REVIEW URRENT C OPINION
Ethical considerations in stroke patients Adam G. Kelly, Bogachan Sahin, and Robert G. Holloway
Purpose of review Medical decision-making in stroke patients can be complex and often involves ethical challenges, from the perspective of healthcare providers as well as patients and their families. Awareness of these challenges and knowledge of current ethical topics in stroke may improve the quality of care provided to stroke patients. Recent findings Predictive scores are increasingly available to estimate prognosis following stroke, though their usefulness in decision-making for individual patients remains unclear. Medical decisions requiring a surrogate decision-maker can be challenging; surrogates may also be susceptible to systematic biases in their decision-making. Variations in care are common and possibly related to under-utilization or over-utilization of resources. However, patient preferences may explain some of the variability as well. Early mortality may be related to patient and family preferences regarding life-sustaining measures rather than the provision of care that is not well tolerated or evidence-based. Summary Ethical challenges are common in the care of stroke patients. An effective understanding of these topics is essential for clinicians to deliver patient-centered, preference-sensitive care. Keywords medical decision-making, quality of care, stroke
INTRODUCTION
ischemic stroke and intracerebral hemorrhage. Recent iterations include the PLAN score [2 ], the Six Simple Variables Model [3 ], the ASTRAL score [4 ,5 ], and the SPAN-100 score [6 ]. Each of these models has its strengths and weaknesses [7 ]. Most are based on simple, readily available, and clinically relevant variables; for example, the PLAN score uses certain premorbid conditions, level of consciousness at the time of presentation, patient age, and specific neurological deficits to predict mortality at 30 days and 1 year after ischemic stroke. Age and some measure of stroke severity are common elements to most predictive models, including earlier versions [8]. In general, c-statistics for both development and validation cohorts is greater than 0.75 across models, suggesting good ability to discriminate among different outcomes. However, validation methods vary across scores, &
Patients and families affected by stroke, and their healthcare providers, are often faced with ethical challenges regarding their care. There can be prognostic uncertainty in the early poststroke period, when providers may be able to offer only broad ranges of possible outcomes when asked about the likelihood and extent of future recovery [1 ]. Even if the prognostic estimate is accurate and precise, it can be difficult for patients and families to effectively imagine the physical, mental, and emotional sequalae of stroke, and decide whether this quality of life would be consistent with the patient’s prior wishes. In this review, we highlight several timely ethical issues in stroke, including predictive models for stroke outcome; the role of surrogate decisionmakers; variations in practice patterns for stroke patients; and the challenges of using mortality as a quality marker in stroke. &
PREDICTIVE MODELS FOR STROKE OUTCOMES: CAN THEY BE USED EFFECTIVELY IN INDIVIDUAL PATIENTS? Over the past few years, several groups have published predictive models to estimate outcomes in
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Department of Neurology, University of Rochester Medical Center, Rochester, New York, USA Correspondence to Adam G. Kelly, MD, University of Rochester Medical Center, 601 Elmwood Avenue, Box 673, Rochester, NY 14642, USA. Tel: +1 585 275 2530; e-mail: [email protected] Curr Opin Neurol 2014, 27:61–65 DOI:10.1097/WCO.0000000000000048
1350-7540 ß 2014 Wolters Kluwer Health | Lippincott Williams & Wilkins
www.co-neurology.com
Copyright © Lippincott Williams & Wilkins. Unauthorized reproduction of this article is prohibited.
Cerebrovascular disease
KEY POINTS Prognostic models of stroke can provide rough estimates of patient outcomes, but differences in derivation and validation techniques may limit their use. Surrogate decision-makers are commonly utilized after stroke, but they may not always reflect patients’ wishes. Practice variations seen in stroke care may reflect under-utilization or over-utilization of resources, or variations in patient preferences regarding available care options. Early poststroke mortality may likewise be reflective of patient and family preferences regarding life-sustaining interventions, rather than deviations from evidencebased practices.
