Predicting outcomes after transient ischemic attack and stroke.

Review Article Address correspondence to Dr David A. Rempe, 621 South New Ballas Road, Tower B, Suite 6005, St Louis, MO 63141, [email protected]. Relationship Disclosure: Dr Rempe reports no disclosure. Unlabeled Use of Products/Investigational Use Disclosure: Dr Rempe reports no disclosure. * 2014, American Academy of Neurology.

Predicting Outcomes After Transient Ischemic Attack and Stroke David A. Rempe, MD, PhD ABSTRACT Purpose of Review: Predicting functional outcome and mortality after stroke, with or without thrombolysis, is a critical role of neurologists. This article reviews the predictors of outcome after ischemic stroke. Recent Findings: Several scores were recently designed to predict (1) mortality and poor functional outcome after ischemic stroke, (2) the functional outcome and risk of symptomatic intracranial hemorrhage (sICH) after thrombolysis, and (3) the risk of stroke following TIA. Validation of these prediction instruments is ongoing, and studies will be critical to determine the general applicability of these scores. Summary: Although several scores were developed to predict mortality and outcome after stroke, it may be premature to employ these prediction scores to determine individual patient outcome. Similarly, prediction scores should not be used to deny patients tissue plasminogen activator (tPA), even if the scores predict that the patient has a high likelihood of sICH or poor outcome after thrombolysis. Continuum (Minneap Minn) 2014;20(2):412–428.

INTRODUCTION After a stroke, providers are often asked to predict both the chance that the patient will recover and their ultimate quality of life. The importance of an accurate assessment cannot be overstated. An overly negative prediction may lead to withdrawal of care in misclassified patients, forming a self-fulfilling prophecy. Traditionally, neurologists have relied on their experience to predict outcome, which may be subject to bias, either positive or negative. With the greater use of neurohospitalists, a lack of experience in following the long-term natural history of stroke patients potentially exists and may lead to errant predictions of outcome. For these reasons, the development of prediction models for stroke patients is a very active area of research. If the accuracy

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of these prediction scores can be verified, they could be an invaluable tool for neurologists and other health care providers. PREDICTING MORTALITY AND OUTCOME IN STROKE PATIENTS Recently, investigators employing various patient registries developed several prediction models for stroke mortality (Table 10-1, Table 10-2, Table 10-3, and Figure 10-1), including the ASTRAL score (age, severity of stroke, stroke onset to admission time, range of visual fields, acute glucose, and level of consciousness)1; the PLAN score (preadmission comorbidity, level of consciousness, age, and neurologic deficit) 3; the Ischemic Stroke Predictive Risk Score (IScore)2,7; the Get With the Guidelines (GWTG) stroke risk model8; the

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a TABLE 10-1 ASTRAL and Ischemic Stroke Predictive Risk Score (IScore) Prediction Instruments

Prediction Instruments 1

ASTRAL

What Is Predicted? Modified Rankin Scale (mRS) score 93 or death at 30 days

IScore 30-day mortality; (30-day score)2 1-year mortality; 30-day death or mRS score 9 3; 30-day mortality or institutionalization at discharge

Components

Calculating Points

C Statistic in Derived Cohort

Age

Every 5 years = 1 point

0.85

NIH Stroke Scale score Every point of NIH Stroke Scale = 1 point Time to presentation

Present 93 hours from onset = 2 points

Visual field cut

Visual deficit = 2 points

Glucose

Glucose 9131 or G66 mg/dL = 1 point

Level of consciousness (LOC)

LOC decreased = 3 points

Age

1 point for each year

Sex

Male = 10 points

0.82Y0.85

Female = 0 points Canadian Neurological Score

0 = 105 points G4 = 65 points 5Y7 = 40 points 98 = 0 points

Stroke subtype

Lacunar = 0 points Nonlacunar = 30 points Undetermined = 35 points

Vascular risk factors

Atrial fibrillation = 10 points Congestive heart failure = 10 points

Comorbid conditions

Cancer = 10 points Renal dialysis = 35 points

Preadmission disability

Independent = 0 points Dependent = 15 points

Glucose on admission

G135 mg/dL = 0 points 9135 mg/dL = 15 points

ASTRAL = age, severity of stroke, stroke onset to admission time, range of visual fields, acute glucose, and level of consciousness score; NIH = National Institutes of Health. a Data from Ntaios G, et al, Neurology,1 www.neurology.org/content/78/24/1916.short?sid=b01602b5-2e1e-4848-aa33-20a22fdf909b and Saposnik G, et al, Circulation.2 circ.ahajournals.org/content/123/7/739.long.

six simple variables (SSV) score4,9; and the Bologna Outcome Algorithm for Stroke (BOAS) score.5 When deriving these prediction models, several independent risk factors were identified as Continuum (Minneap Minn) 2014;20(2):412–428

predicting outcome following stroke. The patient’s age and stroke severity are the two common independent risk factors identified as important predictors of stroke outcome (Figure 10-1). www.ContinuumJournal.com


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TIA and Stroke Outcomes

TABLE 10-2

PLAN, Bologna Outcome Algorithm for Stroke, and Six Simple Variations Prediction Instrumentsa

