Development and validation of multivariable clinical diagnostic models to identify type 1 diabetes requiring rapid insulin therapy in adults aged 18-50 years

This is the final version. Available on open access from BMJ Publishing Group via the DOI in this recordObjective: To develop and validate multivariable clinical diagnostic models to assist distinguishing between type 1 and type 2 diabetes in adults aged 18 to 50. Design: Multivariable logistic regression analysis was used to develop classification models integrating five pre-specified predictor variables, including clinical features (age of diagnosis, BMI) and clinical biomarkers (GADA and Islet Antigen 2 islet autoantibodies, Type 1 Diabetes Genetic Risk Score), to identify type 1 diabetes with rapid insulin requirement using data from existing cohorts. Setting: United Kingdom cohorts recruited from primary and secondary care. Participants: 1,352 (model development) and 582 (external validation) participants diagnosed with diabetes between the age of 18 and 50 years of white European origin. Main outcome measures: Type 1 diabetes was defined by rapid insulin requirement (within 3 years of diagnosis) and severe endogenous insulin deficiency (C-peptide <200pmol/L). Type 2 diabetes was defined by either a lack of rapid insulin requirement or, where insulin treated within 3 years, retained endogenous insulin secretion (C-peptide >600pmol/L at ≥5 years diabetes duration). Model performance was assessed using area under the receiver operating characteristic curve (ROC AUC), and internal and external validation. 4 Results: Type 1 diabetes was present in 13% of participants in the development cohort. All five predictor variables were discriminative and independent predictors of type 1 diabetes (p<0.001 for all) with individual ROC AUC ranging from 0.82 to 0.85. Model performance was high: ROC AUC range 0.90 [95%CI 0.88, 0.93] (clinical features only) to 0.97 [0.96, 0.98] (all predictors) with low prediction error. Results were consistent in external validation (clinical features and GADA ROC AUC 0.93 [0.90, 0.96]). Conclusions: Clinical diagnostic models integrating clinical features with biomarkers have high accuracy for identifying type 1 diabetes with rapid insulin requirement, and could assist clinicians and researchers in accurately identifying patients with type 1 diabetes.National Institute for Health Research (NIHR)European Community FP7Oxford Hospitals Charitable FundWellcome TrustMedical Research Council (MRC

A Monte Carlo simulation model for evaluating the effectiveness of Interventions along the ‘Diabesity’ pathway

The prevalence of obesity has increased worldwide in the past 50 years, reaching pandemic levels (Blüher, 2019). In this paper, we report on the development of an intervention evaluation model and risk assessment tool that has been developed for an Adult Weight Management Service (AWMS) within the U.K.’s National Health Service (NHS). The tool uses Monte Carlo simulation to predict the progress of morbidity and mortality in individual AWMS patients for up to 25 years, with and without intervention. Running the tool on a sample of AWMS patients with known weight loss outcomes indicates that interventions can be evaluated in terms of risk assessment of developing obesity-related health conditions such as diabetes

Data Resource Profile : United Kingdom Optimum Patient Care Research Database

Funding Unfunded project management, medical writing, and statistical support were provided by Momentum Data, UK, a specialist real-world evidence company. Acknowledgements This study is based wholly on data from the Optimum Patient Care Research Database (opcrd.co.uk) obtained under license from Optimum Patient Care Limited and its execution is approved by recognized experts affiliated to the Respiratory Effectiveness Group. However, the interpretation and conclusion contained in this report are those of the author/s alone.Peer reviewedPublisher PD