Disease Progression and Subtype Modeling for Combined Discrete and Continuous Input Data
Organizations: Department of Radiology and Nuclear Medicine, Erasmus MC, Rotterdam, The Netherlands · Department of Epidemiology, Erasmus MC, Rotterdam, The Netherlands · Hawkes Institute, Department of Computer Science, University College London, London, United Kingdom
Abstract
Disease progression modeling provides a robust framework to identify long-term disease trajectories from short-term biomarker data. It is a valuable tool to gain a deeper understanding of diseases with a long disease trajectory, such as Alzheimer's disease. A key limitation of most disease progression models is that they are specific to a single data type (e.g., continuous data), thereby limiting their applicability to heterogeneous, real-world datasets. To address this limitation, we propose the Mixed Events model, a novel disease progression model that handles both discrete and continuous data types. This model is implemented within the Subtype and Stage Inference (SuStaIn) framework, resulting in Mixed-SuStaIn, enabling subtype and progression modeling. We demonstrate the effectiveness of Mixed-SuStaIn through simulation experiments and real-world data from the Alzheimer's Disease Neuroimaging Initiative, showing that it performs well on mixed datasets. The code is available at: https://github.com/ucl-pond/pySuStaIn.
Figures & tables
| Model | Biomarker Trajectory | Input Data Type |
|---|---|---|
| Event-based model | Biomarkers transition from a normal to an abnormal value reflecting an instantaneous switch in disease progression | Biomarker values transformed to probabilities based on a bimodal data distribution (normal vs. abnormal) |
| Scored events model | Biomarkers transition from a normal score (e.g., value zero) to higher scores, reflecting an instantaneous switch between levels of disease severity | Ordinal biomarker scores transformed to probabilities, where each biomarker value in each individual is assigned a probability for each possible score |
| Piecewise linear z -score model | Biomarkers linearly increase from one z -score to another | Biomarker values transformed to z -scores based on a Gaussian reference distribution |
| Characteristic | CN (n=209) | MCI (n=341) | AD (n=91) |
|---|---|---|---|
| Demographics | |||
| Female sex (%) | 54.2 | 44.7 | 42.1 |
| Age (years), ± SD | 72.8 ± 5.8 | 71.2 ± 7.2 | 73.8 ± 8.5 |
| Likelihood, ± SD | |||
| Amyloid- | 0.28 ± 0.35 | 0.47 ± 0.38 | 0.81 ± 0.26 |
| Total tau | 0.12 ± 0.19 | 0.19 ± 0.27 | 0.43 ± 0.31 |