Prediction Instruments PLAN

3

What Is Predicted? 30-day mortality; death or severe disability at discharge modified Rankin Scale (mRS) score 5Y6; 1-year mortality; mRS score 0Y2 at discharge

Components

Calculating Points


Age

1 point per decade

0.80Y0.89

Stroke severity

Severe arm weakness = 2 points

Preadmission medical comorbidities

Severe leg weakness = 2 points Neglect or aphasia = 1 point Atrial fibrillation = 1 point Congestive heart failure = 1 point Cancer = 1.5 points Dependence = 1.5 points

BOAS5

Predicts mRS score 92 or death at 6 months

Level of consciousness (LOC)

LOC reduced = 5.0 points

Age

978 = 1 point

NIH Stroke Scale score

910 = 1 point

Persistent upper limb weakness

If present = 1 point

Paralysis at discharge from stroke unit


Need for oxygen administration


0.89

Need for urinary catheter If present = 1 point SSV

4

Alive at 60 days; alive and independent at 6 months

Age; living alone

Not reported

0.84Y0.88

Independence prestroke (mRS score e2) Verbal component of Glasgow coma scale Able to lift both arms to the horizon Able to walk without help

PLAN = preadmission comorbidity, level of consciousness, age, and neurologic deficit score; BOAS = Bologna Outcome Algorithm for Stroke; NIH = National Institutes of Health; SSV = six simple variables. a Data from O’Donnell MJ, et al, Arch Intern Med,3 archinte.jamanetwork.com/article.aspx?articleid=1391071. Counsell C, et al, Stroke,4 stroke.ahajournals.org/content/33/4/1041.long. and Muscari A, et al, Acta Neurol Scand.5 onlinelibrary.wiley.com/doi/10.1111/j.1600-0404. 2010.01479.x/abstract.

As would be expected, the older the patient, the worse the predicted outcome after stroke. Similarly, patients with more severe strokes have a greater mortality and morbidity. Several other independent risk factors are shared as

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predictors of stroke outcome among the various prediction instruments (Figure 10-1). For example, blood glucose is a predictor of poor outcome in the ASTRAL score1 and IScore.2,7 Premorbid disability is incorporated



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a TABLE 10-3 Get With the Guidelines Stroke Risk Model Prediction Instrument

Prediction Instruments

What Is Predicted?

Get With the Guidelines Stroke Risk Model (GWTG)6

In-hospital mortality

Components

Calculating Points


Age

G60 years = 0 points

0.84

60Y70 years = 1 point 70Y80 years = 5 points 980 years = 9 points NIH Stroke Scale score

0Y2 = 0 points 3Y5 = 10 points 6Y10 = 21 points 11Y15 = 37 points 16Y20 = 48 points 21Y25 = 56 points 925 = 65 points

Mode of arrival

Private transport = 0 points Did not present through emergency department = 16 points Ambulance from scene = 12 points

Sex

Female = 3 points

Vascular risk factors

If no prior stroke/TIA = 2 points If history of diabetes mellitus = 2 points If history of hyperlipidemia = 2 points If history of atrial fibrillation = 2 points If history of coronary artery disease = 5 points If no hyperlipidemia present = 2 points

NIH = National Institutes of Health; TIA = transient ischemic attack. a Data from Smith EE, et al, Circulation.6 circ.ahajournals.org/content/122/15/1496.long.

into the SSV score,4,9 the PLAN score,3 and IScore. The BOAS score5 is unique in that it incorporated the need for oxygen administration and the use of a urinary catheter in patients with stroke outcome. Accuracy of Prediction Scores While a detailed discussion of clinical prediction rules is beyond the scope of this discussion, it is worth briefly considering attributes that make a clinical prediction score reliable and Continuum (Minneap Minn) 2014;20(2):412–428

useful. As discussed elsewhere,10Y13 several factors that make a prediction scale valuable include accuracy of the prediction, validation in separate populations, applicability to the patient population in whom the rule will be applied, ease of use, and its ability to affect treatment decisions and prevent or delay poor outcomes. A popular measure of the accuracy of prediction models is the C statistic, which is a function of the sensitivity

KEY POINT

h The patient’s age and stroke severity are the two common independent predictors of mortality after stroke.



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FIGURE 10-1

Venn diagram demonstrating the overlap of independent predictors for mortality and functional outcome following ischemic stroke. ASTRAL = age, severity of stroke, stroke onset to admission time, range of visual fields, acute glucose, and level of consciousness score; IScore = Ischemic Stroke Predictive Risk Score; GWTG = Get With the Guidelines stroke risk model; LOC = loss of consciousness; PLAN = preadmission comorbidity, level of consciousness, age, and neurologic deficit score; BOAS = Bologna Outcome Algorithm for Stroke score; SSV = six simple variables score; GCS = Glasgow coma scale.

KEY POINTS

h Several factors that make a prediction scale valuable include accuracy of the prediction, validation in separate populations, applicability to the patient population in whom the rule will be applied, ease of use, and its ability to affect treatment decisions and prevent or delay poor outcomes.

h To be clinically useful, a stroke outcomes score should be validated in several patient populations or registries distinct from those used to derive the score.

