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WO2016127185A1 - Procédés et approche permettant la détection et la prédiction au fil du temps d'un changement de la sévérité de la démence ou du diagnostic clinique - Google Patents

Procédés et approche permettant la détection et la prédiction au fil du temps d'un changement de la sévérité de la démence ou du diagnostic clinique Download PDF

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WO2016127185A1
WO2016127185A1 PCT/US2016/017039 US2016017039W WO2016127185A1 WO 2016127185 A1 WO2016127185 A1 WO 2016127185A1 US 2016017039 W US2016017039 W US 2016017039W WO 2016127185 A1 WO2016127185 A1 WO 2016127185A1
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dementia
scores
cognitive
latent
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Donald ROYALL
Raymond Palmer
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    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N33/00—Investigating or analysing materials by specific methods not covered by groups G01N1/00 - G01N31/00
    • G01N33/48—Biological material, e.g. blood, urine; Haemocytometers
    • G01N33/50—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing
    • G01N33/68—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing involving proteins, peptides or amino acids
    • G01N33/6893—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing involving proteins, peptides or amino acids related to diseases not provided for elsewhere
    • G01N33/6896—Neurological disorders, e.g. Alzheimer's disease
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
    • G16H50/30—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for calculating health indices; for individual health risk assessment
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N2800/00—Detection or diagnosis of diseases
    • G01N2800/28—Neurological disorders
    • G01N2800/2814—Dementia; Cognitive disorders
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N2800/00—Detection or diagnosis of diseases
    • G01N2800/60—Complex ways of combining multiple protein biomarkers for diagnosis

Definitions

  • AD Alzheimer's disease
  • dementias dementias
  • An emerging consensus suggests that anti-dementia interventions may have to be applied before the clinical onset of dementia to be effective.
  • Several recent interventions have failed to demonstrate efficacy in the general AD population, but appear to be effective in the earliest cases. Delaying the onset of dementia by only five years would greatly decrease the prevalence of this condition.
  • ⁇ scores are used (i) to measure dementia and track changes in dementia severity over time; (ii) to equate or normalize subjects across groups (e.g., intervention vs. placebo), or recruited across sites or investigators, with regards to their dementia severity; or (iii) to select or classify subjects entering or applying for a study.
  • a bifactor ⁇ homolog targets Instrumental Activities of Daily Living (IADL) or similar functional status measure, in either cross-sectional or longitudinal data, depending on the application.
  • IADL Instrumental Activities of Daily Living
  • a ⁇ ortholog is used to predict an alternative target variable.
  • the ortholog named "dPRE" might target the direction of a patient's future change in ⁇ scores ( ⁇ ) from a baseline cognitive assessment.
  • dPRE models would use the observed longitudinal change in ⁇ scores as the "Target Indicator" of a ⁇ ortholog. dPRE scores are used to select cases most at risk of near-term progression in their dementia severity, or of clinical conversion to a higher stage of their dementing process.
  • FIG. 10 - FIG. 12 illustrate 6's sensitivity to change over one (FIG. 10), two (FIG. 11) and three years (FIG. 12).
  • baseline ⁇ score factor loadings from Visit 1 are applied consistently across waves to generate the future ⁇ score.
  • Each figure is marked by a line of identity. Participants mapping to that line experience no change in their dementia severity over time. Cases to the left of that line have progressed. Those to the right are recovering from a dementing process (even among the NC). A sizable fraction of cases at all diagnostic stages are becoming more demented over time. Even many non-demented controls (NC) are becoming more "demented”. Regardless, other cases are improving in every diagnostic category, including many cases diagnosed with "AD" at baseline.
  • methods described herein can be used with any psychometric assessment (e.g., both paper and pencil or electronic versions), and can be validly applied in the assessment of any recognized dementing illness [including but not limited to Alzheimer's Disease (AD), Dementia with Lewy Bodies (LBD), Vascular dementia (VaD), and /or Fronto-temporal Dementia (FTD)].
  • AD Alzheimer's Disease
  • LBD Dementia with Lewy Bodies
  • VaD Vascular dementia
  • FTD Fronto-temporal Dementia
  • DM Diabetes Mellitus
  • HAV Human Immune Deficiency Virus
  • other infectious/viral diseases post-menopausal cognitive decline, post- operative cognitive decline, post-chemotherapy cognitive decline (also known popularly as "Chemobrain")
  • TBI traumatic brain injury
  • certain neuropsychiatric illnesses e.g., major depression and schizophrenia (previously known as “dementia praecox”
  • the dementia of normal aging also referred to as "senility”
  • the methods can be applied to any psychometric cognitive performance measure in combination with one or more measures of Instrumental Activities of Daily Living (IADL).
  • IADL Instrumental Activities of Daily Living
  • the item set of a single measure is treated as a cognitive battery and a ⁇ -homolog is extracted from it by a proprietary bifactor model structure, which targets the functional status or IADL measure.
