Dataset Information

Leveraging real-world data to investigate multiple sclerosis disease behavior, prognosis, and treatment.

ABSTRACT: Randomized controlled clinical trials and real-world observational studies provide complementary information but with different validity. Some clinical questions (disease behavior, prognosis, validation of outcome measures, comparative effectiveness, and long-term safety of therapies) are often better addressed using real-world data reflecting larger, more representative populations. Integration of disease history, clinician-reported outcomes, performance tests, and patient-reported outcome measures during patient encounters; imaging and biospecimen analyses; and data from wearable devices increase dataset utility. However, observational studies utilizing these data are susceptible to many potential sources of bias, creating barriers to acceptance by regulatory agencies and the medical community. Therefore, data standardization and validation within datasets, harmonization across datasets, and application of appropriate analysis methods are important considerations. We review approaches to improve the scope, quality, and analyses of real-world data to advance understanding of multiple sclerosis and its treatment, as an example of opportunities to better support patient care and research.

SUBMITTER: Cohen JA

PROVIDER: S-EPMC6950891 | biostudies-literature | 2020 Jan

REPOSITORIES: biostudies-literature

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Leveraging real-world data to investigate multiple sclerosis disease behavior, prognosis, and treatment.

Cohen Jeffrey A JA Trojano Maria M Mowry Ellen M EM Uitdehaag Bernard Mj BM Reingold Stephen C SC Marrie Ruth Ann RA

Multiple sclerosis (Houndmills, Basingstoke, England) 20191128 1

Randomized controlled clinical trials and real-world observational studies provide complementary information but with different validity. Some clinical questions (disease behavior, prognosis, validation of outcome measures, comparative effectiveness, and long-term safety of therapies) are often better addressed using real-world data reflecting larger, more representative populations. Integration of disease history, clinician-reported outcomes, performance tests, and patient-reported outcome meas ...[more]

PMID: 31778094

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Project description:BackgroundTherapeutic options targeting inflammation in multiple sclerosis (MS) have evolved rapidly for relapsing-remitting MS, whereas few therapies are available for progressive forms of MS, in particular secondary progressive MS (SPMS). The approval of siponimod for SPMS has allowed for optimism in the otherwise discouraging therapeutic landscape.MethodsWe conducted a retrospective, multicenter, non-interventional study analyzing the efficacy and safety of siponimod under real-world conditions in 227 SPMS patients. According to the retrospective study framework, data was acquired at prespecified time points. Clinical readouts were assessed every three months. Disease progression was determined as increase in expanded disability status scale (EDSS), radiological progression, or the occurrence of new relapses under treatment. For safety analyses, adverse events (AE) and reasons for discontinuation were documented. The collected data points were analyzed at baseline and after 6, 12 and 18 months. However, data were predominately collected at the 6- and 12-month time points as many patients were lost to follow-up. In a group consisting of 41 patients, a more detailed investigation regarding disease progression was conducted, including data from measurement of cognitive and motoric functions.ResultsUnder siponimod therapy, 64.8% of patients experienced sustained clinical disease stability at 12 months. Out of the stable patients 21.4% of patients improved. Of the remaining patients, 31.5% experienced EDSS progression, 3.7% worsened without meeting the threshold for progression. Relapses occurred in 7.4%. Radiological disease activity was detected in 24.1% of patients after six months of treatment and in 29.6% of patients at 12 months follow-up. The in-depth cohort consisting of 41 patients demonstrated no substantial changes in cognitive abilities measured by Paced Auditory Serial Addition Test and Symbol Digit Modalities Test or motoric functions measured with Timed 25-Foot Walk, 100-m timed test, and 9-Hole Peg Test throughout the 12-month study period. Radiological assessment showed a stable volume of white and grey matter, as well as a stable lesion count at 12 months follow-up. AE were observed in nearly half of the included patients, with lymphopenia being the most common. Due to disease progression or AE, 31.2% of patients discontinued therapy.ConclusionTreatment with siponimod had an overall stabilizing effect regarding clinical and radiological outcome measures. However, there is a need for more intensive treatment management and monitoring to identify disease progression and AE.