with some models validated only in internal cohorts whereas others have undergone more extensive validation across heterogeneous populations. Providers should exercise caution when using these tools. First, the population from which a given model is derived may not be representative of the individual patient to whom it is being applied. For example, a predictive model developed in patients who were not treated with thrombolysis would be less relevant in a patient who received thrombolysis, as early thrombolytic treatment is clearly associated with better functional outcomes. Second, provider uncertainty can arise when different predictive tools yield disparate results. Both the ASTRAL score (c-statistic 0.90 in validation cohorts) and PLAN score (c-statistic 0.80 in validation cohorts) have good to excellent discriminatory ability when predicting good functional outcomes after stroke, defined as a modified Rankin Scale of 2 or less. Yet in a hypothetical 48-year-old man with a National Institutes of Health Stroke Scale (NIHSS) score of 15 due to right-sided weakness and aphasia, the PLAN score would predict a 60% chance of good outcome at discharge whereas the ASTRAL tool would predict a 31–40% chance of this outcome at 3 months. Finally, no predictive tool can account for all determinants of survival or functional outcome in a given individual. Underlying cardiovascular disease, end-stage renal disease, terminal cancer, and late-stage dementia or other neurodegenerative conditions are all likely to impact outcomes. Furthermore, patient and family preferences regarding life-sustaining interventions may also have considerable impact on short-term mortality after stroke, and yet these preferences are not included in current predictive tools [9 ]. In summary, models to predict outcomes following stroke are now widely available. Although &
62
www.co-neurology.com
they can provide rough prognostic estimates, providers should be aware that they are populationbased and may not always translate well back for use in individual patients. As with any prognostic estimate, providers should offer a range of possible outcomes, perhaps centered on estimates provided by these tools. Providers should also keep in mind overall life expectancy for a given patient in the absence of stroke, and consider the impact of any other comorbid conditions [10 ,11 ]. Finally, although some models attempt to predict adverse outcomes following treatment with thrombolysis (e.g., SPAN-100), these tools should not be used to exclude patients from thrombolytic treatment. &
&
SURROGATE DECISION-MAKING IN PATIENTS WITH STROKE: HOW RELIABLE IS IT? Patients with stroke or other acute brain injuries are often unable to make their own decisions as a result of aphasia, neglect, impaired level of consciousness, or other cognitive dysfunction. In these situations, surrogate decision-makers must be used; the appointment of a surrogate can be done in advance by the patient (through assignment of a healthcare proxy) or according to local laws/regulations. Ideally, a surrogate is well informed about the patient’s values and preferences or has the assistance of a written living will that outlines the patient’s wishes should certain health states arise. In reality, prior patient wishes, whether oral or written, are often limited to broad and general statements, such as, ‘If I have no chance for recovery, I do not want to be kept alive on machines.’ This can lead to more questions than answers. What does ‘recovery’ mean? As many clinical situations involve at least minimal recovery, what level of recovery – or conversely, what level of dependence and disability – would be acceptable to the patient? Likewise, what does it mean to be ‘kept alive on machines?’ Does this apply to a trial of ventilator assistance for reversible conditions like pneumonia? Compared with other scenarios, surrogate decision-makers appear to be less accurate in estimating patient preferences following stroke [12]. This may be related, in part, to the relatively unpredictable trajectory of functional status following stroke or other acute brain insults. These conditions lead to a rapid loss of function, followed by an unclear pattern that may include some recovery, a static interval, or possible worsening as a result of additional neurological or systemic complications. This is in contrast to the more predictable trajectory of other neurological conditions – the slow and Volume 27 Number 1 February 2014
Copyright © Lippincott Williams & Wilkins. Unauthorized reproduction of this article is prohibited.
