Should We Use the IMPACT-Model for the Outcome Prognostication of TBI Patients? A Qualitative Study Assessing Physicians' Perceptions
Jesse Moskowitz, Thomas Quinn, Muhammad W Khan, Lori Shutter, Robert Goldberg, Nananda Col, Kathleen M Mazor, Susanne Muehlschlegel, Jesse Moskowitz, Thomas Quinn, Muhammad W Khan, Lori Shutter, Robert Goldberg, Nananda Col, Kathleen M Mazor, Susanne Muehlschlegel
Abstract
Introduction. Shared Decision-Making may facilitate information exchange, deliberation, and effective decision-making, but no decision aids currently exist for difficult decisions in neurocritical care patients. The International Patient Decision Aid Standards, a framework for the creation of high-quality decision aids (DA), recommends the presentation of numeric outcome and risk estimates. Efforts are underway to create a goals-of-care DA in critically-ill traumatic brain injury (ciTBI) patients. To inform its content, we examined physicians' perceptions, and use of the IMPACT-model, the most widely validated ciTBI outcome model, and explored physicians' preferences for communicating prognostic information towards families. Methods. We conducted a qualitative study using semi-structured interviews in 20 attending physicians (neurosurgery,neurocritical care,trauma,palliative care) at 7 U.S. academic medical centers. We used performed qualitative content analysis of transcribed interviews to identify major themes. Results. Only 12 physicians (60%) expressed awareness of the IMPACT-model; two stated that they "barely" knew the model. Seven physicians indicated using the model at least some of the time in clinical practice, although none used it exclusively to derive a patient's prognosis. Four major themes emerged: the IMPACT-model is intended for research but should not be applied to individual patients; mistrust in the IMPACT-model derivation data; the IMPACT-model is helpful in reducing prognostic variability among physicians; concern that statistical models may mislead families about a patient's prognosis. Discussion: Our study identified significant variability of the awareness, perception, and use of the IMPACT-model among physicians. While many physicians prefer to avoid conveying numeric prognostic estimates with families using the IMPACT-model, several physicians thought that they "ground" them and reduce prognostic variability among physicians. These findings may factor into the creation and implementation of future ciTBI-related DAs.
Keywords: IMPACT-model; critical care; goals-of-care decisions; outcomes; prognosis; qualitative research; shared decision making; traumatic brain injury.
Conflict of interest statement
None of the authors have any conflicts of interest.
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References
- Hemphill JC, 3rd, Bonovich DC, Besmertis L, Manley GT, Johnston SC. The ICH score: a simple, reliable grading scale for intracerebral hemorrhage. Stroke. 2001;32(4):891–7.
- Clarke JL, Johnston SC, Farrant M, Bernstein R, Tong D, Hemphill JC., 3rd External validation of the ICH score. Neurocrit Care. 2004;1(1):53–60. doi:10.1385/NCC:1:1:53.
- Rost NS, Smith EE, Chang Y, et al. Prediction of functional outcome in patients with primary intracerebral hemorrhage: the FUNC score. Stroke. 2008;39(8):2304–9. doi:10.1161/STROKEAHA.107.512202.
- Steyerberg EW, Mushkudiani N, Perel P, et al. Predicting outcome after traumatic brain injury: development and international validation of prognostic scores based on admission characteristics. PLoS Med. 2008;5(8):e165. doi:10.1371/journal.pmed.0050165.
- Roozenbeek B, Lingsma HF, Lecky FE, et al. Prediction of outcome after moderate and severe traumatic brain injury: external validation of the International Mission on Prognosis and Analysis of Clinical Trials (IMPACT) and Corticoid Randomisation After Significant Head injury (CRASH) prognostic models. Crit Care Med. 2012;40(5):1609–17. doi:10.1097/CCM.0b013e31824519ce00003246-201205000-00028.
- Rabinstein AA, Hemphill JC., 3rd Prognosticating after severe acute brain disease: science, art, and biases. Neurology. 2010;74(14):1086–7. doi:10.1212/WNL.0b013e3181d7d928.
- Zahuranec DB, Fagerlin A, Sanchez BN, et al. Variability in physician prognosis and recommendations after intracerebral hemorrhage. Neurology. 2016;86(20):1864–71. doi:10.1212/WNL.0000000000002676.
- Hemphill JC, 3rd, White DB. Clinical nihilism in neuroemergencies. Emerg Med Clin North Am. 2009;27(1):27–37. doi:10.1016/j.emc.2008.08.009.