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and specificity of the prediction model for discriminating between outcomes. The C statistic ranges from 0.5 to 1.0. A value of 1.0 would indicate perfect discrimination, whereas a value of 0.5 would be no better than chance (no better than flipping a coin). A value of 0.8 is considered to have ‘‘very good discrimination’’ and a value of 0.9 ‘‘excellent discrimination’’ (Table 10-1, Table 10-2, and Table 10-3). Validation of Prediction Scores in Multiple Populations To be clinically useful, a stroke outcomes score should be validated in several patient populations or registries distinct from those used to derive the score. Predictive scores are considered more valid if they are predictive in separate geographic regions, in

patients of different ethnic backgrounds, and in populations with divergent medical systems of care. For example, the GWTG stroke risk model8 (derived from a US population) accurately predicts inpatient stroke mortality when applied to a Chinese registry (C statistic = 0.87).14 Similarly, the ASTRAL score (derived from a Swiss population) predicted a poor outcome (modified Rankin Scale [mRS] score 92) at 3 months, 1 year, and 5 years after stroke in a Chinese registry (C statistic Q0.81).15 Finally, the IScore predicts poor outcome or death in both a Chinese registry (C statistic = 0.82) and a Korean population of patients (C statistic = 0.81).16,17 At the time of this writing, the PLAN and BOAS scores have yet to be validated in diverse geographic populations. It is uncertain whether some of the independent predictors in the BOAS score (eg, need for oxygen and urinary catheter), which are dependent on medical practices, will perform well in various medical systems of care. Convenience of Use To be useful to busy clinicians, clinical scores must be easily accessible and easy to use. Prediction scores should include independent variables that are easily measurable, be readily available to clinicians, have an unambiguous scoring system, and predict outcome without needing a complicated calculation. Regarding the measure of stroke severity, most scales utilize the NIH Stroke Scale (NIHSS) (Appendix A) or the Canadian Neurological Scale (CNS) to measure stroke severity. The PLAN score was designed to avoid this hurdle by using the presence of arm weakness, leg weakness, aphasia, or neglect instead of a formal stroke scale. This may make this scale of particular value to the nonstroke neurologist.3,12 The availability of prediction scores on websites or applications (apps) for personal electronic



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devices make some of these prediction instruments more accessible and easy to use. Currently, the NIHSS and IScore are available both on apps and websites. The predicted stroke outcomes of the ASTRAL score can be approximated using Figure 10-21 and Table 10-1. Predicting Outcome After Stroke Using a Patient Example As an example of how to calculate these stroke prediction scores, and to

FIGURE 10-2

illustrate the agreement (or lack thereof) among these scores, we calculated the predicted outcome and mortality for a 48-year-old man who presented to our hospital with a moderately severe stroke (NIHSS score of 15) (Case 10-1). In this example, the predicted outcomes for a good outcome (mRS score of 0 to 2) varied by almost fourfold, and the predicted 30-day mortality varied by more than fivefold. Thus, predicting individual outcomes with these scores needs to be done with caution.

KEY POINT

h Prediction scores should include independent variables that are easily measurable, be readily available to clinicians, have an unambiguous scoring system, and predict outcome without needing a complicated calculation.

Color chart for the estimation of the predicted probability of unfavorable outcome at 3 months in patients with acute ischemic stroke using the ASTRAL score. NIHSS = National Institutes of Health Stroke Scale. Reprinted with permission from Ntaios G, et al, Neurology.1 B 2012 American Academy of Neurology. www.neurology.org/ content/78/24/1916.short?sid=b01602b5-2e1e-4848-aa33-20a22fdf909b.

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TIA and Stroke Outcomes KEY POINTS

h As the treatment of stroke evolves and improves, the accuracy of prediction scores may diminish.

h Prediction scores are unlikely to incorporate all the factors that are important to outcome for a particular patient.

h Only one of the prediction scores has been demonstrated to improve prediction of patient outcome compared to an experienced clinician’s judgment. The scores may give a false sense of confidence in predictions of poor outcome, leading to early withdrawal of care in some patients.

Case 10-1 A 48-year-old man with no significant past medical history or disability presented with aphasia and right-sided weakness. He had awakened from sleep with right-sided weakness and aphasia and arrived to the hospital by ambulance. Head CT and CT angiography demonstrated a subacute infarct and left internal carotid artery occlusion. His NIH Stroke Scale score was 15, blood glucose was normal, and he had no diminished level of consciousness when he arrived. For the ease of comparison across the many scores, the functional outcome was calculated as the percentage of patients predicted to have an mRS score of 0 to 2 as well as mortality. For this patient, the IScore was 153 with 17% of patients predicted to achieve an mRS score of 0 to 2 at 30 days and predicted mortality of 8% at 30 days. The PLAN score was 7 with 60% of patients predicted to have an mRS score of 0 to 2 at discharge and a predicted 30-day mortality of 1.4%. The ASTRAL score was 28 with 31% to 40% of patients predicted to have an mRS score of 0 to 2 at 3 months (Figure 10-2). The GWTG score was 53, predicting a 5.2% in-hospital mortality rate.6 Comment. The prediction for an outcome of mRS score of 0 to 2 varies from as good as 60% at discharge (PLAN score) to a 17% chance at 30 days (IScore). When calculating the likelihood of death for our patient, the 30-day mortality rate varies from 1.4% to 8% as calculated by the PLAN and IScore, respectively. As such, the predicted functional outcome and mortality varies severalfold among the scores. Because of this variability, predicting individual outcomes with these scores needs to be done with caution. Comparisons of several scores may help the clinician appreciate the potential variability of the outcomes for a given patient.