  • Our method should improve the performance of any psychometric measure, and can be applied post hoc to existing data, or prospectively to newly acquired data. This method should be of interest to researchers, hospitals, insurance companies, pharmaceutical and other corporations, governments, the military and diagnostic test developers
  • FIG. 1 Illustration of a structural equation model (SEM) of two latent factors: "g” and "P. Observed variables are represented by rectangles, while latent constructs are represented by circles. Arrows reflect regression weights, or factor loadings in the case of a latent variable's indicators. Bidirectional arrows represent correlations.
  • ADL Basic Activities of Daily Living
  • CDR Clinical Dementia Rating scale sum of boxes
  • COWA Controlled Oral Word Association Test
  • DST Digit Span Test
  • IADL Instrumental Activities of Daily Living
  • WMS LM II Weschler Memory Scale: Delayed Logical Memory
  • WMS VR II Weschler Memory Scale: Delayed Visual Reproduction. *A11 observed variables are adjusted for age, gender and education. Residuals and their inter- correlations not shown.
  • FIG. 2 Illustration of a structural equation model (SEM) of two latent factors: "g” and "P including the third latent variable "d". Observed variables are represented by rectangles, while latent constructs are represented by circles. Arrows reflect regression weights, or factor loadings in the case of a latent variable's indicators. Bidirectional arrows represent correlations.
  • ADL Basic Activities of Daily Living
  • CDR Clinical Dementia Rating scale sum of boxes
  • COWA Controlled Oral Word Association Test
  • DST Digit Span Test
  • IADL Instrumental Activities of Daily Living
  • WMS LM II Weschler Memory Scale: Delayed Logical Memory
  • WMS VR II Weschler Memory Scale: Delayed Visual Reproduction.
  • FIG. 3 Histogram of g' scores, g' scores, a sizable fraction of the cognitive battery's total variance, are normally distributed because g', unlike d, is orthogonal to dementia status.
  • FIG. 4 Histogram of d scores respectively, d scores are bimodally distributed, as is the TARCC sample itself, which was composed of "dementia cases” and "controls.”
  • FIG. 5 MCI's Boundaries on d's Spectrum.
  • FIG. 6 Wave 1 Histogram (TARCC).
  • FIG. 7. Wave 2 Histogram.
  • FIG. 10 Change in ⁇ Over One Year.
  • FIG. 11 Change in ⁇ Over Two Years.
  • FIG. 12 Change in ⁇ Over Three Years.
  • FIG. 13 An example of a dPRE ortholog. Instead of IADL, future ⁇ scores from a validation cohort are used as the target variable in a ⁇ -like bifactor. dPRE's cognitive indicators are from baseline assessments. The latent variable "dPRE" is strongly associated with future (year 2) ⁇ scores. To output dPRE as a composite variable, we would require knowledge of the future ⁇ score. However, we have shown that a latent ⁇ homolog can be estimated from its cognitive predictors alone, without reference to its target indicator and yet retain its diagnostic accuracy (Royall et al., in press). To estimate future ⁇ scores, we can simply calculate such a "restricted" composite, limited to the baseline cognitive performance.
  • FIG. 14 Illustrates a block diagram of a computer system configured to implement various systems and methods described herein according to some embodiments.
  • FIG. 15 Illustration of various modules that can be used to implement embodiments of the invention.
  • FIG. 16 Illustration of one embodiment of implementing aspects of the invention.
  • FIGs. 17A and 17B (A) 5's incrementally better ROC /AUC relative to their indicators in two different ⁇ -homologs. (B) dMA TARCC hispanics.
  • FIG. 19 dTEXAS.
  • Neurodegenerative pathology is present in non-demented persons. This pathology represents pre-clinical stages in the development of various dementing illnesses, and non- demented persons who exhibit such lesions are at an increased near-term risk of dementia.
  • a variety of serum, plasma, cerebrospinal fluid (CSF) and neuroimaging biomarkers have been associated with clinical dementia, but none have approached latent variable approach's (5's) ROC/AUC for the diagnosis of dementia.
  • MCI mimild cognitive impairment
  • Latent variable "measurement models” offer solutions to many of these problems.
  • a "latent” variable is essentially a "factor” derived from the variance shared across three or more observed variables.
  • the individual observed measures may each have unique vulnerabilities to measurement bias, be it related to the measure's psychometric properties (skewed distributions, ceiling or floor effects), educational or cultural bias, or performance bias (hearing, visual, or motor).
  • measurement bias a latent variable's factor scores represent a continuously varying, and potentially normally distributed phenotype. It can be associated with bio-markers using powerful parametric statistical methods.
  • Cognitive impairment is widely held to be the hallmark of dementia.
  • three conditions are necessary to that diagnosis (Royall et al. (2007) J Neuropsychiatry Clin Neurosci 19, 249-265): (1) there must be acquired cognitive impairment s), (2) there must be functional disability, and (3) the disability must be related to the cognitive impairment(s) that are observed. This implies that the essential feature(s) of dementing processes can be resolved to the cognitive correlates of functional status.
