Study protocol: the Back Pain Outcomes using Longitudinal Data (BOLD) registry

Jeffrey G Jarvik, Bryan A Comstock, Brian W Bresnahan, Srdjan S Nedeljkovic, David R Nerenz, Zoya Bauer, Andrew L Avins, Kathryn James, Judith A Turner, Patrick Heagerty, Larry Kessler, Janna L Friedly, Sean D Sullivan, Richard A Deyo, Jeffrey G Jarvik, Bryan A Comstock, Brian W Bresnahan, Srdjan S Nedeljkovic, David R Nerenz, Zoya Bauer, Andrew L Avins, Kathryn James, Judith A Turner, Patrick Heagerty, Larry Kessler, Janna L Friedly, Sean D Sullivan, Richard A Deyo

Abstract

Background: Back pain is one of the most important causes of functional limitation, disability, and utilization of health care resources for adults of all ages, but especially among older adults. Despite the high prevalence of back pain in this population, important questions remain unanswered regarding the comparative effectiveness of commonly used diagnostic tests and treatments in the elderly. The overall goal of the Back pain Outcomes using Longitudinal Data (BOLD) project is to establish a rich, sustainable registry to describe the natural history and evaluate prospectively the effectiveness, safety, and cost-effectiveness of interventions for patients 65 and older with back pain.

Methods/design: BOLD is enrolling 5,000 patients ≥ 65 years old who present to a primary care physician with a new episode of back pain. We are recruiting study participants from three integrated health systems (Kaiser-Permanente Northern California, Henry Ford Health System in Detroit and Harvard Vanguard Medical Associates/ Harvard Pilgrim Health Care in Boston). Registry patients complete validated, standardized measures of pain, back pain-related disability, and health-related quality of life at enrollment and 3, 6 and 12 months later. We also have available for analysis the clinical and administrative data in the participating health systems' electronic medical records. Using registry data, we will conduct an observational cohort study of early imaging compared to no early imaging among patients with new episodes of back pain. The aims are to: 1) identify predictors of early imaging and; 2) compare pain, functional outcomes, diagnostic testing and treatment utilization of patients who receive early imaging versus patients who do not receive early imaging. In terms of predictors, we will examine patient factors as well as physician factors.

Discussion: By establishing the BOLD registry, we are creating a resource that contains patient-reported outcome measures as well as electronic medical record data for elderly patients with back pain. The richness of our data will allow better matching for comparative effectiveness studies than is currently possible with existing datasets. BOLD will enrich the existing knowledge base regarding back pain in the elderly to help clinicians and patients make informed, evidence-based decisions regarding their care.

Figures

Figure 1
Figure 1
Virtual Data Warehouse data elements.

