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

[This corrects the article DOI: 10.2196/jmir.5130.].

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.

New approach for analyzing self-reporting of insomnia symptoms reveals a high rate of comorbid insomnia across a wide spectrum of chronic diseases.

BACKGROUND: Insomnia is increasingly recognized to be comorbid with one or more medical conditions. This study used an online research platform to characterize insomnia across different mental and physical conditions. METHODS: A custom cross-sectional survey was fielded online to 31,208 users of the patient community PatientsLikeMe. The survey queried members on National Sleep Foundation-defined insomnia risk (waking up feeling unrefreshed, difficulty falling asleep, waking in the middle of the night, or waking too early). RESULTS: Complete results were obtained from 5256 patients with 11 comorbid conditions. Seventy-six percent of US-based respondents were at risk for insomnia. Patients who reported difficulty falling asleep were found to have nearly twice the odds of self-reporting insomnia (odds ratio [OR]: 1.84; 95% confidence interval [CI]: 1.5-2.1) when compared to those who do not have difficulty falling asleep, whereas those who reported waking during the night or waking up unrefreshed were no more likely (OR: 1.025 and 1.032, respectively) to report that they suffered from insomnia than those who did not experience these issues. Although insomnia was self-reported as severe or very severe across most conditions, few respondents had actually been diagnosed with insomnia by a physician. After adjustment for age and gender, there was an independent and strong effect of primary condition severity on insomnia risk, and those with severe epilepsy (0.93), depressive disorders (0.92), and fibromyalgia (0.92) occupied the highest risk probabilities. CONCLUSIONS: The high rate of severity and frequency of insomnia across a multitude of mental and physical conditions reveals an opportunity for better disease management through enhanced insomnia awareness.

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

BACKGROUND: In 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). METHODS: We 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. RESULTS: In 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. CONCLUSIONS: The 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.

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.

Molecular change signal-to-noise criteria for interpreting experiments involving exposure of biological systems to weakly interacting electromagnetic fields.

We describe an approach to aiding the design and interpretation of experiments involving biological effects of weakly interacting electromagnetic fields that range from steady (dc) to microwave frequencies. We propose that if known biophysical mechanisms cannot account for an inferred, underlying molecular change signal-to-noise ratio, (S/N)gen, of a observed result, then there are two interpretation choices: (1) there is an unknown biophysical mechanism with stronger coupling between the field exposure and the ongoing biochemical process, or (2) the experiment is responding to something other than the field exposure. Our approach is based on classical detection theory, the recognition that weakly interacting fields cannot break chemical bonds, and the consequence that such fields can only alter rates of ongoing, metabolically driven biochemical reactions, and transport processes. The approach includes both fundamental chemical noise (molecular shot noise) and other sources of competing chemical change, to be compared quantitatively to the field induced change for the basic case that the field alters a single step in a biochemical network. Consistent with pharmacology and toxicology, we estimate the molecular dose (mass associated with field induced molecular change per mass tissue) resulting from illustrative low frequency field exposures for the biophysical mechanism of voltage gated channels. For perspective, we then consider electric field-mediated delivery of small molecules across human skin and into individual cells. Specifically, we consider the examples of iontophoretic and electroporative delivery of fentanyl through skin and electroporative delivery of bleomycin into individual cells. The total delivered amount corresponds to a molecular change signal and the delivery variability corresponds to generalized chemical noise. Viewed broadly, biological effects due to nonionizing fields may include animal navigation, medical applications, and environmental hazards. Understanding necessary conditions for such effects can be based on a unified approach: quantitative comparison of the estimated chemical change due to a particular electromagnetic field exposure to that due to competing influences, with both estimates based on a biophysical mechanism model within the context of a model of a biological system.

Effects of parameter errors in the simulation of transcranial focused ultrasound.

Previous numerical simulation work has supported experiments showing that a sharply focused transcranial ultrasound field can be generated for noninvasive therapy and surgery in the brain. The predicted pressure gain and optimal sonicating frequency could be affected by uncertainty in the simulation parameters. We estimate the effects of uncertainty in the speed of sound in the skull and brain, and in CT data that specifies the contour of the skull. The results of our simulations indicate that each of these errors may change the predicted pressure gain by up to a few percent, but the predicted optimal frequency is not significantly affected.