  • Latent variable "measurement models” (Cook et al. (2001) Soc Sci Med 53(10): 1275-85) offer the potential for "error free” measures of key constructs.
  • a latent variable model is described herein that provides both a measure of dementia severity and a continuously varying "error free" dementia-specific endophenotype.
  • Target-related outcome variables can be mixed with a battery of predictors to "distill” or “refine” their shared variance into a latent variable of interest.
  • the factor scores of the resulting latent construct can be output to create an error free continuously varying endophenotype, which can then be used as an outcome variable or predictor in its own right.
  • FIG. 1 presents a structural equation model (SEM) of two latent factors: "g" and "P.
  • SEM structural equation model
  • observed variables are represented by rectangles, while latent constructs are represented by circles.
  • Arrows reflect regression weights, or factor loadings in the case of a latent variable's indicators.
  • Bidirectional arrows represent correlations.
  • the latent variable g represents "Spearman's g", i.e., a latent variable representing the shared variance across the observed cognitive performance variables (Spearman (1904) Am J Psychol 15:201-293).
  • TARCC Texas Alzheimer's Research and Care Consortium
  • F represents a latent functional status factor derived from eight observed instrumental activities of daily living (IADL) items and six observed basic ADL (BADL) items.
  • the latent variable f explains 50.67% of the variance in observed variance in care-giver rated IADL/BADL.
  • a third latent variable, a hybrid cognitive/functional status latent construct, is introduced “d" (FIG. 2).
  • the latent construct d represents the variance shared between cognitive and IADL/BADL measures [i.e., any and all dementing process(es) afflicting the sample].
  • the inventors relabeled g as "g' " to acknowledge this effect. Together, g' and d accounted for 59.6% of the variance in our cognitive battery.
  • the latent construct d accounted for 37.2% independently of g' . The remainder was attributable to residual "measurement error”.
  • the latent construct f was also affected by the creation of d.
  • MMSE Mini- Mental State Exam
  • GDS Geriatric Depression Scale
  • the MMSE is a measure of global cognition and should be more strongly associated with a dementing process than the GDS, a measure of depressed mood.
  • d's association with these measures was weakened relative to that with CDR SOB.
  • g's association with MMSE scores was strengthened relative to that with CDR SOB.
  • g' and d were weakly associated with GDS scores.
  • the latent construct f did not contribute significantly to either of those outcomes.
  • the latent variables g', f, and d were tested as independent predictors of TARCC consensus clinical diagnoses (i.e., "AD” vs. "control”).
  • the latent variables d and g' can be output as case-wise factor scores, d scores uniquely can be used as a dementia endophenotype.
  • homologs of d created from other target indicator variables can be output as endophenotypes of their respective target conditions (e.g., age, depression, gender, schizophrenia, alcoholism, mortality, etc.).
  • FIGs. 3 and 4 present histograms of d and g scores respectively, d scores are bimodally distributed, as is the TARCC sample itself, which was composed of "dementia cases" and "controls".
  • g' scores a sizable fraction of the cognitive battery's total variance, are normally distributed because g', unlike d, is orthogonal to dementia status.
  • Endophenotype Applications Once an endophenotype has been created, it can be used as an outcome variable, a predictor, or to make categorical classifications (e.g., diagnoses), d's factor scores can be exported as a "d score". This then becomes a continuously varying dementia specific endophenotype. Thus, the interindividual variability in dementia status can be modeled, i.e., as predictors in biomarker studies.
  • d scores can be used to effectively rank order each individual in a cohort with respect to their relative position along a dementia-specific continuum
  • ROC analysis can be used to define optimal empirical d score boundaries for "normal cognition", "MCI” and "dementia.”
  • MCI normal cognition
  • d scores derived from relatively simple batteries could be used to replicate the diagnoses made by experienced clinicians with full access to comprehensive psychometric data.
  • this can be applied to any latent d score homolog. Depression, schizophrenia, alcoholism etc. could be accurately diagnosed by the same approach.
  • d Model Variations Because Spearman's g is insensitive to the measures employed in the battery, d can be derived from any desired panel of measures, i.e., measures chosen for their ease of administration, to avoid copyright controls, to reduce respondent burden, or to achieve telephone administration. Moreover, because the latent construct d is an error-free construct, it is not vulnerable to factors such as ethnicity, education, or language of administration, which potentially bias the individual measures used to create it.
  • Factor analysis is a statistical method used to describe variability among observed, correlated variables in terms of a potentially lower number of unobserved variables called factors. In other words, it is possible, for example, that variations in three or four observed variables mainly reflect the variations in fewer unobserved variables. Factor analysis searches for such joint variations in response to unobserved latent variables. The observed variables are modeled as linear combinations of the potential factors. The information gained about the interdependencies between observed variables can be used later to reduce the set of variables in a dataset. Computationally this technique is equivalent to low rank approximation of the matrix of observed variables.
