Modeling screening, prevention, and delaying of Alzheimer's disease: an early-stage decision analytic model

Screening for preclinical Alzheimer's disease: Deriving optimal policies using a partially observable Markov model

Alzheimer's Disease (AD) is believed to be the most common type of dementia. Even though screening for AD has been discussed widely, there is no screening program implemented as part of a policy in any country. Current medical research motivates focusing on the preclinical stages of the disease in a modeling initiative. We develop a partially observable Markov decision process model to determine optimal screening programs. The model contains disease free and preclinical AD partially observable states and the screening decision is taken while an individual is in one of those states. An observable diagnosed preclinical AD state is integrated along with observable mild cognitive impairment, AD and death states. Transition probabilities among states are estimated using data from Knight Alzheimer's Disease Research Center (KADRC) and relevant literature. With an objective of maximizing expected total quality-adjusted life years (QALYs), the output of the model is an optimal screening program that specifies at what points in time an individual over 50 years of age with a given risk of AD will be directed to undergo screening. The screening test used to diagnose preclinical AD has a positive disutility, is imperfect and its sensitivity and specificity are estimated using the KADRC data set. We study the impact of a potential intervention with a parameterized effectiveness and disutility on model outcomes for three different risk profiles (low, medium and high). When intervention effectiveness and disutility are at their best, the optimal screening policy is to screen every year between ages 50 and 95, with an overall QALY gain of 0.94, 1.9 and 2.9 for low, medium and high risk profiles, respectively. As intervention effectiveness diminishes and/or its disutility increases, the optimal policy changes to sporadic screening and then to never screening. Under several scenarios, some screening within the time horizon is optimal from a QALY perspective. Moreover, an in-depth analysis of costs reveals that implementing these policies are either cost-saving or cost-effective.

Multifactorial 10-Year Prior Diagnosis Prediction Model of Dementia

Dementia is a neurodegenerative disorder that affects the older adult population. To date, no cure or treatment to change its course is available. Since changes in the brains of affected individuals could be evidenced as early as 10 years before the onset of symptoms, prognosis research should consider this time frame. This study investigates a broad decision tree multifactorial approach for the prediction of dementia, considering 75 variables regarding demographic, social, lifestyle, medical history, biochemical tests, physical examination, psychological assessment and health instruments. Previous work on dementia prognoses with machine learning did not consider a broad range of factors in a large time frame. The proposed approach investigated predictive factors for dementia and possible prognostic subgroups. This study used data from the ongoing multipurpose Swedish National Study on Aging and Care, consisting of 726 subjects (91 presented dementia diagnosis in 10 years). The proposed approach achieved an AUC of 0.745 and Recall of 0.722 for the 10-year prognosis of dementia. Most of the variables selected by the tree are related to modifiable risk factors; physical strength was important across all ages. Also, there was a lack of variables related to health instruments routinely used for the dementia diagnosis.

Increased Prevalence in Alzheimer Disease in the Northeast Tennessee Region of the United States

OBJECTIVES

This study describes the changes in prevalence odds ratios (PORs) for Alzheimer disease (AD) in the northeast Tennessee region (NTR) during a 3-year period, describes the statistical assessment process, and critically assesses the database from which the statistical association was derived. The article also examines several beliefs pertinent to the clinical management of AD in the NTR from the perspective of professionals delivering services.

METHODS

We extracted prevalence data for NTR counties for 2013, 2014, and 2015 from the Centers for Medicare & Medicaid Services Geographic Variation Public Use File. We used the crude prevalence and the 2010 US Census Data fixed population for each county to compute the POR. The 2013 Economic Research Service Rural-Urban Continuum Codes were used to identify rural and urban counties in the NTR. We collected primary data on the perceived observation of the increasing prevalence in the NTR during the last 3 years and barriers to early diagnosis through an online survey from 44 experts and professionals working in AD-related fields within the NTR.

RESULTS

The PORs of AD in rural counties in NTR increased by 18.3%, 4.7%, and 19% compared with urban counties for 2013, 2014, and 2015, respectively. The POR of AD for the entire NTR region increased by 22.7%, 22.5%, and 21.2% compared with other regions in Tennessee for 2013, 2014, and 2015, respectively. Compared with 2012, 68.4% of respondents currently work with more individuals with AD; 71.8% reported that the NTR has a higher number of late-stage diagnoses of AD. A total of 92.3% strongly agreed that early detection of AD is important, and 95% agreed that early diagnosis could prolong the lives of patients with AD; 51.2% were unaware of existing AD screening services. Reported barriers were denial, lack of patient awareness, inefficient screening methods, communication, and lack of community resources.

