Decision making under uncertainty, therapeutic inertia, and physicians' risk preferences in the management of multiple sclerosis (DIScUTIR MS)

Gustavo Saposnik, Angel Perez Sempere, Roula Raptis, Daniel Prefasi, Daniel Selchen, Jorge Maurino, Gustavo Saposnik, Angel Perez Sempere, Roula Raptis, Daniel Prefasi, Daniel Selchen, Jorge Maurino

Abstract

Background: The management of multiple sclerosis (MS) is rapidly changing by the introduction of new and more effective disease-modifying agents. The importance of risk stratification was confirmed by results on disease progression predicted by different risk score systems. Despite these advances, we know very little about medical decisions under uncertainty in the management of MS. The goal of this study is to i) identify whether overconfidence, tolerance to risk/uncertainty, herding influence medical decisions, and ii) to evaluate the frequency of therapeutic inertia (defined as lack of treatment initiation or intensification in patients not at goals of care) and its predisposing factors in the management of MS.

Methods/design: This is a prospective study comprising a combination of case-vignettes and surveys and experiments from Neuroeconomics/behavioral economics to identify cognitive distortions associated with medical decisions and therapeutic inertia. Participants include MS fellows and MS experts from across Spain. Each participant will receive an individual link using Qualtrics platform(©) that includes 20 case-vignettes, 3 surveys, and 4 behavioral experiments. The total time for completing the study is approximately 30-35 min. Case vignettes were selected to be representative of common clinical encounters in MS practice. Surveys and experiments include standardized test to measure overconfidence, aversion to risk and ambiguity, herding (following colleague's suggestions even when not supported by the evidence), physicians' reactions to uncertainty, and questions from the Socio-Economic Panel Study (SOEP) related to risk preferences in different domains. By applying three different MS score criteria (modified Rio, EMA, Prosperini's scheme) we take into account physicians' differences in escalating therapy when evaluating medical decisions across case-vignettes.

Conclusions: The present study applies an innovative approach by combining tools to assess medical decisions with experiments from Neuroeconomics that applies to common scenarios in MS care. Our results will help advance the field by providing a better understanding on the influence of cognitive factors (e.g., overconfidence, aversion to risk and uncertainty, herding) on medical decisions and therapeutic inertia in the management of MS which could lead to better outcomes.

Figures

Fig. 1
Fig. 1
Modified with permission from Sormani et al. defining and scoring response to IFN‑β in multiple sclerosis. Nat. Rev. Neurol. doi:10.1038/nrneurol.2013.146
Fig. 2
Fig. 2
Illustrative comparison of risk aversion changes as a function of wealth and health
Fig. 3
Fig. 3
Framework

