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Yan Song,1 Jian-Yu E,1 Tracy Guo,1 Rahul Sasane,2 Steve Arcona,2 Nirmal Keshava,2 Eric Wu1 1Analysis Group, Inc, Boston, MA, USA; 2Cerevel Therapeutics, Cambridge, MA, USACorrespondence: Yan Song, Analysis Group, Inc, 111 Huntington Ave, 14th Floor, Boston, MA, 02199, USA, Tel +1 617-425-8272, Email yan.song@analysisgroup.comBackground: Studies on real-world treatment patterns and long-term economic burden of Parkinson’s disease (PD) have been limited.Objective: To assess treatment patterns, healthcare resource utilization (HRU), and costs associated with PD symptoms and treatment-related adverse events (AEs) among Medicare beneficiaries in the United States.Methods: A 100% Medicare Fee-For-Service data (2006– 2020) of patients with PD were analyzed. PD treatment patterns were described for the subset of patients who had no previously observed PD treatments or diagnoses (ie, the incident cohort). HRU and healthcare costs associated with PD symptoms were assessed for all patients with PD (ie, the overall cohort) and that associated with treatment-related AEs were assessed for the subset of patients who received PD treatments after PD diagnosis (ie, the active treatment cohort), using longitudinal models with repeated measures.Results: Overall, 318,582 patients were included (mean age at PD diagnosis: 77.4 years; 53.3% female). Among patients in the incident cohort (N=214,829), 51.1% initiated levodopa monotherapy and 5.9% initiated dopamine agonists (DAs) monotherapy as first-line treatment. The proportion of incident patients treated with DAs and other PD therapies generally increased from post-diagnosis years 1 to 10. The median time from diagnosis to PD treatment initiation was 2.0 months; the median time to treatment discontinuation was the longest with levodopa (18.7 months), followed by DAs (9.5 months). In the overall cohort, PD symptoms, especially motor symptoms and severe motor symptoms, were associated with significantly higher rates of HRU and costs.

Clinical translation of reported biomarkers requires reliable and consistent algorithms to derive biomarkers. However, the literature reports statistically significant differences between 1-D MRS measurements from control groups and subjects with disease states but frequently provides little information on the algorithms and parameters used to process the data. The sensitivity of in vivo brain magnetic resonance spectroscopy biomarkers is investigated with respect to parameter values for two key stages of post-acquisitional processing. Our effort is specifically motivated by the lack of consensus on approaches and parameter values for the two critical operations, water resonance removal, and baseline correction. The different stages of data processing also introduce varying levels of uncertainty and arbitrary selection of parameter values can significantly underutilize the intrinsic differences between two classes of signals. The sensitivity of biomarkers points to the need for a better understanding of how all stages of post-acquisitional processing affect biomarker discovery and ultimately, clinical translation. Our results also highlight the possibility of optimizing biomarker discovery by the careful selection of parameters that best reveal class differences. Using previously reported data and biomarkers, our results demonstrate that small changes in parameter values affect the statistical significance and corresponding effect size of biomarkers. Consequently, it is possible to increase the strength of biomarkers by selecting optimal parameter values in different spectral intervals. Our analyses with a previously reported data set demonstrate an increase in effect sizes for wavelet-based biomarkers of up to 36%, with increases in classification performance of up to 12%.

Traditional analyses of in vivo 1D MR spectroscopy of brain metabolites have been limited to the inspection of one-dimensional free induction decay (FID) signals from which only a limited number of metabolites are clearly observable. In this article we introduce a novel set of algorithms to process and characterize two-dimensional in vivo MR correlation spectroscopy (2D COSY) signals. 2D COSY data was collected from phantom solutions of topical metabolites found in the brain, namely glutamine, glutamate, and creatine. A statistical peak-detection and object segmentation algorithm is adapted for 2D COSY signals and applied to phantom solutions containing varied concentrations of glutamine and glutamate. Additionally, quantitative features are derived from peak and object structures, and we show that these measures are correlated with known phantom metabolite concentrations. These results are encouraging for future studies focusing on neurological disorders that induce subtle changes in brain metabolite concentrations and for which accurate quantitation is important.

See ADM001263 for entire Adaptive Sensor Array Processing Workshop on CD-ROM. The original document contains color images. Presented at the Adaptive Sensor Array Processing (ASAP) Workshop (9th), held in Lexington, MA on 13-14 Mar 2001. Contains viewgraphs only.

Hyperspectral data consists of hundreds of contiguous radiometric measurements collected passively from each pixel in a scene. Detection capitalizes on exploiting the difference between target and background spectral signatures. Many detection methods in hyperspectral processing employ signal models commonly used in radar even though it is an active sensor. Starting from a common signal model, we discuss adaptive detection algorithms for hyperspectral data by outlining fundamental similarities and differences with radar. We demonstrate detection using hyperspectral data through experiments with real data and discuss the fundamental applicability of adaptive radar signal models to detection in hyperspectral processing.