CONCLUSIONS

Increased prevalence of AD among inhabitants in the NTR and identified barriers to early screening or diagnosis in the management of AD were identified. Access to early screening techniques must be prioritized in deprived areas within the NTR. Healthcare providers and medical professionals in the NTR must be well equipped with the required training and resources to respond adequately to the increasing prevalence of AD.

Using Cerebrospinal Fluid Biomarker Testing to Target Treatment to Patients with Mild Cognitive Impairment: A Cost-Effectiveness Analysis

OBJECTIVE

Cerebrospinal fluid (CSF) biomarkers are shown to facilitate a risk identification of patients with mild cognitive impairment (MCI) into different risk levels of progression to Alzheimer's disease (AD). Knowing a patient's risk level provides an opportunity for earlier interventions, which could result in potential greater benefits. We assessed the cost effectiveness of the use of CSF biomarkers in MCI patients where the treatment decision was based on patients' risk level.

METHODS

We developed a state-transition model to project lifetime quality-adjusted life-years (QALYs) and costs for a cohort of 65-year-old MCI patients from a US societal perspective. We compared four test-and-treat strategies where the decision to treat was based on a patient's risk level (low, intermediate, high) of progressing to AD with two strategies without testing, one where no patients were treated during the MCI phase and in the other all patients were treated. We performed deterministic and probabilistic sensitivity analyses to evaluate parameter uncertainty.

RESULTS

Testing and treating low-risk MCI patients was the most cost-effective strategy with an incremental cost-effectiveness ratio (ICER) of US$37,700 per QALY. Our results were most sensitive to the level of treatment effectiveness for patients with mild AD and for MCI patients. Moreover, the ICERs for this strategy at the 2.5th and 97.5th percentiles were US$18,900 and US$50,100 per QALY, respectively.

CONCLUSION

Based on the best available evidence regarding the treatment effectiveness for MCI, this study suggests the potential value of performing CSF biomarker testing for early targeted treatments among MCI patients with a narrow range for the ICER.

Machine learning and microsimulation techniques on the prognosis of dementia: A systematic literature review

Background

Dementia is a complex disorder characterized by poor outcomes for the patients and high costs of care. After decades of research little is known about its mechanisms. Having prognostic estimates about dementia can help researchers, patients and public entities in dealing with this disorder. Thus, health data, machine learning and microsimulation techniques could be employed in developing prognostic estimates for dementia.

Objective

The goal of this paper is to present evidence on the state of the art of studies investigating and the prognosis of dementia using machine learning and microsimulation techniques.

Method

To achieve our goal we carried out a systematic literature review, in which three large databases—Pubmed, Socups and Web of Science were searched to select studies that employed machine learning or microsimulation techniques for the prognosis of dementia. A single backward snowballing was done to identify further studies. A quality checklist was also employed to assess the quality of the evidence presented by the selected studies, and low quality studies were removed. Finally, data from the final set of studies were extracted in summary tables.

Results

In total 37 papers were included. The data summary results showed that the current research is focused on the investigation of the patients with mild cognitive impairment that will evolve to Alzheimer’s disease, using machine learning techniques. Microsimulation studies were concerned with cost estimation and had a populational focus. Neuroimaging was the most commonly used variable.

Conclusions

Prediction of conversion from MCI to AD is the dominant theme in the selected studies. Most studies used ML techniques on Neuroimaging data. Only a few data sources have been recruited by most studies and the ADNI database is the one most commonly used. Only two studies have investigated the prediction of epidemiological aspects of Dementia using either ML or MS techniques. Finally, care should be taken when interpreting the reported accuracy of ML techniques, given studies’ different contexts.

Molecular Diagnostics of Ageing and Tackling Age-related Disease

As average life expectancy increases there is a greater focus on health-span and, in particular, how to treat or prevent chronic age-associated diseases. Therapies which were able to control 'biological age' with the aim of postponing chronic and costly diseases of old age require an entirely new approach to drug development. Molecular technologies and machine-learning methods have already yielded diagnostics that help guide cancer treatment and cardiovascular procedures. Discovery of valid and clinically informative diagnostics of human biological age (combined with disease-specific biomarkers) has the potential to alter current drug-discovery strategies, aid clinical trial recruitment and maximize healthy ageing. I will review some basic principles that govern the development of 'ageing' diagnostics, how such assays could be used during the drug-discovery or development process. Important logistical and statistical considerations are illustrated by reviewing recent biomarker activity in the field of Alzheimer's disease, as dementia represents the most pressing of priorities for the pharmaceutical industry, as well as the chronic disease in humans most associated with age.