  • Factor analysis originated in psychometrics, and is used in behavioral sciences, social sciences, marketing, product management, operations research, and other applied sciences that deal with large quantities of data.
  • Latent variable models including factor analysis, use regression modeling techniques to test hypotheses.
  • the factor loadings are the correlation coefficients between the variables and factors. Analogous to Pearson's r, the squared factor loading is the percent of variance in that indicator variable explained by the factor. To get the percent of variance in all the variables accounted for by each factor, add the sum of the squared factor loadings for that factor and divide by the number of variables.
  • methods of determining a score based on a hybrid latent variable can include one or more of the operations described below - in certain aspects these operations are executed in part by instructions provided in a tangible medium, such as a programmed computer; a network comprising one or more programmed computers; or a compact disk.
  • a battery of behavioral measures There must be at least three. They can be any mix of cognitive and/or behavioral measures, preferably continuously distributed, but not necessarily. The selection of behavioral indicators can be selected in order to achieve a particular application.
  • a battery of verbal measures would be selected to achieve telephonic administration.
  • a battery of non-proprietary measures might be used to achieve low cost administration.
  • a battery of bedside measures might be selected to allow data collection in the field.
  • a specific battery might be selected to allow post-hoc evaluation of an existing dataset.
  • Target measure(s) can be selected to achieve the same application(s) as the battery.
  • d's factor loadings can be used to export a "d score" for each individual in the validation cohort.
  • this is a continuously distributed measure of dementia severity. It can be used either as a predictor or an outcome in multivariate regression or other models (i.e., to determine d's biomarkers or to predict dementia-related clinical outcomes).
  • d scores or those of d's ortholog in the case of other targets
  • an optimal d score threshold must be selected by Receiver Operating Curve (ROC) analysis of expert determinations of that diagnosis in the same population used to construct d.
  • ROC Receiver Operating Curve
  • the d score in the unknown case are first administered the same set of measures used to construct d in the validation cohort.
  • the scores are entered into a computer program that encodes d's factor loadings.
  • the program is executed on a suitable platform (phone, tablet, computer, or internet-based server).
  • the unknown case is assigned a d score.
  • the d score is compared to the validated reference threshold.
  • Certain embodiments include the analysis of various cognitive assessment tests and functional assessment tests.
  • the following provide examples of some of the tests that may be provided in isolation or included in a cognitive testing battery.
  • One skilled in such assessments will recognize that other known and novel tests may be applied or used with the methods described herein.
  • the tests may be grouped into specific classifications and groups. The collection and arrangement of tests in a battery may be in accordance with a particular cognitive limitation or other criterion.
  • One of skill in such assessments will recognize that the specific tests may be altered and substituted without affecting the novelty of the methods described herein, as may the groupings and ordering of the tests within a test battery.
  • ⁇ scores are used (i) to measure dementia and track changes in dementia status over time; (ii) to equate or normalize subjects across groups (e.g., intervention vs. placebo), or recruited across sites or investigators, with regards to their dementia severity; or (iii) to select or classify subjects entering or applying for a study.
  • ⁇ ortholog, "dPRE” is used to predict the direction of a patient's future change in ⁇ scores ( ⁇ ) from a baseline cognitive assessment. dPRE models would use the observed longitudinal change in ⁇ scores as the "Target Indicator" of a ⁇ ortholog. dPRE scores are used to select cases most at risk of near-term progression in their dementia severity, or of clinical conversion to a higher stage of their dementing process.
  • Certain embodiments are directed to methods using ⁇ scores (1) as an outcome measure in dementia clinical trials, (2) to equate subjects across groups (i.e., intervention vs. placebo) or recruited across sites or investigators, with regards to their dementia severity, (3) to use a ⁇ ortholog, "dPRE", to predict the direction of a patient's future change in ⁇ scores ( ⁇ ) from a baseline cognitive assessment.
  • dPRE models would use the observed longitudinal change in ⁇ scores as the "Target Indicator" of a ⁇ ortholog.
  • dPRE scores then could be used to select cases most at risk of near-term progression in their dementia severity, or of clinical conversion to a higher stage of their dementing process.
  • FIG. 13 presents an example of a dPRE ortholog.
  • dPRE cognitive indicators are from baseline assessments.
  • the latent variable "dPRE” is strongly associated with future (year 2) ⁇ scores.
  • To output dPRE as a composite variable we would require knowledge of the future ⁇ score.
  • a latent ⁇ homolog can be estimated from its cognitive predictors alone, without reference to its target indicator and yet retain its diagnostic accuracy (Royall et al., in press).
  • To estimate future ⁇ scores we can simply calculate such a "restricted” composite, limited to the baseline cognitive performance. This would allow a highly accurate estimation of future ⁇ scores, and thus, conversion risk.
  • baseline observed cognitive performance, Spearman's g, or ⁇ scores are all associated with future ⁇ scores.
  • dPRE explains additional variance above and beyond them all.