Limitations of Stroke Outcome Prediction Scores These prediction scores have several potential limitations. First, these models were derived from past patient populations. As the treatment of stroke evolves and improves, the accuracy of prediction scores may diminish. For example, many of the scales did not include patients that were treated with tPA, so these scales may be overly pessimistic if trying to predict a poor outcome in patients given tPA. In fact, the PLAN score was demonstrated to be less accurate in predicting outcome in patients who had received tPA.3 Additionally, prediction scores were derived from registries that may not have all predictors that determine outcome in a particular patient and are unlikely to incorporate all the factors that are important for predicting that patient’s outcome. For example, intracranial artery occlusion18 and acute respiratory distress syndrome

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are linked to poor stroke outcome19 but are not included in these models. It is important to note that, to date, only one of these prediction scores has been demonstrated to improve prediction of patient outcome compared with an experienced clinician’s judgment.20 Thus, additional studies comparing these various scores against each other and against experienced clinician judgment are needed. Moreover, these scores may give a false sense of confidence in predictions of poor outcome, leading to early withdrawal of care in some patients. Grouping together poor outcome (mRS score of greater than or equal to 3) and mortality, as is done in ASTRAL, BOAS, and IScore may inadvertently imply to the patient’s family that poor outcome is so severe that it is equivalent to death. Yet, patients with moderate disability do not necessarily have a poor quality of life. For example,



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most patients (or their caregivers) who had a hemicraniectomy for severe stroke indicated that the decision to have the surgery was the correct choice despite the fact that only 14% of them achieved an mRS score of 2 or less.21 PREDICTING FUNCTIONAL OUTCOME AND/OR THE RISK OF SYMPTOMATIC INTRACRANIAL HEMORRHAGE IN PATIENTS TREATED WITH TISSUE PLASMINOGEN ACTIVATOR The seminal National Institutes of Neurological Disorders and Stroke (NINDS) tPA trial revolutionized acute stroke care. For the first time, thrombolysis was demonstrated to improve stroke outcome18; however, the risk of intracerebral hemorrhage, such as symptomatic intracranial hemorrhage (sICH), limits the use of tPA. A risk score that could accurately predict which patients are most likely to benefit, or be harmed, with tPA would be a welcome tool for clinicians. Several scores are available to predict outcome after tPA and/or the probability of sICH after tPA treatment (Table 10-4, Table 10-5, and Appendix A). Instruments that predict outcome include: the DRAGON (dense middle cerebral artery sign or early infarct on CT, baseline modified Rankin Scale score, age, glucose, onset-to-treatment time, NIH Stroke Scale) score22; the Stroke Thrombolytic Predictive Instrument (TPI) score23; and the Multimodal Outcome Score for Stroke Thrombolysis (MOST) score.26 Those developed to predict sICH after tPA include the hemorrhage after thrombolysis (HAT) score,24 the Multicenter Stroke Survey (MSS) score,27 the SEDAN (Sugar, Early infarct sign, hyperDense middle cerebral artery, Age, Neurologic deficit) score,25 and the GRASPS (Glucose at presentation, Race, Age, Sex, Systolic Continuum (Minneap Minn) 2014;20(2):412–428

blood Pressure at presentation, and Severity of stroke at presentation) score.28 Finally, while the IScore was originally developed to predict mortality after stroke (see earlier discussion), recent analyses demonstrated that it also predicts outcome and sICH following tPA treatment.29,30 Stroke severity and glucose abundance are central factors driving both functional outcome and sICH after thrombolysis (Figure 10-3). Age is also common to all scales, except for the HAT score. Three of these scales (DRAGON, SEDAN, and HAT) include imaging data as part of the score. They identified early ischemic changes and/or a dense middle cerebral artery (MCA) sign as predictors of poor stroke outcome or sICH after thrombolysis. Finally, history of diabetes mellitus, higher systolic blood pressure, male sex, time to treatment with tPA, low platelet count, race (specifically Asian), and premorbid disability were found to be predictors of poor functional outcome or sICH in some of the scores.

KEY POINT

h Stroke severity and glucose abundance are central factors driving both functional outcome and symptomatic intracranial hemorrhage after thrombolysis.

Accuracy of Thrombolysis Prediction Scores Most of these scores were derived from patient populations in Europe or North America, and validation in other cohorts is an ongoing process. The Stroke-TPI score was determined to be accurate in predicting outcome with a C statistic of 0.83 for patients within the 3-hour window in a patient population in the Netherlands.31 Yet, this study, and a subsequent study in the UK,32 suggest that the Stroke-TPI slightly overestimates good outcome and underestimates poor outcome. In its original publication, the SEDAN score was externally validated using data from 880 patients in three Swiss cohorts with a C statistic of 0.77.25 Yet, in a subsequent publication it only had low www.ContinuumJournal.com


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a TABLE 10-4 The Stroke-Thrombolytic Predictive Instrument and DRAGON Prediction Instrument

Prediction Instruments

What Is Predicted?

