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The ICD and Shared Decision Making: Nothing Is Ever Easy
Accepted Manuscript Title: The ICD and Shared Decision Making: Nothing is Ever Easy Author: Paul W. Goetz, Kathleen L. Grady, Clyde W. Yancy PII: DOI: Reference:
S1071-9164(17)30150-1 http://dx.doi.org/doi: 10.1016/j.cardfail.2017.05.012 YJCAF 3966
To appear in:
Journal of Cardiac Failure
Received date: Accepted date:
17-5-2017 18-5-2017
Please cite this article as: Paul W. Goetz, Kathleen L. Grady, Clyde W. Yancy, The ICD and Shared Decision Making: Nothing is Ever Easy, Journal of Cardiac Failure (2017), http://dx.doi.org/doi: 10.1016/j.cardfail.2017.05.012. This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.
The ICD and Shared Decision Making: Nothing is Ever Easy Paul W. Goetz, Ph.D.1 Kathleen L. Grady, Ph.D.1, 2 Clyde W. Yancy, MD, MSc2 Division of Cardiac Surgery1 and Division of Cardiology2, Northwestern University, Feinberg School of Medicine, Chicago, Illinois
Corresponding author Paul W. Goetz, Ph.D. Division of Cardiac Surgery 201 East Huron Street Suite 11-140 Chicago, IL 60611 Email: [email protected] Tel: 312-695-2734
1 Page 1 of 8
For many therapeutic interventions in contemporary cardiovascular medicine, from the treatment of dyslipidemia to the management of advanced heart failure, the prevailing wisdom is to enter into a shared decision making conversation with the patient.1 This very noble consideration is made without sufficient thought given to how a shared decision making conversation may or may not adequately create a mutual exchange of ideas and create an environment in which patient preferences, values, and goals are fully considered. Previous investigators have made clear the role of cognition and emotions in clinical decision making in various disciplines of medicine but less commonly do we consider these variables in the care of patients with cardiac diseases, including those with heart failure.2, 3 In the current issue of the Journal of Cardiac Failure, Matlock, et al add to the body of literature examining factors that influence a patient’s decision by exploring how the decision to undergo implantable-cardioverter defibrillator (ICD) treatment might depend on cognitive bias - a “selective or non-veridical processing of emotionrelevant information”.4 The authors recruited and interviewed 48 patients with heart failure who had accepted or declined ICDs at four outpatient settings in Denver. They used the Framework Method to conduct qualitative analyses of semi-structured interviews and identified the nine most salient cognitive biases. The authors determined that a majority of those biases measured seemed to encourage ICD treatment. Specifically, their findings suggest the way in which the provider presents the information about the need for an ICD (framing), the patient’s perception that the default answer to the question of whether or not to get an ICD is “yes” (default), and the patient’s very 2 Page 2 of 8
favorable perception of the provider (halo effect), influence the decision to undergo ICD implantation. These are non-trivial observations as the benefit of ICD therapy is not universal and has been called into question based on recent trial data exploring a lesser efficacy in non-ischemic and older patients.5 There is a dearth of research examining psychological factors that contribute to ICD decision making.6 Matlock et al. bring to light the importance of understanding patient perceptions and provide a window into relevant cognitive biases in shared decision making regarding ICD implantation. From those relevant biases, the patient’s provider becomes a central factor. The authors did a commendable job of highlighting the importance of a more balanced, less paternalistic approach to ICD education in discussions with patients. Recognizing that evidence-based guidelines addressing appropriate use of ICDs frequently inform physician recommendations in favor of this treatment, there are patients who none-the-less don’t have a clear indication or may decline ICD implant. It is incumbent upon clinicians to ensure that patients who make this decision are fully informed.7 The authors delve into the complexities of decision making noting that a more direct approach (i.e., binary decision making) may be appropriate at times, for certain patients, when discussing certain interventions (e.g., smoking, to use their example), but not appropriate or helpful when it comes to discussion about ICD implantation, where the correct answer is almost always contextual. The health risks of ICD implantation include the potential for important morbidity with a benefit that might be uncertain. Moreover, the psychological risk associated with ICD treatment cannot be discounted.8, 9 The authors have provided a valuable 3 Page 3 of 8
