Examination compliance and screening for diabetic retinopathy: a 2-year follow-up study

Early detection and timely treatment of diabetic retinopathy can preserve vision, yet many people with diabetes do not have their eyes examined regularly. The purpose of this study was to examine eye care practices of people with diabetes who had not previously accessed eye care services on a regular basis. Screening with non-mydriatic retinal photography for diabetic retinopathy was initiated in 1996, and targeted people with diabetes who did not access eye care services on a regular basis. Each test area was revisited 2 years after the initial screening. Patients that did not attend the biennial screening were followed up by mail survey. Although none of the participants in this study had been previously accessing eye care services on a regular basis, 87% did so after attending the screening. These results indicate that mobile screening with non-mydriatic photography, as an adjunct to current eye care services, has the potential to increase examination compliance for diabetic retinopathy and to achieve sustained behaviour change.

Recruitment methods for community-based screening for diabetic retinopathy

Regular screening of all people with diabetes is the most efficient and cost-effective way to detect early stages of diabetic retinopathy so that laser treatment can be performed at the optimal time. A major aim of the Program for the Early Detection of Diabetic Retinopathy was to increase compliance with guidelines for screening for diabetic retinopathy. This community-based screening program used non-mydriatic retinal photography and was initiated in four areas of Victoria, Australia from 1996-1998. Recruitment strategies included targeted mail-outs, provision of the program brochure in English and the main languages spoken in the areas and media promotion in ethnic newspapers and on ethnic radio stations. In Victoria, only 55% of the population with diabetes currently access eye care services at the recommended intervals. This program was able to increase compliance with guidelines to 70% among people with diabetes that had not had a recent eye examination. A total of 1,197 people with diabetes were screened for diabetic retinopathy. Of the 1,197 people who were screened, 620 (15% of the estimated number of people with diabetes) had not had their eyes examined in the past two years. This pilot study identified strategies to encourage people with diabetes to have their eyes examined at the recommended intervals.

Enhancing predictive accuracy of cardiac autonomic neuropathy using blood biochemistry features and iterative multitier ensembles

Blood biochemistry attributes form an important class of tests, routinely collected several times per year for many patients with diabetes. The objective of this study is to investigate the role of blood biochemistry for improving the predictive accuracy of the diagnosis of cardiac autonomic neuropathy (CAN) progression. Blood biochemistry contributes to CAN, and so it is a causative factor that can provide additional power for the diagnosis of CAN especially in the absence of a complete set of Ewing tests. We introduce automated iterative multitier ensembles (AIME) and investigate their performance in comparison to base classifiers and standard ensemble classifiers for blood biochemistry attributes. AIME incorporate diverse ensembles into several tiers simultaneously and combine them into one automatically generated integrated system so that one ensemble acts as an integral part of another ensemble. We carried out extensive experimental analysis using large datasets from the diabetes screening research initiative (DiScRi) project. The results of our experiments show that several blood biochemistry attributes can be used to supplement the Ewing battery for the detection of CAN in situations where one or more of the Ewing tests cannot be completed because of the individual difficulties faced by each patient in performing the tests. The results show that AIME provide higher accuracy as a multitier CAN classification paradigm. The best predictive accuracy of 99.57% has been obtained by the AIME combining decorate on top tier with bagging on middle tier based on random forest. Practitioners can use these findings to increase the accuracy of CAN diagnosis.