy suggested. Wright et al. (2014) ( 58 ) compared the performance of database search strategies and citation search strategies and found that using this combination of resources stated in this protocol could identify 99.7% of total citations found by a traditional database search. Any future amendments to this protocol that result from knowledge acquired during the pilot data extraction stage or during initial iterations of citation searches will be documented in a separate section with justification for any changes included. Abbreviations T2DM: type 2 diabetes LRA : Leicester Risk Assessment score LRP : Leicester Practice Risk Score BMI : body mass index NICE : National Institute for Health and Care Excellence PICOTS : Population Index Prediction Model, Comparator, Outcomes, Timing, Setting PRISMA-P : Preferred Reporting Items for Systematic Reviews and Meta-Analyses Protocols CHARMS: Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modelling Studies TRIPOD-SRMA : Transparent reporting of multivariable prediction models for individual prognosis or diagnosis: checklist for systematic reviews and meta-analyses AUROC : area under receiver operating curve PROBAST : Prediction study of Risk Of Bias Assessment Tool GRADE: Grading of Recommendations, Assessment, Development, and Evaluations Declarations Availability of data and materials All data generated from this review will be available from the corresponding author on reasonable request. Acknowledgements This study was supported by the National Institute for Health and Care Research (NIHR) Applied Research Collaboration East Midlands (ARC EM) and Leicester NIHR Biomedical Research Centre (BRC). The views expressed are those of the author(s) and not necessarily those of the NIHR or the Department of Health and Social Care. Contributions LH is the guarantor and drafted the manuscript. All authors provided input on methodological issues; KK provided expertise on clinical issues. LG and JE provided statistical expertise on evidence synthesis methods and the review process. All authors provided advice and input regarding the protocol and have contributed, read and approved the final manuscript. Ethics declarations Ethics approval and consent to participate Not applicable Consent for publication Not applicable Competing interests LG and KK developed the original models. KK was Chair of the National Institute for Care and Excellence Type 2 diabetes: prevention in people at high risk Guidelines. Funding LH is funded by the Medical Research Council [grant number MR/W007002/1] as part of the Advanced Inter-Disciplinary Models (AIM) doctoral training partnership. LG is funded by the National Institute for Health and Care Research (NIHR) Applied Research Collaboration East Midlands (ARC EM) and Leicester NIHR Biomedical Research Centre (BRC). LG is an NIHR Senior Investigator. The views expressed are those of the author(s) and not necessarily those of the NIHR or the Department of Health and Social Care. JE is funded by the Birmingham NIHR Biomedical Research Centre (BRC). KK is supported by the National Institute for Health Research (NIHR) Applied Research Collaboration East Midlands (ARC EM), NIHR Global Research Centre for Multiple Long Term Conditions, NIHR Cross NIHR Collaboration for Multiple Long Term Conditions, NIHR Leicester Biomedical Research Centre (BRC) and the British Heart Foundation (BHF) Centre of Excellence. References Risk factors for pre-diabetes and undiagnosed type 2 diabetes in England - Office for National Statistics. Office for National Statistics 19.2.24. Magliano DJ, Co-Chair, Boyko EJ, Co-Chair. IDF Diabetes Atlas 10th edition. IDF Diabetes Atlas 10th Edition scientific committee 2021. Harris MI, Klein R, Welborn TA, Knuiman MW. Onset of NIDDM Occurs at Least 4–7 Yr Before Clinical Diagnosis. Porta M, Curletto G, Cipullo D, Rigault De La Longrais R, Trento M, Passera P, et al. Estimating the Delay Between Onset and Diagnosis of Type 2 Diabetes From the Time Course of Retinopathy Prevalence. 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Riley RD, Snell KIE, Archer L, Ensor J, Debray TPA, Van Calster B, et al. Evaluation of clinical prediction models (part 3): calculating the sample size required for an external validation study. BMJ 2024-01-22. Efthimiou O, Seo M, Chalkou K, Debray T, Egger M, Salanti G. Developing clinical prediction models: a step-by-step guide. BMJ 2024-09-03. Steyerberg EW. Clinical prediction models: a practical approach to development, validation, and updating. 1st ed. New York: Springer; 2009. Jenkins DA, Martin GP, Sperrin M, Riley RD, Debray TPA, Collins GS, et al. Continual updating and monitoring of clinical prediction models: time for dynamic prediction systems? Diagn Progn Res 2021-01-11;5(1). Steyerberg EW, Moons KGM, Van Der Windt DA, Hayden JA, Perel P, Schroter S, et al. Prognosis Research Strategy (PROGRESS) 3: Prognostic Model Research. Collins GS, Mallett S, Omar O, Yu L. Developing risk prediction models for type 2 diabetes: a systematic review of methodology and reporting. Asgari S, Khalili D, Hosseinpanah F, Hadaegh F. Prediction Models for Type 2 Diabetes Risk in the General Population: A Systematic Review of Observational Studies. Int J Endocrinol Metab 2021-03-22;19(3). Binuya MAE, Engelhardt EG, Schats W, Schmidt MK, Steyerberg EW. Methodological guidance for the evaluation and updating of clinical prediction models: a systematic review. BMC Med Res Methodol 2022-12-12;22(1). Gray LJ, Taub NA, Khunti K, Gardiner E, Hiles S, Webb DR, et al. The Leicester Risk Assessment score for detecting undiagnosed Type 2 diabetes and impaired glucose regulation for use in a multiethnic UK setting. Diabetic Medicine 2010-07-15;27(8):887. Gray LJ, Davies MJ, Hiles S, Taub NA, Webb DR, Srinivasan BT, et al. Detection of impaired glucose regulation and/or type 2 diabetes mellitus, using primary care electronic data, in a multiethnic UK community setting. Diabetologia 2012-01-10;55(4):959. Diabetes UK – Know Your Risk of Type 2 diabetes. Available at: https://riskscore.diabetes.org.uk . Accessed Feb 14, 2025. Barber SR, Dhalwani NN, Davies MJ, Khunti K, Gray LJ. External national validation of the Leicester Self-Assessment score for Type 2 diabetes using data from the English Longitudinal Study of Ageing. Diabet Med 2017-07-20;34(11):1575. Liu X, Littlejohns TJ, Bešević J, Bragg F, Clifton L, Collister JA, et al. Incorporating polygenic risk into the Leicester Risk Assessment score for 10-year risk prediction of type 2 diabetes. Diabetes & Metabolic Syndrome: Clinical Research & Reviews 2024-04-01;18(4):102996. Gray LJ, Khunti K, Wilmot EG, Yates T, Davies MJ. External validation of two diabetes risk scores in a young UK South Asian population. Diabetes Research and Clinical Practice 2014-04-02;104(3):451. Moher D, Shamseer L, Clarke M, Ghersi D, Liberati A, Petticrew M, et al. Preferred reporting items for systematic review and meta-analysis protocols (PRISMA-P) 2015 statement. Systematic reviews 2015;4:1–9. Snell KIE, Levis B, Damen JAA, Dhiman P, Debray TPA, Hooft L, et al. Transparent reporting of multivariable prediction models for individual prognosis or diagnosis: checklist for systematic reviews and meta-analyses (TRIPOD-SRMA). BMJ 2023-05-03. Moons KG, Hooft L, Williams K, Hayden JA, Damen JA, Riley RD. Implementing systematic reviews of prognosis studies in Cochrane. Cochrane Database of Systematic Reviews 2018-10-11. Debray TP, Damen JA, Snell KI, Ensor J, Hooft L, Reitsma JB, et al. A guide to systematic review and meta-analysis of prediction model performance. BMJ 2017;356. Hirt J, Nordhausen T, Appenzeller-Herzog C, Ewald H. Citation tracking for systematic literature searching: A scoping review. Research Synthesis Methods 2023;14(3):563–579. Janssens ACJW, Gwinn M. Novel citation-based search method for scientific literature: application to meta-analyses. BMC Med Res Methodol 2015-10-13;15(1). Veritas Health Innovation, Melbourne, Australia. Available at www.covidence.org. Covidence systematic review software. 