Jj risk engine7/3/2023 ![]() ![]() ![]() 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. doi: 10.1136/ the increasing incidence of diabetic retinopathy and its improved detection, there is increased demand for diabetic retinopathy treatment services. Severe hypoglycaemia and cardiovascular disease: systematic review and meta-analysis with bias analysis. Goto A, Arah OA, Goto M, Terauchi Y, Noda M. Tanaka S, Tanaka S, Iimuro S, Yamashita H, Katayama S, Akanuma Y, et al. Long-term multiple risk factor interventions in Japanese elderly diabetic patients: the Japanese elderly diabetes intervention trial–study design, baseline characteristics, and effects of intervention. Effects of lifestyle modifications on patients with type 2 diabetes: the Japan diabetes complications study (JDCS) study design, baseline analysis and three year-interim report. Sone H, Katagiri A, Ishibashi S, Abe R, Saito Y, Murase T, et al. Predicting macro- and microvascular complications in type 2 diabetes: the Japan diabetes complications study/the Japanese elderly diabetes intervention trial risk engine. Therefore, it is necessary to create a new risk engine that requires fewer input items than the JJ risk engine and is applicable to several patients.Ĭ-statistic Calibration Coronary heart disease Discrimination Hypoglycemia JJ risk engine Type 2 diabetes. ![]() The JJ risk engine has several input items (variables), and it is difficult to satisfy them all unless the environment is well-equipped with testing facilities, such as a university hospital. Therefore, it is difficult to accurately predict the complication rate of patients using the JJ risk engine based on the diabetes treatment policy after the Kumamoto Declaration 2013. However, in the group expected to have a low frequency of hypoglycemia, the C-statistic was 0.646 the predictability of the JJ risk engine was relatively accurate. ![]() The observed value of coronary heart disease (CHD) incidence after 5 years and the predicted value by the JJ risk engine as of 2013 were compared and verified using the discrimination and calibration values.Īmong the total cases analyzed, the C-statistic was 0.588, and the calibration was p < 0.05 thus, the JJ risk engine could not correctly predict the risk of CHD. In 2018, we conducted a retrospective survey using the medical records of 484 patients with type 2 diabetes. ![]()
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