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.

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 (NDH) that does not meet the threshold for a diabetes diagnosis. The Leicester Risk Assessment score (LRA) and Leicester Practice Risk score (LPR) are diagnostic risk prediction models that estimate an individual's risk of undiagnosed T2DM and NDH, developed for use in community and primary care settings respectively. The LRA is also used as a prognostic model; neither model has been updated since development. This study will systematically review all applications of these models as diagnostic and prognostic tools 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 NDH 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.