  • 5's factor scores can be exported as a composite variable, i.e., as a " ⁇ Score”. This then becomes a continuously varying dementia specific phenotype. It can be used to effectively rank order each individual in a cohort with respect to their relative position along a "dementia"-specific continuum.
  • MCI is likely then to represent the middle ground of 5's distribution. Its boundaries can be estimated by ROC analysis. Optimal empirical boundaries have been calculated for "normal cognition”, “MCI” and “dementia” using another ⁇ homolog in a Japanese sample (“dJ”). dJ is indicated by CLOX: An Executive Clock-Drawing Task (CLOX) (Royall et al. Journal of Neurology, Neurosurgery and Psychiatry (1988) 64:588- 94), the Executive Interview (EXIT25) (Royall et al.
  • CLOX An Executive Clock-Drawing Task
  • EXIT25 the Executive Interview
  • dJ is constructed from a 30 minute bedside battery that does not overlap with the 80 minute psychometric battery used to construct d in TARCC.
  • a dJ score of 0.85 best discriminates between AD v. MCI, with 78% sensitivity and 83% specificity.
  • a dJ score of 1.12 best discriminates between NC v. MCI, with 73% sensitivity and 77% specificity.
  • FIG. 5 presents these thresholds for the discriminations between AD v. MCI (a) and NC v. MCI (b). dJ's AUCs for these discriminations are in Table 2.
  • ⁇ scores predict longitudinal cognitive decline: ⁇ scores predict longitudinal cognitive decline among initially non-demented persons (Royall and Palmer Journal of Neuropsychiatry and Clinical Neurosciences (2012) 24:37-46). A latent growth curve of change in ⁇ scores as a predictor of prospective Year 4 dementia status was constructed.
  • FIG. 6 - FIG. 9 show the distribution of ⁇ scores in TARCC over four waves of longitudinal follow-up. Over time, a dementia group emerges in 5's lower (demented) range.
  • FIG. 10 - FIG. 12 show the correlations between baseline ⁇ scores and those obtained after 1, 2, and 3 years of follow-up (respectively).
  • Baseline ⁇ score factor loadings from Visit 1 are applied consistently across waves to generate the future ⁇ score.
  • Each figure is marked by a line of identity - participants mapping to that line experience no change in their dementia severity over time. Cases to the left of that line have progressed. Those to the right are recovering from a dementing process (even among the NC). It is easily determined that a sizable fraction of cases at all diagnostic stages are becoming more demented over time. Even many non-demented controls (NC) are becoming more "demented”. Regardless, other cases are improving in every diagnostic category, including many cases diagnosed with "AD" at baseline.
  • FIG. 9 - FIG. 11 also code clinical conversions from "NC” to MCI or AD (open circle), and from MCI to AD (closed circle). These conversions are concentrated in an intermediate range of d Scores and among cases with worsening d Scores (to the left of the line of identity). Furthermore, MCI conversions to AD occur at lower (more "demented") baseline d Scores than do conversions from NC status.
  • ⁇ scores are sensitive to change at all stages of dementia (NC, MCI and AD), (2) ⁇ scores can detect clinically salient change among MCI and NC, and (3) ⁇ scores can detect improvement or progression of dementia status at intervals as short as one year and (4) ⁇ scores can predict conversion to MCI or AD from non-demented baselines over up to four years of follow-up.
  • ⁇ score This findings also illustrate the ⁇ score's potential to serve as a sensitive measure of functionally (IADL) salient (dementing) cognitive change in demented and pre-demented subjects, and at intervals as short as 1 year.
  • IADL functionally
  • ⁇ scores are continuously distributed, they obviate the need for difficult to make categorical clinical distinctions (such as "MCI").
  • MCI categorical clinical distinctions
  • Any desired ⁇ score can be used to select cases for treatment and to ensure the recruitment of comparably demented subjects across sites, investigators (even across linguistic or cultural barriers (Royall et al., Journal of Alzheimer 's Disease 2016;49:561-570).
  • subjects can be selected at some confidence level that a selected subject's untreated dementia status will worsen over time.
  • dTEXAS and dCLOX have a higher AUC/ROC than the traditionally sum scored measures from which their indicators were taken.
  • dTEXAS 's AUC 0.92 for the diagnosis of dementia vs, 0.89 for the EXIT25 (Matsuoka et al., 2014).
  • dCLOX's AUC 0.92, vs. 0.78 for the CLOX (Matsuoka et al., 2014).
  • the latent variable ⁇ will necessarily improve upon any observed measure or battery's diagnostic performance because it is free of that measurement error.
  • the latent variable ⁇ can be constructed from any cognitive battery. This is because our proprietary bifactor model's structure extract's ⁇ from Spearman's general intelligence factor g. It is widely accepted that g is "indifferent to its indicators" meaning that it contributes variance to every cognitive performance measure, regardless of its face validity as a measure of any individual cognitive domain (e.g., memory, attention, executive function, etc). 3.