2025. Corporation for Digital Scholarship. Zotero version 7.0.11. Moons KG, de Groot JA, Bouwmeester W, Vergouwe Y, Mallett S, Altman DG, et al. Critical appraisal and data extraction for systematic reviews of prediction modelling studies: the CHARMS checklist. PLoS medicine 2014;11(10):e1001744. Derezea E, Ades AE, Rogers G, Sutton AJ, Cooper NJ, Hamilton J, et al. TECHNICAL SUPPORT DOCUMENT 25: EVIDENCE SYNTHESIS OF DIAGNOSTIC TEST ACCURACY FOR DECISION MAKING REPORT BY THE DECISION SUPPORT UNIT NOVEMBER 2024. Debray TP, Damen JA, Riley RD, Snell K, Reitsma JB, Hooft L, et al. A framework for meta-analysis of prediction model studies with binary and time-to-event outcomes. Stat Methods Med Res 2018-07-23;28(9):2768. Stata package to be amended. Wolff RF, Moons KG, Riley RD, Whiting PF, Westwood M, Collins GS, et al. PROBAST: a tool to assess the risk of bias and applicability of prediction model studies. Ann Intern Med 2019;170(1):51–58. Riley RD, Ensor J, Snell KI, Debray TP, Altman DG, Moons KG, et al. External validation of clinical prediction models using big datasets from e-health records or IPD meta-analysis: opportunities and challenges. BMJ 2016;353. Heus P, Damen JAAG, Pajouheshnia R, Scholten RJPM, Reitsma JB, Collins GS, et al. Poor reporting of multivariable prediction model studies: towards a targeted implementation strategy of the TRIPOD statement. BMC Med 2018-07-19;16(1). Debray TP, Moons KG, Riley RD. Detecting small-study effects and funnel plot asymmetry in meta‐analysis of survival data: a comparison of new and existing tests. Research synthesis methods 2018;9(1):41–50. Damen JAA, Moons KGM, Van Smeden M, Hooft L. How to conduct a systematic review and meta-analysis of prognostic model studies. Clinical Microbiology and Infection 2022-08-04;29(4):434. Inthout J, Pa J, Borm GF. The Hartung-Knapp-Sidik-Jonkman method for random effects meta-analysis is straightforward and considerably outperforms the standard DerSimonian-Laird method. Foroutan FP, Guyatt G, Trivella M, Kreuzberger N, Skoetz N, Riley RD, et al. GRADE concept paper 2: Concepts for judging certainty on the calibration of prognostic models in a body of validation studies. Journal of Clinical Epidemiology 2021-11-18;143:202. Foroutan F, Mayer M, Guyatt G, Riley RD, Mustafa R, Kreuzberger N, et al. GRADE concept paper 8: judging the certainty of discrimination performance estimates of prognostic models in a body of validation studies. Journal of Clinical Epidemiology 2024-04-03;170. Berlin JA, Santanna J, Schmid CH, Szczech LA, Feldman HI. Individual patient-versus group‐level data meta‐regressions for the investigation of treatment effect modifiers: ecological bias rears its ugly head. Stat Med 2002;21(3):371–387. Burke DL, Ensor J, Riley RD. Meta-analysis using individual participant data: one‐stage and two‐stage approaches, and why they may differ. Stat Med 2017;36(5):855–875. Type 2 diabetes: prevention in people at high risk Public health guideline. 2017-09-15. Hippisley-Cox J, Coupland C, Robson J, Sheikh A, Brindle P. Predicting risk of type 2 diabetes in England and Wales: prospective derivation and validation of QDScore. BMJ 2009;338. Wright K, Golder S, Rodriguez-Lopez R. Citation searching: a systematic review case study of multiple risk behaviour interventions. Additional Declarations Competing interest reported. LG and KK developed the original models. KK was Chair of the National Institute for Care and Excellence Type 2 diabetes: prevention in people at high risk Guidelines. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7252002","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"protocol","associatedPublications":[],"authors":[{"id":496111336,"identity":"9c668df8-c1aa-432c-b05b-7d7123a7b8b2","order_by":0,"name":"Louise Haddon","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA8ElEQVRIiWNgGAWjYBACPgmGxAcJFRYMbMiijA14tLBJMDw2+HBGAqLlAHFaGJ9JzmyTgPCI0yLdnCbNO09Cjo/98OHPHyruMfC3H2CTnIFPi8yxZGvebRLGbDxpaRIHzhQzSJxJYJPcgNdhOYm3gVoS2yR4zBgOtiUwMNxgYJN8gFdL/gdp3jkS9W0S/J8/HPyXwCBPWEtCkuTMBokENgkeBomDDQkMBiAt+B2WkGzw4ZiEYRtPmpnEmWMJPIZnEpst8XmfXyIBGJU1NvLy7Ycff6ioSZCTO3744M0ePFowAA+BWBkFo2AUjIJRQAwAAKUYR5Oop/cvAAAAAElFTkSuQmCC","orcid":"","institution":"University of Leicester","correspondingAuthor":true,"prefix":"","firstName":"Louise","middleName":"","lastName":"Haddon","suffix":""},{"id":496111337,"identity":"15b7f7b3-1fa5-4996-aad1-57f53f68a18f","order_by":1,"name":"Joie Ensor","email":"","orcid":"","institution":"University of Birmingham","correspondingAuthor":false,"prefix":"","firstName":"Joie","middleName":"","lastName":"Ensor","suffix":""},{"id":496111339,"identity":"22b45384-08ed-4511-9cf5-6cea1675c9a8","order_by":2,"name":"Kamlesh Khunti","email":"","orcid":"","institution":"University Hospitals of Leicester NHS Trust","correspondingAuthor":false,"prefix":"","firstName":"Kamlesh","middleName":"","lastName":"Khunti","suffix":""},{"id":496111345,"identity":"8a399876-6ef2-4731-806e-e094e52eb767","order_by":3,"name":"Gray LJ","email":"","orcid":"","institution":"University of Leicester","correspondingAuthor":false,"prefix":"","firstName":"Gray","middleName":"","lastName":"LJ","suffix":""}],"badges":[],"createdAt":"2025-07-30 10:53:38","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7252002/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7252002/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s41512-026-00219-w","type":"published","date":"2026-01-15T16:29:08+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":100614508,"identity":"9d5e1db0-495e-413a-86e4-35755e9345fc","added_by":"auto","created_at":"2026-01-19 17:21:02","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":764246,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7252002/v1/50467776-1162-4867-8919-baad0e9ac85c.pdf"},{"id":88550732,"identity":"f6334731-47b8-4ca2-9b06-b6f9fd7943fb","added_by":"auto","created_at":"2025-08-07 15:25:26","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":15665,"visible":true,"origin":"","legend":"","description":"","filename":"Appendix1.docx","url":"https://assets-eu.researchsquare.com/files/rs-7252002/v1/c4619fbcf0dba9dd73afcb2a.docx"},{"id":88550738,"identity":"a23c72ae-c098-40bb-8df0-66203fcb1699","added_by":"auto","created_at":"2025-08-07 15:25:26","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":23705,"visible":true,"origin":"","legend":"","description":"","filename":"Protocolsupportinginformation.docx","url":"https://assets-eu.researchsquare.com/files/rs-7252002/v1/ebbf0cce2297ff09b90965ab.docx"}],"financialInterests":"Competing interest reported. LG and KK developed the original models.\nKK was Chair of the National Institute for Care and Excellence Type 2 diabetes: prevention in people at high risk Guidelines.","formattedTitle":"Performance of the Leicester Risk Assessment and Leicester Practice Risk Scores for assessing the risk of undiagnosed type 2 diabetes or prediabetes in diverse populations: protocol for a systematic review of published validations and updates","fulltext":[{"header":"Background","content":"\u003cp\u003eIt is estimated that approximately one million adults in the UK have undiagnosed type 2 diabetes mellitus (T2DM) (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e); prediabetes and non-diabetic hyperglycaemia are terms used to describe raised blood glucose levels which do not meet the criteria for diagnosis of T2DM (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e), and there are a further 5.1\u0026nbsp;million adults in the UK with hyperglycaemia that does not meet the threshold for a diabetes diagnosis (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e). Whilst type 1 diabetes classically presents with symptoms such as excessive thirst, frequent urination and weight loss, T2DM may be completely asymptomatic, or any symptoms may be much less overt (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e). The lack of symptoms and the difficulty in determining an exact date of onset can mean that there is a prolonged period without a diagnosis where complications can occur. Despite the time to diagnosis reported to have reduced from 9–12 years (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e) to 4–7 years (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e), as many as 37% of individuals with a new T2DM diagnosis may present with chronic complications (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eLeft untreated, T2DM can cause serious complications that place a significant burden on the individual, with reported life expectancy up to ten years less for an individual with a T2DM diagnosis, compared to individuals without a diabetes diagnosis (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e). Where individuals do not progress to a diabetes diagnosis, prediabetes is still associated with an elevated long-term risk of complications including cardiovascular and renal disease (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e). There is also significant burden on the wider health economy, with approximately 10% of total NHS expenditure spent on T2DM (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e). The presence of these indicates a higher risk of developing T2DM in the future as well as an increased cardiovascular disease risk compared to those with glucose levels in the normal range (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eIt is believed that up to 50% of T2DM diagnoses can be prevented or delayed (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e). Age, family history and ethnicity are non-modifiable risk factors but there are risk factors for developing T2DM that can be addressed by the individual. There is evidence that offering people with prediabetes a lifestyle intervention programme is effective at both an individual (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e)(\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e)(\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e) and at a population level (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e) in reducing the rate of progression from prediabetes to T2DM. Therefore, early identification of individuals who are at high risk of developing or have undiagnosed T2DM benefits both the individual and the wider health economy.\u003c/p\u003e\u003cp\u003eCurrent National Institute for Health and Care Excellence (NICE) guidelines for identification of adults at high risk of T2DM (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e) recommend a two-stage strategy: individuals over 18 years old who receive a high-risk score from a validated risk-assessment tool or self-assessment questionnaire should be offered a fasting plasma glucose or HbA1c test (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e). Thus, an individual’s risk of developing T2DM should first be evaluated with a simple clinical prediction model before a more invasive intervention is requested only for those with deemed to have an elevated risk.\u003c/p\u003e\u003cp\u003eClinical prediction models, such as risk assessment tools, are statistical equations that generate the outcome risk, or likelihood, that an individual will develop a health condition within a specified time-period, usually by combining several predictors. Predictors are often patient characteristics such as age, sex, ethnicity (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e). Models can be broadly split into two types: diagnostic models estimate an individual’s risk (or probability) of a specific health condition currently being present, whilst prognostic models estimate the risk of developing a specific health outcome over a specified time-period (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e). For example, a diagnostic prediction model could be used to estimate an individual’s risk of currently having undiagnosed (prevalent) T2DM and a prognostic prediction model would estimate the risk of an individual developing (incident) T2DM within a 10-year period.\u003c/p\u003e\u003cp\u003ePrediction models can affect outcomes, for example one may lead to withholding a test or intervention that could be beneficial to a subset of the population and have a cost to implement. The recommended process of developing and validating a clinical prediction model has been well described in the literature (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e)(\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e)(\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e)(\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e). A dataset representing the underlying or target population is used to develop a clinical prediction model; evaluating the performance of the model in the same dataset is internal validation. External validation evaluates the model in a new dataset to determine whether model predictions are accurate in another population or setting and may be referred to as generalizability or transportability (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e). Validation of a clinical prediction model usually considers key performance measures: calibration, which examines the agreement between the observed outcome risk and the risk predicted by the model, and discrimination, which considers how well the model predictions separate those individuals who do and do not develop the outcome of interest (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e). As populations and care pathways change over time, the performance of a clinical prediction model may also change and deteriorate (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e) so it is recommended that model impact on both health outcomes and also cost effectiveness should be evaluated, although few model impact studies have been published for prediction models (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThere have been many clinical prediction models published that evaluate the risk of an individual having T2DM. In 2011, a systematic review found 39 studies reporting 43 different diagnostic and prognostic models for predicting the risk of incident or prevalent T2DM (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e). More recently, Asgari et al. (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e) found a further 19 models predicting the risk of undiagnosed (prevalent) T2DM and 24 models predicting the risk of developing (incident) T2DM had been published between 2011 and 2019, highlighting that new prediction models continue to be developed at a significant rate. This is despite recommendations to recalibrate, update or aggregate existing models; model updating is seen as a better option than continually discarding prediction models and developing new ones, where previous research and historical data is ignored (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e)(\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e). Clinical prediction models are often not updated (with or without information from new predictors) or evaluated properly in specific settings (\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e). Without this type of model evaluation, it cannot be confirmed that models in current use remain relevant and accurate.\u003c/p\u003e\u003cp\u003eThis review will focus on two prediction models developed by Gray et al.(\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e)(\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e). The Leicester Risk Assessment score (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e)(LRA) is a validated non-invasive risk assessment tool that detects undiagnosed T2DM and prediabetes and includes age, sex, ethnicity, body mass index (BMI), waist circumference, family history of diabetes, hypertension as predictors. It was originally developed to be completed by hand as a simple scoring system and is recommended by NICE. The LRA is the only self-assessment tool listed by NHS England where an individual can find out whether they are at risk of developing T2DM (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e). The majority of use of the LRA is as a web-based tool where the model is used on the Diabetes UK website(\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e) and has been completed over 3\u0026nbsp;million times. In subsequent external validations, the LRA was shown to reliably identify individuals that may develop T2DM within the next 10 years (\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e)(\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e). Since the model was developed, it has been implemented and modified for use in different populations.