Modelling Decline in Cognition to Decline in Function in Alzheimer's Disease.

OBJECTIVES: The study aimed to evaluate and quantify the temporal link between cognitive and functional decline, and assess the impact of the apolipoprotein E4 (APOE-e4) genotype on Alzheimer's disease (AD) progression. METHODS: A nonlinear mixed-effects Emax model was developed using longitudinal data from 659 patients with dementia due to AD sourced from the Alzheimer's disease neuroimaging initiative (ADNI) database. A cognitive decline model was first built using a cognitive subscale of the AD assessment scale (delayed word recall) as the endpoint, followed by a functional decline model, using the functional assessment questionnaire (FAQ) as the endpoint. Individual and population cognitive decline from the first model drove a functional decline in the second model. The impact of the APOE-e4 genotype status on the dynamics of AD progression was evaluated using the model. RESULTS: Mixed-effects Emax models adequately quantified population average and individual disease trajectories. The model captured a higher initial cognitive impairment and final functional impairment in APOE-e4 carriers than non-carriers. The age at cognitive decline and diagnosis of dementia due to AD was significantly lower in APOE-e4 carriers than that of non-carriers. The average [standard deviation] time shift between cognitive and functional decline, i.e. the time span between half of the maximum cognitive decline and half of the maximum functional decline, was estimated as 1.5 [1.6] years. CONCLUSION: The present analysis quantifies the temporal link between a cognitive and functional decline in AD progression at the population and individual level, and provides information about the potential benefits of pre-clinical AD treatments on both cognition and function.

Development and validation of a Bayesian survival model for inclusion body myositis.

BACKGROUND: Associations between disease characteristics and payer-relevant outcomes can be difficult to establish for rare and progressive chronic diseases with sparse available data. We developed an exploratory bridging model to predict premature mortality from disease characteristics, and using inclusion body myositis (IBM) as a representative case study. METHODS: Candidate variables that may be potentially associated with premature mortality were identified by disease experts and from the IBM literature. Interdependency between candidate variables in IBM patients were assessed using existing patient-level data. A Bayesian survival model for the IBM population was developed with identified variables as predictors for premature mortality in the model. For model selection and external validation, model predictions were compared to published mortality data in IBM patient cohorts. After validation, the final model was used to simulate the increased risk of premature death in IBM patients. Baseline survival was based on age- and gender-specific survival curves for the general population in Western countries as reported by the World Health Organisation. RESULTS: Presence of dysphagia, aspiration pneumonia, falls, being wheelchair-bound and 6-min walking distance (6MWD in meters) were identified as candidate variables to be used as predictors for premature mortality based on inputs received from disease experts and literature. There was limited correlation between these functional performance measures, which were therefore treated as independent variables in the model. Based on the Bayesian survival model, among all candidate variables, presence of dysphagia and decrease in 6MWD [m] were associated with poorer survival with contributing hazard ratios (HR) 1.61 (95% credible interval [CrI]: 0.84-3.50) and 2.48 (95% CrI: 1.27-5.00) respectively. Excess mortality simulated in an IBM cohort vs. an age- and gender matched general-population cohort was 4.03 (95% prediction interval 1.37-10.61). CONCLUSIONS: For IBM patients, results suggest an increased risk of premature death compared with the general population of the same age and gender. In the absence of hard data, bridging modelling generated survival predictions by combining relevant information. The methodological principle would be applicable to the analysis of associations between disease characteristics and payer-relevant outcomes in progressive chronic and rare diseases. Studies with lifetime follow-up would be needed to confirm the modelling results.

Predicting Chronic Spontaneous Urticaria Symptom Return After Omalizumab Treatment Discontinuation: Exploratory Analysis.

BACKGROUND: Omalizumab is highly effective in controlling chronic spontaneous urticaria (CSU) symptoms; however, patients can experience symptom return on treatment discontinuation. Pivotal clinical trials have identified 2 categories of patients who experience symptom return: rapid and slow. OBJECTIVE: The objective of this study was to identify potential predictors of the speed of symptom return after stopping omalizumab treatment. METHODS: Phase III randomized controlled trial (RCT) data from ASTERIA I (n = 319; 6 × 4 weekly injections of omalizumab 75, 150, 300 mg or placebo; NCT01287117) and ASTERIA II (n = 323; 3 × 4 weekly injections of omalizumab 75, 150, 300 mg, or placebo; NCT01292473) were pooled to identify predictors of symptom return after stopping omalizumab treatment (16-week follow-up). The least absolute shrinkage and selection operator regularization regression model was used to select predictive variables, and relapse probability was represented using heatmap visualizations. Model accuracy was tested using data from the GLACIAL phase III RCT (n = 336; 6 × 4 weekly injections of omalizumab 300 mg or placebo; NCT0126493). RESULTS: Of 746 variables assessed, 2 were selected by the model as predictors of symptom return: baseline urticaria activity score over 7 days (UAS7) and early area above the curve (AAC; determined by plotting the UAS7 scores across time points). Results suggest that high baseline UAS7 and low UAS7 AAC (slow decrease of symptoms) indicate a higher probability of rapid symptom return than low baseline UAS7 and high UAS7 AAC. CONCLUSIONS: These results suggest that the probability of rapid symptom return in patients with CSU who discontinue treatment with omalizumab can be estimated based on baseline UAS7 and early treatment response.

