Deep learning for clustering of multivariate clinical patient trajectories with missing values

BACKGROUND: Precision medicine requires a stratification of patients by disease presentation that is sufficiently informative to allow for selecting treatments on a per-patient basis. For many diseases, such as neurological disorders, this stratification problem translates into a complex problem of clustering multivariate and relatively short time series because (i) these diseases are multifactorial and not well described by single clinical outcome variables and (ii) disease progression needs to be monitored over time. Additionally, clinical data often additionally are hindered by the presence of many missing values, further complicating any clustering attempts. FINDINGS: The problem of clustering multivariate short time series with many missing values is generally not well addressed in the literature. In this work, we propose a deep learning-based method to address this issue, variational deep embedding with recurrence (VaDER). VaDER relies on a Gaussian mixture variational autoencoder framework, which is further extended to (i) model multivariate time series and (ii) directly deal with missing values. We validated VaDER by accurately recovering clusters from simulated and benchmark data with known ground truth clustering, while varying the degree of missingness. We then used VaDER to successfully stratify patients with Alzheimer disease and patients with Parkinson disease into subgroups characterized by clinically divergent disease progression profiles. Additional analyses demonstrated that these clinical differences reflected known underlying aspects of Alzheimer disease and Parkinson disease. CONCLUSIONS: We believe our results show that VaDER can be of great value for future efforts in patient stratification, and multivariate time-series clustering in general

Generation of realistic synthetic data using Multimodal Neural Ordinary Differential Equations

Abstract Individual organizations, such as hospitals, pharmaceutical companies, and health insurance providers, are currently limited in their ability to collect data that are fully representative of a disease population. This can, in turn, negatively impact the generalization ability of statistical models and scientific insights. However, sharing data across different organizations is highly restricted by legal regulations. While federated data access concepts exist, they are technically and organizationally difficult to realize. An alternative approach would be to exchange synthetic patient data instead. In this work, we introduce the Multimodal Neural Ordinary Differential Equations (MultiNODEs), a hybrid, multimodal AI approach, which allows for generating highly realistic synthetic patient trajectories on a continuous time scale, hence enabling smooth interpolation and extrapolation of clinical studies. Our proposed method can integrate both static and longitudinal data, and implicitly handles missing values. We demonstrate the capabilities of MultiNODEs by applying them to real patient-level data from two independent clinical studies and simulated epidemiological data of an infectious disease

Forecast Alzheimer's disease progression to better select patients for clinical trials

International audienceObjectivesSubject recruitment is a burden that hampers clinical trials, especially in neurodegenerative diseases, where worsening of abilities is subtle, long-term and heterogeneous. Targeting the right patients during trial screening is a way to reduce the needed sample size or conversely to improve the proven effect size.MethodsFrom Alzheimer’s disease (AD) observational cohorts, we selected longitudinal data that matched AD trials (inclusion and exclusion criteria, trial duration and primary endpoint). We modeled EMERGE, a phase 3 trial in pre-clinical AD, and a mild AD trial, using 4 research cohorts (ADNI, Memento, PharmaCog, AIBL). For each patient, we simulated its treated counterpart by applying an individual treatment effect. It consisted in a linear improvement of outcome for effective decliners, calibrated on our data so to match the expected trial effect size. Next, we built a multimodal AD course map that grasped long-term disease progression in a mixed-effects fashion [1] with Leaspy. We used it to forecast never-seen individuals’ outcomes from their screening biomarkers. Based on these individual screening predictions, we selected clinically relevant sub-groups [2]. Finally, we compared the effective sample size that would have been needed for the trial, with and without our selections. We evaluated dispersion of this metric using a bootstrap procedure.ResultsIn all investigated setups and cohorts, we found a decrease in needed sample sizes with selection. For EMERGE trial, we showed that selecting patients having a predicted CDR-SoB changed between 0.5 and 1.5 points per year enabled to reduce the needed sample size by 38.2 ± 3.3 %. For the mild AD trial, we showed that selecting patients having a predicted MMSE changed between 1 and 2 points per year enabled to reduce the needed sample size by 38.9 ± 2.2 %.ConclusionsWe build a modelling framework for forecasting individual outcomes from their multimodal screening assessments. Using them as an extra inclusion criterion in clinical trials, we can better control trial population and thus reduce the needed sample size for a given treatment effect

Forecast Alzheimer's disease progression to better select patients for clinical trials