service by reacquainting us with the challenges of decision making -- even for class I recommended therapies. This qualitative investigation has several strengths. First, this study used a robust qualitative data analytic method that is both case and theme based which optimizes data analysis through summarization and synthesis, while retaining links to the original data. The authors collected data from several sites, enhancing the external validity of their findings. Additionally, through targeted recruitment strategies, both older adults and women were enrolled, both of whom are often under-represented in studies. Furthermore, patients who declined ICD implantation were recruited, which enhanced sampling by more completely representing real world patient decision making. There are also a number of limitations to the study. The authors acknowledge the retrospective nature of the data as a limitation. Thus, they could not indicate the adequacy of the qualitative sample (i.e., whether sampling reached saturation [i.e., no new data emerged]). Also, the salience and reasons (i.e., biases) related to undergoing implantation are likely quite different for the two groups of patients (i.e., those who considered ICD implant for primary prevention versus secondary indications) who were surveyed. Patients may have been much more indecisive in the moment of considering ICD implantation than the information recalled during the interview. -i.e., confirmation bias.10 Furthermore, the open-ended questions on the semi-structured interview were created and used 5-10 years earlier to explore “patient experiences with decision making”. The current study focuses on cognitive biases, so the interview questions may not have sufficiently 4 Page 4 of 8
captured data on the many types of cognitive bias. While the authors do acknowledge there are other relevant cognitive factors they did not include, it is unclear how the authors narrowed the biases down from 15 to 9. Finally, inclusion of a saturation grid for the nine cognitive biases identified in their framework would have provided much stronger evidence of the prevalence of these biases versus simply including one quote for each bias. Other factors consistently emerge from the health decision making literature that are worth mentioning and may be weaved into the heuristics described by Matlock et al in future research. For example, one of those biases in the original 15 that was not included was perceived risk. Perceived risk is a concept that has garnered considerable attention in the health decision literature, and for which, there is much empirical support for its role in health decisions (3). Perceived risk, a complex construct, involves elements that are objective (mathematical probabilities,11 and subjective (assessment of loss and significance of that loss.12 There is evidence to suggest low perceived risk of sudden cardiac death can be a barrier to primary prevention ICD candidates.6 The authors indicated that it was difficult to assess the level of emotion involved based on ascertained patient statements. Affect and emotion have been central in health-related judgment and decision making research.13 Specifically, mood may bias one’s interpretation of a risk that he/she is unsure of regarding features such as likelihood, severity, and importance of treatment. We often selectively attend to mood-congruent sources of information- i.e., experiences that validate a prevailing mood whether pertinent or not. As well, mood-congruent 5 Page 5 of 8
memories affect decisions about current and future events, which may involve risk.14 As such, in addition to a patient’s cognitive biases influencing decision making about ICD implantation; emotion-laden memories, current emotional state, as well as the emotion involved in delivery of information from a provider could be influential in a patient’s decision to move forward with this procedure. With respect to specific emotions, fear is powerful and may lead to adaptive health behaviors or avoidance, depending on whether the information being delivered includes discussion of strategies to reduce risks associated with an ICD,15 and risks of living without an ICD. In sum, in addition to understanding the cognitive biases that influence decision making, it is important to integrate emotions underlying health and treatment associated with ICD decision making. The important take-home message merits our attention. Although we follow an evidence-based construct based on rigorously acquired data from clinical trials, extrapolating that evidence to patient level decision making must necessarily account for clinical indications, overt bias, implicit or subconscious bias, cognitive biases, mood and emotions. Thus, the decision to permanently place life-saving devices in a patient is challenging and involves many more variables than the prevailing ejection fraction or clinical practice guideline statement. This is as it should be; we should refine the shared decision making space. Let us seek opportunities to coach rather than dictate, to inform rather than insist, and to listen rather than coerce.