Stroke-Thrombolytic Good outcome modified Predictive Instrument23 Rankin Scale (mRS) score 0Y1; poor outcome mRS score 5Y6

Components

Calculating Points


Age

Not reported

0.77Y0.78

Dense middle cerebral artery sign or early infarct on head CT

Both = 2 points

0.84

Baseline mRS score 91

Yes = 1 point

Age

980 years = 2 points

Sex NIH Stroke Scale score Time to treatment Glucose Diabetes mellitus Systolic blood pressure Prior stroke

DRAGON

22

Good outcome mRS score 0Y2; poor outcome mRS score 5Y6

Either = 1 point Neither = 0 points

65Y79 years = 1 point G65 years = 0 points Glucose

9144 mg/dL = 1 point

Onset-to-treatment time

990 minutes = 1 point


915 = 3 points 10Y14 = 2 points 5Y9 = 1 point 0Y4 = 0 points

NIH = National Institutes of Health; DRAGON = dense middle cerebral artery sign or early infarct on CT, baseline Modified Rankin Scale, age, glucose, onset-to-treatment time, NIH Stroke Scale score. a Data from Strbian D, et al, Neurology,22 www.neurology.org/content/78/6/427.short?sid=69cd245d-7a2c-43a0-b108-8a79a5debff9 and Kent DM et al, Stroke.23 stroke.ahajournals.org/cgi/pmidlookup?view=long&pmid=17068305.

(0.60) to moderate (0.66) discriminatory capability in two other cohorts.33 The HAT score is reasonably accurate at predicting outcome in the patients in the NINDS trial and in a series of patients from a single institution (C statistic of 0.74 for predicting sICH, 0.75 for predicting good outcome,

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and 0.78 for predicting catastrophic outcome [mRS score of 5 to 6]). Yet, the HAT score had limited to moderate discriminatory power in patients from two clinical trials.34 Similarly, the MSS score had limited discriminatory power (C statistic of 0.61) when examined in patients from the StrokeYAcute Ischemic



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a TABLE 10-5 SEDAN and HAT Prediction Instruments

Prediction Instruments SEDAN (Sugar, early infarct sign, hyperdense middle cerebral artery, age, neurologic deficit)25

HAT (Hemorrhage after thrombolysis)24

What Is Predicted? Components Symptomatic intracerebral hemorrhage risk

Symptomatic intracerebral hemorrhage risk; modified Rankin Scale (mRS) score 0Y1 (90 day); mRS score 0Y2 (90 day); mRS score 5Y6 (90 day)


Calculating Points

0.77

Sugar (glucose)

145Y216 mg/dL = 1 point; 9216 mg/dL = 2 points

Early infarct sign


Hyperdense middle cerebral artery (MCA)


Age

975 years = 1 point

Neurologic deficit

NIH Stroke Scale 910 = 1 point

Glucose

History of diabetes mellitus or blood glucose 9200 = 1 point


G15 = 0 points; 15Y20 = 1 point; 920 = 2 points

Early ischemic changes on head CT

If not present = 0 points; G One-third of MCA territory = 1 point; 9 One-third of MCA territory = 2 points

0.72Y0.78

NIH = National Institutes of Health; CT = computed tomography. a Data from Lou M, et al, Neurology,24 www.neurology.org/content/71/18/1417.short?sid=5f3a0dd1-ee88-433c-b43b-2b0dc59a5634 and Strbian D, et al, Ann Neurol.25 onlinelibrary.wiley.com/doi/10.1002/ana.23546/abstract.

NXY Treatment (SAINT) trials.34 In patients from the NINDS trial, the GRASPS score had a C statistic of 0.68. In summary, the ability of these scores to predict outcome or sICH risk following tPA administration needs to be viewed with caution as their accuracy in validation cohorts is variable and lower than in their derivation studies. While the IScore was developed as a predictor of mortality in patients with ischemic stroke, it also predicts functional outcome and sICH risk in patients treated with tPA.29 Patients with a low (less than 139 points) or medium (140 to 179 points) IScore appeared to benefit from tPA treatment, while those patients with an IScore of greater than 200 did not benefit. Similarly, patients in the NINDS trial with Continuum (Minneap Minn) 2014;20(2):412–428

an IScore of greater than 200 points did not appear to benefit from tPA treatment.30 Examples of Treating Patients With Thrombolysis: Comparing the Prediction Instruments For illustrative purposes, we examined the predicted functional outcome and risk of sICH of two patients with moderate to severe stroke severity who were treated with tPA (Case 10-2, Case 10-3). To calculate the DRAGON, HAT, IScore, and Stroke-TPI scores, commercially available apps were used. The GRASPS score is available online at www.strokeassociation.org/STROKEORG/ General/Get-With-The-Guidelines-StrokesICH-Calculator_UCM_453748_ SubHomePage.jsp. The SEDAN score is

KEY POINT

h The ability of scores to predict outcome or symptomatic intracranial hemorrhage risk following tissue plasminogen activator administration needs to be viewed with caution, as their accuracy in validation cohorts is variable and lower than in their derivation studies.



