AIMS: To evaluate our proposed multivariate approach to identify patients who will develop sight-threatening diabetic retinopathy (STDR) within a 1-year screen interval, and explore the impact of simple stratification rules on prediction. MATERIALS AND METHODS: A 7-year dataset (2009-2016) from people with diabetes (PWD) was analysed using a novel multivariate longitudinal discriminant approach. Level of diabetic retinopathy, assessed from routine digital screening photographs of both eyes, was jointly modelled using clinical data collected over time. Simple stratification rules based on retinopathy level were also applied and compared with the multivariate discriminant approach. RESULTS: Data from 13 103 PWD (49 520 screening episodes) were analysed. The multivariate approach accurately predicted whether patients developed STDR or not within 1 year from the time of prediction in 84.0% of patients (95% confidence interval [CI] 80.4-89.7), compared with 56.7% (95% CI 55.5-58.0) and 79.7% (95% CI 78.8-80.6) achieved by the two stratification rules. While the stratification rules detected up to 95.2% (95% CI 92.2-97.6) of the STDR cases (sensitivity) only 55.6% (95% CI 54.5-56.7) of patients who did not develop STDR were correctly identified (specificity), compared with 85.4% (95% CI 80.4-89.7%) and 84.0% (95% CI 80.7-87.6%), respectively, achieved by the multivariate risk model. CONCLUSIONS: Accurate prediction of progression to STDR in PWD can be achieved using a multivariate risk model whilst also maintaining desirable specificity. While simple stratification rules can achieve good levels of sensitivity, the present study indicates that their lower specificity (high false-positive rate) would therefore necessitate a greater frequency of eye examinations.

• Keywords
• cohort study, diabetic retinopathy, observational study, primary care,
• MeSH
• Early Diagnosis MeSH
• Datasets as Topic MeSH
• Diabetes Mellitus, Type 2 complications   diagnosis   epidemiology   pathology   MeSH
• Diabetic Retinopathy diagnosis   epidemiology   MeSH
• Adult MeSH
• Individuality MeSH
• Precision Medicine methods   MeSH
• Middle Aged MeSH
• Humans MeSH
• Follow-Up Studies MeSH
• Mass Screening methods   MeSH
• Disease Progression MeSH
• Risk Factors MeSH
• Aged MeSH
• Sensitivity and Specificity MeSH
• Check Tag
• Adult MeSH
• Middle Aged MeSH
• Humans MeSH
• Male MeSH
• Aged MeSH
• Female MeSH
• Publication type
• Journal Article MeSH
• Evaluation Study MeSH
• Research Support, Non-U.S. Gov't MeSH

OBJECTIVE: To identify people with epilepsy who will not achieve a 12-month seizure remission within 5 years of starting treatment. METHODS: The Standard and New Antiepileptic Drug (SANAD) study is the largest prospective study in patients with epilepsy to date. We applied a recently developed multivariable approach to the SANAD dataset that takes into account not only baseline covariates describing a patient's history before diagnosis but also follow-up data as predictor variables. RESULTS: Changes in number of seizures and treatment history were the most informative time-dependent predictors and were associated with history of neurologic insult, epilepsy type, age at start of treatment, sex, and having a first-degree relative with epilepsy. Our model classified 95% of patients. Of those classified, 95% of patients observed not to achieve remission at 5 years were correctly classified (95% confidence interval [CI] 89.5%-100%), with 51% identified by 3 years and 90% within 4 years of follow-up. Ninety-seven percent (95% CI 93.3%-98.8%) of patients observed to achieve a remission within 5 years were correctly classified. Of those predicted not to achieve remission, 76% (95% CI 58.5%-88.2%) truly did not achieve remission (positive predictive value). The predictive model achieved similar accuracy levels via external validation in 2 independent United Kingdom-based datasets. CONCLUSION: Our approach generates up-to-date predictions of the patient's risk of not achieving seizure remission whenever new clinical information becomes available that could influence patient counseling and management decisions.

• MeSH
• Anticonvulsants therapeutic use   MeSH
• Adult MeSH
• Middle Aged MeSH
• Humans MeSH
• Multivariate Analysis MeSH
• Prospective Studies MeSH
• Drug Resistant Epilepsy diagnosis   drug therapy   MeSH
• Risk Factors MeSH
• Check Tag
• Adult MeSH
• Middle Aged MeSH
• Humans MeSH
• Male MeSH
• Female MeSH
• Publication type
• Journal Article MeSH
• Multicenter Study MeSH
• Research Support, Non-U.S. Gov't MeSH
• Randomized Controlled Trial MeSH
• Names of Substances
• Anticonvulsants MeSH