Ethical considerations in stroke patients Kelly et al.
steady decline observed in neurodegenerative conditions like dementia and Parkinson’s disease, or the more rapid change seen in amyotrophic lateral sclerosis and glioblastoma [10 ]. Even when outcomes are more certain, surrogates may consciously or unconsciously misinterpret prognostic information. A recent study of critically ill patients, including some with stroke, found that surrogate decision-makers consistently over-estimated the chances of survival of their loved ones, even when clinicians offered little chance for recovery [13 ]. For example, when told, ‘It is very unlikely he/she will survive,’ the median chance of survival assigned by 80 surrogates was still above 30%. When the chance of survival was stated specifically by the provider to be 5%, the chance of survival assigned by surrogates was significantly higher (median value 15%, maximum 40%). It is important for healthcare providers to acknowledge some of these limitations inherent in the use of surrogate decision-makers. Patients should make their wishes known as much as possible, so that designated surrogates can be best prepared should their services be needed. Clinicians may consider having surrogates ‘teach back’ the prognostic information relayed to them, in order to ensure that messages have been effectively received. Personalized strategies should be developed for managing challenging situations, such as surrogate decision-makers requesting impractical or futile treatments [14 ] or multiple surrogates struggling to reach a consensus due to divergent viewpoints [15 ]. &
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PRACTICE VARIATIONS: ARE WE UNDER-UTILIZING OR OVER-UTILIZING RESOURCES? COULD WE BE MISDIAGNOSING PATIENT PREFERENCES? Geographic variations in the delivery of medical care have received attention through the widespread availability of the Dartmouth Atlas program (www.dartmouthatlas.org). In addition to geographical or regional differences, variation can also be seen across patient demographics, facility characteristics, and between individual providers. Variations based on race and ethnicity can often coexist with variations based on socioeconomic status or hospital characteristics [16 ]. A large study of carotid artery imaging rates among stroke patients admitted to Veterans’ Affairs hospitals found that in hospitals not serving a high proportion of minority patients, there was no difference in the rate of carotid artery imaging between black and white patients [17 ]. However, at the 10% of hospitals &
&
caring for the highest proportion of minorities, carotid artery imaging rates were significantly lower for both white patients (78.0%) and black patients (70.5%) compared with white patients at nonminority hospitals (89.7%). Using a large sample of nearly one million stroke admissions entered into the Get With The Guidelines national quality improvement program, researchers found differences in some stroke-related performance measures by region [18 ]. Specifically, patients in the Midwest and Southern regions of the United States were less likely to receive treatment with intravenous thrombolysis compared with those residing in the Northeast (adjusted odds ratios 0.82 for both regions). Conversely, patients in these regions were more likely to be treated with lipidlowering medications following their strokes. The reasons for these differences are unclear. Although possible, it seems unlikely that providers in certain regions are unfamiliar with the benefits of thrombolytic treatment and statins following stroke, so differences in utilization may not be explained from the provider’s perspective. An alternative explanation could be that patients in certain regions may be more risk averse regarding certain treatment options (statin use in the Northeast, thrombolysis in the South and Midwest). When variation is observed, it is important to identify any systematic differences in the approach to medical care across providers, institutions, patient populations, and geographic regions. However, one must also consider that differences in patient preferences could result in practice or outcome variations. For example, rates of life-sustaining interventions following stroke (e.g., mechanical ventilation or gastrostomy tube placement) are higher in black patients compared with whites [19]. Is this related to an over-utilization of these procedures in black stroke patients, or their underutilization in white patients? Alternatively, is it possible that there is a broad spectrum of preferences in each group’s approach to the intensity of care following stroke, but this internal diversity is not well represented in the data used in some studies? Finally, it is also conceivable that there are implicit, perhaps unconscious, provider misconceptions about those preferences. A disadvantage to using large administrative datasets to study practice variations is that patient preferences, provider impressions about patient preferences, and the scope of provider–patient discussions regarding options for care are not typically included. Finding ways to measure whether well informed, patient-centered, and preference-sensitive decisions have occurred is of vital importance, as this may partly explain observed practice variations [20 ].
1350-7540 ß 2014 Wolters Kluwer Health | Lippincott Williams & Wilkins
&
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www.co-neurology.com
63
Copyright © Lippincott Williams & Wilkins. Unauthorized reproduction of this article is prohibited.
