![]() ![]() In the United Kingdom, diabetic retinopathy (DR) services are organised into diabetic eye screening programmes (DESP) and hospital eye services. While services may be organised differently from country to country, the care pathways are likely to be similar with patients at higher risk being provided closer monitoring and care. The detection of retinopathy has also increased through better population screening. There has been a global increase in the number of people with diabetes, rising from 108 million in 1980 to 422 million in 2014. However, these models will also need updating and external validation in multiple hospital settings before being implemented into clinical practice. Most models focussed on lower-risk patients, the majority had high risk of bias and doubtful applicability, but three models had some applicability for higher-risk patients. Participants, outcomes, predictors handling and modelling methods varied. ![]() Studies ranged from low to high risk of bias, mostly due to the need for external validation or missing data. Discriminative ability with c-statistics ranged from 0.57 to 0.91. Eleven models had internal validation, eight had external validation and one had neither. ![]() Twenty-two articles reporting on 14 prognostic models (including four updates) met the selection criteria. They were assessed for quality using criteria specified by PROBAST and CHARMS checklists, independently by two reviewers. Included studies had data extracted on model characteristics, predictive ability and validation. Search results were screened for relevance to the review question. We searched MEDLINE, EMBASE, COCHRANE CENTRAL, conference abstracts and reference lists of included publications for studies of any design using search terms related to diabetes, diabetic retinopathy and prognostic models. We wanted to look into the predictive ability and applicability of the existing models for the higher-risk patients referred into hospitals. Prognostic prediction models have been used to optimise services but these were intended for early detection of sight-threatening retinopathy and are mostly used in diabetic retinopathy screening services. The risk engine accurately predicts macro- and microvascular complications and would provide helpful information in risk classification and health economic simulations.With the increasing incidence of diabetic retinopathy and its improved detection, there is increased demand for diabetic retinopathy treatment services. By combining macro- and microvascular risks, the classification of low- and high-risk patients was improved by a net reclassification improvement of 5.7% (P = 0.02). C statistics in our Japanese patients were high for CHD, noncardiovascular mortality, and overt nephropathy (0.725, 0.696, and 0.767) but moderate for stroke and progression of retinopathy (0.636 and 0.614). In contrast, the UK Prospective Diabetes Study (UKPDS) risk engine overestimated CHD risk (O/P ratios: 0.30 for CHD and 0.72 for stroke). The observed-to-predicted (O/P) ratios for each event were between 0.93 and 1.08, and Hosmer-Lemeshow tests showed no significant deviations between observed and predicted events. Sex, age, HbA1c, years after diagnosis, BMI, systolic blood pressure, non-HDL cholesterol, albumin-to-creatinine ratio, atrial fibrillation, current smoker, and leisure-time physical activity were risk factors for macro- and microvascular complications and were incorporated into the risk engine. The predictive accuracy of the calculated 5-year risks was cross-validated. We fit a multistate Cox regression model to derive an algorithm for prediction. End points were coronary heart disease (CHD), stroke, noncardiovascular mortality, overt nephropathy defined by persistent proteinuria, and progression of retinopathy. We analyzed pooled data from two clinical trials on 1,748 Japanese type 2 diabetic patients without diabetes complications other than mild diabetic retinopathy with a median follow-up of 7.2 years. To develop and validate a risk engine that calculates the risks of macro- and microvascular complications in type 2 diabetes.
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