Timothy Vaughan

Sharing Health Data for Better Outcomes on PatientsLikeMe

Background PatientsLikeMe is an online quantitative personal research platform for patients with life-changing illnesses to share their experience using patient-reported outcomes, find other patients like them matched on demographic and clinical characteristics, and learn from the aggregated data reports of others to improve their outcomes. The goal of the website is to help patients answer the question: “Given my status, what is the best outcome I can hope to achieve, and how do I get there?” Objective Using a cross-sectional online survey, we sought to describe the potential benefits of PatientsLikeMe in terms of treatment decisions, symptom management, clinical management, and outcomes. Methods Almost 7,000 members from six PatientsLikeMe communities (amyotrophic lateral sclerosis [ALS], Multiple Sclerosis [MS], Parkinson’s Disease, human immunodeficiency virus [HIV], fibromyalgia, and mood disorders) were sent a survey invitation using an internal survey tool (PatientsLikeMe Lens). Results Complete responses were received from 1323 participants (19% of invited members). Between-group demographics varied according to disease community. Users perceived the greatest benefit in learning about a symptom they had experienced; 72% (952 of 1323) rated the site “moderately” or “very helpful.” Patients also found the site helpful for understanding the side effects of their treatments (n = 757, 57%). Nearly half of patients (n = 559, 42%) agreed that the site had helped them find another patient who had helped them understand what it was like to take a specific treatment for their condition. More patients found the site helpful with decisions to start a medication (n = 496, 37%) than to change a medication (n = 359, 27%), change a dosage (n = 336, 25%), or stop a medication (n = 290, 22%). Almost all participants (n = 1,249, 94%) were diagnosed when they joined the site. Most (n = 824, 62%) experienced no change in their confidence in that diagnosis or had an increased level of confidence (n = 456, 34%). Use of the site was associated with increasing levels of comfort in sharing personal health information among those who had initially been uncomfortable. Overall, 12% of patients (n = 151 of 1320) changed their physician as a result of using the site; this figure was doubled in patients with fibromyalgia (21%, n = 33 of 150). Patients reported community-specific benefits: 41% of HIV patients (n = 72 of 177) agreed they had reduced risky behaviors and 22% of mood disorders patients (n = 31 of 141) agreed they needed less inpatient care as a result of using the site. Analysis of the Web access logs showed that participants who used more features of the site (eg, posted in the online forum) perceived greater benefit. Conclusions We have established that members of the community reported a range of benefits, and that these may be related to the extent of site use. Third party validation and longitudinal evaluation is an important next step in continuing to evaluate the potential of online data-sharing platforms.

Accelerated clinical discovery using self-reported patient data collected online and a patient-matching algorithm

Patients with serious diseases may experiment with drugs that have not received regulatory approval. Online patient communities structured around quantitative outcome data have the potential to provide an observational environment to monitor such drug usage and its consequences. Here we describe an analysis of data reported on the website PatientsLikeMe by patients with amyotrophic lateral sclerosis (ALS) who experimented with lithium carbonate treatment. To reduce potential bias owing to lack of randomization, we developed an algorithm to match 149 treated patients to multiple controls (447 total) based on the progression of their disease course. At 12 months after treatment, we found no effect of lithium on disease progression. Although observational studies using unblinded data are not a substitute for double-blind randomized control trials, this study reached the same conclusion as subsequent randomized trials, suggesting that data reported by patients over the internet may be useful for accelerating clinical discovery and evaluating the effectiveness of drugs already in use.

Patient-reported outcomes as a source of evidence in off-label prescribing: analysis of data from PatientsLikeMe.

