Visual signs and symptoms of dementia with Lewy bodies

Dementia with Lewy bodies ('Lewy body dementia' or 'diffuse Lewy body disease') (DLB) is the second most common form of dementia to affect elderly people, after Alzheimer's disease. A combination of the clinical symptoms of Alzheimer's disease and Parkinson's disease is present in DLB and the disorder is classified as a 'parkinsonian syndrome', a group of diseases which also includes Parkinson's disease, progressive supranuclear palsy, corticobasal degeneration and multiple system atrophy. Characteristics of DLB are fluctuating cognitive ability with pronounced variations in attention and alertness, recurrent visual hallucinations and spontaneous motor features, including akinesia, rigidity and tremor. In addition, DLB patients may exhibit visual signs and symptoms, including defects in eye movement, pupillary function and complex visual functions. Visual symptoms may aid the differential diagnoses of parkinsonian syndromes. Hence, the presence of visual hallucinations supports a diagnosis of Parkinson's disease or DLB rather than progressive supranuclear palsy. DLB and Parkinson's disease may exhibit similar impairments on a variety of saccadic and visual perception tasks (visual discrimination, space-motion and object-form recognition). Nevertheless, deficits in orientation, trail-making and reading the names of colours are often significantly greater in DLB than in Parkinson's disease. As primary eye-care practitioners, optometrists should be able to work with patients with DLB and their carers to manage their visual welfare.

Application of Naïve Bayesian sequential analysis to primary care optometry

The objective of this study was to investigate the effects of circularity, comorbidity, prevalence and presentation variation on the accuracy of differential diagnoses made in optometric primary care using a modified form of naïve Bayesian sequential analysis. No such investigation has ever been reported before. Data were collected for 1422 cases seen over one year. Positive test outcomes were recorded for case history (ethnicity, age, symptoms and ocular and medical history) and clinical signs in relation to each diagnosis. For this reason only positive likelihood ratios were used for this modified form of Bayesian analysis that was carried out with Laplacian correction and Chi-square filtration. Accuracy was expressed as the percentage of cases for which the diagnoses made by the clinician appeared at the top of a list generated by Bayesian analysis. Preliminary analyses were carried out on 10 diagnoses and 15 test outcomes. Accuracy of 100% was achieved in the absence of presentation variation but dropped by 6% when variation existed. Circularity artificially elevated accuracy by 0.5%. Surprisingly, removal of Chi-square filtering increased accuracy by 0.4%. Decision tree analysis showed that accuracy was influenced primarily by prevalence followed by presentation variation and comorbidity. Analysis of 35 diagnoses and 105 test outcomes followed. This explored the use of positive likelihood ratios, derived from the case history, to recommend signs to look for. Accuracy of 72% was achieved when all clinical signs were entered. The drop in accuracy, compared to the preliminary analysis, was attributed to the fact that some diagnoses lacked strong diagnostic signs; the accuracy increased by 1% when only recommended signs were entered. Chi-square filtering improved recommended test selection. Decision tree analysis showed that accuracy again influenced primarily by prevalence, followed by comorbidity and presentation variation. Future work will explore the use of likelihood ratios based on positive and negative test findings prior to considering naïve Bayesian analysis as a form of artificial intelligence in optometric practice.