A Disease State Fingerprint for Evaluation of Alzheimer's Disease

Abstract

Abstract. Diagnostic processes of Alzheimer's disease (AD) are evolving. Knowledge about disease-specific biomarkers is constantly increasing and larger volumes of data are being measured from patients. To gain additional benefits from the collected data, a novel statistical modeling and data visualization system is proposed for supporting clinical diagnosis of AD. The proposed system computes an evidence-based estimate of a patient's AD state by comparing his or her heterogeneous neuropsychological, clinical, and biomarker data to previously diagnosed cases. The AD state in this context denotes a patient's degree of similarity to a previously diagnosed disease population. A summary of patient data and results of the computation are displayed in a succinct Disease State Fingerprint (DSF) visualization. The visualization clearly discloses how patient data contributes to the AD state, facilitating rapid interpretation of the information. To model the AD state from complex and heterogeneous patient data, a statistical Disease State Index (DSI) method underlying the DSF has been developed. Using baseline data from the Alzheimer's Disease Neuroimaging Initiative (ADNI), the ability of the DSI to model disease progression from elderly healthy controls to AD and its ability to predict conversion from mild cognitive impairment (MCI) to AD were assessed. It was found that the DSI provides well-behaving AD state estimates, corresponding well with the actual diagnoses. For predicting conversion from MCI to AD, the DSI attains performance similar to state-of-the-art reference classifiers. The results suggest that the DSF establishes an effective decision support and data visualization framework for improving AD diagnostics, allowing clinicians to rapidly analyze large quantities of diverse patient data.

The PredictAD project: development of novel biomarkers and analysis software for early diagnosis of the Alzheimer's disease

“…CDSSs will help in objectively defining the point where enough evidence has been collected and no potentially expensive, laborious or even risky further test are needed. Evidence suggesting solid cost versus benefit gains when using [18] was found based on the cost per procedure data from project partner hospitals. These results are yet to be published.…”

“…Utilization of clinical disease support systems, such as discussed in Mattila et al [18], has potential economic impact in several ways. Accurate an early diagnosis would delay the progression AD, and thus likely reduce the need for institutional care significantly.…”

“…We addressed these needs by developing a novel framework combining heterogeneous multi-source data and providing an evidence-based index reflecting the probability of the disease in the case of an individual subject [18]. This index, called the Disease State Index (DSI), is computed by comparing the measurements of a yet undiagnosed case against a large number of measurements from healthy and diseased subjects collected to databases.…”

“…In the study of Mattila et al [18], we used the baseline data from the Alzheimer's disease neuroimaging initiative (ADNI; http://www.adni-info.org/, accessed 26 October 2012.) to benchmark the ability of the DSI to model disease progression from elderly healthy controls to AD and its ability to predict conversion from MCI to AD.…”

Integrative approaches to computational biomedicine

“…Currently, a definitive diagnosis of this disease is only possible after death. Researchers in this project are collecting omics, electromagnetic and imaging data from AD patients and controls and developing mathematical models of disease progression as tools for early and differential diagnosis and prognosis prediction [19].…”