Background Evaluating a new use for an existing drug can be expensive and time consuming. Providers and patients must all too often rely upon their own individual-level experience to inform clinical practice, which generates only anecdotal and unstructured data. While academic-led clinical trials are occasionally conducted to test off-label uses of drugs with expired patents, this is relatively rare. In this work, we explored how a patient-centered online research platform could supplement traditional trials to create a richer understanding of medical products postmarket by efficiently aggregating structured patient-reported data. PatientsLikeMe is a tool for patients, researchers, and caregivers (currently 82,000 members across 11 condition-based communities) that helps users make treatment decisions, manage symptoms, and improve outcomes. Members enter demographic information, longitudinal treatment, symptoms, outcome data, and treatment evaluations. These are reflected back as longitudinal health profiles and aggregated reports. Over the last 3 years, patients have entered treatment histories and evaluations on thousands of medical products. These data may aid in evaluating the effectiveness and safety of some treatments more efficiently and over a longer period of time course than is feasible through traditional trials. Objective The objective of our study was to examine the illustrative cases of amitriptyline and modafinil – drugs commonly used off-label. Methods We analyzed patient-reported treatment histories and drug evaluations for each drug, examining prevalence, treatment purpose, and evaluations of effectiveness, side effects, and burden. Results There were 1948 treatment histories for modafinil and 1394 treatment reports for amitriptyline reported across five PatientsLikeMe communities (multiple sclerosis, Parkinsons disease, mood conditions, fibromyalgia/chronic fatigue syndrome, and amyotrophic lateral sclerosis). In these reports, the majority of members reported taking the drug for off-label uses. Only 34 of the 1755 (1%) reporting purpose used modafinil for an approved purpose (narcolepsy or sleep apnea). Only 104 out of 1197 members (9%) reported taking amitriptyline for its approved indication, depression. Members taking amitriptyline for off-label purposes rated the drug as more effective than those who were taking it for its approved indication. While dry mouth is a commonly reported side effect of amitriptyline for most patients, 88 of 220 (40%) of people with amyotrophic lateral sclerosis on the drug reported taking advantage of this side effect to treat their symptom of excess saliva. Conclusions Patient-reported outcomes, like those entered within PatientsLikeMe, offer a unique real-time approach to understand utilization and performance of treatments across many conditions. These patient-reported data can provide a new source of evidence about secondary uses and potentially identify targets for treatments to be studied systematically in traditional efficacy trials.

Evaluation of an Online Platform for Multiple Sclerosis Research: Patient Description, Validation of Severity Scale, and Exploration of BMI Effects on Disease Course

Objectives To assess the potential of an online platform, PatientsLikeMe.com (PLM), for research in multiple sclerosis (MS). An investigation of the role of body mass index (BMI) on MS disease course was conducted to illustrate the utility of the platform. Methods First, we compared the demographic characteristics of subjects from PLM and from a regional MS center. Second, we validated PLM’s patient-reported outcome measure (MS Rating Scale, MSRS) against standard physician-rated tools. Finally, we analyzed the relation of BMI to the MSRS measure. Results Compared with 4,039 MS Center patients, the 10,255 PLM members were younger, more educated, and less often male and white. Disease course was more often relapsing remitting, with younger symptom onset and shorter disease duration. Differences were significant because of large sample sizes but small in absolute terms. MSRS scores for 121 MS Center patients revealed acceptable agreement between patient-derived and physician-derived composite scores (weighted kappa = 0.46). The Walking domain showed the highest weighted kappa (0.73) and correlation (rs = 0.86) between patient and physician scores. Additionally, there were good correlations between the patient-reported MSRS composite and walking scores and physician-derived measures: Expanded Disability Status Scale (composite rs = 0.61, walking rs = 0.74), Timed 25 Foot Walk (composite rs = 0.70, walking rs = 0.69), and Ambulation Index (composite rs = 0.81, walking rs = 0.84). Finally, using PLM data, we found a modest correlation between BMI and cross-sectional MSRS (rho = 0.17) and no association between BMI and disease course. Conclusions The PLM population is comparable to a clinic population, and its patient-reported MSRS is correlated with existing clinical instruments. Thus, this online platform may provide a venue for MS investigations with unique strengths (frequent data collection, large sample sizes). To illustrate its applicability, we assessed the role of BMI in MS disease course but did not find a clinically meaningful role for BMI in this setting.

Subjects no more: what happens when trial participants realize they hold the power?