References

    1. English C, Aloi JJ. New fda-approved disease-modifying therapies for multiple sclerosis. Clin Ther. 2015;37:691–715. doi: 10.1016/j.clinthera.2015.03.001.
    1. Bruck W, Gold R, Lund BT, Oreja-Guevara C, Prat A, Spencer CM, et al. Therapeutic decisions in multiple sclerosis: moving beyond efficacy. JAMA Neurol. 2013;70:1315–24.
    1. Sormani MP, Bruzzi P. Can we measure long-term treatment effects in multiple sclerosis? Nat Rev Neurol. 2015;11:176–82. doi: 10.1038/nrneurol.2014.237.
    1. D’Amico E, Leone C, Caserta C, Patti F. Oral drugs in multiple sclerosis therapy: an overview and a critical appraisal. Expert Rev Neurother. 2015;15:803–24. doi: 10.1586/14737175.2015.1058162.
    1. Feinstein A, Freeman J, Lo AC. Treatment of progressive multiple sclerosis: What works, what does not, and what is needed. Lancet Neurol. 2015;14:194–207. doi: 10.1016/S1474-4422(14)70231-5.
    1. Hartung HP, Aktas O, Boyko AN. Alemtuzumab: a new therapy for active relapsing-remitting multiple sclerosis. Mult Scler. 2015;21:22–34. doi: 10.1177/1352458514549398.
    1. Sempere AP, Gimenez-Martinez J. Safety considerations when choosing the appropriate treatment for patients with multiple sclerosis. Expert Opin Drug Saf. 2014;13:1287–9. doi: 10.1517/14740338.2014.955012.
    1. Ontaneda D, Cohn S, Fox RJ. Risk stratification and mitigation in multiple sclerosis. Mult Scler Relat Disord. 2014;3:639–49.
    1. Ransohoff RM, Hafler DA, Lucchinetti CF. Multiple sclerosis-a quiet revolution. Nat Rev Neurol. 2015;11:134–42. doi: 10.1038/nrneurol.2015.14.
    1. Sormani MP, De Stefano N. Defining and scoring response to ifn-beta in multiple sclerosis. Nat Rev Neurol. 2013;9:504–12. doi: 10.1038/nrneurol.2013.146.
    1. Sormani MP, Rio J, Tintore M, Signori A, Li D, Cornelisse P, et al. Scoring treatment response in patients with relapsing multiple sclerosis. Mult Scler. 2013;19:605–12. doi: 10.1177/1352458512460605.
    1. Freedman MS, Forrestal FG. Canadian treatment optimization recommendations (tor) as a predictor of disease breakthrough in patients with multiple sclerosis treated with interferon beta-1a: analysis of the prisms study. Mult Scler. 2008;14:1234–41. doi: 10.1177/1352458508093892.
    1. Freedman MS, Selchen D, Arnold DL, Prat A, Banwell B, Yeung M, et al. Treatment optimization in ms: Canadian ms working group updated recommendations. Can J Neurol Sci. 2013;40:307–23. doi: 10.1017/S0317167100014244.
    1. Bermel RA, You X, Foulds P, Hyde R, Simon JH, Fisher E, et al. Predictors of long-term outcome in multiple sclerosis patients treated with interferon beta. Ann Neurol. 2013;73:95–103. doi: 10.1002/ana.23758.
    1. Prosperini L, Mancinelli CR, De Giglio L, De Angelis F, Barletta V, Pozzilli C. Interferon beta failure predicted by ema criteria or isolated mri activity in multiple sclerosis. Mult Scler. 2014;20:566–76. doi: 10.1177/1352458513502399.
    1. O’Connor PJ, Sperl-Hillen JAM, Johnson PE, Rush WA, Biltz G. Clinical inertia and outpatient medical errors. In: Henriksen K, Battles JB, Marks ES, Lewin DI, editors. Advances in patient safety: from research to implementation (volume 2: Concepts and methodology). Rockville: Agency for Healthcare Research and Quality (US); 2005.
    1. Mohan AV, Phillips LS. Clinical inertia and uncertainty in medicine. JAMA. 2011;306:383. doi: 10.1001/jama.2011.1044.
    1. Phillips LS, Branch WT, Cook CB, Doyle JP, El-Kebbi IM, Gallina DL, et al. Clinical inertia. Ann Intern Med. 2001;135:825–34. doi: 10.7326/0003-4819-135-9-200111060-00012.
    1. Okonofua EC, Simpson KN, Jesri A, Rehman SU, Durkalski VL, Egan BM. Therapeutic inertia is an impediment to achieving the healthy people 2010 blood pressure control goals. Hypertension. 2006;47:345–51. doi: 10.1161/01.HYP.0000200702.76436.4b.
    1. Huang LY, Shau WY, Yeh HL, Chen TT, Hsieh JY, Su S, et al. A model measuring therapeutic inertia and the associated factors among diabetes patients: A nationwide population-based study in taiwan. J Clin Pharmacol. 2015;55:17–24. doi: 10.1002/jcph.367.
    1. Escobar C, Barrios V, Alonso-Moreno FJ, Llisterri JL, Rodriguez-Roca GC, Prieto MA, et al. New blood pressure control goals, more rational but facilitating therapeutic inertia? J Hypertens. 2013;31:2462. doi: 10.1097/HJH.0000000000000002.
    1. Turner BJ, Hollenbeak CS, Weiner M, Ten Have T, Tang SS. Effect of unrelated comorbid conditions on hypertension management. Ann Intern Med. 2008;148:578–86. doi: 10.7326/0003-4819-148-8-200804150-00002.
    1. Kerr EA, Zikmund-Fisher BJ, Klamerus ML, Subramanian U, Hogan MM, Hofer TP. The role of clinical uncertainty in treatment decisions for diabetic patients with uncontrolled blood pressure. Ann Intern Med. 2008;148:717–27. doi: 10.7326/0003-4819-148-10-200805200-00004.
    1. Glimcher P, Fehr E. Neuroeconomics: decision making and the brain. San Diego: Academic; 2014.
    1. Croskerry P. The importance of cognitive errors in diagnosis and strategies to minimize them. Acad Med. 2003;78:775–80. doi: 10.1097/00001888-200308000-00003.
