Progression to dementia in a population with amnestic mild cognitive impairment: clinical variables associated with conversion.

The aim of this study was to investigate clinical predictors of, and rates of conversion to, dementia syndrome in a case series of patients with amnestic mild cognitive impairment (aMCI). Two hundred and eight aMCI subjects were followed over a six-year period. A lower Mini Mental State Examination score was a significant predictor of dementia, and mild cognitive impairment patients with behavioral and psychiatric symptoms showed a faster conversion rate.

An electronic diary on a palm device for headache monitoring: a preliminary experience.

Patients suffering from headache are usually asked to use charts to allow monitoring of their disease. These diaries, providing they are regularly filled in, become crucial in the diagnosis and management of headache disorders because they provide further information on attack frequency and temporal pattern, drug intake, trigger factors, and short-/long-term responses to treatment. Electronic tools could facilitate diary monitoring and thus the management of headaches. Medication overuse headache (MOH) is a chronic and disabling condition that can be treated by withdrawing the overused drug(s) and adopting specific approaches that focus on the development of a close doctor-patient relationship in the post-withdrawal phase. Although the headache diary is, in this context, an essential tool for the constant, reliable monitoring of these patients to prevent relapses, very little is known about the applicability of electronic diaries in MOH patients. The purpose of this study was to evaluate the acceptability of and patient compliance with an electronic headache diary (palm device) as compared with a traditional diary chart in a group of headache inpatients with MOH. A palm diary device, developed in accordance with the ICHD-II criteria, was given to 85 MOH inpatients during the detoxification phase. On the first day of hospitalization, the patients were instructed in the use of the diary and were then required to fill it in daily for the following 7 days. Data on the patients' opinions on the electronic diary and the instructions given, its screen and layout, as well as its convenience and ease of use, in comparison with the traditional paper version, were collected using a numerical rating scale. A total of 504 days with headache were recorded in both the electronic and the traditional headache diaries simultaneously. The level of patient compliance was good. The patients appreciated the electronic headache diary, deeming it easy to understand and to use (fill in); most of the patients rated the palm device handier than the traditional paper version.

A Machine Learning Approach for the Differential Diagnosis of Alzheimer and Vascular Dementia Fed by MRI Selected Features.

Among dementia-like diseases, Alzheimer disease (AD) and vascular dementia (VD) are two of the most frequent. AD and VD may share multiple neurological symptoms that may lead to controversial diagnoses when using conventional clinical and MRI criteria. Therefore, other approaches are needed to overcome this issue. Machine learning (ML) combined with magnetic resonance imaging (MRI) has been shown to improve the diagnostic accuracy of several neurodegenerative diseases, including dementia. To this end, in this study, we investigated, first, whether different kinds of ML algorithms, combined with advanced MRI features, could be supportive in classifying VD from AD and, second, whether the developed approach might help in predicting the prevalent disease in subjects with an unclear profile of AD or VD. Three ML categories of algorithms were tested: artificial neural network (ANN), support vector machine (SVM), and adaptive neuro-fuzzy inference system (ANFIS). Multiple regional metrics from resting-state fMRI (rs-fMRI) and diffusion tensor imaging (DTI) of 60 subjects (33 AD, 27 VD) were used as input features to train the algorithms and find the best feature pattern to classify VD from AD. We then used the identified VD-AD discriminant feature pattern as input for the most performant ML algorithm to predict the disease prevalence in 15 dementia patients with a "mixed VD-AD dementia" (MXD) clinical profile using their baseline MRI data. ML predictions were compared with the diagnosis evidence from a 3-year clinical follow-up. ANFIS emerged as the most efficient algorithm in discriminating AD from VD, reaching a classification accuracy greater than 84% using a small feature pattern. Moreover, ANFIS showed improved classification accuracy when trained with a multimodal input feature data set (e.g., DTI + rs-fMRI metrics) rather than a unimodal feature data set. When applying the best discriminant pattern to the MXD group, ANFIS achieved a correct prediction rate of 77.33%. Overall, results showed that our approach has a high discriminant power to classify AD and VD profiles. Moreover, the same approach also showed potential in predicting earlier the prevalent underlying disease in dementia patients whose clinical profile is uncertain between AD and VD, therefore suggesting its usefulness in supporting physicians' diagnostic evaluations.

Driving habits in patients with dementia: a report from Alzheimer's disease assessment units in northern Italy.

The aim of this study was to characterize the driving behavior of a sample of patients with dementia. Demographic and clinical characteristics and parameters considered to be the most significant predictors of driving ability were collected. Of the total 198 patients enrolled, 172 were still driving. Many subjects (30-65%) were found to have modified their driving habits (reducing driving time and mileage, avoiding driving at night and during rush hours, sticking to familiar routes). The patients' own rating of their driving ability was significantly higher than their caregivers' rating (51% versus 29%). Crash history was not a significant variable. The patients' restriction of their driving increased significantly (p<0.01) with age and increasing worsening of cognitive, functional and behavioral variables. In the absence of a gold standard for determining fitness to drive, the patients' driving habits were self-regulated and, in particular, regulated by their caregivers. Age and degree of dementia can be considered among the best predictors of driving safety.

Almotriptan for the treatment of acute migraine: a review of early intervention trials.

Almotriptan is a serotonin (5-hydroxytryptamine)(1B/1D) receptor agonist (triptan) that has shown consistent efficacy in the acute treatment of migraine with excellent tolerability. It is an effective, well-tolerated and cost-effective triptan, as demonstrated by improvement in rigorous, patient-orientated end points, such as 'sustained pain-free without adverse events'. Results from post hoc analyses, observational studies and well-controlled, prospective clinical trials have shown that significant improvements can be achieved if almotriptan 12.5 mg is administered within an hour of migraine onset, particularly when pain is mild, rather than waiting until pain is moderate-to-severe. Benefits were also achieved with early treatment of moderate-to-severe pain. Time-to-treatment was the best predictor of headache duration, whereas initial headache intensity best predicted most other efficacy outcomes. Early administration of almotriptan 12.5 mg not only produced rapid symptomatic relief, it also improved the patient's quality of life and ability to resume normal daily functioning. Furthermore, the efficacy of almotriptan is not significantly affected by allodynia (purported to reduce the efficacy of triptans). Thus, the excellent efficacy and tolerability profile of almotriptan administered early in a migraine attack indicate that it may be a first-line treatment option in this common, underdiagnosed and undertreated disorder.