International audienceObjectivesSubject recruitment is a burden that hampers clinical trials, especially in neurodegenerative diseases, where worsening of abilities is subtle, long-term and heterogeneous. Targeting the right patients during trial screening is a way to reduce the needed sample size or conversely to improve the proven effect size.MethodsFrom Alzheimer’s disease (AD) observational cohorts, we selected longitudinal data that matched AD trials (inclusion and exclusion criteria, trial duration and primary endpoint). We modeled EMERGE, a phase 3 trial in pre-clinical AD, and a mild AD trial, using 4 research cohorts (ADNI, Memento, PharmaCog, AIBL). For each patient, we simulated its treated counterpart by applying an individual treatment effect. It consisted in a linear improvement of outcome for effective decliners, calibrated on our data so to match the expected trial effect size. Next, we built a multimodal AD course map that grasped long-term disease progression in a mixed-effects fashion [1] with Leaspy. We used it to forecast never-seen individuals’ outcomes from their screening biomarkers. Based on these individual screening predictions, we selected clinically relevant sub-groups [2]. Finally, we compared the effective sample size that would have been needed for the trial, with and without our selections. We evaluated dispersion of this metric using a bootstrap procedure.ResultsIn all investigated setups and cohorts, we found a decrease in needed sample sizes with selection. For EMERGE trial, we showed that selecting patients having a predicted CDR-SoB changed between 0.5 and 1.5 points per year enabled to reduce the needed sample size by 38.2 ± 3.3 %. For the mild AD trial, we showed that selecting patients having a predicted MMSE changed between 1 and 2 points per year enabled to reduce the needed sample size by 38.9 ± 2.2 %.ConclusionsWe build a modelling framework for forecasting individual outcomes from their multimodal screening assessments. Using them as an extra inclusion criterion in clinical trials, we can better control trial population and thus reduce the needed sample size for a given treatment effect

Forecast Alzheimer's disease progression to better select patients for clinical trials

Objectives Subject recruitment is a burden that hampers clinical trials, especially in neurodegenerative diseases, where worsening of abilities is subtle, long-term and heterogeneous. Targeting the right patients during trial screening is a way to reduce the needed sample size or conversely to improve the proven effect size. Methods From Alzheimer’s disease (AD) observational cohorts, we selected longitudinal data that matched AD trials (inclusion and exclusion criteria, trial duration and primary endpoint). We modeled EMERGE, a phase 3 trial in pre-clinical AD, and a mild AD trial, using 4 research cohorts (ADNI, Memento, PharmaCog, AIBL). For each patient, we simulated its treated counterpart by applying an individual treatment effect. It consisted in a linear improvement of outcome for effective decliners, calibrated on our data so to match the expected trial effect size. Next, we built a multimodal AD course map that grasped long-term disease progression in a mixed-effects fashion [1] with Leaspy. We used it to forecast never-seen individuals’ outcomes from their screening biomarkers. Based on these individual screening predictions, we selected clinically relevant sub-groups [2]. Finally, we compared the effective sample size that would have been needed for the trial, with and without our selections. We evaluated dispersion of this metric using a bootstrap procedure. Results In all investigated setups and cohorts, we found a decrease in needed sample sizes with selection. For EMERGE trial, we showed that selecting patients having a predicted CDR-SoB changed between 0.5 and 1.5 points per year enabled to reduce the needed sample size by 38.2 ± 3.3 %. For the mild AD trial, we showed that selecting patients having a predicted MMSE changed between 1 and 2 points per year enabled to reduce the needed sample size by 38.9 ± 2.2 %. Conclusions We build a modelling framework for forecasting individual outcomes from their multimodal screening assessments. Using them as an extra inclusion criterion in clinical trials, we can better control trial population and thus reduce the needed sample size for a given treatment effect

Remote monitoring technologies in Alzheimer's disease:design of the RADAR-AD study

BACKGROUND: Functional decline in Alzheimer's disease (AD) is typically measured using single-time point subjective rating scales, which rely on direct observation or (caregiver) recall. Remote monitoring technologies (RMTs), such as smartphone applications, wearables, and home-based sensors, can change these periodic subjective assessments to more frequent, or even continuous, objective monitoring. The aim of the RADAR-AD study is to assess the accuracy and validity of RMTs in measuring functional decline in a real-world environment across preclinical-to-moderate stages of AD compared to standard clinical rating scales. METHODS: This study includes three tiers. For the main study, we will include participants (n = 220) with preclinical AD, prodromal AD, mild-to-moderate AD, and healthy controls, classified by MMSE and CDR score, from clinical sites equally distributed over 13 European countries. Participants will undergo extensive neuropsychological testing and physical examination. The RMT assessments, performed over an 8-week period, include walk tests, financial management tasks, an augmented reality game, two activity trackers, and two smartphone applications installed on the participants' phone. In the first sub-study, fixed sensors will be installed in the homes of a representative sub-sample of 40 participants. In the second sub-study, 10 participants will stay in a smart home for 1 week. The primary outcome of this study is the difference in functional domain profiles assessed using RMTs between the four study groups. The four participant groups will be compared for each RMT outcome measure separately. Each RMT outcome will be compared to a standard clinical test which measures the same functional or cognitive domain. Finally, multivariate prediction models will be developed. Data collection and privacy are important aspects of the project, which will be managed using the RADAR-base data platform running on specifically designed biomedical research computing infrastructure. RESULTS: First results are expected to be disseminated in 2022. CONCLUSION: Our study is well placed to evaluate the clinical utility of RMT assessments. Leveraging modern-day technology may deliver new and improved methods for accurately monitoring functional decline in all stages of AD. It is greatly anticipated that these methods could lead to objective and real-life functional endpoints with increased sensitivity to pharmacological agent signal detection