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cohorts were treated with tPA within 3 hours of stroke onset. As such, the accuracy of these scales within the 3- to 4.5-hour window is likely to be reduced.

FIGURE 10-3

Venn diagram demonstrating the overlap of independent predictors for functional outcome and risk of symptomatic intracerebral hemorrhage when treating patients with thrombolysis. DRAGON = dense middle cerebral artery sign or early infarct on CT, baseline modified Rankin scale, age, glucose, onset-totreatment time, NIH Stroke Scale score; SEDAN = sugar, early infarct sign, hyperdense middle cerebral artery, age, neurologic deficit score; MCA = middle cerebral artery; Stroke-TPI = StrokeThrombolytic Predictive Instrument; early CT $ = early ischemic changes on the head CT; HAT = hemorrhage after thrombolysis score; MSS = Multicenter Stroke Survey; GRASPS = glucose at presentation, race, age, sex, systolic blood pressure at presentation, and severity of stroke at presentation.

KEY POINT

h It is premature to use scores from various prediction instruments as a means to exclude patients from thrombolysis. Similarly, it would be premature to bypass tissue plasminogen activator and perform other interventions based on these scores.

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calculated as detailed in Table 10-5. For the SEDAN score, the absolute risk of sICH with thrombolysis is as follows: 1.4%, 2.9%, 8.5%, 12.2%, 21.7%, and 33.3% for score points of 0, 1, 2, 3, 4, and 5, respectively. As can be appreciated by the case examples (Table 10-6 and Table 10-7), variation occurs among these prediction instruments to estimate sICH risk and functional outcome in patients receiving tPA for acute strokes. Until these scores are better validated to predict sICH and outcome, it is premature to use these various scores as a means to exclude patients from thrombolysis. Similarly, it would be premature to bypass tPA and perform other interventions based on these scores. It is also worth noting that most of the patients in the derivation

RISK OF STROKE FOLLOWING TIA Two of the most common questions that neurologists are asked by primary care providers, medical hospitalists, and emergency medicine physicians are (1) Was the patient’s transient neurologic episode a TIA? and (2) Does the patient need to be admitted to the hospital for an evaluation? Approximately 200,000 to 500,000 TIAs occur in the United States yearly with about 5 million patients in the United States having had a TIA.35 Historically, there was a general misconception that TIAs were benign; yet TIAs represent a transient diminished blood flow to the brain that shares the same pathophysiologic mechanisms as stroke. In addition, patients with TIAs have an increased risk of disability and death.36 Several disease processes mimic TIAs (eg, complicated migraine, focal seizure, tumor, subdural hematoma, metabolic derangements). Migraine aura, with or without headache, is commonly encountered and can usually be distinguished by a history of migraine, or the typical visual obscurations that occur with migraine (scintillating scotomas, fortification spectra). TIAs are less likely to be repeated frequently, to be stereotypic (as opposed to seizure or migraine), or be accompanied by headache. While sensory symptoms or dizziness are less specific and less suggestive of TIA, transient language problems and/or weakness are more suggestive of TIA. TIAs typically last several minutes or 1 hour or more as opposed to a few seconds. Obviously, older patients, especially those with vascular disease risk factors, are at higher risk of TIA. Risk of Recurrent Stroke After TIA To decide whether the patient needs to be admitted for evaluation after the



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Case 10-2 A 75-year-old man with a past medical history of hyperlipidemia, hypertension, and distant prior stroke but without premorbid disability presented with left-sided weakness and slurred speech. The patient had been normal when he awoke but suddenly became weak later that morning. Upon arrival to the emergency department (via ambulance), he had significant slurred speech, left facial weakness, left visual field cut, right gaze preference, and left arm weakness, with a NIH Stroke Scale score of 11. Chronic right cerebellar and bilateral occipital lobe infarcts were identified on head CT, but no intracranial hemorrhage (ICH), early ischemic changes, or dense MCA sign was found. The international normalized ratio (INR) was 1.0 and blood glucose was 107 mg/dL. His blood pressure was 151/83 mm Hg. He was treated with tPA 1 hour and 14 minutes after symptom onset. Brain MRI the next day demonstrated a right MCA stroke primarily involving the posterior division of the MCA (Figure 10-4). During several days in the hospital, he slowly improved Diffusion-weighted MRI sequences (A and B) of the patient in Case 10-2 and upon discharge FIGURE 10-4 demonstrating an ischemic infarct primarily involving the posterior division of the right middle cerebral artery territory. he had an NIHSS score of 4. Comment. The GRASPS points were 73, the SEDAN score was 2, and the IScore was 155. The predicted risk of symptomatic ICH varied from 2% to 8.5%, but the predicted good outcome (mRS score of 0 to 2) was in general agreement.