  • ⁇ can be constructed from item-level data, without loss of its diagnostic performance. Therefore, the diagnostic performance of ANY cognitive performance measure might be improved by rescoring it as a ⁇ homolog composite.
  • the individual observed measures may each have unique vulnerabilities to measurement bias, be it related to the measure's psychometric properties (skewed distributions, ceiling or floor effects), educational or cultural bias, or performance bias (hearing, visual, or motor). However, they do not all share these attributes. Thus, their shared variance is arguably measurement "error-free", and can be explicitly modeled in an SEM framework.
  • a latent variable's factor scores represent a continuously varying, and potentially normally distributed phenotype. It can be associated with bio-markers using powerful parametric statistical methods.
  • Certain aspects of the methods described herein improve the diagnostic performance of psychometric measures by using their itemsets to generate ⁇ scores. This should both improve diagnostic accuracy while potentially also reducing administration burden, and /or improving the ease of administration.
  • 5's factor scores can be exported as a composite variable, i.e., as a " ⁇ Score". This then becomes a continuously varying dementia specific phenotype. It can be used to effectively rank order each individual in a cohort with respect to their relative position along a "dementia"-specific continuum.
  • the itemset of any cognitive performance measure is amenable to this approach because all measures are affected by Spearman's general intelligence factor g, from which ⁇ can be derived. It is further unprecedented to apply this approach to item level data. That opens the possibility of rescoring and cognitive performance measures as a ⁇ homolog, thereby improving the original measure's sensitivity to functionally salient cognitive changes and dementia.
  • Biomarkers can be used to both define a disease state as well as to provide a means to predict physiological and clinical manifestations of a disease.
  • Three commonly discussed ways in which biomarkers could be used clinically are: (1) to characterize a disease state, i.e. establish a diagnosis, (2) to demonstrate the progression of a disease, and (3) to predict the progression of a disease, i.e. establish a prognosis.
  • Establishing putative biomarkers for such uses typically requires a statistical analysis of relative changes in biomarker expression either cross-sectionally and/or over time (longitudinally). For example, in a state or diagnostic biomarker analysis, levels of one or more biomarkers are measured cross-sectionally, e.g.
  • biomarker expression in patients with disease and in normal control subjects, at one point in time and then related to the clinical status of the groups. Statistically significant differences in biomarker expression can be linked to presence or absence of disease, and would indicate that the biomarkers could subsequently be used to diagnose patients as either having disease or not having disease.
  • levels of one or more biomarkers and clinical status are both measured longitudinally. Statistically significant changes over time in both biomarker expression and clinical status would indicate that the biomarkers under study could be used to monitor the progression of the disease.
  • levels of one or more biomarkers are measured at one point in time and related to the change in clinical status from that point in time to another subsequent point in time. A statistical relationship between biomarker expression and subsequent change in clinical status would indicate that the biomarkers under study could be used to predict disease progression.
  • Results from prognostic analyses can also be used for disease staging and for monitoring the effects of drugs.
  • the prediction of variable rates of decline for various groups of patients allows them to be identified as subgroups that are differentiated according to disease severity (i.e., less versus more) or stage (i.e., early versus late).
  • patients treated with a putative disease-modifying therapy may demonstrate an observed rate of cognitive decline that does not match the rate of decline predicted by the prognostic analysis. This could be considered evidence of drug or treatment efficacy.
  • WO 2004/104597 “Method for Prediction, Diagnosis, and Differential Diagnosis of AD” describes methods of predicting disease status via an x/y ratio of ⁇ peptides
  • WO 2005/047484 “Biomarkers for Alzheimer's Disease” describes a series of markers that can be used for the assessment of disease state
  • WO 2005/052592 “Methods and Compositions for Diagnosis, Stratification, and Monitoring of Alzheimer's Disease and Other Neurological Disorders in Body Fluids” teaches methods and markers gleaned from plasma for the monitoring of Alzheimer's disease
  • WO 2006/009887 "Evaluation of a Treatment to Decrease the Risk of a Progressive Brain Disorder or to Slow Brain Aging” teaches methods and ways to use brain imaging to measure brain activity and/or structural changes to determine efficacy of putative treatments for brain-related disorders.
  • Embodiments of the current invention can be used to improve and identify novel biomarkers and methods for the treatment and assessment of a variety of disease states that result in cognitive impairments, alterations, and/or deficiencies.
  • the protein thrombopoeitin, measured in serum has been shown to predict future d-scores, but only via the contemporaneously measured intercept of the longitudinally measured growth process of d-score change, not the growth process' slope (Royall & Palmer, in press).
  • our method can be used to ascertain the time point in a dementing illness when a specific biomarker is involved (Royall and Palmer, Alzheimer 's & Dementia: Diagnosis, Assessment & Disease Monitoring, in press.).
  • ⁇ scores can be used (1) as an outcome measure in dementia clinical trials, and (2) to equate subjects across groups (i.e., intervention vs. placebo) or recruited across sites or investigators, with regards to their dementia severity.