\u003c/p\u003e\u003cp\u003eThe Leicester Practice Risk score (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e) (LPR) is similar but was developed for use within a primary care setting, using predictor variables restricted to those routinely collected in health records including age, sex, ethnicity, BMI, family history of diabetes and hypertension; waist circumference was not as this is poorly recorded in primary care records (\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e). It has been included in electronic health records in primary care, where a GP practice population can be ranked according to risk so the highest risk group can be invited for targeted screening. This model differs from the LRA in how some of the predictors are treated; age and BMI are categorised in the LRA but are treated as continuous variables in the LPR.\u003c/p\u003e\u003cp\u003eThe aim of this systematic review is to identify and summarise all studies implementing, modifying or validating the LRA and/or LPR to assess model performance, in terms of discrimination, calibration and clinical utility, to predict the risk of undiagnosed T2DM and/or prediabetes in different populations. It will be the first to bring together all external validations of the models in different populations, including the addition of any new predictor variables. Thus, this review is important as it will yield a summary measure of both model’s discrimination and calibration in a wider population, including the level of uncertainty surrounding these, making this highly relevant to individuals and stakeholders who recommend and implement the models.\u003c/p\u003e\u003cp\u003eThe conclusions of the review will also inform a potential update and recalibration of the models. As the LRA is now increasingly used as a web-based tool, it provides the opportunity for a more complex model that can still be completed by a lay person. This will ultimately lead to improved outcomes for those with undiagnosed T2DM and prediabetes through earlier diagnosis and management.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003eThe protocol is registered in PROSPERO (CRD420251005841) and reporting follows the Preferred Reporting Items for Systematic Reviews and Meta-Analyses Protocols (PRISMA-P) 2015 statement (\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e). Reporting of this review will adhere to Transparent reporting of multivariable prediction models for individual prognosis or diagnosis: checklist for systematic reviews and meta-analyses \u003cb\u003e(\u003c/b\u003eTRIPOD-SRMA) guidelines (\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e), and the guidelines provided by the Cochrane Prognosis Methods Group (\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cb\u003eAims and objectives\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThis review aims to a.) assess the performance of the LRA and LPR, in terms of discrimination, calibration and clinical utility, to predict an individual’s risk of undiagnosed T2DM and/or prediabetes by summarising available data in a meta-analysis to assess each model’s predictive performance, and b.) assess the effect of any additional predictor variables added to either model, including the strength of association for existing predictor variables. Gray et al. (2010) (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e) did not publish the full LRA model equation, therefore model validations may have been based on the score generated by the model rather than the model itself. Thus, the review also aims to investigate whether validations based on the outcome of applying the model (the score) or the model itself affects conclusions on model performance.\u003c/p\u003e\u003cp\u003eThe review question has been outlined in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, according to the PICOTS format (\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e). To achieve these aims, we will conduct a systematic review to identify external validation studies published since August 2010, where the LRA or LPR has been implemented, with or without updating, and model impact studies that applied the LRA model and/or the LPR, or any modified version of, that was originally developed by Gray et al. (2010) and Gray et al. (2012). We will include all studies assessing the performance of the model, whether the primary objective of the study was to validate the LRA/LPR or to use the LRA/LPR as a comparator for other prognostic models.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cdiv class=\"gridtable\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eReview question in PICOTS format\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"2\"\u003e\u003c/colgroup\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCharacteristics\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eDetails of what will be considered\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e1. Population\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eIndividuals without a confirmed diagnosis of T2DM and no confirmed diagnosis of non-diabetic hyperglycaemia/prediabetes\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2. Index prediction model(s)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLeicester Diabetes Risk score (LRA) published by Gray et al. 2010\u003c/p\u003e\u003cp\u003eLeicester Practice Risk score (LPR) published by Gray et al. 2012\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e3. Comparator prediction model(s)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eComparing predictive accuracy of all applications of LRA or LRP, including those updated with additional predictors and recalibrated\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e4. Outcomes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eDiagnosis of type 2 diabetes or non-diabetic hyperglycaemia / prediabetes\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e5. Timing (two elements)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eBaseline: no T2DM or persistent hyperglycaemia diagnosis\u003c/p\u003e\u003cp\u003eOutcome: risk of having either undiagnosed T2DM or prediabetes or developing T2DM over any time period\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e6. Setting\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTo fully assess model performance in all populations it has been implemented, all studies regardless of care setting and geographical location will be included.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/table\u003e\u003c/div\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eStudy eligibility criteria\u003c/b\u003e\u003c/p\u003e\u003cp\u003eStudies will be eligible for inclusion in this review if they meet the following criteria: full-text publications in English, assessment of the performance of the LRA or LPR including diagnostic, calibration, discrimination and cost utility performance measures. Studies available in abstract form only will not be included as it is unlikely that enough information to adequately describe the target population and assess study quality and model performance would be included.\u003c/p\u003e\u003cp\u003eStudies involving participants with an existing T2DM or prediabetes diagnosis will be excluded. No restrictions will be placed on the target population in order to fully assess model performance in all settings.\u003c/p\u003e\u003cp\u003e\u003cb\u003eSearch strategy\u003c/b\u003e\u003c/p\u003e\u003cp\u003eA forwards citation search strategy focused on the LRA and LPR will be used. Key publications will be identified as the primary papers (seed references) \u003cem\u003ea priori\u003c/em\u003e. Titles and abstracts of published papers that have cited any of the key publications will be collected; relevant retrieved articles will then be used as new key references for a further iteration of citation searching. The process will repeat until no further eligible studies are identified (\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e). The number of iterations will be reported.