possible TIA, one must consider the risk of recurrent stroke after TIA. One of the most commonly used scores to predict stroke risk following TIA is the ABCD2 score, which was derived from combining the ABCD and California score.37 The ABCD2 score is calculated as follows: age greater than or equal to 60 years (1 point); blood pressure greater than or equal to 140/90 mm Hg upon presentation (1 point); clinical features: unilateral weakness (2 points) or speech/language impairment without weakness (1 point); duration greater than or equal to 60 minutes Continuum (Minneap Minn) 2014;20(2):412–428

(2 points) or 10 to 59 minutes (1 point); and diabetes mellitus (1 point).37 The ABCD2 score can vary from 0 to 7, and increasing scores predict increased risk of stroke. For example, the risk of stroke 2 days after TIA is 0% if the ABCD2 score is only 0 to 1, but the risk increases to greater than 8% if the ABCD2 score is 6 to 7. The ABCD2 score is easy to calculate, and the risk of recurrent stroke can be calculated using available apps. The ABCD2 score is widely employed and supported by American Heart Association and American Stroke Association

KEY POINT

h One of the most commonly used scores to predict stroke risk following TIA is the ABCD2 score.



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Case 10-3 An 88-year-old woman with a history of atrial fibrillation, congestive heart failure, and hypertension was in a nursing home recovering from a vertebral compression fracture but was scheduled for discharge in 2 days to live independently. She was not taking warfarin because of a risk of falling. She had last been seen normal at 6:00 AM and at 6:50 AM was noted to have dysarthric speech and no movement on her left side. She arrived to the emergency department via ambulance 1 hour and 20 minutes after last seen normal. An initial head CT showed a dense MCA sign on the right (Figure 10-5) with some minimal patchy low attenuation of the basal ganglia on the right. No intracranial hemorrhage (ICH) was seen on CT, and the early ischemic changes were less than one-third of the MCA territory. The INR was 1.1; blood FIGURE 10-5 Head CT of the patient in Case 10-3 glucose level was demonstrating a dense middle cerebral artery sign. 131 mg/dL; systolic blood pressure was 182 mm Hg, but this reduced to 155 mm Hg without treatment. The patient was lethargic, had no movement on the left side, neglected the left side, and was dysarthric with a NIHSS score of 23. She was treated with tPA 1 hour and 56 minutes after last seen normal. Given the patient’s advanced age and family’s wishes, no endovascular therapy was pursued. The patient’s symptoms showed no improvement, and a large stroke evolved in her right hemisphere. A few days following the stroke the family elected to pursue comfort care measures. No ICH was identified on follow-up imaging. Comment. The GRASPS score was 83, the SEDAN score was 4, and the IScore was 258. The predicted risk of ICH varied widely from 5% to 21.7%, the predicted good outcome (mRS score of 0 to 2) varied from 0% to 13%, but the prediction of catastrophic outcome (mRS score of 5 to 6) was in good agreement.

guidelines to triage TIA patients for hospitalization. It is reasonable to hospitalize TIA patients if they present within 72 hours of the event and any

424

of the following criteria are present: an ABCD2 score greater than or equal to 3, an ABCD2 score less than or equal to 2 and uncertainty that diagnostic workup



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TABLE 10-6 Calculating the Outcome and Risk of Symptomatic Intracerebral Hemorrhage With Tissue Plasminogen Activator for the Patient in Case 10-2

Symptomatic Intracerebral Prediction Hemorrhage Instrument Risk

Modified Rankin Scale Score 0Y1

DRAGON

NA

Stroke-TPI

2%

52%

HAT

2%

66%

GRASPS

5%

SEDAN

8.5%

IScore

7.7%

Modified Rankin Scale Score 0Y2

Modified Rankin Scale Score 30-Day Death or 5Y6 Institutionalization

74%

5%

30-Day Death or Modified Rankin Scale Score 92

12.9% 77%

6%

20%

76%

DRAGON = dense middle cerebral artery sign or early infarct on CT, baseline modified Rankin Scale, age, glucose, onset-to-treatment time, NIH Stroke Scale score; NA = not applicable; Stroke-TPI = Stroke-Thrombolytic Predictive Instrument; HAT = hemorrhage after thrombolysis score; GRASPS = glucose at presentation, race, age, sex, systolic blood pressure at presentation, and severity of stroke at presentation]; SEDAN = sugar, early infarct sign, hyperdense middle cerebral artery, age, neurologic deficit score; IScore = Ischemic Stroke Predictive Risk Score.

can be completed within 2 days as an outpatient, or an ABCD2 score of 0 to 2 and other evidence that indicates the patient’s event was caused by focal ischemia (Class IIa recommendations).35 Regarding the evaluation of TIA patients, all of the following are Class Ia recom-

mendations: neuroimaging should be obtained within 24 hours (with MRI the preferred modality); noninvasive imaging of the cervicocephalic vessels should be obtained; noninvasive evaluation of the intracranial vasculature is reasonable if likely to alter management; patients

TABLE 10-7 Calculating the Outcome and Risk of Symptomatic Intracerebral Hemorrhage With Tissue Plasminogen Activator for the Patient in Case 10-3 30-Day Death or Symptomatic Modified Intracerebral Modified Modified Modified Rankin Prediction Hemorrhage Rankin Scale Rankin Scale Rankin Scale 30-Day Death or Scale Instrument Risk Score 0Y1 Score 0Y2 Score 5Y6 Institutionalization Score 92 DRAGON