  • a ⁇ ortholog, "dPRE” can also be used to predict the direction of a patient's future change in ⁇ scores ( ⁇ ) from a baseline cognitive assessment. dPRE models would use the observed longitudinal change in ⁇ scores as the "Target Indicator" of a ⁇ ortholog. dPRE scores then could be used to select cases most at risk of near-term progression in their dementia severity, or of clinical conversion to a higher stage of their dementing process.
  • 6's factor scores can be exported as a composite variable, i.e., as a " ⁇ Score”. This then becomes a continuously varying dementia specific phenotype. It can be used to effectively rank order each individual in a cohort with respect to their relative position along a "dementia"-specific continuum.
  • ⁇ ortholog [000100] ⁇ ortholog, "dPRE", can be used to predict the direction of a patient's future change in ⁇ scores ( ⁇ ) from a baseline cognitive assessment. dPRE models would use the observed longitudinal change in ⁇ scores as the "Target Indicator" of a ⁇ ortholog. dPRE scores then could be used to select cases most at risk of near-term progression in their dementia severity, or of clinical conversion to a higher stage of their dementing process.
  • Embodiments of hybrid latent variable system may be implemented or executed by one or more computer systems.
  • One such computer system is illustrated in FIG. 14.
  • computer system may be a server, a mainframe computer system, a workstation, a network computer, a desktop computer, a laptop, or the like.
  • the system shown in FIG. 14, FIG. 15, FIG. 16 or the like may be implemented as computer system.
  • one or more of servers or devices may include one or more computers or computing devices generally in the form of a computer system.
  • these various computer systems may be configured to communicate with each other in any suitable way, such as, for example, via a network.
  • the computer system includes one or more processors 510 coupled to a system memory 520 via an input/output (I/O) interface 530.
  • Computer system 500 further includes a network interface 540 coupled to I/O interface 530, and one or more input/output devices 550, such as cursor control device 560, keyboard 570, and display(s) 580.
  • a given entity e.g., hybrid latent variable system
  • some elements may be implemented via one or more nodes of computer system 500 that are distinct from those nodes implementing other elements (e.g., a first computer system may implement an assessment of a hybrid latent variable assessment or system while another computer system may implement data gathering, scaling, classification etc.).
  • computer system 500 may be a single-processor system including one processor 510, or a multi-processor system including two or more processors 510 (e.g., two, four, eight, or another suitable number).
  • processors 510 may be any processor capable of executing program instructions.
  • processors 510 may be general -purpose or embedded processors implementing any of a variety of instruction set architectures (ISAs), such as the x86, POWERPC®, ARM®, SPARC®, or MIPS® ISAs, or any other suitable ISA.
  • ISAs instruction set architectures
  • each of processors 510 may commonly, but not necessarily, implement the same ISA.
  • at least one processor 510 may be a graphics-processing unit (GPU) or other dedicated graphics-rendering device.
  • GPU graphics-processing unit
  • System memory 520 may be configured to store program instructions and/or data accessible by processor 510.
  • system memory 520 may be implemented using any suitable memory technology, such as static random access memory (SRAM), synchronous dynamic RAM (SDRAM), nonvolatile/Flash-type memory, or any other type of memory.
  • SRAM static random access memory
  • SDRAM synchronous dynamic RAM
  • program instructions and data implementing certain operations may be stored within system memory 520 as program instructions 525 and data storage 535, respectively.
  • program instructions and/or data may be received, sent or stored upon different types of computer-accessible media or on similar media separate from system memory 520 or computer system 500.
  • a computer-accessible medium may include any tangible storage media or memory media such as magnetic or optical media— e.g., disk or CD/DVD-ROM coupled to computer system 500 via I/O interface 530.
  • Program instructions and data stored on a tangible computer-accessible medium in non-transitory form may further be transmitted by transmission media or signals such as electrical, electromagnetic, or digital signals, which may be conveyed via a communication medium such as a network and/or a wireless link, such as may be implemented via network interface 540.
  • I/O interface 530 may be configured to coordinate I/O traffic between processor 510, system memory 520, and any peripheral devices in the device, including network interface 540 or other peripheral interfaces, such as input/output devices 550.
  • I/O interface 530 may perform any necessary protocol, timing or other data transformations to convert data signals from one component (e.g., system memory 520) into a format suitable for use by another component (e.g., processor 510).
  • I/O interface 530 may include support for devices attached through various types of peripheral buses, such as a variant of the Peripheral Component Interconnect (PCI) bus standard or the Universal Serial Bus (USB) standard, for example.
  • PCI Peripheral Component Interconnect
  • USB Universal Serial Bus
  • I/O interface 530 may be split into two or more separate components, such as a north bridge and a south bridge, for example.
  • some or all of the functionality of I/O interface 530 such as an interface to system memory 520, may be incorporated directly into processor 510.
  • Network interface 540 may be configured to allow data to be exchanged between computer system 500 and other devices attached to a network, such as other computer systems, or between nodes of computer system 500.
