Research Article: Personalized predictive modeling for patients with Alzheimer’s disease using an extension of Sullivan’s life table model

Date Published: September 20, 2017

Publisher: BioMed Central

Author(s): Eric Stallard, Bruce Kinosian, Yaakov Stern.

http://doi.org/10.1186/s13195-017-0302-6

Abstract

Alzheimer’s disease (AD) progression varies substantially among patients, hindering calculation of residual total life expectancy (TLE) and its decomposition into disability-free life expectancy (DFLE) and disabled life expectancy (DLE) for individual patients with AD. The objective of the present study was to assess the accuracy of a new synthesis of Sullivan’s life table (SLT) and longitudinal Grade of Membership (L-GoM) models that estimates individualized TLEs, DFLEs, and DLEs for patients with AD. If sufficiently accurate, such information could enhance the quality of important decisions in AD treatment and patient care.

We estimated a new SLT/L-GoM model of the natural history of AD over 10 years in the Predictors 2 Study cohort: N = 229 with 6 fixed and 73 time-varying covariates over 21 examinations covering 11 measurement domains including cognitive, functional, behavioral, psychiatric, and other symptoms/signs. Total remaining life expectancy was censored at 10 years. Disability was defined as need for full-time care (FTC), the outcome most strongly associated with AD progression. All parameters were estimated via weighted maximum likelihood using data-dependent weights designed to ensure that the estimates of the prognostic subtypes were of high quality. Goodness of fit was tested/confirmed for survival and FTC disability for five relatively homogeneous subgroups defined to cover the range of patient outcomes over the 21 examinations.

The substantial heterogeneity in initial patient presentation and AD progression was captured using three clinically meaningful prognostic subtypes and one terminal subtype exhibiting highly differentiated symptom severity on 7 of the 11 measurement domains. Comparisons of the observed and estimated survival and FTC disability probabilities demonstrated that the estimates were accurate for all five subgroups, supporting their use in AD life expectancy calculations. Mean 10-year TLE differed widely across subgroups: range 3.6–8.0 years, average 6.1 years. Mean 10-year DFLE differed relatively even more widely across subgroups: range 1.2–6.5 years, average 4.0 years. Mean 10-year DLE was relatively much closer: range 1.5–2.3 years, average 2.1 years.

The SLT/L-GoM model yields accurate maximum likelihood estimates of TLE, DFLE, and DLE for patients with AD; it provides a realistic, comprehensive modeling framework for endpoint and resource use/cost calculations.

The online version of this article (doi:10.1186/s13195-017-0302-6) contains supplementary material, which is available to authorized users.

Partial Text

The rate of progression of Alzheimer’s disease (AD) varies across patients, making it difficult to generate accurate estimates of the course of disease or time until specific disease endpoints for individual patients [1]. Moreover, differences in group-specific rates of progression and treatment efficacies in therapeutic trials may be confounded by individual variation in rates of progression, making it difficult to evaluate the effectiveness of randomization [2]. All of these difficulties are exacerbated by two additional factors: (1) the clinical presentation at diagnosis is highly variable over individual patients with AD—involving cognitive, functional, behavioral, psychiatric, and other symptoms; and (2) the neuropathological substrates of AD—involving neuronal dysfunction, neurodegeneration, synaptic dysfunction, cerebral atrophy, and other pathologies—differentially influence the clinical course of AD in ways that are poorly understood [3]. For example, there are no known biomarkers that closely track the progression of AD clinical signs/symptoms or uniquely identify their presence [4]. Thus, the development of a realistic, comprehensive, multidomain model of the progression of AD clinical signs/symptoms and outcomes in a well-defined cohort of patients with AD dementia could yield new insights into the process and accelerate the development of disease-modifying therapies. The need for such development was recognized in the call for new models of AD progression/outcomes in the recommendations from the 2015 National Institutes of Health AD Research Summit [5]. The model reported in this paper is intended to advance this development.

This study provides the first published estimates of the L-GoM extension of the SLT model. Our motivation for this extension was fourfold. First, our analysis supports the hypothesis that patients with AD are heterogeneous in initial presentation and in rates of progression [1], implying that adequate characterization of the clinical course of AD requires a parsimonious multivariate latent-variable model such as L-GoM [10]. Second, the ability to directly map the GoM scores to TLE, DFLE, and DLE focuses attention on these readily understood, familiar metrics. This contrasts with existing factor analytic models [9] that cannot incorporate the SLT model and cannot extract TLE, DFLE, and DLE from patient-level longitudinal data [10, 19]. Third, predictions of TLE, DFLE, DLE, and associated survival curves for many types of disability, especially FTC, are central to important decisions in AD treatment and patient care; they represent information that patients with AD, their families, and caregivers want to know. Fourth, the L-GoM extension of the SLT model can be used to assess the effects of treatment on disability-free and disabled survival. Lifetime costs can be calculated by combining estimated survival curves and cost functions for selected disability measures, implying that the SLT/L-GoM model can be used as a realistic, comprehensive modeling framework for endpoint and resource use/cost calculations for individual patients with AD and subgroups. The appendix in Additional file 1 provides all parametric estimates needed for hypothesis generation and further exploration of AD using the SLT/L-GoM model (Additional file 1: Tables A.3–A.6).

The objective of the present study was to assess the accuracy of the estimates produced by the SLT/L-GoM model. This required that we generate for the first time a comprehensive, individualized multidomain model of AD progression covering the first 10 years following study intake and incorporating a composite mapping leading directly from the longitudinal data to the individual-specific TLEs, DFLEs, and DLEs. The substantial heterogeneity in initial patient presentation and AD progression was captured using three clinically meaningful prognostic subtypes (subtypes 1–3) and one terminal subtype (subtype 4) exhibiting highly differentiated symptom severity on 7 of the 11 measurement domains in the model (Table 2). The rates of progression for subtype 2 (mild-moderate severity at examination 1) were found to be substantially faster than for subtype 3 (moderate severity at examination 1), underscoring the need to distinguish these subtypes in clinical prognostication. The mixed membership property of the model was used to define five relatively homogeneous but diverse patient subgroups, four of which (1–4) had high GoM scores on the respective prognostic/terminal subtypes, with the fifth defined as a residual subgroup. The model yielded accurate maximum likelihood estimates of TLE, DFLE, and DLE and associated survival and disability probabilities for all five subgroups. Thus, the model provides a realistic, comprehensive framework for endpoint and resource use/cost calculations for patients with AD.

 

Source:

http://doi.org/10.1186/s13195-017-0302-6

 

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