NA

StrokeYTPI

5%

5%

HAT

15%

13%

GRASPS

11%

SEDAN

21.7%

IScore

11.2%

0%

70% 64%

13%

62%

89%

98%

DRAGON = dense middle cerebral artery sign or early infarct on CT, baseline modified Rankin Scale, age, glucose, onset-to-treatment time, NIH Stroke Scale score; NA = not applicable; Stroke-TPI = Stroke-Thrombolytic Predictive Instrument; HAT = hemorrhage after thrombolysis score; GRASPS = glucose at presentation, race, age, sex, systolic blood pressure at presentation, and severity of stroke at presentation]; SEDAN = sugar, early infarct sign, hyperdense middle cerebral artery, age, neurologic deficit score; IScore = Ischemic Stroke Predictive Risk Score.




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TIA and Stroke Outcomes KEY POINTS

h While the ABCD2 score is widely used for stratifying risk of stroke recurrence in TIA patients, some studies have suggested a limited predictive value of the ABCD2.

h The identification of diffusion-weighted imagingYpositive strokes in TIA patients significantly increases the risk of recurrent stroke.

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with suspected TIA should be evaluated as soon as possible after an event. Estimating the Risk of Stroke After TIA While the ABCD2 score is widely used for stratifying risk of stroke recurrence in TIA patients, some studies have suggested a limited predictive value of the ABCD2.38 Recurrent strokes are observed in some patients with low ABCD2 scores. Several validation studies of the ABCD2 have been performed, yet they have not demonstrated C statistics of 0.8 or greater.39 Because of these limitations of the ABCD2 score, several studies have examined the ability of imaging to improve its predictive ability. The identification of diffusion-weighted imaging (DWI)Ypositive strokes in patients with TIA significantly increases the risk of recurrent stroke.40Y42 The 7-day risk of recurrent stroke in patients with an ABCD2 score of 6 to 7 was 12.5% when DWI-positive lesions were seen on MRI, whereas it was only 0.4% in patients without these lesions.40 Similarly, patients with a low ABCD2 score but with DWIpositive lesions on MRI were at a similar risk of stroke as those with high ABCD2 scores but without DWI-positive lesions. By including imaging as part of the score the C statistic of the ABCD2 score improves from approximately 0.66 to 0.8, indicating a very good ability to discriminate patients at risk of stroke.40Y42 This ABCD2 score incorporating stroke imaging is referred to as the ABCD2I score. Recent studies have also incorporated dual and multiple events into the prediction scores, and/or the presence of carotid stenosis.43 Incorporating this additional information improves the C statistic to greater than 0.8,43 which has been validated in a Chinese population.44 However, since MRI is many times not immediately available prior to making admission decisions, the utility of this approach is reduced.

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Secondary prevention after ischemic stroke or transient ischemic attack.

Pioglitazone after Ischemic Stroke or Transient Ischemic Attack.

Screening for diabetes after stroke and transient ischemic attack.

Metabolomics predicts stroke recurrence after transient ischemic attack.

Dual antiplatelet therapy after noncardioembolic ischemic stroke or transient ischemic attack: pros and cons.

Occurrence and predictors of persistent impaired glucose tolerance after acute ischemic stroke or transient ischemic attack.

In-hospital stroke recurrence and stroke after transient ischemic attack: frequency and risk factors.

Transient ischemic attacks: predictability of future ischemic stroke or transient ischemic attack events.

Minor Stroke and Transient Ischemic Attack: Research and Practice.

Silent atrial fibrillation after ischemic stroke or transient ischemic attack: interest of continuous ECG monitoring.

Blood Biomarkers in Minor Stroke and Transient Ischemic Attack.

Stroke subtypes and interventional studies for transient ischemic attack.

Readmission for infective endocarditis after ischemic stroke or transient ischemic attack.

Safety and feasibility of post-stroke care and exercise after minor ischemic stroke or transient ischemic attack: MotiveS & MoveIT.

Predicting asymptomatic coronary artery disease in patients with ischemic stroke and transient ischemic attack: the PRECORIS score.

Clopidogrel With Aspirin in Acute Minor Stroke or Transient Ischemic Attack (CHANCE) Trial: One-Year Outcomes.

Association between hospitalization and care after transient ischemic attack or minor stroke.

Cost burden of stroke mimics and transient ischemic attack after intravenous tissue plasminogen activator treatment.

Risk factors for incident dementia after stroke and transient ischemic attack.

Exercise stress testing after stroke or transient ischemic attack: a scoping review.

Location of Cerebral Microbleeds May Predict Subsequent Stroke after Transient Ischemic Attack.

Antithrombotic selection and risk factor management in ischemic stroke and transient ischemic attack.

The Effects of Statins on Infections after Stroke or Transient Ischemic Attack: A Meta-Analysis.

Copeptin and Long-Term Risk of Recurrent Vascular Events After Transient Ischemic Attack and Ischemic Stroke: Population-Based Study.