A multi-modal phase plane method for constructing multivariate disease trajectories.

Understanding the sequential order and timing of different biomarkers in the progression of Alzheimer's disease (AD) is paramount for understanding the pathophysiology of the disease, leading to better staging and improved prediction of clinical progression, providing crucial knowledge for the design and timing of effective clinical therapeutic trials. This study developed and evaluated a multi-modal phase plane (MMPP) method to construct long-term multivariate disease trajectory curves from short term longitudinal data for neuro-degenerative diseases like AD. The MMPP method is an extension to a previously presented four-step method for constructing single variable disease trajectories. A novel anchoring step which uses study participants' multivariate data to infer the staging of the separate single variable progression trajectories allows multivariate disease trajectory curves to be generated. Further, the anchoring step provides disease staging at the individual level. A bootstrapping protocol was employed, providing confidence limits on the predictions. We demonstrate that the MMPP method is able to accurately reconstruct multivariate disease trajectory curves and individuals' disease stage from simulated noisy short term longitudinal data. Specifically, the method successfully estimated the delay times between distinct progressing variables and reliably predicted individual baseline disease times (r2 = 0.981) for participants exhibiting significant early biomarker deviations.