Improving the quality of cognitive screening assessments: ACEmobile, an iPad-based version of the Addenbrooke's Cognitive Examination-III.

INTRODUCTION: Ensuring reliable administration and reporting of cognitive screening tests are fundamental in establishing good clinical practice and research. This study captured the rate and type of errors in clinical practice, using the Addenbrooke's Cognitive Examination-III (ACE-III), and then the reduction in error rate using a computerized alternative, the ACEmobile app. METHODS: In study 1, we evaluated ACE-III assessments completed in National Health Service (NHS) clinics (n = 87) for administrator error. In study 2, ACEmobile and ACE-III were then evaluated for their ability to capture accurate measurement. RESULTS: In study 1, 78% of clinically administered ACE-IIIs were either scored incorrectly or had arithmetical errors. In study 2, error rates seen in the ACE-III were reduced by 85%-93% using ACEmobile. DISCUSSION: Error rates are ubiquitous in routine clinical use of cognitive screening tests and the ACE-III. ACEmobile provides a framework for supporting reduced administration, scoring, and arithmetical error during cognitive screening.

Delta-9-tetrahydrocannabinol protects against MPP+ toxicity in SH-SY5Y cells by restoring proteins involved in mitochondrial biogenesis.

Proliferator-activated receptor γ (PPARγ) activation can result in transcription of proteins involved in oxidative stress defence and mitochondrial biogenesis which could rescue mitochondrial dysfunction in Parkinson's disease (PD).The PPARγ agonist pioglitazone is protective in models of PD; however side effects have limited its clinical use. The cannabinoid Δ9-tetrahydrocannabinol (Δ9-THC) may have PPARγ dependent anti-oxidant properties. Here we investigate the effects of Δ9-THC and pioglitazone on mitochondrial biogenesis and oxidative stress. Differentiated SH-SY5Y neuroblastoma cells were exposed to the PD relevant mitochondrial complex 1 inhibitor 1-methyl-4-phenylpyridinium iodide (MPP+). We found that only Δ9-THC was able to restore mitochondrial content in MPP+ treated SH-SY5Y cells in a PPARγ dependent manner by increasing expression of the PPARγ co-activator 1α (PGC-1α), the mitochondrial transcription factor (TFAM) as well as mitochondrial DNA content. Co-application of Δ9-THC with pioglitazone further increased the neuroprotection against MPP+ toxicity as compared to pioglitazone treatment alone. Furthermore, using lentiviral knock down of the PPARγ receptor we showed that, unlike pioglitazone, Δ9-THC resulted in a PPARγ dependent reduction of MPP+ induced oxidative stress. We therefore suggest that, in contrast to pioglitazone, Δ9-THC mediates neuroprotection via PPARγ-dependent restoration of mitochondrial content which may be beneficial for PD treatment.

A review of clinical trial designs used to detect a disease-modifying effect of drug therapy in Alzheimer's disease and Parkinson's disease.

BACKGROUND: Disease-modification clinical trials in neurodegenerative disorders have struggled to separate symptomatic effects of putative agents from disease-modification. In response, a variety of clinical trial designs have been developed. A systematic review was undertaken to examine which trial designs have been used in Alzheimer's disease (AD) and Parkinson's disease (PD) to detect disease-modifying, as opposed to symptomatic, drug effects. In addition we aimed to identify novel clinical trial designs used in the past or planned for use in the future. We aimed to critique whether the methods used would have identified true disease-modification. METHODS: MEDLINE, Embase and CENTRAL (1980-2015) were searched to identify papers meriting review in full. ClinicalTrials.gov was searched to identify unpublished or planned randomised controlled trials (RCTs). We included RCTs in PD or AD which aimed to demonstrate the disease-modifying properties of drug therapy and differentiate that benefit from any symptomatic effect. RESULTS: 128 RCTs were finally included: 84 in AD (59 published, 25 unpublished); 44 in PD (36 published, 8 unpublished). A variety of clinical trial designs were applied including long-term follow-up, wash-in and wash-out analyses, randomised delayed-start, the use of time-to-event outcome measures and surrogate disease progression biomarkers. Deficiencies in each of these design strategies, the quantity of missing data in included RCTs and the methods used to deal with missing data, meant that none of the included studies convincingly demonstrated disease-modification. No truly novel clinical trial designs were identified. CONCLUSION: We currently believe that the best clinical trial design available to demonstrate disease-modification is a long-term follow-up study, in which an examination is made for sustained divergence in outcome measures between treatment arms over the study period.

A systematic review of biomarkers for disease progression in Alzheimer's disease.