Cerebrovascular disease
Although patient factors may explain some differences in practice, other variation is more closely related to the provider. A recent study showed considerable practice variation among providers performing neurointerventional procedures for stroke [21 ]. A survey of 140 interventionalists revealed wide ranges in the minimum NIHSS score required to undertake an intervention (from no minimum score to a score >10); the time window for consideration of a procedure following anterior circulation stroke (from
Ethical considerations in stroke patients Adam G. Kelly, Bogachan Sahin, and Robert G. Holloway
Purpose of review Medical decision-making in stroke patients can be complex and often involves ethical challenges, from the perspective of healthcare providers as well as patients and their families. Awareness of these challenges and knowledge of current ethical topics in stroke may improve the quality of care provided to stroke patients. Recent findings Predictive scores are increasingly available to estimate prognosis following stroke, though their usefulness in decision-making for individual patients remains unclear. Medical decisions requiring a surrogate decision-maker can be challenging; surrogates may also be susceptible to systematic biases in their decision-making. Variations in care are common and possibly related to under-utilization or over-utilization of resources. However, patient preferences may explain some of the variability as well. Early mortality may be related to patient and family preferences regarding life-sustaining measures rather than the provision of care that is not well tolerated or evidence-based. Summary Ethical challenges are common in the care of stroke patients. An effective understanding of these topics is essential for clinicians to deliver patient-centered, preference-sensitive care. Keywords medical decision-making, quality of care, stroke
INTRODUCTION
ischemic stroke and intracerebral hemorrhage. Recent iterations include the PLAN score [2 ], the Six Simple Variables Model [3 ], the ASTRAL score [4 ,5 ], and the SPAN-100 score [6 ]. Each of these models has its strengths and weaknesses [7 ]. Most are based on simple, readily available, and clinically relevant variables; for example, the PLAN score uses certain premorbid conditions, level of consciousness at the time of presentation, patient age, and specific neurological deficits to predict mortality at 30 days and 1 year after ischemic stroke. Age and some measure of stroke severity are common elements to most predictive models, including earlier versions [8]. In general, c-statistics for both development and validation cohorts is greater than 0.75 across models, suggesting good ability to discriminate among different outcomes. However, validation methods vary across scores, &
Patients and families affected by stroke, and their healthcare providers, are often faced with ethical challenges regarding their care. There can be prognostic uncertainty in the early poststroke period, when providers may be able to offer only broad ranges of possible outcomes when asked about the likelihood and extent of future recovery [1 ]. Even if the prognostic estimate is accurate and precise, it can be difficult for patients and families to effectively imagine the physical, mental, and emotional sequalae of stroke, and decide whether this quality of life would be consistent with the patient’s prior wishes. In this review, we highlight several timely ethical issues in stroke, including predictive models for stroke outcome; the role of surrogate decisionmakers; variations in practice patterns for stroke patients; and the challenges of using mortality as a quality marker in stroke. &
PREDICTIVE MODELS FOR STROKE OUTCOMES: CAN THEY BE USED EFFECTIVELY IN INDIVIDUAL PATIENTS? Over the past few years, several groups have published predictive models to estimate outcomes in
&&
&
&&
&
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Department of Neurology, University of Rochester Medical Center, Rochester, New York, USA Correspondence to Adam G. Kelly, MD, University of Rochester Medical Center, 601 Elmwood Avenue, Box 673, Rochester, NY 14642, USA. Tel: +1 585 275 2530; e-mail: [email protected] Curr Opin Neurol 2014, 27:61–65 DOI:10.1097/WCO.0000000000000048
1350-7540 ß 2014 Wolters Kluwer Health | Lippincott Williams & Wilkins
www.co-neurology.com
Copyright © Lippincott Williams & Wilkins. Unauthorized reproduction of this article is prohibited.
Cerebrovascular disease
KEY POINTS Prognostic models of stroke can provide rough estimates of patient outcomes, but differences in derivation and validation techniques may limit their use. Surrogate decision-makers are commonly utilized after stroke, but they may not always reflect patients’ wishes. Practice variations seen in stroke care may reflect under-utilization or over-utilization of resources, or variations in patient preferences regarding available care options. Early poststroke mortality may likewise be reflective of patient and family preferences regarding life-sustaining interventions, rather than deviations from evidencebased practices.