Patients will hold us all accountable in new and necessary ways

Quantifying Short-Term Dynamics of Parkinson's Disease Using Self-Reported Symptom Data From an Internet Social Network

Background Parkinson’s disease (PD) is an incurable neurological disease with approximately 0.3% prevalence. The hallmark symptom is gradual movement deterioration. Current scientific consensus about disease progression holds that symptoms will worsen smoothly over time unless treated. Accurate information about symptom dynamics is of critical importance to patients, caregivers, and the scientific community for the design of new treatments, clinical decision making, and individual disease management. Long-term studies characterize the typical time course of the disease as an early linear progression gradually reaching a plateau in later stages. However, symptom dynamics over durations of days to weeks remains unquantified. Currently, there is a scarcity of objective clinical information about symptom dynamics at intervals shorter than 3 months stretching over several years, but Internet-based patient self-report platforms may change this. Objective To assess the clinical value of online self-reported PD symptom data recorded by users of the health-focused Internet social research platform PatientsLikeMe (PLM), in which patients quantify their symptoms on a regular basis on a subset of the Unified Parkinson’s Disease Ratings Scale (UPDRS). By analyzing this data, we aim for a scientific window on the nature of symptom dynamics for assessment intervals shorter than 3 months over durations of several years. Methods Online self-reported data was validated against the gold standard Parkinson’s Disease Data and Organizing Center (PD-DOC) database, containing clinical symptom data at intervals greater than 3 months. The data were compared visually using quantile-quantile plots, and numerically using the Kolmogorov-Smirnov test. By using a simple piecewise linear trend estimation algorithm, the PLM data was smoothed to separate random fluctuations from continuous symptom dynamics. Subtracting the trends from the original data revealed random fluctuations in symptom severity. The average magnitude of fluctuations versus time since diagnosis was modeled by using a gamma generalized linear model. Results Distributions of ages at diagnosis and UPDRS in the PLM and PD-DOC databases were broadly consistent. The PLM patients were systematically younger than the PD-DOC patients and showed increased symptom severity in the PD off state. The average fluctuation in symptoms (UPDRS Parts I and II) was 2.6 points at the time of diagnosis, rising to 5.9 points 16 years after diagnosis. This fluctuation exceeds the estimated minimal and moderate clinically important differences, respectively. Not all patients conformed to the current clinical picture of gradual, smooth changes: many patients had regimes where symptom severity varied in an unpredictable manner, or underwent large rapid changes in an otherwise more stable progression. Conclusions This information about short-term PD symptom dynamics contributes new scientific understanding about the disease progression, currently very costly to obtain without self-administered Internet-based reporting. This understanding should have implications for the optimization of clinical trials into new treatments and for the choice of treatment decision timescales.

The multiple sclerosis rating scale, revised (MSRS-R): Development, refinement, and psychometric validation using an online community

BackgroundIn developing the PatientsLikeMe online platform for patients with Multiple Sclerosis (MS), we required a patient-reported assessment of functional status that was easy to complete and identified disability in domains other than walking. Existing measures of functional status were inadequate, clinician-reported, focused on walking, and burdensome to complete. In response, we developed the Multiple Sclerosis Rating Scale (MSRS).MethodsWe adapted a clinician-rated measure, the Guy’s Neurological Disability Scale, to a self-report scale and deployed it to an online community. As part of our validation process we reviewed discussions between patients, conducted patient cognitive debriefing, and made minor improvements to form a revised scale (MSRS-R) before deploying a cross-sectional survey to patients with relapsing-remitting MS (RRMS) on the PatientsLikeMe platform. The survey included MSRS-R and comparator measures: MSIS-29, PDDS, NARCOMS Performance Scales, PRIMUS, and MSWS-12.ResultsIn total, 816 RRMS patients responded (19% response rate). The MSRS-R exhibited high internal consistency (Cronbach’s alpha = .86). The MSRS-R walking item was highly correlated with alternative walking measures (PDDS, ρ = .84; MSWS-12, ρ = .83; NARCOMS mobility question, ρ = .86). MSRS-R correlated well with comparison instruments and differentiated between known groups by PDDS disease stage and relapse burden in the past two years. Factor analysis suggested a single factor accounting for 51.5% of variance.ConclusionsThe MSRS-R is a concise measure of MS-related functional disability, and may have advantages for disease measurement over longer and more burdensome instruments that are restricted to a smaller number of domains or measure quality of life. Studies are underway describing the use of the instrument in contexts outside our online platform such as clinical practice or trials. The MSRS-R is released for use under creative commons license.