BACKGROUND: Using surrogate biomarkers for disease progression as endpoints in neuroprotective clinical trials may help differentiate symptomatic effects of potential neuroprotective agents from true slowing of the neurodegenerative process. A systematic review was undertaken to determine what biomarkers for disease progression in Alzheimer's disease exist and how well they perform. METHODS: MEDLINE and Embase (1950-2011) were searched using five search strategies. Abstracts were assessed to identify papers meriting review in full. Studies of participants with probable Alzheimer's disease diagnosed by formal criteria were included. We made no restriction on age, disease duration, or drug treatment. We only included studies with a longitudinal design, in which the putative biomarker and clinical measure were both measured at least twice, as this is the only appropriate study design to use when developing a disease progression biomarker. We included studies which attempted to draw associations between the changes over time in the biomarker used to investigate disease progression and a clinical measure of disease progression. RESULTS: Fifty-nine studies were finally included. The commonest biomarker modality examined was brain MRI (17/59, 29% of included studies). Median follow-up in included studies was only 1.0 (IQR 0.8-1.7) year and most studies only measured the putative biomarker and clinical measure twice. Included studies were generally of poor quality with small numbers of participants (median 31 (IQR 17 to 64)), applied excessively restrictive study entry criteria, had flawed methodologies and conducted overly simplistic statistical analyses without adjusting for confounding factors. CONCLUSIONS: We found insufficient evidence to recommend the use of any biomarker as an outcome measure for disease progression in Alzheimer's disease trials. However, further investigation into the efficacy of using MRI measurements of ventricular volume and whole brain volume appeared to be merited. A provisional 'roadmap' to improve the quality of future disease progression biomarker studies is presented.

A systematic review of biomarkers for disease progression in Parkinson's disease.

BACKGROUND: Using surrogate biomarkers for disease progression as endpoints in neuroprotective clinical trials may help differentiate symptomatic effects of potential neuroprotective agents from true disease-modifying effects. A systematic review was undertaken to determine what biomarkers for disease progression in Parkinson's disease (PD) exist. METHODS: MEDLINE and EMBASE (1950-2010) were searched using five search strategies. Abstracts were assessed to identify papers meriting review in full. Studies of participants with idiopathic PD diagnosed by formal criteria or clearly described clinical means were included. We made no restriction on age, disease duration, drug treatment, or study design. We included studies which attempted to draw associations between any tests used to investigate disease progression and any clinical measures of disease progression. The electronic search was validated by hand-searching the two journals from which most included articles came. RESULTS: 183 studies were included: 163 (89%) cross-sectional, 20 (11%) longitudinal. The electronic search strategy had a sensitivity of 71.4% (95% CI 51.1-86.0) and a specificity of 97.1% (95% CI 96.5-97.7). In longitudinal studies median follow-up was 2.0 years (IQR 1.1-3.5). Included studies were generally poor quality--cross-sectional with small numbers of participants, applying excessive inclusion/exclusion criteria, with flawed methodologies and simplistic statistical analyses. CONCLUSION: We found insufficient evidence to recommend the use of any biomarker for disease progression in PD clinical trials, which may simply reflect the poor quality of research in this area. We therefore present a provisional 'roadmap' for conducting future disease progression biomarker studies, and recommend new quality criteria by which future studies may be judged.

Machine learning-based method for personalized and cost-effective detection of Alzheimer's disease.

Diagnosis of Alzheimer's disease (AD) is often difficult, especially early in the disease process at the stage of mild cognitive impairment (MCI). Yet, it is at this stage that treatment is most likely to be effective, so there would be great advantages in improving the diagnosis process. We describe and test a machine learning approach for personalized and cost-effective diagnosis of AD. It uses locally weighted learning to tailor a classifier model to each patient and computes the sequence of biomarkers most informative or cost-effective to diagnose patients. Using ADNI data, we classified AD versus controls and MCI patients who progressed to AD within a year, against those who did not. The approach performed similarly to considering all data at once, while significantly reducing the number (and cost) of the biomarkers needed to achieve a confident diagnosis for each patient. Thus, it may contribute to a personalized and effective detection of AD, and may prove useful in clinical settings.

Bioprofile analysis: a new approach for the analysis of biomedical data in Alzheimer's disease.