    1. Elstein AS, Schwartz A. Clinical problem solving and diagnostic decision making: selective review of the cognitive literature. BMJ. 2002;324:729–32. doi: 10.1136/bmj.324.7339.729.
    1. Reach G. Clinical inertia, uncertainty and individualized guidelines. Diabetes Metab. 2014;40:241–5. doi: 10.1016/j.diabet.2013.12.009.
    1. Ye S. Medical decision making and the counting of uncertainty. Circulation. 2014;129:2500–2. doi: 10.1161/CIRCULATIONAHA.114.010152.
    1. Fontana M, Asaria P, Moraldo M, Finegold J, Hassanally K, Manisty CH, et al. Patient-accessible tool for shared decision making in cardiovascular primary prevention: balancing longevity benefits against medication disutility. Circulation. 2014;129:2539–46. doi: 10.1161/CIRCULATIONAHA.113.007595.
    1. Platt ML, Huettel SA. Risky business: the neuroeconomics of decision making under uncertainty. Nat Neurosci. 2008;11:398–403. doi: 10.1038/nn2062.
    1. Tur C, Tintore M, Vidal-Jordana A, Bichuetti D, Nieto Gonzalez P, Arevalo MJ, et al. Risk acceptance in multiple sclerosis patients on natalizumab treatment. PLoS One. 2013;8:e82796. doi: 10.1371/journal.pone.0082796.
    1. Lee D. Neuroeconomics: Best to go with what you know? Nature. 2006;441:822–3. doi: 10.1038/441822a.
    1. d’Acremont M, Bossaerts P. Decision making: How the brain weighs the evidence. Curr Biol. 2012;22:R808–10. doi: 10.1016/j.cub.2012.07.031.
    1. Saposnik G, Johnston SC. Decision making in acute stroke care: learning from neuroeconomics, neuromarketing, and poker players. Stroke. 2014;45:2144–50. doi: 10.1161/STROKEAHA.114.005462.
    1. Ariely D. Predictably irrational: the hidden forces that shape our decisions. New York: HarperCollins Publishers; 2008.
    1. Hu J, Yu R. The neural correlates of the decoy effect in decisions. Front Behav Neurosci. 2014;8:271.
    1. Wagner GG, Frick JR, Schupp J, Panel DIfWPDS-Ö . The German socio-economic panel study (soep): scope, evolution and enhancements. Berlin: DIW Berlin; 2007.
    1. Dohmen T, Falk A, Huffman D, Sunde U, Schupp J, Wagner GG. Individual risk attitudes: measurement, determinants, and behavioral consequences. 2011.
    1. Panel DIfWPDS-Ö. Sozialforschung TI . Soep 2014 – erhebungsinstrumente 2014 (welle 31) des sozio-oekonomischen panels: Personenfragebogen, altstichproben. Berlin: DIW Berlin / SOEP; 2014.
    1. Saposnik G, Redelmeier D, Ruff CC, Tobler PN. Cognitive distortions and associated with medical desicions: a systematic review. BMC Med. 2015.
    1. Gerrity MS, DeVellis RF, Earp JA. Physicians’ reactions to uncertainty in patient care. A new measure and new insights. Med Care. 1990;28:724–36. doi: 10.1097/00005650-199008000-00005.
    1. Gerrity M, White K, DeVellis R, Dittus R. Physicians’ reactions to uncertainty: refining the constructs and scales. Motiv Emot. 1995;19:175–91. doi: 10.1007/BF02250510.
    1. Levy I, Snell J, Nelson AJ, Rustichini A, Glimcher PW. Neural representation of subjective value under risk and ambiguity. J Neurophysiol. 2010;103:1036–47. doi: 10.1152/jn.00853.2009.
    1. Anderson LR, Mellor JM. Predicting health behaviors with an experimental measure of risk preference. J Health Econ. 2008;27:1260–74. doi: 10.1016/j.jhealeco.2008.05.011.
    1. David E, Jon K. Information cascades (chapter 16). Cambridge: Cambridge University Press; 2010.
    1. Cesarini D, Sandewall Ö, Johannesson M. Confidence interval estimation tasks and the economics of overconfidence. J Econ Behav Organ. 2006;61:453–70. doi: 10.1016/j.jebo.2004.10.010.
    1. Ontaneda D, Fox RJ, Chataway J. Clinical trials in progressive multiple sclerosis: lessons learned and future perspectives. Lancet Neurol. 2015;14:208–23. doi: 10.1016/S1474-4422(14)70264-9.
    1. Mahad DH, Trapp BD, Lassmann H. Pathological mechanisms in progressive multiple sclerosis. Lancet Neurol. 2015;14:183–93. doi: 10.1016/S1474-4422(14)70256-X.
    1. Kopke S, Solari A, Khan F, Heesen C, Giordano A. Information provision for people with multiple sclerosis. Cochrane Database Syst Rev. 2014;4:CD008757.
    1. Studdert DM, Mello MM, Sage WM, DesRoches CM, Peugh J, Zapert K, et al. Defensive medicine among high-risk specialist physicians in a volatile malpractice environment. JAMA. 2005;293:2609–17. doi: 10.1001/jama.293.21.2609.
    1. Dijkstra IS, Pols J, Remmelts P, Brand PL. Preparedness for practice: a systematic cross-specialty evaluation of the alignment between postgraduate medical education and independent practice. Med Teach. 2015;37:153–61. doi: 10.3109/0142159X.2014.929646.
    1. Saposnik G, Redelmeier D, Ruff CC, Tobler PN. Cognitive distortions and associated with medical desicions: a systematic review Med Decis Mak. 2015.
    1. Kalincik T, Cutter G, Spelman T, Jokubaitis V, Havrdova E, Horakova D, et al. Defining reliable disability outcomes in multiple sclerosis. Brain. 2015;138:3287–98. doi: 10.1093/brain/awv258.
    1. Lublin FD, Reingold SC, Cohen JA, Cutter GR, Sorensen PS, Thompson AJ, et al. Defining the clinical course of multiple sclerosis: the 2013 revisions. Neurology. 2014;83:278–86. doi: 10.1212/WNL.0000000000000560.

Source: PubMed

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