TY - JOUR
T1 - Harnessing Real-World Data to Inform Decision-Making
T2 - Multiple Sclerosis Partners Advancing Technology and Health Solutions (MS PATHS)
AU - Mowry, Ellen M.
AU - Bermel, Robert A.
AU - Williams, James R.
AU - Benzinger, Tammie L.S.
AU - de Moor, Carl
AU - Fisher, Elizabeth
AU - Hersh, Carrie M.
AU - Hyland, Megan H.
AU - Izbudak, Izlem
AU - Jones, Stephen E.
AU - Kieseier, Bernd C.
AU - Kitzler, Hagen H.
AU - Krupp, Lauren
AU - Lui, Yvonne W.
AU - Montalban, Xavier
AU - Naismith, Robert T.
AU - Nicholas, Jacqueline A.
AU - Pellegrini, Fabio
AU - Rovira, Alex
AU - Schulze, Maximilian
AU - Tackenberg, Björn
AU - Tintore, Mar
AU - Tivarus, Madalina E.
AU - Ziemssen, Tjalf
AU - Rudick, Richard A.
N1 - Funding Information:
This study was funded by Biogen (Cambridge, MA, USA). Biogen provided funding for medical writing and editorial support in the development of this manuscript. Linda Wagner (Excel Scientific Solutions, Fairfield, CT, USA) wrote the first draft of the manuscript based on input from the authors, and Elizabeth Wassmer (Excel Scientific Solutions, Fairfield, CT, USA) copyedited and styled the manuscript per journal requirements. Biogen reviewed and provided feedback on the paper to the authors. The authors had full editorial control of the paper and provided their final approval of all content. The authors thank all of the patients who are participating in the MS PATHS initiative. Funding. Biogen provides funding for MS PATHS. As the sponsor of MS PATHS, Biogen facilitated the planning process and funded the development and ongoing operation/support of the MSPT and health information exchange architecture. Biogen entered into a research agreement with Siemens Healthineers to co-develop the MRI metrics prototype. Biogen also compensates participating healthcare institutions for data and samples contributed to MS PATHS at fair market value rates. Biogen serves as the hub for contracting and data sharing. All the participating healthcare institutions contracted with Biogen to receive consented patient data and allow Biogen to share the data with other researchers for uses consistent with the informed consent. As the data aggregator, Biogen curates the data and samples and then makes them available to researchers in the network who have an approved data or sample use proposal. To maintain the scientific integrity of the network, Biogen plays no role in adjudicating data and sample requests from academic collaborators or in conducting any scientific or editorial review of resultant publications. Biogen also does not permit its commercial employees to access the data.
Publisher Copyright:
© Copyright © 2020 Mowry, Bermel, Williams, Benzinger, de Moor, Fisher, Hersh, Hyland, Izbudak, Jones, Kieseier, Kitzler, Krupp, Lui, Montalban, Naismith, Nicholas, Pellegrini, Rovira, Schulze, Tackenberg, Tintore, Tivarus, Ziemssen and Rudick.
PY - 2020/8/7
Y1 - 2020/8/7
N2 - 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.
AB - 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.
KW - MS PATHS
KW - digital health technology
KW - learning health system
KW - multiple sclerosis
KW - standardized brain magnetic resonance imaging
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U2 - 10.3389/fneur.2020.00632
DO - 10.3389/fneur.2020.00632
M3 - Article
C2 - 32849170
AN - SCOPUS:85087315620
SN - 1664-2295
VL - 11
JO - Frontiers in Neurology
JF - Frontiers in Neurology
M1 - 632
ER -