6 Page 6 of 8
References 1. Allen LA, Stevenson LW, Grady KL, Goldstein NE, Matlock DD, Arnold RM, et al. Decision making in advanced heart failure. Circulation 2012;125:1928-52. 2. Kiviniemi MT, Ellis EM. Worry about skin cancer mediates the relation of perceived cancer risk and sunscreen use. J Behav Med 2014;37:1069-74. 3. Waters EA, McQueen A, Cameron LD. Perceived risk and its relationship to healthrelated decisions and behavior. In Martin LR, DiMatteo MR, editors. The oxford handbook of health communication, behavior change, and treatment adherence. Oxford University Press; 2013. p. 1-42. 4. Mineka S, Tomarken A. The role of cognitive biases in the origins and maintenance of fear and anxiety disorders. In Archer T, Nilsson L-G, editors. Aversion, avoidance, and anxiety: Perspectives on aversively motivated behavior. Erlbaum; 1989. p. 195-221. 5. Køber L, Thune JJ, Nielsen JC, Haarbro J, Videbæk L, Korup E, et al. Defibrillator implantation in patients with nonischemic systolic heart failure. N Engl J Med 2016;375:1221-30. 6. Yuhas J, Mattocks K, Gravelin L, Remetz M, Foley J, Fazio R, et al. Patients’ attitudes and perceptions of implantable cardioverter-defibrillators: potential barriers to appropriate primary prophylaxis. Pace 2012;35:1179-87. 7. Russo AM, Stainback RF, Bailey SR, Epstein AE, Heidenreich PA, Jessup M. ACCF/HRS/AHA/ASE/HFSA/SCAI/SCCT/SCMR 2013 Appropriate Use Criteria for Implantable Cardioverter-Defibrillators and Cardiac Resynchronization Therapy. Heart Rhythm 2013;10:1-48. 7 Page 7 of 8
8. Hallas C N, Burke JL, White DG, Connelly DT. Pre-ICD illness beliefs affect postimplant perceptions of control and patient quality of life. Pace 2010;33:256-65. 9. Hoogwegt MT, Kupper N, Theuns DAMJ, Zijlstra WP, Jordaens L, Pedersen SS. Undertreatment of anxiety and depression in patients with an implantable cardioverter-defibrillator: impact on health status. Health Psychol 2012;31:745-53. 10. Jonas E, Schulz-Hardt S, Frey D, Thelen N. Confirmation bias in sequential information search after preliminary decisions: an expansion of dissonance theoretical research on selective exposure to information. J Personality Soc Psychol 2001;80:557-71. 11. Brun W. Risk perception: main issues, approaches, and findings. In Wright G, Ayton P, editors. Subjective probability. Johen Wiley & Sons Ltd.; 1994. p 295-320. 12. Yates JF, Stone ER. The risk construct. In Yates JF, editor. Risk-taking behavior. John Wiley & Sons Ltd.; 1992. p. 1-25. 13. Leventhal H, Weinman J, Leventhal EA, Phillips LA. Health psychology: the search for pathways between behavior and health. Annu Rev Psychol 2008;59:477-505. 14. Bower GH. Mood and memory. Am Psychol 1981;36:129-48. 15. Witte K, Allen M. A meta-analysis of fear appeals: implications for effective public health campaigns. Health Ed Behav 2000;27:591-615.
8 Page 8 of 8
S1071-9164(17)30150-1 http://dx.doi.org/doi: 10.1016/j.cardfail.2017.05.012 YJCAF 3966
To appear in:
Journal of Cardiac Failure
Received date: Accepted date:
17-5-2017 18-5-2017
Please cite this article as: Paul W. Goetz, Kathleen L. Grady, Clyde W. Yancy, The ICD and Shared Decision Making: Nothing is Ever Easy, Journal of Cardiac Failure (2017), http://dx.doi.org/doi: 10.1016/j.cardfail.2017.05.012. This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.
The ICD and Shared Decision Making: Nothing is Ever Easy Paul W. Goetz, Ph.D.1 Kathleen L. Grady, Ph.D.1, 2 Clyde W. Yancy, MD, MSc2 Division of Cardiac Surgery1 and Division of Cardiology2, Northwestern University, Feinberg School of Medicine, Chicago, Illinois
Corresponding author Paul W. Goetz, Ph.D. Division of Cardiac Surgery 201 East Huron Street Suite 11-140 Chicago, IL 60611 Email: [email protected] Tel: 312-695-2734
1 Page 1 of 8
For many therapeutic interventions in contemporary cardiovascular medicine, from the treatment of dyslipidemia to the management of advanced heart failure, the prevailing wisdom is to enter into a shared decision making conversation with the patient.1 This very noble consideration is made without sufficient thought given to how a shared decision making conversation may or may not adequately create a mutual exchange of ideas and create an environment in which patient preferences, values, and goals are fully considered. Previous investigators have made clear the role of cognition and emotions in clinical decision making in various disciplines of medicine but less commonly do we consider these variables in the care of patients with cardiac diseases, including those with heart failure.2, 3 In the current issue of the Journal of Cardiac Failure, Matlock, et al add to the body of literature examining factors that influence a patient’s decision by exploring how the decision to undergo implantable-cardioverter defibrillator (ICD) treatment might depend on cognitive bias - a “selective or non-veridical processing of emotionrelevant information”.4 The authors recruited and interviewed 48 patients with heart failure who had accepted or declined ICDs at four outpatient settings in Denver. They used the Framework Method to conduct qualitative analyses of semi-structured interviews and identified the nine most salient cognitive biases. The authors determined that a majority of those biases measured seemed to encourage ICD treatment. Specifically, their findings suggest the way in which the provider presents the information about the need for an ICD (framing), the patient’s perception that the default answer to the question of whether or not to get an ICD is “yes” (default), and the patient’s very 2 Page 2 of 8