References

    1. Docking RE, Fleming J, Brayne C, Zhao J, Macfarlane GJ, Jones GT. Epidemiology of back pain in older adults: prevalence and risk factors for back pain onset. Rheumatology (Oxford) 2011;50(9):1645–1653. doi: 10.1093/rheumatology/ker175.
    1. Dionne CE, Dunn KM, Croft PR. Does back pain prevalence really decrease with increasing age? A systematic review. Age Ageing. 2006;35(3):229–234. doi: 10.1093/ageing/afj055.
    1. Initial National Priorities for Comparative Effectiveness Research. In. Institute of Medicine, Washington DC; 2009.
    1. Schappert SM, Rechtsteiner EA. Ambulatory medical care utilization estimates for 2006. Natl Health Stat Report. 2008;8:1–29.
    1. Martin BI, Deyo RA, Mirza SK, Turner JA, Comstock BA, Hollingworth W, Sullivan SD. Expenditures and health status among adults with back and neck problems. JAMA. 2008;299(6):656–664. doi: 10.1001/jama.299.6.656.
    1. Deyo RA, Diehl AK. Cancer as a cause of back pain: frequency, clinical presentation, and diagnostic strategies. J Gen Intern Med. 1988;3:230–238. doi: 10.1007/BF02596337.
    1. Friedly J, Bresnahan BW, Comstock B, Turner JA, Deyo RA, Sullivan SD, Heagerty P, Bauer Z, Nedeljkovic SS, Avins AL. et al.Study Protocol- Lumbar Epidural Steroid Injections for Spinal Stenosis (LESS): a double-blind randomized controlled trial of epidural steroid injections for lumbar spinal stenosis among older adults. BMC Musculoskelet Disord. 2012;13(1):48. doi: 10.1186/1471-2474-13-48.
    1. International Classification of Diseases, 9th Revision, 5th Edition, Clinical Modification. Practice Management Information Corp, Los Angeles, Calif; 1993.
    1. Roland M, Morris R. A study of the natural history of back pain. Part 1: Development of a reliable and sensitive measure of disability in low back pain. Spine. 1983;8:141–144. doi: 10.1097/00007632-198303000-00004.
    1. Cleeland CS, Nakamura Y, Mendoza TR, Edwards KR, Douglas J, Serlin RC. Dimensions of the impact of cancer pain in a four country sample: new information from multidimensional scaling. Pain. 1996;67(2–3):267–273.
    1. Cleeland CS, Ryan KM. Pain assessment: global use of the Brief Pain Inventory. Ann Acad Med Singapore. 1994;23(2):129–138.
    1. Kroenke K, Spitzer RL, Williams JB, Lowe B. An ultra-brief screening scale for anxiety and depression: the PHQ-4. Psychosomatics. 2009;50(6):613–621.
    1. Brooks R. EuroQOL: the current state of play. Health Policy. 1996;37(1):53–72. doi: 10.1016/0168-8510(96)00822-6.
    1. Centers for Disease Control and Prevention. Self-reported falls and fall-related injuries among persons aged > or =65 years--United States, 2006. MMWR Morb Mortal Wkly Rep. 2008;57(9):225–229.
    1. Bergner M, Bobbitt RA, Carter WB, Gilson BS. The Sickness Impact Profile: Development and final revision of a health status measure. Med Care. 1981;19:787–805. doi: 10.1097/00005650-198108000-00001.
    1. Patrick DL, Deyo RA, Atlas SJ, Singer DE, Chapin A, Keller RB. Assessing health-related quality of life in patients with sciatica. Spine. 1995;20(17):1899–1908. doi: 10.1097/00007632-199509000-00011.
    1. Patrick DL, Deyo RA. Generic and disease-specific mesures in assessing health status and quality of life. Med Care. 1989;27(suppl):S217–S232. doi: 10.1097/00005650-198903001-00018.
    1. Deyo RA, Andersson G, Bombardier C, Cherkin DC, Keller RB, Lee CK, Liang MH, Lipscomb B, Shekelle P, Spratt KF. et al.Outcome measures for studying patients with low back pain. Spine. 1994;19(18 Suppl):2032S–2036S.
    1. Deyo RA, Diehr P, Patrick DL. Reproducibility and responsiveness of health status measures. Statistics and strategies for evaluation. Control Clin Trials. 1991;12(4 Suppl):142S–158S.
