Deep learning-based patient stratification for prognostic enrichment of clinical dementia trials
- Published November 27, 2023
- Birkenbihl, C., de Jong, J., Yalchyk, I., & Fröhlich, H.
- medRxiv
- https://doi.org/10.1101/2023.11.25.23299015
Highlights
- AI-based clustering of AD dementia patient trajectories revealed two distinct progression subgroups
- Subgroups were externally validated. Additional, independent replication showed high consistency of the results
- Classifier predicts individuals' progression subgroup from cross-sectional data available at the time of dementia diagnosis
- Highlights the impact of patient stratification onto clinical dementia trials with respect to possible sample size reduction
- First estimation of economical savings achieved by pursuing prognostic enrichment trials in the dementia space (~13%)
Abstract
Dementia probably due to Alzheimer's disease (AD) is a progressive condition that manifests in cognitive decline and impairs patients' daily life. Affected patients show great heterogeneity in their symptomatic progression, which hampers the identification of efficacious treatments in clinical trials. Using artificial intelligence approaches to enable clinical enrichment trials serves a promising avenue to identify treatments.
In this work, we used a deep learning method to cluster the multivariate disease trajectories of 283 early dementia patients along cognitive and functional scores. Two distinct subgroups were identified that separated patients into 'slow' and 'fast' progressing individuals. These subgroups were externally validated and independently replicated in a dementia cohort comprising 2779 patients. We trained a machine learning model to predict the progression subgroup of a patient from cross-sectional data at their time of dementia diagnosis. The classifier achieved a prediction performance of 0.70 ± 0.01 AUC in external validation.
By emulating a hypothetical clinical trial conducting patient enrichment using the proposed classifier, we estimate its potential to decrease the required sample size. Furthermore, we balance the achieved enrichment of the trial cohort against the accompanied demand for increased patient screening. Our results show that enrichment trials targeting cognitive outcomes offer improved chances of trial success and are more than 13% cheaper compared to conventional clinical trials. The resources saved could be redirected to accelerate drug development and expand the search for remedies for cognitive impairment.
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