Background: Postural instability is an intractable sign of Parkinson’s disease, associated with poor disease prognosis, fall risk, and decreased quality of life. Objective: 1) Characterize verbatim reports of postural instability and associated symptoms (gait disorder, balance, falling, freezing, and posture), 2) compare reports with responses to three pre-specified questions from Part II of the Movement Disorder Society Unified Parkinson Disease Rating Scale (MDS-UPDRS), and 3) examine postural instability symptoms and MDS-UPDRS responses as predictors of future falls. Methods: Fox Insight research participants reported their problems attributed to PD in their own words using the Parkinson Disease Patient Reports of Problems (PD-PROP). Natural language processing, clinical curation, and data mining techniques were applied to classify text into problem domains and clinically-curated symptoms. Baseline postural instability symptoms were mapped to MDS-UPDRS questions 2.11–2.13. T-tests and chi-square tests were used to compare postural instability reporters and non-reporters, and Cochran-Armitage trend tests were used to evaluate associations between PD-PROP and MDS-UPDRS responses; survival methods were utilized to evaluate the predictive utility of PD-PROP and MDS-UPDRS responses in time-to-fall analyses. Results: Of participants within 10 years of PD diagnosis, 9,692 (56.0%) reported postural instability symptoms referable to gait unsteadiness, balance, falling, freezing, or posture at baseline. Postural instability symptoms were significantly associated with patient-reported measures from the MDS-UPDRS questions. Balance problems reported on PD-PROP and MDS-UPDRS 2.11–2.13 measures were predictive of future falls. Conclusion: Verbatim-reported problems captured by the PD-PROP and categorized by natural language processing and clinical curation and MDS-UPDRS responses predicted falls. The PD-PROP output was more granular than, and as informative as, the categorical responses.

Background: The Parkinson’s Disease Patient Report of Problems (PD-PROP) captures the problems and functional impact that patients report verbatim. Online research participation and advances in language analysis have enabled longitudinal collection and classification of symptoms as trial outcomes. Objective: Analyze verbatim reports longitudinally to examine postural-instability symptoms as 1) precursors of subsequent falling and 2) newly occurring symptoms that could serve as outcome measures in randomized controlled trials. Methods: Problems reported by >25,000 PD patients in their own words were collected online in the Fox Insight observational study and classified into symptoms by natural language processing, clinical curation, and machine learning. Symptoms of gait, balance, falling, and freezing and associated reports of having fallen in the last month were analyzed over three years of longitudinal observation by a Cox regression model in a cohort of 8,287 participants. New onset of gait, balance, falling, and freezing symptoms was analyzed by Kaplan-Meier survival techniques in 4,119 participants who had not previously reported these symptoms. Results: Classified verbatim symptoms of postural instability were significant precursors of subsequent falling among participants who were older, female, and had longer PD duration. New onset of symptoms steadily increased and informed sample size estimates for clinical trials to reduce the onset of these symptoms. Conclusion: The tools to analyze symptoms reported by PD patients in their own words and capacity to enroll large numbers of research participants online support the feasibility and statistical power for conducting randomized clinical trials to detect effects of therapeutic interventions.

Background: Free-text, verbatim replies in the words of people with Parkinson’s disease (PD) have the potential to provide unvarnished information about their feelings and experiences. Challenges of processing such data on a large scale are a barrier to analyzing verbatim data collection in large cohorts. Objective: To develop a method for curating responses from the Parkinson’s Disease Patient Report of Problems (PD-PROP), open-ended questions that asks people with PD to report their most bothersome problems and associated functional consequences. Methods: Human curation, natural language processing, and machine learning were used to develop an algorithm to convert verbatim responses to classified symptoms. Nine curators including clinicians, people with PD, and a non-clinician PD expert classified a sample of responses as reporting each symptom or not. Responses to the PD-PROP were collected within the Fox Insight cohort study. Results: Approximately 3,500 PD-PROP responses were curated by a human team. Subsequently, approximately 1,500 responses were used in the validation phase; median age of respondents was 67 years, 55% were men and median years since PD diagnosis was 3 years. 168,260 verbatim responses were classified by machine. Accuracy of machine classification was 95% on a held-out test set. 65 symptoms were grouped into 14 domains. The most frequently reported symptoms at first report were tremor (by 46% of respondents), gait and balance problems (>39%), and pain/discomfort (33%). Conclusion: A human-in-the-loop method of curation provides both accuracy and efficiency, permitting a clinically useful analysis of large datasets of verbatim reports about the problems that bother PD patients.