with some models validated only in internal cohorts whereas others have undergone more extensive validation across heterogeneous populations. Providers should exercise caution when using these tools. First, the population from which a given model is derived may not be representative of the individual patient to whom it is being applied. For example, a predictive model developed in patients who were not treated with thrombolysis would be less relevant in a patient who received thrombolysis, as early thrombolytic treatment is clearly associated with better functional outcomes. Second, provider uncertainty can arise when different predictive tools yield disparate results. Both the ASTRAL score (c-statistic 0.90 in validation cohorts) and PLAN score (c-statistic 0.80 in validation cohorts) have good to excellent discriminatory ability when predicting good functional outcomes after stroke, defined as a modified Rankin Scale of 2 or less. Yet in a hypothetical 48-year-old man with a National Institutes of Health Stroke Scale (NIHSS) score of 15 due to right-sided weakness and aphasia, the PLAN score would predict a 60% chance of good outcome at discharge whereas the ASTRAL tool would predict a 31–40% chance of this outcome at 3 months. Finally, no predictive tool can account for all determinants of survival or functional outcome in a given individual. Underlying cardiovascular disease, end-stage renal disease, terminal cancer, and late-stage dementia or other neurodegenerative conditions are all likely to impact outcomes. Furthermore, patient and family preferences regarding life-sustaining interventions may also have considerable impact on short-term mortality after stroke, and yet these preferences are not included in current predictive tools [9 ]. In summary, models to predict outcomes following stroke are now widely available. Although &
62
www.co-neurology.com
they can provide rough prognostic estimates, providers should be aware that they are populationbased and may not always translate well back for use in individual patients. As with any prognostic estimate, providers should offer a range of possible outcomes, perhaps centered on estimates provided by these tools. Providers should also keep in mind overall life expectancy for a given patient in the absence of stroke, and consider the impact of any other comorbid conditions [10 ,11 ]. Finally, although some models attempt to predict adverse outcomes following treatment with thrombolysis (e.g., SPAN-100), these tools should not be used to exclude patients from thrombolytic treatment. &
&
SURROGATE DECISION-MAKING IN PATIENTS WITH STROKE: HOW RELIABLE IS IT? Patients with stroke or other acute brain injuries are often unable to make their own decisions as a result of aphasia, neglect, impaired level of consciousness, or other cognitive dysfunction. In these situations, surrogate decision-makers must be used; the appointment of a surrogate can be done in advance by the patient (through assignment of a healthcare proxy) or according to local laws/regulations. Ideally, a surrogate is well informed about the patient’s values and preferences or has the assistance of a written living will that outlines the patient’s wishes should certain health states arise. In reality, prior patient wishes, whether oral or written, are often limited to broad and general statements, such as, ‘If I have no chance for recovery, I do not want to be kept alive on machines.’ This can lead to more questions than answers. What does ‘recovery’ mean? As many clinical situations involve at least minimal recovery, what level of recovery – or conversely, what level of dependence and disability – would be acceptable to the patient? Likewise, what does it mean to be ‘kept alive on machines?’ Does this apply to a trial of ventilator assistance for reversible conditions like pneumonia? Compared with other scenarios, surrogate decision-makers appear to be less accurate in estimating patient preferences following stroke [12]. This may be related, in part, to the relatively unpredictable trajectory of functional status following stroke or other acute brain insults. These conditions lead to a rapid loss of function, followed by an unclear pattern that may include some recovery, a static interval, or possible worsening as a result of additional neurological or systemic complications. This is in contrast to the more predictable trajectory of other neurological conditions – the slow and Volume 27 Number 1 February 2014
Copyright © Lippincott Williams & Wilkins. Unauthorized reproduction of this article is prohibited.
Ethical considerations in stroke patients Kelly et al.