favorable perception of the provider (halo effect), influence the decision to undergo ICD implantation. These are non-trivial observations as the benefit of ICD therapy is not universal and has been called into question based on recent trial data exploring a lesser efficacy in non-ischemic and older patients.5 There is a dearth of research examining psychological factors that contribute to ICD decision making.6 Matlock et al. bring to light the importance of understanding patient perceptions and provide a window into relevant cognitive biases in shared decision making regarding ICD implantation. From those relevant biases, the patient’s provider becomes a central factor. The authors did a commendable job of highlighting the importance of a more balanced, less paternalistic approach to ICD education in discussions with patients. Recognizing that evidence-based guidelines addressing appropriate use of ICDs frequently inform physician recommendations in favor of this treatment, there are patients who none-the-less don’t have a clear indication or may decline ICD implant. It is incumbent upon clinicians to ensure that patients who make this decision are fully informed.7 The authors delve into the complexities of decision making noting that a more direct approach (i.e., binary decision making) may be appropriate at times, for certain patients, when discussing certain interventions (e.g., smoking, to use their example), but not appropriate or helpful when it comes to discussion about ICD implantation, where the correct answer is almost always contextual. The health risks of ICD implantation include the potential for important morbidity with a benefit that might be uncertain. Moreover, the psychological risk associated with ICD treatment cannot be discounted.8, 9 The authors have provided a valuable 3 Page 3 of 8
service by reacquainting us with the challenges of decision making -- even for class I recommended therapies. This qualitative investigation has several strengths. First, this study used a robust qualitative data analytic method that is both case and theme based which optimizes data analysis through summarization and synthesis, while retaining links to the original data. The authors collected data from several sites, enhancing the external validity of their findings. Additionally, through targeted recruitment strategies, both older adults and women were enrolled, both of whom are often under-represented in studies. Furthermore, patients who declined ICD implantation were recruited, which enhanced sampling by more completely representing real world patient decision making. There are also a number of limitations to the study. The authors acknowledge the retrospective nature of the data as a limitation. Thus, they could not indicate the adequacy of the qualitative sample (i.e., whether sampling reached saturation [i.e., no new data emerged]). Also, the salience and reasons (i.e., biases) related to undergoing implantation are likely quite different for the two groups of patients (i.e., those who considered ICD implant for primary prevention versus secondary indications) who were surveyed. Patients may have been much more indecisive in the moment of considering ICD implantation than the information recalled during the interview. -i.e., confirmation bias.10 Furthermore, the open-ended questions on the semi-structured interview were created and used 5-10 years earlier to explore “patient experiences with decision making”. The current study focuses on cognitive biases, so the interview questions may not have sufficiently 4 Page 4 of 8
captured data on the many types of cognitive bias. While the authors do acknowledge there are other relevant cognitive factors they did not include, it is unclear how the authors narrowed the biases down from 15 to 9. Finally, inclusion of a saturation grid for the nine cognitive biases identified in their framework would have provided much stronger evidence of the prevalence of these biases versus simply including one quote for each bias. Other factors consistently emerge from the health decision making literature that are worth mentioning and may be weaved into the heuristics described by Matlock et al in future research. For example, one of those biases in the original 15 that was not included was perceived risk. Perceived risk is a concept that has garnered considerable attention in the health decision literature, and for which, there is much empirical support for its role in health decisions (3). Perceived risk, a complex construct, involves elements that are objective (mathematical probabilities,11 and subjective (assessment of loss and significance of that loss.12 There is evidence to suggest low perceived risk of sudden cardiac death can be a barrier to primary prevention ICD candidates.6 The authors indicated that it was difficult to assess the level of emotion involved based on ascertained patient statements. Affect and emotion have been central in health-related judgment and decision making research.13 Specifically, mood may bias one’s interpretation of a risk that he/she is unsure of regarding features such as likelihood, severity, and importance of treatment. We often selectively attend to mood-congruent sources of information- i.e., experiences that validate a prevailing mood whether pertinent or not. As well, mood-congruent 5 Page 5 of 8
memories affect decisions about current and future events, which may involve risk.14 As such, in addition to a patient’s cognitive biases influencing decision making about ICD implantation; emotion-laden memories, current emotional state, as well as the emotion involved in delivery of information from a provider could be influential in a patient’s decision to move forward with this procedure. With respect to specific emotions, fear is powerful and may lead to adaptive health behaviors or avoidance, depending on whether the information being delivered includes discussion of strategies to reduce risks associated with an ICD,15 and risks of living without an ICD. In sum, in addition to understanding the cognitive biases that influence decision making, it is important to integrate emotions underlying health and treatment associated with ICD decision making. The important take-home message merits our attention. Although we follow an evidence-based construct based on rigorously acquired data from clinical trials, extrapolating that evidence to patient level decision making must necessarily account for clinical indications, overt bias, implicit or subconscious bias, cognitive biases, mood and emotions. Thus, the decision to permanently place life-saving devices in a patient is challenging and involves many more variables than the prevailing ejection fraction or clinical practice guideline statement. This is as it should be; we should refine the shared decision making space. Let us seek opportunities to coach rather than dictate, to inform rather than insist, and to listen rather than coerce.
