- Email: [email protected]
A Blood-based Biomarker Panel to Detect Acute Stroke
A Blood-Based Biomarker Panel to Detect Acute Stroke Richa Sharma, MD, MPH,* Stephanie Macy, BA,† Kara Richardson, BA,† Yuliya Lokhnygina, PhD,‡ and Daniel T. Laskowitz, MD, MHS*†‡
Background: The aim of this study was to develop an adjunctive, peripheral biomarker test to differentiate ischemic strokes, intracranial hemorrhages (ICHs), and stroke mimics in the acute setting. Methods: Serum samples were collected from 167 patients who presented with an acute neurologic deficit within 24 hours of symptom onset. Patients were adjudicated to ischemic stroke, ICH, and mimic pathology groups based on clinical and radiographic findings. Samples were tested for levels of 262 potential markers. A multivariate Cox proportional hazards regression model of 5 biomarkers was built by stepwise selection and validated by bootstrapping. Its discriminative capacity was quantified by C index and net reclassification improvement (NRI). Results: The final model consisted of eotaxin, epidermal growth factor receptor, S100A12, metalloproteinase inhibitor-4, and prolactin. It demonstrated a discriminative capacity for ischemic stroke versus mimic (C 5 .92), ischemic stroke and ICH versus mimic (C 5 .93), and ischemic stroke versus ICH (C 5 .82). The inclusion of biomarkers to a model consisting of age, race, and gender resulted in an NRI of 161% when detecting ischemic stroke versus mimic (P ,.0001), an improvement of 171% when detecting ischemic strokes plus ICH versus mimic (P ,.0001), and an improvement of 56% when detecting ischemic strokes versus ICH (P 5.1419). Conclusions: These results suggest that information obtained from a 5-biomarker panel may add valuable information in the early evaluation and management of patients with stroke-like symptoms. Key Words: Ischemic stroke— diagnostic biomarkers—stroke mimic—intracranial hemorrhage. Ó 2013 by National Stroke Association
Introduction Stroke is the fourth leading cause of death in the United States and remains the sixth most common cause of lost From the *Duke University School of Medicine, Durham, North Carolina; †Department of Medicine (Neurology), Duke University Medical Center, Durham, North Carolina; and ‡Duke Clinical Research Institute, Durham, North Carolina. Received June 4, 2013; revision received July 17, 2013; accepted July 25, 2013. Funding: The study was subsidized by a research grant from Astute Medical, Inc. The Duke University Stead Scholarship was also a funding source. Disclosures: The authors served as site investigators in this study ,and D.T.L. has served as a scientific consultant for Astute Medical, Inc., which performed the antibody-based assays used in this study. Address correspondence to Daniel T. Laskowitz, MD, MHS, Department of Medicine, Duke University School of Medicine, Box 2900, Durham, NC 27710. E-mail: [email protected]. 1052-3057/$ - see front matter Ó 2013 by National Stroke Association http://dx.doi.org/10.1016/j.jstrokecerebrovasdis.2013.07.034
disability-adjusted life-years globally.1,2 A number of conditions present with stroke-like symptoms and timely diagnosis of the etiology of an acute neurologic deficit is critical to make appropriate management decisions. The only approved pharmacologic intervention for acute ischemic stroke is recombinant tissue plasminogen activator (rt-PA).3 However, administration of r-tPA is time limited, and diagnostic uncertainty may contribute to underutilization of this therapy.4 Although 99% of emergency physicians believe rt-PA should be the standard of care for acute stroke, two thirds feel uncomfortable making the diagnosis and administering rt-PA without a neurologic consultation.4 In addition to ischemic stroke, there are a number of neurologic conditions that may present with similar clinical presentations, include transient ischemic attack (TIA), intracranial hemorrhage (ICH), and subarachnoid hemorrhage (SAH), and nonvascular mimics such as postictal paresis, complex migraine, toxic–metabolic conditions, peripheral neuropathy, mass lesions, and conversion disorder.