This article presents a new approach for the analysis of biomedical data to support the management and care of patients with Alzheimer's disease (AD). The increase in prevalence of neurodegenerative disorders such as AD has led to a need for objective means to assist clinicians with the analysis and interpretation of complex biomedical data. To this end, we propose a "Bioprofile" analysis to reveal the pattern of disease in the subject's biodata. From the Bioprofile, personal "Bioindices" that indicate how closely a subject's data resemble the pattern of AD can be derived. We used an unsupervised technique (k-means) to cluster variables of the ADNI database so that subjects are divisible into those with the Bioprofile of AD and those without it. Results revealed that there is an "AD pattern" in the biodata of most AD and mild cognitive impairment (MCI) patients and some controls. This pattern agrees with a recent hypothetical model of AD evolution. We also assessed how the Bioindices changed with time and we found that the Bioprofile was associated with the risk of progressing from MCI to AD. Hence, the Bioprofile analysis is a promising methodology that may potentially provide a complementary new way of interpreting biomedical data. Furthermore, the concept of the Bioprofile could be extended to other neurodegenerative diseases.

The impact of motor and non motor symptoms on health state values in newly diagnosed idiopathic Parkinson's disease.

The aim of the study was to estimate health state utility values in newly diagnosed idiopathic Parkinson's disease (PD) for use in the assessment of health-related quality-of-life (HRQL), and in the estimation of quality-adjusted life-years (QALYs). Data from 162 patients enrolled in a community-based incidence study of PD were used to estimate health state utility values. Self-report data from the EQ-5D, a generic measure of HRQL, were used to derive preference-based health state utility values. The impact of motor and non motor symptoms, and other clinical and demographic factors, on the derived EQ-5D health state values was examined in univariate and multivariate analyses. The mean health state utility value for recently diagnosed PD patients was estimated at 0.65 ± 0.27. Significant reductions in health state values were attributable to pain (-0.18), motor functioning (-0.16), depression (-0.12), and insomnia (-0.11). Depression had its greatest impact (-0.19) in patients in the less severe stages of PD (i.e. Hoehn Yahr stages ≤2.5). This study shows, through the presentation of QALY values, that there is scope to achieve significant health gains in newly diagnosed idiopathic PD patients via improved management of pain, depression and insomnia, alongside the treatment of primary motor symptoms.

Health state values for use in the economic evaluation of treatments for Alzheimer's disease.

Alzheimer's disease (AD) is a chronic, progressive, neurodegenerative disease that places a heavy burden on people with the condition, their families and carers, health care systems and society in general. Health-related quality of life (HR-QOL) in patients deteriorates as the cognitive, behavioural and functional symptoms of AD develop. The human and financial cost of AD is forecast to grow rapidly as populations age, and those responsible for planning and financing health care face the challenge of allocating increasingly scarce resources against current and future interventions targeted towards AD. These include calls for early detection and diagnosis, preventative strategies, new medications, residential care, supportive care, and meeting the needs of carers as well as patients. Health care funders in many health systems now require a demonstration of the value of new interventions through a comparison of benefits in terms of improvements in HR-QOL and costs relative to those of competing or existing practices. Changes in HR-QOL provide the basis for the calculation of the quality-adjusted life-year (QALY), a key outcome used in economic evaluations to compare treatments within and between different disease conditions. The objective of this systematic review was to provide a summary of the published health state values (utilities) for AD patients and their carers that are currently available to estimate QALYs for use in health economic evaluations of interventions in AD. The health care literature was searched for articles published in English between 2000 and 2011, using keywords and variants including 'quality-adjusted life years', 'health state indicators', 'health utilities' and the specific names of generic measures of HR-QOL and health state valuation techniques. Databases searched included MEDLINE, EMBASE, NHS EED, PsycINFO and ISI Web of Science. This review identified 12 studies that reported utility values associated with health states in AD. Values for AD health states categorized according to cognitive impairment (where 1 = perfect health and 0 = dead) ranged from mild AD (0.52-0.73) to moderate AD (0.30-0.53) to severe AD (0.12-0.49). Utility values were almost all based on two generic measures of HR-QOL: the EQ-5D and Health Utility Index mark 2/3 (HUI2/3). There were no health state values estimated from condition- or disease-specific measures of HR-QOL. The review also identified 18 published cost-utility analyses (CUAs) of treatments for AD. The CUAs incorporated results from only three of the identified health state valuation studies. Twelve CUAs relied on the same study for health state values. We conclude that the literature on health state values in AD is limited and overly reliant on a single symptom (cognition) to describe disease progression. Other approaches to characterizing disease progression in AD based on multiple outcomes or dependency may be better predictors of costs and utilities in economic evaluations. Patient and proxy ratings were poorly correlated, particularly in patients with more advanced AD. However, proxy ratings displayed the validity and reliability across the entire range of AD severity needed to detect long-term changes relevant to economic evaluation. Further longitudinal research of patient and carer HR-QOL based on multidimensional measures of outcome and utilities is needed.