A novel multi-tissue RNA diagnostic of healthy ageing relates to cognitive health status

BACKGROUND

Diagnostics of the human ageing process may help predict future healthcare needs or guide preventative measures for tackling diseases of older age. We take a transcriptomics approach to build the first reproducible multi-tissue RNA expression signature by gene-chip profiling tissue from sedentary normal subjects who reached 65 years of age in good health.

RESULTS

One hundred and fifty probe-sets form an accurate classifier of young versus older muscle tissue and this healthy ageing RNA classifier performed consistently in independent cohorts of human muscle, skin and brain tissue (n = 594, AUC = 0.83-0.96) and thus represents a biomarker for biological age. Using the Uppsala Longitudinal Study of Adult Men birth-cohort (n = 108) we demonstrate that the RNA classifier is insensitive to confounding lifestyle biomarkers, while greater gene score at age 70 years is independently associated with better renal function at age 82 years and longevity. The gene score is 'up-regulated' in healthy human hippocampus with age, and when applied to blood RNA profiles from two large independent age-matched dementia case-control data sets (n = 717) the healthy controls have significantly greater gene scores than those with cognitive impairment. Alone, or when combined with our previously described prototype Alzheimer disease (AD) RNA 'disease signature', the healthy ageing RNA classifier is diagnostic for AD.

CONCLUSIONS

We identify a novel and statistically robust multi-tissue RNA signature of human healthy ageing that can act as a diagnostic of future health, using only a peripheral blood sample. This RNA signature has great potential to assist research aimed at finding treatments for and/or management of AD and other ageing-related conditions.

Alzheimer's disease biomarker discovery using SOMAscan multiplexed protein technology

Blood proteins and their complexes have become the focus of a great deal of interest in the context of their potential as biomarkers of Alzheimer's disease (AD). We used a SOMAscan assay for quantifying 1001 proteins in blood samples from 331 AD, 211 controls, and 149 mild cognitive impaired (MCI) subjects. The strongest associations of protein levels with AD outcomes were prostate-specific antigen complexed to α1-antichymotrypsin (AD diagnosis), pancreatic prohormone (AD diagnosis, left entorhinal cortex atrophy, and left hippocampus atrophy), clusterin (rate of cognitive decline), and fetuin B (left entorhinal atrophy). Multivariate analysis found that a subset of 13 proteins predicted AD with an accuracy of area under the curve of 0.70. Our replication of previous findings provides further evidence that levels of these proteins in plasma are truly associated with AD. The newly identified proteins could be potential biomarkers and are worthy of further investigation.

Cost-effectiveness of magnetic resonance imaging with a new contrast agent for the early diagnosis of Alzheimer's disease

Background

Used as contrast agents for brain magnetic resonance imaging (MRI), markers for beta-amyloid deposits might allow early diagnosis of Alzheimer's disease (AD). We evaluated the cost-effectiveness of such a diagnostic test, MRI+CLP (contrastophore-linker-pharmacophore), should it become clinically available.

Methodology/Principal Findings

We compared the cost-effectiveness of MRI+CLP to that of standard diagnosis using currently available cognition tests and of standard MRI, and investigated the impact of a hypothetical treatment efficient in early AD. The primary analysis was based on the current French context for 70-year-old patients with Mild Cognitive Impairment (MCI). In alternative “screen and treat” scenarios, we analyzed the consequences of systematic screenings of over-60 individuals (either population-wide or restricted to the ApoE4 genotype population). We used a Markov model of AD progression; model parameters, as well as incurred costs and quality-of-life weights in France were taken from the literature. We performed univariate and probabilistic multivariate sensitivity analyses.

The base-case preferred strategy was the standard MRI diagnosis strategy. In the primary analysis however, MRI+CLP could become the preferred strategy under a wide array of scenarios involving lower cost and/or higher sensitivity or specificity. By contrast, in the “screen and treat” analyses, the probability of MRI+CLP becoming the preferred strategy remained lower than 5%.

Conclusions/Significance

It is thought that anti-beta-amyloid compounds might halt the development of dementia in early stage patients. This study suggests that, even should such treatments become available, systematically screening the over-60 population for AD would only become cost-effective with highly specific tests able to diagnose early stages of the disease. However, offering a new diagnostic test based on beta-amyloid markers to elderly patients with MCI might prove cost-effective.