  • network interface 540 may support communication via wired or wireless general data networks, such as any suitable type of Ethernet network, for example; via telecommunications/telephony networks such as analog voice networks or digital fiber communications networks; via storage area networks such as Fiber Channel SANs, or via any other suitable type of network and/or protocol.
  • Input/output devices 550 may, in some embodiments, include one or more display terminals, keyboards, keypads, touch screens, scanning devices, voice or optical recognition devices, or any other devices suitable for entering or retrieving data by one or more computer system 500. Multiple input/output devices 550 may be present in computer system 500 or may be distributed on various nodes of computer system 500. In some embodiments, similar input/output devices may be separate from computer system 500 and may interact with one or more nodes of computer system 500 through a wired or wireless connection, such as over network interface 540.
  • memory 520 may include program instructions 525, configured to implement certain embodiments described herein, and data storage 535, comprising various data accessible by program instructions 525.
  • program instructions 525 may include software elements of embodiments described in the claims or illustrated in FIG. 14, FIG. 15, or the like.
  • program instructions 525 may be implemented in various embodiments using any desired programming language, scripting language, or combination of programming languages and/or scripting languages (e.g., C, C++, C#, JAVA®, JAVASCRIPT®, PERL®, etc).
  • Data storage 535 may include data that may be used in these embodiments. In other embodiments, other or different software elements and data may be included.
  • computer system 500 is merely illustrative and is not intended to limit the scope of the disclosure described herein.
  • the computer system and devices may include any combination of hardware or software that can perform the indicated operations.
  • the operations performed by the illustrated components may, in some embodiments, be performed by fewer components or distributed across additional components.
  • the operations of some of the illustrated components may not be performed and/or other additional operations may be available. Accordingly, systems and methods described herein may be implemented or executed with other computer system configurations.

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Abstract

L'invention porte, dans certains modes de réalisation, sur des procédés permettant de produire et d'évaluer des scores δ qui sont utilisés (i) pour mesurer la démence et suivre au fil du temps des changements de l'état démentiel ; (ii) pour associer ou normaliser des sujets par le biais de groupes, ou recrutés par le biais de sites ou d'enquêteurs, en ce qui concerne leur sévérité démentielle ; ou (iii) pour sélectionner ou classer des sujets s'inscrivant à une étude ou sollicitant une étude.
PCT/US2016/017039 2015-02-06 2016-02-08 Procédés et approche permettant la détection et la prédiction au fil du temps d'un changement de la sévérité de la démence ou du diagnostic clinique Ceased WO2016127185A1 (fr)

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Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN114420300A (zh) * 2022-01-20 2022-04-29 北京大学第六医院 中国老年认知损害预测模型
US12205582B2 (en) 2020-10-08 2025-01-21 Mastercard International Incorporated System and method for implementing a virtual caregiver

Citations (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20130224117A1 (en) * 2012-02-24 2013-08-29 The Board Of Regents Of The University Of Texas System Latent variable approach to the identification and/or diagnosis of cognitive disorders and/or behaviors and their endophenotypes
US20140018446A1 (en) * 2012-07-16 2014-01-16 The Board Of Regents Of The University Of Texas System Serum Biomarker Screen for the Diagnosis of Clinical and Preclinical Alzheimer's Disease

Patent Citations (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20130224117A1 (en) * 2012-02-24 2013-08-29 The Board Of Regents Of The University Of Texas System Latent variable approach to the identification and/or diagnosis of cognitive disorders and/or behaviors and their endophenotypes
US20140018446A1 (en) * 2012-07-16 2014-01-16 The Board Of Regents Of The University Of Texas System Serum Biomarker Screen for the Diagnosis of Clinical and Preclinical Alzheimer's Disease

Non-Patent Citations (2)

* Cited by examiner, † Cited by third party
Title
LANGBAUM ET AL.: "An empirically derived composite cognitive test score with improved power to track and evaluate treatments for preclinical Alzheimers disease", ALZHEIMERS DEMENT., vol. 10, no. 6, 2014, Retrieved from the Internet <URL:http://europepmc.org/backend/ptpmcrender.fcgi?accid=PMC4201904&blobtype=pdf> [retrieved on 20160401] *
ROYALL ET AL.: "Getting Past ''q'': Testing a New Model of Domonting Processes in Persons Without Dementia", J. NEUROPSYCHIATRY CLIN. NEUROSCI., vol. 24, no. 1, 2012, Retrieved from the Internet <URL:http://neuro.psychiatryonline.org/doi/pdf/10.1176/appi.neuropsych.111040078> [retrieved on 20160401] *

Cited By (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US12205582B2 (en) 2020-10-08 2025-01-21 Mastercard International Incorporated System and method for implementing a virtual caregiver
CN114420300A (zh) * 2022-01-20 2022-04-29 北京大学第六医院 中国老年认知损害预测模型
CN114420300B (zh) * 2022-01-20 2023-08-04 北京大学第六医院 中国老年认知损害预测模型

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