- Hwang DY, Dell CA, Sparks MJ, et al. Clinician judgment vs formal scales for predicting intracerebral hemorrhage outcomes. Neurology. 2016;86(2):126–33. doi:10.1212/WNL.0000000000002266.
- Turgeon AF, Lauzier F, Burns KE, et al. Determination of neurologic prognosis and clinical decision making in adult patients with severe traumatic brain injury: a survey of Canadian intensivists, neurosurgeons, and neurologists. Crit Care Med. 2013;41(4):1086–93. doi:10.1097/CCM.0b013e318275d046.
- Turgeon AF, Lauzier F, Simard JF, et al. Mortality associated with withdrawal of life-sustaining therapy for patients with severe traumatic brain injury: a Canadian multicentre cohort study. CMAJ. 2011;183(14):1581–8. doi:10.1503/cmaj.101786.
- Cote N, Turgeon AF, Lauzier F, et al. Factors associated with the withdrawal of life-sustaining therapies in patients with severe traumatic brain injury: a multicenter cohort study. Neurocrit Care. 2013;18(1):154–60. doi:10.1007/s12028-012-9787-9.
- Izzy S, Compton R, Carandang R, Hall W, Muehlschlegel S. Self-fulfilling prophecies through withdrawal of care: do they exist in traumatic brain injury, too? Neurocrit Care. 2013;19(3):347–63. doi:10.1007/s12028-013-9925-z.
- Stacey D, Legare F, Col NF, et al. Decision aids for people facing health treatment or screening decisions. Cochrane Database Syst Rev. 2014;(1):CD001431. doi:10.1002/14651858.CD001431.pub4.
- Cox CE, Lewis CL, Hanson LC, et al. Development and pilot testing of a decision aid for surrogates of patients with prolonged mechanical ventilation. Crit Care Med. 2012;40(8):2327–34. doi:10.1097/CCM.0b013e3182536a63.
- Cox CE, White DB, Abernethy AP. A universal decision support system. Addressing the decision-making needs of patients, families, and clinicians in the setting of critical illness. Am J Respir Crit Care Med. 2014;190(4):366–73. doi:10.1164/rccm.201404-0728CP.
- Muehlschlegel S, Shutter L, Col N, Goldberg R. Decision aids and shared decision-making in neurocritical care: an unmet need in our neuro-ICUs. Neurocrit Care. 2015;23(1):127–30. doi:10.1007/s12028-014-0097-2.
- Kon AA, Davidson JE, Morrison W, et al. Shared decision making in ICUs: an American College of Critical Care Medicine and American Thoracic Society policy statement. Crit Care Med. 2016;44(1):188–201. doi:10.1097/CCM.0000000000001396.
- Shutter LA, Timmons SD. Intracranial pressure rescued by decompressive surgery after traumatic brain injury. N Engl J Med. 2016;375(12):1183–4. doi:10.1056/NEJMe1609722.
- Ottawa Hospital Research Institute (OHRI). Decision aid development toolkit [cited 28 November 2016]. Available from:
- Elwyn G, O’Connor A, Stacey D, et al. Developing a quality criteria framework for patient decision aids: online international Delphi consensus process. BMJ. 2006;333(7565):417. doi:10.1136/.
- Quinn T, Moskowitz J, Khan MW, et al. What families need and physicians deliver: contrasting communication preferences between surrogate decision-makers and physicians during outcome prognostication in critically ill TBI patients. Neurocrit Care. 2017;27(2):154–62. doi:10.1007/s12028-017-0427-2.
- Murray GD, Butcher I, McHugh GS, et al. Multivariable prognostic analysis in traumatic brain injury: results from the IMPACT study. J Neurotrauma. 2007;24(2):329–37. doi:10.1089/neu.2006.0035.
- Lingsma HF, Roozenbeek B, Steyerberg EW, Murray GD, Maas AI. Early prognosis in traumatic brain injury: from prophecies to predictions. Lancet Neurol. 2010;9(5):543–54. doi:10.1016/S1474-4422(10)70065-X.
- Hallen SA, Hootsmans NA, Blaisdell L, Gutheil CM, Han PK. Physicians’ perceptions of the value of prognostic models: the benefits and risks of prognostic confidence. Health Expect. 2015;18(6):2266–77. doi:10.1111/hex.12196.
- Skrobik Y, Kavanagh BP. Scoring systems for the critically ill: use, misuse and abuse. Can J Anaesth. 2006;53(5):432–6. doi:10.1007/BF03022613.