How common are ALS plateaus and reversals

Objective: To determine the frequency of amyotrophic lateral sclerosis (ALS) plateaus and reversals in the Pooled Resource Open-Access ALS Clinical Trials (PRO-ACT) database. Methods: We analyzed Amyotrophic Lateral Sclerosis Functional Rating Scale (ALSFRS) and ALSFRS–revised (ALSFRS-R) data from PRO-ACT participants. The frequencies of participants experiencing plateaus (periods where scores did not change) were calculated over 6-, 12-, and 18-month epochs. The percentage of participants ever experiencing reversals (periods where scores improved) of different lengths were also calculated and plotted. Results: Over 6 months, 25% of 3,132 participants did not decline. Over 12 months, 16% of 2,105 participants did not decline. Over 18 months, 7% of 1,218 participants did not decline. Small ALS reversals were also common, especially over shorter follow-up intervals; 14% of 1,343 participants had a 180-day interval where their ALSFRS-R slope was greater than zero. Fewer than 1% of participants ever experienced improvements of 4 or more ALSFRS-R points lasting at least 12 months. Conclusion: ALS plateaus and small reversals are common, especially over brief intervals. In light of these data, stable disease, especially for a short period of time, should not be interpreted as an ALS treatment effect. Large sustained ALS reversals, on the other hand, are rare, potentially important, and warrant further study.

Patients report worse MS symptoms after menopause: findings from an online cohort.

BACKGROUND Many women with multiple sclerosis (MS) are postmenopausal, yet the impact of menopause on MS symptoms is unknown. OBJECTIVE To investigate patient-reported impact of menopause in a large online research platform, PatientsLikeMe (PLM). METHODS A detailed reproductive history survey was deployed to PLM members, and responses were linked to PLM׳s prospectively collected patient-reported severity score (MS Rating Scale, MSRS). The MSRS has previously shown good correlation with physician-derived EDSS scores. RESULTS Of the 513 respondents, 55% were postmenopausal; 54% of these reported induced menopause. Median age at natural menopause was 51. Surgical menopause occurred at an earlier age (p<0.001) and was associated with more hormone replacement therapy use (p=0.02) than natural menopause. Postmenopausal status, surgical menopause, and earlier age at menopause were all associated with worse MSRS scores (p≤0.01) in regressions adjusting for age, disease type and duration. CONCLUSION Postmenopausal patients in this study reported worse MS disease severity. Further, this study highlights a utility for online research platforms, which allow for rapid generation of hypotheses that then require validation in clinical settings.

Exploring Concordance of Patient-Reported Information on PatientsLikeMe and Medical Claims Data at the Patient Level.

Background With the emergence of data generated by patient-powered research networks, it is informative to characterize their correspondence with health care system-generated data. Objectives This study explored the linking of 2 disparate sources of real-world data: patient-reported data from a patient-powered research network (PatientsLikeMe) and insurance claims. Methods Active patients within the PatientsLikeMe community, residing in the United States, aged 18 years or older, with a self-reported diagnosis of multiple sclerosis or Parkinson’s disease (PD) were invited to participate during a 2-week period in December 2014. Patient-reported data were anonymously matched and compared to IMS Health medical and pharmacy claims data with dates of service between December 2009 and December 2014. Patient-level match (identity), diagnosis, and usage of disease-modifying therapies (DMTs) were compared between data sources. Results Among 603 consenting patients, 94% had at least 1 record in the IMS Health dataset; of these, there was 93% agreement rate for multiple sclerosis diagnosis. Concordance on the use of any treatment was 59%, and agreement on reports of specific treatment usage (within an imputed 5-year period) ranged from 73.5% to 100%. Conclusions It is possible to match patient identities between the 2 data sources, and the high concordance at multiple levels suggests that the matching process was accurate. Likewise, the high degree of concordance suggests that these patients were able to accurately self-report their diagnosis and, to a lesser degree, their treatment usage. Further studies of linked data types are warranted to evaluate the use of enriched datasets to generate novel insights.