\u003c/p\u003e\u003cp\u003eIt is acknowledged that citation searching is only an effective strategy if all eligible studies cite relevant early work (\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e). Key references will include the original model development papers and three subsequent validations of one or both models completed by or with the original authors to maximise the likelihood of finding all relevant papers, included in appendix 1.\u003c/p\u003e\u003cp\u003eFor each iteration of citation search, abstracts will be screened and grouped by model into one of three categories by two independent reviewers: papers that validate or implement the original clinical prediction model; papers that modify/amend the original prediction model (e.g. by amending or adding predictor variables); papers that are not relevant to the focus of this review.\u003c/p\u003e\u003cp\u003e\u003cb\u003eInformation sources\u003c/b\u003e\u003c/p\u003e\u003cp\u003eCitations and references from the key publications will be identified using the following databases to maximise the possible number of citations:\u003c/p\u003e\u003cul\u003e\u003cli\u003e\u003cp\u003eScopus\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eWeb of Science\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eGoogle Scholar\u003c/p\u003e\u003c/li\u003e\u003c/ul\u003e\u003cp\u003eHow the model is described (name and keywords) within included papers will be extracted and then a keyword search in the PubMed database using these key descriptors will be performed to ensure the search strategy is as comprehensive as possible. In addition to the electronic searches, reference and citation lists of the retrieved articles will be hand-searched for further papers of interest.\u003c/p\u003e\u003cp\u003e\u003cb\u003eData management\u003c/b\u003e\u003c/p\u003e\u003cp\u003eScreening will be performed using Covidence systematic review software (\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e) and selected articles, including PDF files, will be managed using Zotero (\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cb\u003eSelection process\u003c/b\u003e\u003c/p\u003e\u003cp\u003eTwo reviewers will independently screen titles and abstracts for eligibility, followed by full text assessment. Disagreements will be resolved by discussion; in cases of no consensus, final resolution will be achieved by involving a third reviewer. Study selection will be documented in a flow chart (PRISMA) (\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cb\u003eData collection process\u003c/b\u003e\u003c/p\u003e\u003cp\u003eData will be extracted independently by two reviewers, in a piloted data extraction form based on the Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modelling Studies \u003cb\u003e(\u003c/b\u003eCHARMS) checklist (\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e); the applicability and appropriateness of the extraction form will be tested using the first five included studies. Any disagreements that cannot be resolved by discussion will be referred to a third reviewer.\u003c/p\u003e\u003cp\u003e\u003cb\u003eData items\u003c/b\u003e\u003c/p\u003e\u003cp\u003eData extraction will include:\u003c/p\u003e\u003cul\u003e\u003cli\u003e\u003cp\u003eStudy information: author, title, publication date, date of validation, source, country\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eSource of data: e.g. registry data, existing cohort\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eWhich model is being assessed (LRA, LPR or both, whether the model or score is being used)\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eStudy design characteristics (prospective or retrospective, type)\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003ePatient characteristics including mean age, % male, family history of diabetes, mean BMI, % history of hypertension, ethnicity\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003ePredictors included in the model including continuous or categorised, methods of measurement, thresholds for continuous predictors\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eCompleteness of data (missing data, losses to follow up) and how missing data are dealt with (complete case or type of imputation)\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eModel validation: total sample size, total number of events, target population, setting, whether model was recalibrated, predictor effects adjusted\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eOutcome measures used for diagnosis including HbA1c, fasting plasma glucose, random plasma glucose, oral glucose tolerance test and cut-off points used\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eTimespan of prediction will be recorded to assess how the model is being used in practice.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eModel performance measures, including validation performance statistics for calibration, discrimination and overall performance measures including 95% confidence intervals or standard errors\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eFor calibration: calibration slope, intercepts, goodness-of-fit test or observed/expected (O/E) outcome ratios if reported. The number of observed events and the number of expected events will be extracted or derived from plots. As a binary outcome, Brier score will be extracted if available\u003csup\u003e38\u003c/sup\u003e.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eFor discrimination: the c-statistic, including uncertainty measures (confidence interval or standard error.). As the c-statistic is identical to the area under the receiver operating curver (AUROC), this will also be used.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eMean values and standard deviations of the LRA, LPR and their predictors will be extracted to explore the effect of patient characteristics / case-mix on LRA/LPR performance.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eSensitivity and specificity (also reported as true positive fraction and false positive fraction respectively), defined as the probability of correct classification in individuals with and without the target condition, are used to assess accuracy of a diagnostic test and are recommended for meta-analysis (\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e)\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003ePositive and negative predictive values, the probability of the health outcome given a positive test result and the probability of not having the health outcome given a negative test result for threshold values are described as more interpretable and clinically relevant than sensitivity and specificity (\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e)\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eThe distribution of the linear predictor to determine how different the study sample is from the original develop sample (this can inform how we interpret the performance at validation).\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eStatistical methods used, including if/how any new predictors were included and any recalibration\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eAny clinical utility measures reported e.g. net benefit or decision curve analysis\u003c/p\u003e\u003c/li\u003e\u003c/ul\u003e\u003cp\u003e\u003cb\u003eMissing data\u003c/b\u003e\u003c/p\u003e\u003cp\u003eIf performance measures are not explicitly stated, methods outlined by Debray et al. (2017 and 2018) (\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e)(\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e) will be used to estimate these; missing performance measures and confidence intervals or standard errors will be calculated if the required information is reported. Missing total O:E ratio and standard error will be estimated using statistical equations using the total number of observed and expected events and the total sample size. Calibration-in-the-large will be estimated using the linear predictor and regression co-efficients of the model.