Model-based economic evaluation in Alzheimer's disease: a review of the methods available to model Alzheimer's disease progression.

OBJECTIVE: To consider the methods available to model Alzheimer's disease (AD) progression over time to inform on the structure and development of model-based evaluations, and the future direction of modelling methods in AD. METHODS: A systematic search of the health care literature was undertaken to identify methods to model disease progression in AD. Modelling methods are presented in a descriptive review. RESULTS: The literature search identified 42 studies presenting methods or applications of methods to model AD progression over time. The review identified 10 general modelling frameworks available to empirically model the progression of AD as part of a model-based evaluation. Seven of these general models are statistical models predicting progression of AD using a measure of cognitive function. The main concerns with models are on model structure, around the limited characterization of disease progression, and on the use of a limited number of health states to capture events related to disease progression over time. None of the available models have been able to present a comprehensive model of the natural history of AD. CONCLUSIONS: Although helpful, there are serious limitations in the methods available to model progression of AD over time. Advances are needed to better model the progression of AD and the effects of the disease on peoples' lives. Recent evidence supports the need for a multivariable approach to the modelling of AD progression, and indicates that a latent variable analytic approach to characterising AD progression is a promising avenue for advances in the statistical development of modelling methods.

The use of decision-analytic models in Parkinson's disease: a systematic review and critical appraisal.

The aims of this review were to review decision-analytic models used to evaluate interventions in idiopathic Parkinson's disease (PD), and to consider the future directions for development of methods to model the progression of PD over time. A systematic search of the healthcare literature up to June 2010 identified model-based economic evaluations in PD. The modelling methods used in the identified studies were appraised using good practice guidelines for decision-analytic modelling. The review identified 18 model-based evaluations of interventions in PD. All models evaluated treatments targeted towards the motor symptoms of PD or the motor complications of PD treatment. There were no models identified that evaluated interventions targeted towards the non-motor symptoms of PD, such as neuropsychiatric problems or autonomic dysfunction. Consequently, models characterized disease progression in PD using clinical measures of motor functioning. Most studies (n = 13) evaluated medications, three evaluated diagnostic technologies and two examined surgical procedures. Overall, the models reported structural components and data inputs appropriately and clearly, although limited evidence was provided to support choices made on the structures used in the models or the data synthesis reported. Models did not adequately consider structural uncertainty or internal/external consistency. Modelling methods used to date do not capture the full impact of PD. The emphasis in the current literature is on the motor symptoms of PD, characterizing the clinical nature of disease progression, largely neglecting the important impacts of non-motor symptoms. Modelling methods reported for the motor symptoms of PD may not be suitable for future interventions targeted towards modifying disease progression in PD across the entire spectrum of PD. More comprehensive models of disease progression, including both motor and non-motor symptoms will be needed where it is important to capture the effects of interventions more broadly.

Role of cannabinoids in multiple sclerosis.

Although extracts from the cannabis plant have been used medicinally for thousands of years, it is only within the last 2 decades that our understanding of cannabinoid physiology and the provision of evidence for therapeutic benefit of cannabinoids has begun to accumulate. This review provides a background to advances in our understanding of cannabinoid receptors and the endocannabinoid system, and then considers how cannabinoids may help in the management of multiple sclerosis (MS). The relative paucity of treatments for MS-related symptoms has led to experimentation by patients with MS in a number of areas including the use of cannabis extracts. An increasing amount of evidence is now emerging to confirm anecdotal reports of symptomatic improvement, particularly for muscle stiffness and spasms, neuropathic pain and sleep and bladder disturbance, in patients with MS treated with cannabinoids. Trials evaluating a role in treating other symptoms such as tremor and nystagmus have not demonstrated any beneficial effects of cannabinoids. Safety profiles of cannabinoids seem acceptable, although a slow prolonged period of titration improves tolerability. No serious safety concerns have emerged. Methodological issues in trial design and treatment delivery are now being addressed. In addition, recent experimental evidence is beginning to suggest an effect of cannabinoids on more fundamental processes important in MS, with evidence of anti-inflammation, encouragement of remyelination and neuroprotection. Trials are currently under way to test whether cannabinoids may have a longer term role in reducing disability and progression in MS, in addition to symptom amelioration, where indications are being established.

Early detection and characterization of Alzheimer's disease in clinical scenarios using Bioprofile concepts and K-means.