steady decline observed in neurodegenerative conditions like dementia and Parkinson’s disease, or the more rapid change seen in amyotrophic lateral sclerosis and glioblastoma [10 ]. Even when outcomes are more certain, surrogates may consciously or unconsciously misinterpret prognostic information. A recent study of critically ill patients, including some with stroke, found that surrogate decision-makers consistently over-estimated the chances of survival of their loved ones, even when clinicians offered little chance for recovery [13 ]. For example, when told, ‘It is very unlikely he/she will survive,’ the median chance of survival assigned by 80 surrogates was still above 30%. When the chance of survival was stated specifically by the provider to be 5%, the chance of survival assigned by surrogates was significantly higher (median value 15%, maximum 40%). It is important for healthcare providers to acknowledge some of these limitations inherent in the use of surrogate decision-makers. Patients should make their wishes known as much as possible, so that designated surrogates can be best prepared should their services be needed. Clinicians may consider having surrogates ‘teach back’ the prognostic information relayed to them, in order to ensure that messages have been effectively received. Personalized strategies should be developed for managing challenging situations, such as surrogate decision-makers requesting impractical or futile treatments [14 ] or multiple surrogates struggling to reach a consensus due to divergent viewpoints [15 ]. &
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&
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PRACTICE VARIATIONS: ARE WE UNDER-UTILIZING OR OVER-UTILIZING RESOURCES? COULD WE BE MISDIAGNOSING PATIENT PREFERENCES? Geographic variations in the delivery of medical care have received attention through the widespread availability of the Dartmouth Atlas program (www.dartmouthatlas.org). In addition to geographical or regional differences, variation can also be seen across patient demographics, facility characteristics, and between individual providers. Variations based on race and ethnicity can often coexist with variations based on socioeconomic status or hospital characteristics [16 ]. A large study of carotid artery imaging rates among stroke patients admitted to Veterans’ Affairs hospitals found that in hospitals not serving a high proportion of minority patients, there was no difference in the rate of carotid artery imaging between black and white patients [17 ]. However, at the 10% of hospitals &
&
caring for the highest proportion of minorities, carotid artery imaging rates were significantly lower for both white patients (78.0%) and black patients (70.5%) compared with white patients at nonminority hospitals (89.7%). Using a large sample of nearly one million stroke admissions entered into the Get With The Guidelines national quality improvement program, researchers found differences in some stroke-related performance measures by region [18 ]. Specifically, patients in the Midwest and Southern regions of the United States were less likely to receive treatment with intravenous thrombolysis compared with those residing in the Northeast (adjusted odds ratios 0.82 for both regions). Conversely, patients in these regions were more likely to be treated with lipidlowering medications following their strokes. The reasons for these differences are unclear. Although possible, it seems unlikely that providers in certain regions are unfamiliar with the benefits of thrombolytic treatment and statins following stroke, so differences in utilization may not be explained from the provider’s perspective. An alternative explanation could be that patients in certain regions may be more risk averse regarding certain treatment options (statin use in the Northeast, thrombolysis in the South and Midwest). When variation is observed, it is important to identify any systematic differences in the approach to medical care across providers, institutions, patient populations, and geographic regions. However, one must also consider that differences in patient preferences could result in practice or outcome variations. For example, rates of life-sustaining interventions following stroke (e.g., mechanical ventilation or gastrostomy tube placement) are higher in black patients compared with whites [19]. Is this related to an over-utilization of these procedures in black stroke patients, or their underutilization in white patients? Alternatively, is it possible that there is a broad spectrum of preferences in each group’s approach to the intensity of care following stroke, but this internal diversity is not well represented in the data used in some studies? Finally, it is also conceivable that there are implicit, perhaps unconscious, provider misconceptions about those preferences. A disadvantage to using large administrative datasets to study practice variations is that patient preferences, provider impressions about patient preferences, and the scope of provider–patient discussions regarding options for care are not typically included. Finding ways to measure whether well informed, patient-centered, and preference-sensitive decisions have occurred is of vital importance, as this may partly explain observed practice variations [20 ].
1350-7540 ß 2014 Wolters Kluwer Health | Lippincott Williams & Wilkins
&
&&
www.co-neurology.com
63
Copyright © Lippincott Williams & Wilkins. Unauthorized reproduction of this article is prohibited.
Cerebrovascular disease
Although patient factors may explain some differences in practice, other variation is more closely related to the provider. A recent study showed considerable practice variation among providers performing neurointerventional procedures for stroke [21 ]. A survey of 140 interventionalists revealed wide ranges in the minimum NIHSS score required to undertake an intervention (from no minimum score to a score >10); the time window for consideration of a procedure following anterior circulation stroke (from
