H004 Using real-world data to understand symptoms and treatment care patterns among patients with Huntington’s Disease
Bibliographic record
Abstract
Background Huntington’s Disease (HD)—a genetic, neurological disorder with psychiatric, cognitive, and motor symptoms—impacts functioning and quality of life. An ongoing, US-based, real-world registry study, RISE-HD (Real-world Integrated Evidence Study in HD), explores HD diagnosis, treatment care patterns, and patient journeys. Aims Describe HD symptom timing, frequency, and care management. Methods RISE-HD enrolled adults with clinician-confirmed HD diagnosis or mutant huntingtin gene carriers using PicnicHealth’s research platform. Clinical data from medical records were abstracted and aggregated using human-validated machine learning. Results Overall, 385 patients (data as of October 2023) were included (mean CAG repeat length=43.99 [SD=3.90]; N=288). Diagnostic evaluations included Patient Health Questionnaire-9 (available for 37% of patients), Montreal Cognitive Assessment (24%) and Mini-Mental State Examination (10%). Common symptoms (1-year pre-/post-diagnosis) were psychiatric (20% vs 29%), motor (13% vs 21%), and cognitive (8% vs 14%). During 1-year post-diagnosis, care visits (N=385) included: neurology (44%), primary care (26%), genetics (8%), psychiatry (7%), physical therapy (6%), social work (6%), psychology (5%), speech pathology (4%). Over total post-diagnosis period (variable by patient), antidepressant (sertraline 22%, escitalopram 12%), anxiolytic (clonazepam 15%, lorazepam 11%), and anti-psychotic (risperidone 14%, aripiprazole 12%, olanzapine 10%) utilization was generally higher than VMAT2 inhibitors (deutetrabenazine 15%, tetrabenazine 8%, valbenazine 1%). Conclusions Despite high HD disease burden and greater symptom prevalence post-diagnosis, ≤1/3 of patients had each symptom and pharmacological treatment recorded over 1-year post-diagnosis, underestimating patient burden. Supplementing medical records with patient/caregiver-reported outcomes may help understand holistic patient experiences. Drug utilization patterns in clinical practice should be further investigated. Disclosures VS has served as a consultant for Sage Therapeutics, Inc, Genentech, Teva Neuroscience, and Neurocrine. RS, AL, KP, YS, and JP are employees of Sage Therapeutics, Inc. and may hold stock/stock options. PK is an employee of PicnicHealth, a vendor to Sage Therapeutics, Inc. which received fees from Sage Therapeutics, Inc. for the conduct of this study. Funding This study was funded by Sage Therapeutics, Inc. (Cambridge, MA, USA). Medical writing and logistical support were provided by Boston Strategic Partners, Inc. (funded by Sage Therapeutics, Inc.).
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".