The association of timing of disease-modifying drug initiation and relapse in patients with multiple sclerosis using electronic health records

References

• Compston A, Coles A. Multiple sclerosis. Lancet 2008;372:1502-17
   PubMed Web of Science ®Google Scholar
• Relapsing–remitting MS (RRMS). National Multiple Sclerosis Society, 2016. Available at: http://www.nationalmssociety.org/What-is-MS/Types-of-MS/Relapsing-remitting-MS [Last accessed 16 March 2016]
   Google Scholar
• Coles AJ, Twyman CL, Arnold DL, et al. Alemtuzumab for patients with relapsing multiple sclerosis after disease-modifying therapy: a randomised controlled phase 3 trial. Lancet 2012;380:1829-39
   PubMed Web of Science ®Google Scholar
• Gold R, Kappos L, Arnold DL, et al. Placebo-controlled phase 3 study of oral BG-12 for relapsing multiple sclerosis. N Engl J Med 2012;367:1098-107
   PubMed Web of Science ®Google Scholar
• PRISMS Study Group. Randomised double-blind placebo-controlled study of interferon beta-1a in relapsing/remitting multiple sclerosis. Lancet 1998;352:1498-504
   PubMed Web of Science ®Google Scholar
• Sellebjerg F, Sondergaard HB, Koch-Henriksen N, et al. Prediction of response to interferon therapy in multiple sclerosis. Acta Neurol Scand 2014;130:268-75
   PubMed Web of Science ®Google Scholar
• Cocco E, Sardu C, Spinicci G, et al. Influence of treatments in multiple sclerosis disability: a cohort study. Mult Scler 2015;21:433-41
   PubMed Web of Science ®Google Scholar
• Coyle PK. Early treatment of multiple sclerosis to prevent neurologic damage. Neurology 2008;71(24Suppl3):S3-7
   Web of Science ®Google Scholar
• Goodin DS, Bates D. Treatment of early multiple sclerosis: the value of treatment initiation after a first clinical episode. Mult Scler 2009;15:1175-82
   PubMed Web of Science ®Google Scholar
• Margolis JM, Fowler R, Johnson BH, et al. Disease-modifying drug initiation patterns in commercially insured multiple sclerosis patients: a retrospective cohort study. BMC Neurol 2011;11:122
   PubMed Web of Science ®Google Scholar
• Davis MF, Haines JL. The intelligent use and clinical benefits of electronic medical records in multiple sclerosis. Expert Rev Clin Immunol 2015;11:205-11
   PubMed Web of Science ®Google Scholar
• Electronic Health Records. Centers for Medicare & Medicaid Services (CMS), 2012. Available at: https://www.cms.gov/Medicare/E-Health/EHealthRecords/index.html?redirect=/ehealthrecords [Last accessed 24 February 2017]
   Google Scholar
• Krysko KM, Ivers NM, Young J, et al. Identifying individuals with multiple sclerosis in an electronic medical record. Mult Scler 2015;21:217-24
   PubMed Web of Science ®Google Scholar
• Maraganore DM, Frigerio R, Kazmi N, et al. Quality improvement and practice-based research in neurology using the electronic medical record. Neurol Clin Pract 2015;5:419-29
   PubMedGoogle Scholar
• Hayrinen K, Saranto K, Nykanen P. Definition, structure, content, use and impacts of electronic health records: a review of the research literature. Int J Med Inform 2008;77:291-304
   PubMed Web of Science ®Google Scholar
• Emir B, Masters ET, Mardekian J, et al. Identification of a potential fibromyalgia diagnosis using random forest modeling applied to electronic medical records. J Pain Res 2015;8:277-88
   PubMed Web of Science ®Google Scholar
• Bayley KB, Belnap T, Savitz L, et al. Challenges in using electronic health record data for CER: experience of 4 learning organizations and solutions applied. Med Care 2013;51(8Suppl3):S80-6
   Web of Science ®Google Scholar
• Davis MF, Sriram S, Bush WS, et al. Automated extraction of clinical traits of multiple sclerosis in electronic medical records. J Am Med Inform Assoc 2013;20(e2):e334-40
   PubMed Web of Science ®Google Scholar