Alzheimer's Disease (AD) is the most common neurodegenerative disease in elderly people. There is a need for objective means to detect AD early to allow targeted interventions and to monitor response to treatment. To help clinicians in these tasks, we propose the creation of the Bioprofile of AD. A Bioprofile should reveal key patterns of a disease in the subject's biodata. We applied k-means clustering to data features taken from the ADNI database to divide the subjects into pathologic and non-pathologic groups in five clinical scenarios. The preliminary results confirm previous findings and show that there is an important AD pattern in the biodata of controls, AD, and Mild Cognitive Impairment (MCI) patients. Furthermore, the Bioprofile could help in the early detection of AD at the MCI stage since it divided the MCI subjects into groups with different rates of conversion to AD.

Machine Learning classification of MRI features of Alzheimer's disease and mild cognitive impairment subjects to reduce the sample size in clinical trials.

There is a need for objective tools to help clinicians to diagnose Alzheimer's Disease (AD) early and accurately and to conduct Clinical Trials (CTs) with fewer patients. Magnetic Resonance Imaging (MRI) is a promising AD biomarker but no single MRI feature is optimal for all disease stages. Machine Learning classification can address these challenges. In this study, we have investigated the classification of MRI features from AD, Mild Cognitive Impairment (MCI), and control subjects from ADNI with four techniques. The highest accuracy rates for the classification of controls against ADs and MCIs were 89.2% and 72.7%, respectively. Moreover, we used the classifiers to select AD and MCI subjects who are most likely to decline for inclusion in hypothetical CTs. Using the hippocampal volume as an outcome measure, we found that the required group sizes for the CTs were reduced from 197 to 117 AD patients and from 366 to 215 MCI subjects.

Patient-orientated longitudinal study of multiple sclerosis in south west England (The South West Impact of Multiple Sclerosis Project, SWIMS) 1: protocol and baseline characteristics of cohort.

BACKGROUND: There is a need for greater understanding of the impact of multiple sclerosis (MS) from the perspective of individuals with the condition. The South West Impact of MS Project (SWIMS) has been designed to improve understanding of disease impact using a patient-centred approach. The purpose is to (1) develop improved measurement instruments for clinical trials, (2) evaluate longitudinal performance of a variety of patient-reported outcome measures, (3) develop prognostic predictors for use in individualising drug treatment for patients, particularly early on in the disease course. METHODS: This is a patient-centred, prospective, longitudinal study of multiple sclerosis and clinically isolated syndrome (CIS) in south west England. The study area comprises two counties with a population of approximately 1.7 million and an estimated 1,800 cases of MS. Self-completion questionnaires are administered to participants every six months (for people with MS) or 12 months (CIS). Here we present descriptive statistics of the baseline data provided by 967 participants with MS. RESULTS: Seventy-five percent of those approached consented to participate. The male:female ratio was 1.00:3.01 (n = 967). Average (standard deviation) age at time of entry to SWIMS was 51.6 (11.5) years (n = 961) and median (interquartile range) time since first symptom was 13.3 (6.8 to 24.5) years (n = 934). Fatigue was the most commonly reported symptom, with 80% of participants experiencing fatigue at baseline. Although medication use for symptom control was common, there was little evidence of effectiveness, particularly for fatigue. Nineteen percent of participants were unable to classify their subtype of MS. When patient-reported subtype was compared to neurologist assessment for a sample of participants (n = 396), agreement in disease sub-type was achieved in 63% of cases. There were 836 relapses, reported by 931 participants, in the twelve months prior to baseline. Twenty-three percent of the relapsing-remitting group and 12% of the total sample were receiving disease-modifying therapy at baseline. CONCLUSIONS: Demographics of this sample were similar to published data for the UK. Overall, the results broadly reflect clinical experience in confirming high symptom prevalence, with relatively little complete symptom relief. Participants often had difficulty in defining MS relapses and their own MS type.

Rating scales as outcome measures for clinical trials in neurology: problems, solutions, and recommendations.

Have state-of-the-art clinical trials failed to deliver treatments for neurodegenerative diseases because of shortcomings in the rating scales used? This Review assesses two methodological limitations of rating scales that might help to answer this question. First, the numbers generated by most rating scales do not satisfy the criteria for rigorous measurements. Second, we do not really know which variables most rating scales measure. We use clinical examples to highlight concerns about the limitations of rating scales, examine their underlying rationales, clarify their implications, explore potential solutions, and make some recommendations for future research. We show that improvements in the scientific rigour of rating scales can improve the chances of reaching the correct conclusions about the effectiveness of treatments.