- Di Deo P, Lingsma H, Nieboer D, et al. Clinical results and outcome improvement over time in traumatic brain injury. J Neurotrauma. 2016;33(22):2019–25. doi:10.1089/neu.2015.4026.
- Han J, King NK, Neilson SJ, Gandhi MP, Ng I. External validation of the CRASH and IMPACT prognostic models in severe traumatic brain injury. J Neurotrauma. 2014;31(13):1146–52. doi:10.1089/neu.2013.3003.
- Steyerberg EW, Murray G, Maas A. Datasets included in IMPACT [cited 2 February 2017]. Available from:
- Maas AI. Standardisation of data collection in traumatic brain injury: key to the future? Crit Care. 2009;13(6):1016. doi:10.1186/cc8163.
- Maas AI, Harrison-Felix CL, Menon D, et al. Common data elements for traumatic brain injury: recommendations from the interagency working group on demographics and clinical assessment. Arch Phys Med Rehabil. 2010;91(11):1641–9. doi:10.1016/j.apmr.2010.07.232.
- Wilde EA, Whiteneck GG, Bogner J, et al. Recommendations for the use of common outcome measures in traumatic brain injury research. Arch Phys Med Rehabil. 2010;91(11):1650–60.e17. doi:10.1016/j.apmr.2010.06.033.
- NIH/NINDS. NINDS Common Data Elements—Traumatic brain injury 2012; version 2.0 [updated June 2012; cited 22 January 2017]. Available from:
- Yue JK, Vassar MJ, Lingsma HF, et al. Transforming research and clinical knowledge in traumatic brain injury pilot: multicenter implementation of the common data elements for traumatic brain injury. J Neurotrauma. 2013;30(22):1831–44. doi:10.1089/neu.2013.2970.
- Maas AI, Menon DK, Steyerberg EW, et al. Collaborative European NeuroTrauma Effectiveness Research in Traumatic Brain Injury (CENTER-TBI): a prospective longitudinal observational study. Neurosurgery. 2015;76(1):67–80. doi:10.1227/NEU.0000000000000575.
- Muehlschlegel S, Carandang R, Ouillette C, Hall W, Anderson F, Goldberg R. Frequency and impact of intensive care unit complications on moderate-severe traumatic brain injury: early results of the Outcome Prognostication in Traumatic Brain Injury (OPTIMISM) Study. Neurocrit Care. 2013;18(3):318–31. doi:10.1007/s12028-013-9817-2.
- Politi MC, Han PK, Col NF. Communicating the uncertainty of harms and benefits of medical interventions. Med Decis Making. 2007;27(5):681–95. doi:10.1177/0272989X07307270.
- Diamond GA. What price perfection? Calibration and discrimination of clinical prediction models. J Clin Epidemiol. 1992;45(1):85–9.
- Rockhill B. Theorizing about causes at the individual level while estimating effects at the population level: implications for prevention. Epidemiology. 2005;16(1):124–9.
- Zier LS, Sottile PD, Hong SY, Weissfield LA, White DB. Surrogate decision makers’ interpretation of prognostic information: a mixed-methods study. Ann Intern Med. 2012;156(5):360–6. doi:10.7326/0003-4819-156-5-201203060-00008.
- Boyd EA, Lo B, Evans LR, et al. “It’s not just what the doctor tells me:” factors that influence surrogate decision-makers’ perceptions of prognosis. Crit Care Med. 2010;38(5):1270–5. doi:10.1097/CCM.0b013e3181d8a217.
- Lee Char SJ, Evans LR, Malvar GL, White DB. A randomized trial of two methods to disclose prognosis to surrogate decision makers in intensive care units. Am J Respir Crit Care Med. 2010;182(7):905–9. doi:10.1164/rccm.201002-0262OC.
- Reyna VF. Theories of medical decision making and health: an evidence-based approach. Med Decis Making. 2008;28(6):829–33. doi:10.1177/0272989X08327069.
- Reyna VF. A theory of medical decision making and health: fuzzy trace theory. Med Decis Making. 2008;28(6):850–65. doi:10.1177/0272989X08327066.
- Cox CE, Wysham NG, Walton B, et al. Development and usability testing of a Web-based decision aid for families of patients receiving prolonged mechanical ventilation. Ann Intensive Care. 2015;5:6. doi:10.1186/s13613-015-0045-0.
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