\u003c/p\u003e\u003cp\u003eVariance or standard deviation of key patient characteristics can be estimated from reported ranges or histograms. Missing positive and negative predictive values will be generated from sensitivity and specificity (\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e). Where relevant information is not reported, efforts will be made to contact the study authors to request this. It is possible that some applications of the model may include additional predictor variables or exclude some key parameters. The effect of this on model performance will be examined in subgroup and sensitivity analyses.\u003c/p\u003e\u003cp\u003eEstimates will be obtained using methods implemented in Stata package (\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e). If five or more studies are available, the same package will be used to meta-analyse model performance measures. If meta-analysis is not possible, results will be presented as a narrative.\u003c/p\u003e\u003cp\u003e\u003cb\u003eRisk-of-bias assessment\u003c/b\u003e\u003c/p\u003e\u003cp\u003eRisk of bias of included studies will be assessed using the PROBAST (Prediction study of Risk of Bias Assessment Tool) (\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e) by two independent reviewers according to the following PROBAST domains: participants, predictors, outcome, analysis. Based on answers to the signalling questions within each domain, studies will be rated as low, high or unclear risk of bias. If information required to assess risk of bias is missing, study authors will be contacted to request additional information.\u003c/p\u003e\u003cp\u003e\u003cb\u003eAssessment of heterogeneity\u003c/b\u003e\u003c/p\u003e\u003cp\u003ePossible causes of heterogeneity may be differences in study characteristic or quality, difference in baseline risk of the prevalence of T2DM across different populations, performance estimates may be based on validations of a modified model with adjusted parameters (\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e) or if a predictor variable included in the model is not measured or not included in one or more studies (systematically missing predictor variables) (\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e). It is possible that proxy predictor variables may have been used in some studies, for example if waist circumference is not recorded.\u003c/p\u003e\u003cp\u003e\u003cb\u003eAssessment of reporting deficiencies\u003c/b\u003e\u003c/p\u003e\u003cp\u003eWhilst it is recommended that all prediction models report calibration and discrimination\u003csup\u003e30\u003c/sup\u003e, it has been reported that calibration and discrimination measures are often poorly reported or not reported at all (\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e). Therefore, this review will evaluate reporting deficiencies in included studies. Funnel plots will be used to visually assess for any evidence of publication bias (\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cb\u003eData analysis and synthesis\u003c/b\u003e\u003c/p\u003e\u003cp\u003eAll following analyses will be performed for the LRA and LPR separately so that each individual model is evaluated.\u003c/p\u003e\u003cp\u003ePerformance measures will be summarized in tables and illustrated graphically. As the LRA and LPR are diagnostic models, they can be evaluated following methods described for meta-analysis of accuracy of a diagnostic test for use in NICE decision models. This uses a Bayesian approach, allowing more complex analyses considering multiple thresholds i.e. specific thresholds for risk grouping, fitting a random effects model for sensitivity and specificity (\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eA random-effects meta-analysis of calibration (O:E ratio, calibration slope) and discrimination (c-statistic or AUROC) (\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e, \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e) will be performed if more than five studies are sufficiently similar (\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e). The c-statistics will be transformed with a logit transformation of the c-statistic, using statistical package Stata (\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e). If performance measures are not reported, they will be estimated according to methods outlined by Debray et al. (\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e) (\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eWhere appropriate, meta-analyses will be performed using a random-effects model to account for heterogeneity; between-study variance (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\tau\\:}^{2}\\)\u003c/span\u003e\u003c/span\u003e) and the percentage total variation in the study estimate due to between-study heterogeneity (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{I}^{2}\\)\u003c/span\u003e\u003c/span\u003e) will be calculated. An approximate 95% prediction interval for discrimination (C-statistic) and calibration (O:E ratio) measures will be calculated (\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eAlthough no Grading of Recommendations, Assessment, Development, and Evaluations (GRADE) guidance currently exists for rating certainty of prediction model performance, a GRADE approach to assessing certainty in model calibration (assessed by the O:E ratio) (\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e) and proposing different methods to choose a threshold for rating certainty of evidence when assessing discrimination (\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e) has recently been published.\u003c/p\u003e\u003cp\u003e\u003cb\u003eSubgroup analysis and investigation of heterogeneity\u003c/b\u003e\u003c/p\u003e\u003cp\u003eMeta-regression and subgroup analyses will be used to evaluate model performance if the number of identified studies supports this. Foroutan et al. (2021) (\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e) suggest reporting calibration separately for different risk categories or clinically meaningful subgroups as an overall O:E ratio may appear adequate despite a model underestimating the risk in half of the population and overestimating the risk in the other half.\u003c/p\u003e\u003cp\u003eTo identify potential sources of heterogeneity, we plan to compare the model performance in the whole study population with that in subgroups of studies that included participants with the following characteristics:\u003c/p\u003e\u003cul\u003e\u003cli\u003e\u003cp\u003eBy country\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eBy method/criteria used to diagnose diabetes\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eValidation based on score or model\u003c/p\u003e\u003c/li\u003e\u003c/ul\u003e\u003cp\u003eIt is expected that most studies will report aggregate data. Meta regression to investigate the association between patient-level characteristics and intervention effect can lead to ecological bias when using aggregate data (\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e) (\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e) and so characteristics in the target populations will be summarised and explored in subgroup analyses if little overlap exists, unless individual patient-level data is available.\u003c/p\u003e\u003cp\u003e\u003cb\u003eSensitivity Analysis\u003c/b\u003e\u003c/p\u003e\u003cp\u003eSensitivity analysis will be used to test the effects of:\u003c/p\u003e\u003cul\u003e\u003cli\u003e\u003cp\u003eestimation of any missing performance measures, if this method is used\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003erisk-of-bias rating, if a sufficient number of studies within each rating is found\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eeffect of estimating 95% prediction intervals and 95% confidence intervals on levels of certainty by exploring any changes between reported and estimated\u003c/p\u003e\u003c/li\u003e\u003c/ul\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe results of this review will identify all studies that have implemented, modified or validated the LRA and LPR for the risk of undiagnosed T2DM and/or prediabetes in different populations. This will allow summary measures, including level of uncertainty, of model performance to be calculated, making this highly relevant to individuals and stakeholders who recommend and implement these models. The conclusions of the review will also inform the potential update and recalibration of the models. As the LRA is now increasingly used as a web-based tool, it provides the opportunity for a more complex model that can still be completed by a lay person. This will ultimately lead to improved outcomes for those with undiagnosed T2DM and prediabetes through earlier diagnosis and management.