6 Page 6 of 8
References 1. Allen LA, Stevenson LW, Grady KL, Goldstein NE, Matlock DD, Arnold RM, et al. Decision making in advanced heart failure. Circulation 2012;125:1928-52. 2. Kiviniemi MT, Ellis EM. Worry about skin cancer mediates the relation of perceived cancer risk and sunscreen use. J Behav Med 2014;37:1069-74. 3. Waters EA, McQueen A, Cameron LD. Perceived risk and its relationship to healthrelated decisions and behavior. In Martin LR, DiMatteo MR, editors. The oxford handbook of health communication, behavior change, and treatment adherence. Oxford University Press; 2013. p. 1-42. 4. Mineka S, Tomarken A. The role of cognitive biases in the origins and maintenance of fear and anxiety disorders. In Archer T, Nilsson L-G, editors. Aversion, avoidance, and anxiety: Perspectives on aversively motivated behavior. Erlbaum; 1989. p. 195-221. 5. Køber L, Thune JJ, Nielsen JC, Haarbro J, Videbæk L, Korup E, et al. Defibrillator implantation in patients with nonischemic systolic heart failure. N Engl J Med 2016;375:1221-30. 6. Yuhas J, Mattocks K, Gravelin L, Remetz M, Foley J, Fazio R, et al. Patients’ attitudes and perceptions of implantable cardioverter-defibrillators: potential barriers to appropriate primary prophylaxis. Pace 2012;35:1179-87. 7. Russo AM, Stainback RF, Bailey SR, Epstein AE, Heidenreich PA, Jessup M. ACCF/HRS/AHA/ASE/HFSA/SCAI/SCCT/SCMR 2013 Appropriate Use Criteria for Implantable Cardioverter-Defibrillators and Cardiac Resynchronization Therapy. Heart Rhythm 2013;10:1-48. 7 Page 7 of 8
8. Hallas C N, Burke JL, White DG, Connelly DT. Pre-ICD illness beliefs affect postimplant perceptions of control and patient quality of life. Pace 2010;33:256-65. 9. Hoogwegt MT, Kupper N, Theuns DAMJ, Zijlstra WP, Jordaens L, Pedersen SS. Undertreatment of anxiety and depression in patients with an implantable cardioverter-defibrillator: impact on health status. Health Psychol 2012;31:745-53. 10. Jonas E, Schulz-Hardt S, Frey D, Thelen N. Confirmation bias in sequential information search after preliminary decisions: an expansion of dissonance theoretical research on selective exposure to information. J Personality Soc Psychol 2001;80:557-71. 11. Brun W. Risk perception: main issues, approaches, and findings. In Wright G, Ayton P, editors. Subjective probability. Johen Wiley & Sons Ltd.; 1994. p 295-320. 12. Yates JF, Stone ER. The risk construct. In Yates JF, editor. Risk-taking behavior. John Wiley & Sons Ltd.; 1992. p. 1-25. 13. Leventhal H, Weinman J, Leventhal EA, Phillips LA. Health psychology: the search for pathways between behavior and health. Annu Rev Psychol 2008;59:477-505. 14. Bower GH. Mood and memory. Am Psychol 1981;36:129-48. 15. Witte K, Allen M. A meta-analysis of fear appeals: implications for effective public health campaigns. Health Ed Behav 2000;27:591-615.
8 Page 8 of 8