Linked Patient-Reported Outcomes Data From Patients With Multiple Sclerosis Recruited on an Open Internet Platform to Health Care Claims Databases Identifies a Representative Population for Real-Life Data Analysis in Multiple Sclerosis.

BACKGROUND: An enormous amount of information relevant to public health is being generated directly by online communities. OBJECTIVE: To explore the feasibility of creating a dataset that links patient-reported outcomes data, from a Web-based survey of US patients with multiple sclerosis (MS) recruited on open Internet platforms, to health care utilization information from health care claims databases. The dataset was generated by linkage analysis to a broader MS population in the United States using both pharmacy and medical claims data sources. METHODS: US Facebook users with an interest in MS were alerted to a patient-reported survey by targeted advertisements. Eligibility criteria were diagnosis of MS by a specialist (primary progressive, relapsing-remitting, or secondary progressive), ≥12-month history of disease, age 18-65 years, and commercial health insurance. Participants completed a questionnaire including data on demographic and disease characteristics, current and earlier therapies, relapses, disability, health-related quality of life, and employment status and productivity. A unique anonymous profile was generated for each survey respondent. Each anonymous profile was linked to a number of medical and pharmacy claims datasets in the United States. Linkage rates were assessed and survey respondents' representativeness was evaluated based on differences in the distribution of characteristics between the linked survey population and the general MS population in the claims databases. RESULTS: The advertisement was placed on 1,063,973 Facebook users' pages generating 68,674 clicks, 3719 survey attempts, and 651 successfully completed surveys, of which 440 could be linked to any of the claims databases for 2014 or 2015 (67.6% linkage rate). Overall, no significant differences were found between patients who were linked and not linked for educational status, ethnicity, current or prior disease-modifying therapy (DMT) treatment, or presence of a relapse in the last 12 months. The frequencies of the most common MS symptoms did not differ significantly between linked patients and the general MS population in the databases. Linked patients were slightly younger and less likely to be men than those who were not linkable. CONCLUSIONS: Linking patient-reported outcomes data, from a Web-based survey of US patients with MS recruited on open Internet platforms, to health care utilization information from claims databases may enable rapid generation of a large population of representative patients with MS suitable for outcomes analysis.

Patterns of Treatment Switching in Multiple Sclerosis Therapies in US Patients Active on Social Media: Application of Social Media Content Analysis to Health Outcomes Research.

BACKGROUND: Social media analysis has rarely been applied to the study of specific questions in outcomes research. OBJECTIVE: The aim was to test the applicability of social media analysis to outcomes research using automated listening combined with filtering and analysis of data by specialists. After validation, the process was applied to the study of patterns of treatment switching in multiple sclerosis (MS). METHODS: A comprehensive listening and analysis process was developed that blended automated listening with filtering and analysis of data by life sciences-qualified analysts and physicians. The population was patients with MS from the United States. Data sources were Facebook, Twitter, blogs, and online forums. Sources were searched for mention of specific oral, injectable, and intravenous (IV) infusion treatments. The representativeness of the social media population was validated by comparison with community survey data and with data from three large US administrative claims databases: MarketScan, PharMetrics Plus, and Department of Defense. RESULTS: A total of 10,260 data points were sampled for manual review: 3025 from Twitter, 3771 from Facebook, 2773 from Internet forums, and 691 from blogs. The demographics of the social media population were similar to those reported from community surveys and claims databases. Mean age was 39 (SD 11) years and 14.56% (326/2239) of the population was older than 50 years. Women, patients aged 30 to 49 years, and those diagnosed for more than 10 years were represented by more data points than other patients were. Women also accounted for a large majority (82.6%, 819/991) of reported switches. Two-fifths of switching patients had lived with their disease for more than 10 years since diagnosis. Most reported switches (55.05%, 927/1684) were from injectable to oral drugs with switches from IV therapies to orals the second largest switch (15.38%, 259/1684). Switches to oral drugs accounted for more than 80% (927/1114) of the switches away from injectable therapies. Four reasons accounted for more than 90% of all switches: severe side effects, lack of efficacy, physicians' advice, and greater ease of use. Side effects were the main reason for switches to oral or to injectable therapies and search for greater efficacy was the most important factor in switches to IV therapies. Cost of medication was the reason for switching in less than 0.5% of patients. CONCLUSIONS: Social intelligence can be applied to outcomes research with power to analyze MS patients' personal experiences of treatments and to chart the most common reasons for switching between therapies.