\u003c/p\u003e\u003cp\u003eNICE guidelines (PH38) for the prevention of T2DM in people at high-risk list three examples of risk assessment tools: the LRA, LRP and QDiabetes risk calculator (\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e). This review focuses on the LRA and LRP models as these were developed for use in the UK to identify undiagnosed (prevalent) T2DM and/or prediabetes, allowing interventions such as the NHS Diabetes Prevention Programme to be targeted to individuals at high-risk. The QDiabetes risk calculator is a prognostic model to identify the (incident) risk of developing diabetes over the next 10 years (\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e) and so was developed for a different outcome, explaining the decision not to include this model in the review.\u003c/p\u003e\u003cp\u003eAs this review focuses on implementation and validation of two specific clinical prediction models, the research team deemed there to be a high probability that the original papers describing the model developments would be referenced in relevant studies. Therefore, it is expected that a high percentage of titles from the searches will be included, given the citation search strategy suggested. Wright et al. (2014) (\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e) compared the performance of database search strategies and citation search strategies and found that using this combination of resources stated in this protocol could identify 99.7% of total citations found by a traditional database search.\u003c/p\u003e\u003cp\u003eAny future amendments to this protocol that result from knowledge acquired during the pilot data extraction stage or during initial iterations of citation searches will be documented in a separate section with justification for any changes included.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003e\u003cstrong\u003eT2DM:\u0026nbsp;\u003c/strong\u003etype 2 diabetes\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eLRA\u003c/strong\u003e: Leicester Risk Assessment score\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eLRP\u003c/strong\u003e: Leicester Practice Risk Score\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eBMI\u003c/strong\u003e: body mass index\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eNICE\u003c/strong\u003e: National Institute for Health and Care Excellence\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePICOTS\u003c/strong\u003e: Population Index Prediction Model, Comparator, Outcomes, Timing, Setting\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePRISMA-P\u003c/strong\u003e: Preferred Reporting Items for Systematic Reviews and Meta-Analyses Protocols\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCHARMS:\u0026nbsp;\u003c/strong\u003eCritical Appraisal and Data Extraction for Systematic Reviews of Prediction Modelling Studies\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTRIPOD-SRMA\u003c/strong\u003e: Transparent reporting of multivariable prediction models for individual prognosis or diagnosis: checklist for systematic reviews and meta-analyses\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAUROC\u003c/strong\u003e: area under receiver operating curve\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePROBAST\u003c/strong\u003e: Prediction study of Risk Of Bias Assessment Tool\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eGRADE:\u003c/strong\u003e Grading of Recommendations, Assessment, Development, and Evaluations\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll data generated from this review will be available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was supported\u0026nbsp;by the National Institute for Health and Care Research (NIHR) Applied Research Collaboration East Midlands (ARC EM) and Leicester NIHR Biomedical Research Centre (BRC). The views expressed are those of the author(s) and not necessarily those of the NIHR or the Department of Health and Social Care.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eContributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eLH is the guarantor and drafted the manuscript. All authors provided input on methodological issues; KK provided expertise on clinical issues. LG and JE provided statistical expertise on evidence synthesis methods and the review process. All authors provided advice and input regarding the protocol and have contributed, read and approved the final manuscript.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics declarations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eLG and KK developed the original models.\u003c/p\u003e\n\u003cp\u003eKK was Chair of the National Institute for Care and Excellence Type 2 diabetes: prevention in people at high risk Guidelines.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eLH is funded by the Medical Research Council [grant number MR/W007002/1] as part of the Advanced Inter-Disciplinary Models (AIM) doctoral training partnership.\u003c/p\u003e\n\u003cp\u003eLG is funded by the National Institute for Health and Care Research (NIHR) Applied Research Collaboration East Midlands (ARC EM) and Leicester NIHR Biomedical Research Centre (BRC). LG is an NIHR Senior Investigator. The views expressed are those of the author(s) and not necessarily those of the NIHR or the Department of Health and Social Care.\u003c/p\u003e\n\u003cp\u003eJE is funded by the Birmingham NIHR Biomedical Research Centre (BRC).\u003c/p\u003e\n\u003cp\u003eKK is supported by the National Institute for Health Research (NIHR) Applied Research Collaboration East Midlands (ARC EM), NIHR Global Research Centre for Multiple Long Term Conditions, NIHR Cross NIHR Collaboration for Multiple Long Term Conditions, NIHR Leicester Biomedical Research Centre (BRC) and the British Heart Foundation (BHF) Centre of Excellence.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eRisk factors for pre-diabetes and undiagnosed type 2 diabetes in England - Office for National Statistics. Office for National Statistics 19.2.24.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMagliano DJ, Co-Chair, Boyko EJ, Co-Chair. IDF Diabetes Atlas 10th edition. IDF Diabetes Atlas 10th Edition scientific committee 2021.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHarris MI, Klein R, Welborn TA, Knuiman MW. Onset of NIDDM Occurs at Least 4\u0026ndash;7 Yr Before Clinical Diagnosis.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePorta M, Curletto G, Cipullo D, Rigault De La Longrais R, Trento M, Passera P, et al. Estimating the Delay Between Onset and Diagnosis of Type 2 Diabetes From the Time Course of Retinopathy Prevalence. Diabetes Care 2014-05-10;37(6):1668.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePalladino R, Tabak AG, Khunti K, Valabhji J, Majeed A, Millett C, et al. Association between pre-diabetes and microvascular and macrovascular disease in newly diagnosed type 2 diabetes. BMJ Open Diab Res Care 2020 -04;8(1).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eNICE 2023. Definition | Background information | Diabetes - type 2 | CKS | NICE. 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Critical appraisal and data extraction for systematic reviews of prediction modelling studies: the CHARMS checklist. PLoS medicine 2014;11(10):e1001744.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eDerezea E, Ades AE, Rogers G, Sutton AJ, Cooper NJ, Hamilton J, et al. TECHNICAL SUPPORT DOCUMENT 25: EVIDENCE SYNTHESIS OF DIAGNOSTIC TEST ACCURACY FOR DECISION MAKING REPORT BY THE DECISION SUPPORT UNIT NOVEMBER 2024.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eDebray TP, Damen JA, Riley RD, Snell K, Reitsma JB, Hooft L, et al. A framework for meta-analysis of prediction model studies with binary and time-to-event outcomes. Stat Methods Med Res 2018-07-23;28(9):2768.