J. Med. Internet Res. - Quantifying short-term dynamics of Parkinson's disease using self-reported symptom data from an Internet social network.

Tópicos

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Resumo

CKGROUND: 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.

Resumo Limpo

ckground parkinson diseas pd incur neurolog diseas approxim preval hallmark symptom gradual movement deterior current scientif consensus diseas progress hold symptom will worsen smooth time unless treat accur inform symptom dynam critic import patient caregiv scientif communiti design new treatment clinic decis make individu diseas manag longterm studi character typic time cours diseas earli linear progress gradual reach plateau later stage howev symptom dynam durat day week remain unquantifi current scarciti object clinic inform symptom dynam interv shorter month stretch sever year internetbas patient selfreport platform may chang thisobject assess clinic valu onlin selfreport pd symptom data record user healthfocus internet social research platform patientslikem plm patient quantifi symptom regular basi subset unifi parkinson diseas rate scale updr analyz data aim scientif window natur symptom dynam assess interv shorter month durat sever yearsmethod onlin selfreport data valid gold standard parkinson diseas data organ center pddoc databas contain clinic symptom data interv greater month data compar visual use quantilequantil plot numer use kolmogorovsmirnov test use simpl piecewis linear trend estim algorithm plm data smooth separ random fluctuat continu symptom dynam subtract trend origin data reveal random fluctuat symptom sever averag magnitud fluctuat versus time sinc diagnosi model use gamma general linear modelresult distribut age diagnosi updr plm pddoc databas broad consist plm patient systemat younger pddoc patient show increas symptom sever pd state averag fluctuat symptom updr part ii point time diagnosi rise point year diagnosi fluctuat exceed estim minim moder clinic import differ respect patient conform current clinic pictur gradual smooth chang mani patient regim symptom sever vari unpredict manner underw larg rapid chang otherwis stabl progressionconclus inform shortterm pd symptom dynam contribut new scientif understand diseas progress current cost obtain without selfadminist internetbas report understand implic optim clinic trial new treatment choic treatment decis timescal

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