    1. Jensen M, Karoly P. Handbook of Pain Assessment Second ed. The Guilford Press, New York; 2001. Self-report scales and procedures for assessing pain in adults; pp. 15–34.
    1. Dworkin RH, Turk DC, Farrar JT, Haythornthwaite JA, Jensen MP, Katz NP, Kerns RD, Stucki G, Allen RR, Bellamy N. et al.Core outcome measures for chronic pain clinical trials: IMMPACT recommendations. Pain. 2005;113(1–2):9–19.
    1. Kroenke K, Spitzer RL, Williams JB. The Patient Health Questionnaire-2: validity of a two-item depression screener. Med Care. 2003;41(11):1284–1292. doi: 10.1097/01.MLR.0000093487.78664.3C.
    1. Barton GR, Sach TH, Avery AJ, Jenkinson C, Doherty M, Whynes DK, Muir KR. A comparison of the performance of the EQ-5D and SF-6D for individuals aged > or = 45 years. Health Econ. 2008;17(7):815–832. doi: 10.1002/hec.1298.
    1. Buchner DM, Hornbrook MC, Kutner NG, Tinetti ME, Ory MG, Mulrow CD, Schechtman KB, Gerety MB, Fiatarone MA, Wolf SL. et al.Development of the common data base for the FICSIT trials. J Am Geriatr Soc. 1993;41(3):297–308.
    1. Ganz DA, Higashi T, Rubenstein LZ. Monitoring falls in cohort studies of community-dwelling older people: effect of the recall interval. J Am Geriatr Soc. 2005;53(12):2190–2194. doi: 10.1111/j.1532-5415.2005.00509.x.
    1. Hannan MT, Gagnon MM, Aneja J, Jones RN, Cupples LA, Lipsitz LA, Samelson EJ, Leveille SG, Kiel DP. Optimizing the tracking of falls in studies of older participants: comparison of quarterly telephone recall with monthly falls calendars in the MOBILIZE Boston Study. Am J Epidemiol. 2010;171(9):1031–1036. doi: 10.1093/aje/kwq024.
    1. Mackenzie L, Byles J, D’Este C. Validation of self-reported fall events in intervention studies. Clin Rehabil. 2006;20(4):331–339. doi: 10.1191/0269215506cr947oa.
    1. Pluijm SM, Smit JH, Tromp EA, Stel VS, Deeg DJ, Bouter LM, Lips P. A risk profile for identifying community-dwelling elderly with a high risk of recurrent falling: results of a 3-year prospective study. Osteoporos Int. 2006;17(3):417–425. doi: 10.1007/s00198-005-0002-0.
    1. Tinetti ME, Baker DI, McAvay G, Claus EB, Garrett P, Gottschalk M, Koch ML, Trainor K, Horwitz RI. A multifactorial intervention to reduce the risk of falling among elderly people living in the community. N Engl J Med. 1994;331(13):821–827. doi: 10.1056/NEJM199409293311301.
    1. Harris PA, Taylor R, Thielke R, Payne J, Gonzalez N, Conde JG. Research electronic data capture (REDCap)–a metadata-driven methodology and workflow process for providing translational research informatics support. J Biomed Inform. 2009;42(2):377–381. doi: 10.1016/j.jbi.2008.08.010.
    1. Rosenbaum P, Rubin DB. The central role of the propensity score in observational studies for causal effects. Biometrika. 1983;70:41–55. doi: 10.1093/biomet/70.1.41.
    1. Dehejia RH, Wahba S. Propensity score-matching methods for nonexperimental causal studies. Rev Econ Stat. 2002;84(1):151–161. doi: 10.1162/003465302317331982.
    1. Pearl J. Causality: Models, Reasoning and Inference. Cambridge University Press, Cambridge; 2000.
    1. Rosenbaum PR. Design of observational studies. 1. Springer, New York; 2009.
    1. Rosenbaum PR, Rubin DB. The bias due to incomplete matching. Biometrics. 1985;41(1):103–116. doi: 10.2307/2530647.
    1. Rowan KM, Welch CA, North E, Harrison DA. Drotrecogin alfa (activated): real-life use and outcomes for the UK. Crit Care. 2008;12(2):R58. doi: 10.1186/cc6879.
    1. Stukel TA, Fisher ES, Wennberg DE, Alter DA, Gottlieb DJ, Vermeulen MJ. Analysis of observational studies in the presence of treatment selection bias: effects of invasive cardiac management on AMI survival using propensity score and instrumental variable methods. JAMA. 2007;297(3):278–285. doi: 10.1001/jama.297.3.278.