• Xia Z, Secor E, Chibnik LB, et al. Modeling disease severity in multiple sclerosis using electronic health records. PLoS One 2013;8:e78927
   PubMed Web of Science ®Google Scholar
• Humedica Life Sciences Detailed Clinical Data. Optum, 2016. Available at: http://www.humedica.com/solutions/life-sciences/detailed-clinical-data/ [Last accessed 16 March 2016]
   Google Scholar
• Phillips AL, Fang J, Corvino FA, Oliveri D. The impact of timing of disease-modifying drug treatment initiation on multiple sclerosis relapse rates in newly diagnosed patients. Presented at: AMCP Nexus 2015, Orlando, FL, USA, 26–29 October 2015, Poster G15
   Google Scholar
• Deyo RA, Cherkin DC, Ciol MA. Adapting a clinical comorbidity index for use with ICD-9-CM administrative databases. J Clin Epidemiol 1992;45:613-19
   PubMed Web of Science ®Google Scholar
• Quan H, Sundararajan V, Halfon P, et al. Coding algorithms for defining comorbidities in ICD-9-CM and ICD-10 administrative data. Med Care 2005;43:1130-9
   PubMed Web of Science ®Google Scholar
• Capkun G, Lahoz R, Verdun E, et al. Expanding the use of administrative claims databases in conducting clinical real-world evidence studies in multiple sclerosis. Curr Med Res Opin 2015;31:1029-39
   PubMed Web of Science ®Google Scholar
• Chastek BJ, Oleen-Burkey M, Lopez-Bresnahan MV. Medical chart validation of an algorithm for identifying multiple sclerosis relapse in healthcare claims. J Med Econ 2010;13:618-25
   PubMedGoogle Scholar
• Kavaliunas A, Manouchehrinia A, Stawiarz L, et al. Importance of early treatment initiation in the clinical course of multiple sclerosis. Mult Scler 2016; doi: 10.1177/1352458516675039; published online 17 October 2016
   Google Scholar
• Ziemssen T, Hillert J, Butzkueven H. The importance of collecting structured clinical information on multiple sclerosis. BMC Med 2016;14:81
   PubMed Web of Science ®Google Scholar
• Schneeweiss S, Gagne JJ, Glynn RJ, et al. Assessing the comparative effectiveness of newly marketed medications: methodological challenges and implications for drug development. Clin Pharmacol Ther 2011;90:777-90
   PubMed Web of Science ®Google Scholar
• Riley GF. Administrative and claims records as sources of health care cost data. Med Care 2009;47(7Suppl1):S51-5
   Web of Science ®Google Scholar
• Stausberg J, Koch D, Ingenerf J, et al. Comparing paper-based with electronic patient records: lessons learned during a study on diagnosis and procedure codes. J Am Med Inform Assoc 2003;10:470-7
   PubMed Web of Science ®Google Scholar
• Patti F. Optimizing the benefit of multiple sclerosis therapy: the importance of treatment adherence. Patient Prefer Adherence 2010;4:1-9
   PubMed Web of Science ®Google Scholar
• Martin LR, Williams SL, Haskard KB, et al. The challenge of patient adherence. Ther Clin Risk Manag 2005;1:189-99
   PubMedGoogle Scholar
• Menzin J, Caon C, Nichols C, et al. Narrative review of the literature on adherence to disease-modifying therapies among patients with multiple sclerosis. J Manag Care Pharm 2013;19(1SupplA):S24-40
   Web of Science ®Google Scholar
• Gellad WF, Grenard J, McGlynn EA. A Review of Barriers to Medication Adherence: A Framework for Driving Policy Options. 2009. Available at: http://www.rand.org/content/dam/rand/pubs/technical_reports/2009/RAND_TR765.pdf [Last accessed 16 March 2016]
   Google Scholar
• Moller HJ. Effectiveness studies: advantages and disadvantages. Dialogues Clin Neurosci 2011;13:199-207
   PubMedGoogle Scholar
• Kern R, Haase R, Eisele JC, et al. Designing an electronic patient management system for multiple sclerosis: building a next generation multiple sclerosis documentation system. Interact J Med Res 2016;5:e2
   PubMed Web of Science ®Google Scholar