Project description:BACKGROUND:Internet search data on health-related terms can reflect people's concerns about their health status in near real time, and hence serve as a supplementary metric of disease characteristics. However, studies using internet search data to monitor and predict chronic diseases at a geographically finer state-level scale are sparse. OBJECTIVE:The aim of this study was to explore the associations of internet search volumes for lung cancer with published cancer incidence and mortality data in the United States. METHODS:We used Google relative search volumes, which represent the search frequency of specific search terms in Google. We performed cross-sectional analyses of the original and disease metrics at both national and state levels. A smoothed time series of relative search volumes was created to eliminate the effects of irregular changes on the search frequencies and obtain the long-term trends of search volumes for lung cancer at both the national and state levels. We also performed analyses of decomposed Google relative search volume data and disease metrics at the national and state levels. RESULTS:The monthly trends of lung cancer-related internet hits were consistent with the trends of reported lung cancer rates at the national level. Ohio had the highest frequency for lung cancer-related search terms. At the state level, the relative search volume was significantly correlated with lung cancer incidence rates in 42 states, with correlation coefficients ranging from 0.58 in Virginia to 0.94 in Oregon. Relative search volume was also significantly correlated with mortality in 47 states, with correlation coefficients ranging from 0.58 in Oklahoma to 0.94 in North Carolina. Both the incidence and mortality rates of lung cancer were correlated with decomposed relative search volumes in all states excluding Vermont. CONCLUSIONS:Internet search behaviors could reflect public awareness of lung cancer. Research on internet search behaviors could be a novel and timely approach to monitor and estimate the prevalence, incidence, and mortality rates of a broader range of cancers and even more health issues.

Project description:Background: Multiple Sclerosis Partners Advancing Technology and Health Solutions (MS PATHS) is the first example of a learning health system in multiple sclerosis (MS). This paper describes the initial implementation of MS PATHS and initial patient characteristics. Methods: MS PATHS is an ongoing initiative conducted in 10 healthcare institutions in three countries, each contributing standardized information acquired during routine care. Institutional participation required the following: active MS patient census of ≥500, at least one Siemens 3T magnetic resonance imaging scanner, and willingness to standardize patient assessments, share standardized data for research, and offer universal enrolment to capture a representative sample. The eligible participants have diagnosis of MS, including clinically isolated syndrome, and consent for sharing pseudonymized data for research. MS PATHS incorporates a self-administered patient assessment tool, the Multiple Sclerosis Performance Test, to collect a structured history, patient-reported outcomes, and quantitative testing of cognition, vision, dexterity, and walking speed. Brain magnetic resonance imaging is acquired using standardized acquisition sequences on Siemens 3T scanners. Quantitative measures of brain volume and lesion load are obtained. Using a separate consent, the patients contribute DNA, RNA, and serum for future research. The clinicians retain complete autonomy in using MS PATHS data in patient care. A shared governance model ensures transparent data and sample access for research. Results: As of August 5, 2019, MS PATHS enrolment included participants (n = 16,568) with broad ranges of disease subtypes, duration, and severity. Overall, 14,643 (88.4%) participants contributed data at one or more time points. The average patient contributed 15.6 person-months of follow-up (95% CI: 15.5-15.8); overall, 166,158 person-months of follow-up have been accumulated. Those with relapsing-remitting MS demonstrated more demographic heterogeneity than the participants in six randomized phase 3 MS treatment trials. Across sites, a significant variation was observed in the follow-up frequency and the patterns of disease-modifying therapy use. Conclusions: Through digital health technology, it is feasible to collect standardized, quantitative, and interpretable data from each patient in busy MS practices, facilitating the merger of research and patient care. This approach holds promise for data-driven clinical decisions and accelerated systematic learning.

Dataset Information

Leveraging real-world data to investigate multiple sclerosis disease behavior, prognosis, and treatment.

Publications

Leveraging real-world data to investigate multiple sclerosis disease behavior, prognosis, and treatment.

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