    1. Jarvik JG, Hollingworth W, Martin B, Emerson SS, Gray DT, Overman S, Robinson D, Staiger T, Wessbecher F, Sullivan SD. et al.Rapid magnetic resonance imaging vs radiographs for patients with low back pain: a randomized controlled trial. JAMA. 2003;289(21):2810–2818. doi: 10.1001/jama.289.21.2810.
    1. Webster BS, Cifuentes M. Relationship of early magnetic resonance imaging for work-related acute low back pain with disability and medical utilization outcomes. J Occup Environ Med. 2010;52(9):900–907. doi: 10.1097/JOM.0b013e3181ef7e53.
    1. Modic MT, Obuchowski NA, Ross JS, Brant-Zawadzki MN, Grooff PN, Mazanec DJ, Benzel EC. Acute low back pain and radiculopathy: MR imaging findings and their prognostic role and effect on outcome. Radiology. 2005;237(2):597–604. doi: 10.1148/radiol.2372041509.
    1. Rosenbaum PR, Rubin DB. Constructing a Control Group Using Multivariate Matched Sampling Methods That Incorporate the Propensity Score. Am Stat. 1985;39(1):33–38.
    1. Diggle PJ, Heagerty P, Zeger SL. Analysis of longitudinal data. Oxford University Press, Oxford; 1993.
    1. Cummings P, McKnight B. Analysis of matched cohort data. Stata J. 2004;4:274–281.
    1. Drummond MF, O’Brien B, Stoddart GL, Torrance GW. Methods for the economic evaluatio of health care programmes. 2. Oxford University Press, Oxford; 1997.
    1. Briggs A, Claxton K, Sculpher M. Decision modeling for health economic evaluation. Oxford University Press, Oxford; 2008.
    1. Graves JM, Fulton-Kehoe D, Martin DP, Jarvik JG, Franklin GM. Factors Associated with Early MRI Utilization for Acute Occupational Low Back Pain: A Population-Based Study from Washington State workers compensation. Spine (Phila Pa 1976) 2011.
    1. Glick HA, Doshi JA, Sonnad SS, Polsky D. Economic evaluation in clinical trials. Oxford University Press, Oxford; 2007.
    1. Melloh M, Staub L, Aghayev E, Zweig T, Barz T, Theis JC, Chavanne A, Grob D, Aebi M, Roeder C. The international spine registry SPINE TANGO: status quo and first results. Eur Spine J. 2008;17(9):1201–1209. doi: 10.1007/s00586-008-0665-2.
    1. Stromqvist B, Fritzell P, Hagg O, Jonsson B. The Swedish Spine Register: development, design and utility. Eur Spine J. 2009;18(Suppl 3):294–304.
    1. Schluessmann E, Diel P, Aghayev E, Zweig T, Moulin P, Roder C. SWISSspine: a nationwide registry for health technology assessment of lumbar disc prostheses. Eur Spine J. 2009;18(6):851–861. doi: 10.1007/s00586-009-0934-8.
    1. Costa Lda C, Maher CG, McAuley JH, Hancock MJ, Herbert RD, Refshauge KM, Henschke N. Prognosis for patients with chronic low back pain: inception cohort study. BMJ. 2009;339:b3829. doi: 10.1136/bmj.b3829.
    1. Scheele J, Luijsterburg PA, Ferreira ML, Maher CG, Pereira L, Peul WC, van Tulder MW, Bohnen AM, Berger MY, Bierma-Zeinstra SM. et al.Back complaints in the elders (BACE); design of cohort studies in primary care: an international consortium. BMC Musculoskelet Disord. 2010;12:193.
    1. Roovers JP. Registries: what level of evidence do they provide? Int Urogynecol J Pelvic Floor Dysfunct. 2007;18(10):1119–1120. doi: 10.1007/s00192-007-0434-5.
    1. U.S. Preventive Services Task Force. Screening for breast cancer: U.S. Preventive Services Task Force recommendation statement. Ann Intern Med. 2009;151(10):716–726. W-236.
    1. Jarvik JJ, Hollingworth W, Heagerty P, Haynor DR, Deyo RA. The Longitudinal Assessment of Imaging and Disability of the Back (LAIDBack) Study: baseline data. Spine. 2001;26(10):1158–1166. doi: 10.1097/00007632-200105150-00014.

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