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eStata package to be amended.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWolff RF, Moons KG, Riley RD, Whiting PF, Westwood M, Collins GS, et al. PROBAST: a tool to assess the risk of bias and applicability of prediction model studies. Ann Intern Med 2019;170(1):51\u0026ndash;58.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eRiley RD, Ensor J, Snell KI, Debray TP, Altman DG, Moons KG, et al. External validation of clinical prediction models using big datasets from e-health records or IPD meta-analysis: opportunities and challenges. BMJ 2016;353.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHeus P, Damen JAAG, Pajouheshnia R, Scholten RJPM, Reitsma JB, Collins GS, et al. Poor reporting of multivariable prediction model studies: towards a targeted implementation strategy of the TRIPOD statement. BMC Med 2018-07-19;16(1).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eDebray TP, Moons KG, Riley RD. Detecting small-study effects and funnel plot asymmetry in meta‐analysis of survival data: a comparison of new and existing tests. Research synthesis methods 2018;9(1):41\u0026ndash;50.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eDamen JAA, Moons KGM, Van Smeden M, Hooft L. How to conduct a systematic review and meta-analysis of prognostic model studies. Clinical Microbiology and Infection 2022-08-04;29(4):434.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eInthout J, Pa J, Borm GF. The Hartung-Knapp-Sidik-Jonkman method for random effects meta-analysis is straightforward and considerably outperforms the standard DerSimonian-Laird method.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eForoutan FP, Guyatt G, Trivella M, Kreuzberger N, Skoetz N, Riley RD, et al. GRADE concept paper 2: Concepts for judging certainty on the calibration of prognostic models in a body of validation studies. Journal of Clinical Epidemiology 2021-11-18;143:202.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eForoutan F, Mayer M, Guyatt G, Riley RD, Mustafa R, Kreuzberger N, et al. 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Stat Med 2017;36(5):855\u0026ndash;875.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eType 2 diabetes: prevention in people at high risk Public health guideline. 2017-09-15.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHippisley-Cox J, Coupland C, Robson J, Sheikh A, Brindle P. Predicting risk of type 2 diabetes in England and Wales: prospective derivation and validation of QDScore. BMJ 2009;338.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWright K, Golder S, Rodriguez-Lopez R. Citation searching: a systematic review case study of multiple risk behaviour interventions.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"diagnostic-and-prognostic-research","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"dapr","sideBox":"Learn more about [Diagnostic and Prognostic Research](https://diagnprognres.biomedcentral.com/)","snPcode":"41512","submissionUrl":"https://submission.springernature.com/new-submission/41512/3","title":"Diagnostic and Prognostic Research","twitterHandle":"@MedicalEvidence","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Type 2 diabetes mellitus, Prediction, Prognosis, Systematic Review, Risk, Study Protocol, Diagnostic","lastPublishedDoi":"10.21203/rs.3.rs-7252002/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7252002/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eApproximately one million adults in the UK are estimated to have undiagnosed type 2 diabetes mellitus (T2DM), with a further 5.1 million adults with nondiabetic hyperglycaemia (prediabetes) that does not meet the threshold for a diabetes diagnosis. As T2DM may by asymptomatic, diagnoses can be delayed. The Leicester Risk Assessment score (LRA) and Leicester Practice Risk score (LPR) are diagnostic risk prediction models that use a combination of patient characteristics to predict an individual’s risk of undiagnosed T2DM and prediabetes, developed for use in community and primary care settings respectively. This study will systematically review all applications of these models and any published updates to evaluate their performance in different populations. This review has been registered with PROSPERO (CRD420251005841).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe will implement a citation search strategy to search Scopus, Web of Science and Google Scholar, restricted to full text, English language papers. Eligible papers will validate, update or modify either model. Data will be extracted using a form based on the Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modelling Studies\u003cstrong\u003e (\u003c/strong\u003eCHARMS) checklist; missing information will be sought from authors or estimated from other available information where possible. Meta-analysis of predictive performance measures will be completed if sufficient data exist. Subgroup and sensitivity analyses will be used to explore between-study heterogeneity and risk-of-bias impact.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDiscussion\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis review will identify studies that have implemented, modified or validated the LRA and LPR for the risk of undiagnosed T2DM and prediabetes in different populations. This will allow summary measures, including level of uncertainty, of model performance to be calculated, making this highly relevant to individuals and stakeholders who recommend and implement these models. Review conclusions will also inform the potential update and recalibration of the models. This will ultimately lead to improved outcomes through earlier diagnosis and management.\u003c/p\u003e","manuscriptTitle":"Performance of the Leicester Risk Assessment and Leicester Practice Risk Scores for assessing the risk of undiagnosed type 2 diabetes or prediabetes in diverse populations: protocol for a systematic review of published validations and updates","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-08-07 15:25:21","doi":"10.21203/rs.3.rs-7252002/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-11-28T15:40:43+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-11-28T13:26:00+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"340141088284729017692522676123826392688","date":"2025-10-27T08:32:31+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-10-09T06:41:18+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"21007736237094787585185396664581412469","date":"2025-10-07T07:44:17+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-08-05T07:53:05+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-07-30T23:23:15+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-07-30T23:22:23+00:00","index":"","fulltext":""},{"type":"submitted","content":"Diagnostic and Prognostic Research","date":"2025-07-30T10:51:01+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"diagnostic-and-prognostic-research","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"dapr","sideBox":"Learn more about [Diagnostic and Prognostic Research](https://diagnprognres.biomedcentral.com/)","snPcode":"41512","submissionUrl":"https://submission.springernature.com/new-submission/41512/3","title":"Diagnostic and Prognostic Research","twitterHandle":"@MedicalEvidence","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"bd1f1a8b-b577-4fb9-ac14-18c6d3aea758","owner":[],"postedDate":"August 7th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2026-01-19T16:46:30+00:00","versionOfRecord":{"articleIdentity":"rs-7252002","link":"https://doi.org/10.1186/s41512-026-00219-w","journal":{"identity":"diagnostic-and-prognostic-research","isVorOnly":false,"title":"Diagnostic and Prognostic Research"},"publishedOn":"2026-01-15 16:29:08","publishedOnDateReadable":"January 15th, 2026"},"versionCreatedAt":"2025-08-07 15:25:21","video":"","vorDoi":"10.1186/s41512-026-00219-w","vorDoiUrl":"https://doi.org/10.1186/s41512-026-00219-w","workflowStages":[]},"version":"v1","identity":"rs-7252002","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7252002","identity":"rs-7252002","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}