Journal of Stroke and Cerebrovascular Diseases, Vol. -, No. - (---), 2013: pp 1-9
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Once in the hospital, there are established diagnostic algorithms for ICH and SAH as computed tomography (CT) is quite sensitive for the detection of acute intracranial blood. However, the diagnosis of acute stroke is still largely clinical. Standardized diagnostic scales are helpful in the preclinical setting and in hospitals without a neurologist on site. For example, the Recognition of Stroke in the Emergency Room Scale and the Face Arm Speech Test5,6 have utility in these settings; however, these tools demonstrate suboptimal specificity.5,6 Adjunctive imaging also plays an important role in stroke diagnosis. However, radiographic studies such as noncontrasted CT and magnetic resonance imaging (MRI) vary in terms of discriminative capacity and interobserver reliability.7,8 Furthermore, the technology and expertise necessary to operate and interpret sophisticated multimodal imaging may not be universally available in the emergent setting. Ultimately, a significant number of patients initially diagnosed with stroke suffer from stroke mimics such as seizures, systemic infection, brain tumors, and toxic– metabolic syndromes, for which thrombolytic therapy is not appropriate and may potentially be harmful.9 Given these limitations, there is a need for a rapid test to expedite the diagnosis and guide early management decisions in suspected acute cerebrovascular ischemia. One possibility is a blood-based test measuring biomarkers associated with stroke and cerebral injury. Precedents for this approach exist for emergent medical conditions such as the use of troponin for myocardial infarction, D-dimer for pulmonary embolism, and B-type natriuretic factor for congestive heart failure.10-12 To date, no single stroke biomarker with clinically useful accuracy has been identified in the setting of acute stroke. Challenges involve the heterogeneity of central nervous system cell populations and their abilities to withstand ischemia, the complexity of inflammatory and secondary injury cascades, and the presence of the blood–brain barrier.13 Thus, one approach has been to simultaneously evaluate a number of analytes associated with acute stroke. These panels incorporate biomarkers that are reflective of different aspects of the ischemic cascade. Although early work in this area has been promising and has demonstrated the feasibility of this approach, one of the few large multicenter stroke biomarker trials evaluating the use of matrix metalloproteinase-9, brain natriuretic protein, D-dimer, and S100B failed to establish a discriminative capacity that was optimal for clinical use.14 Thus, the aim of this study is to identify a panel of biomarkers differentiating ischemic strokes, ICHs, TIAs, and mimics within 24 hours of symptom onset. We hypothesize that because acute stroke is associated with inflammation, oxidative stress, primary and secondary neuronal damage, and compromise of vascular integrity, markers of these processes will be differentially represented in patients with ischemic strokes, ICH, TIAs, and mimics.
Methods Study Design and Participants This study was approved by the Duke Institutional Review Board. Patients were enrolled from August 2007 to February 2012 at the Duke University Medical Center. Inclusion criteria consisted of age 18 years or older, no recent history of trauma, documented brain imaging, and having blood draw within 24 hours of symptom onset. As per Duke IRB policy, exclusion criteria included hemoglobin less than 12.5 g/dL for women and 13.5 g/dL for men, untreated systolic blood pressure less than 90 mm Hg, and untreated diastolic blood pressure less than 50 mm Hg. Consent forms were signed by the patient or a legal representative. Blood samples were obtained from venous puncture or from an existing vascular catheter using sterile technique, collected in EDTAcontaining tubes, and centrifuged within 1 hour of collection at 10,000 g. The supernatant serum was frozen at 280 C and sent to Astute Medical, Inc., San Diego, CA, to determine the biomarker levels. Demographic, clinical, laboratory, and radiographic data were extracted from electronic medical records into case report forms. All cases were adjudicated by 2 physicians (D.T.L. and R.S.) who were blinded to biomarker results. Stroke was defined as a persistent neurologic deficit in a vascular distribution lasting 24 hours or more or less than 24 hours but with CT and/or MRI confirmation. The diagnosis of ICH (excluding SAH) was based on CT findings. All radiology studies were performed by a clinical neuroradiologist blinded to biomarker values as part of standard clinical practice. National Institutes of Health Stroke Scale scores and pertinent demographic information were collected by the clinical co-ordinator at study entry. Patients with a demonstrated nonvascular condition as a source of their symptoms and negative imaging were adjudicated as stroke mimics. Patients with neurologic findings lasting less than 24 hours in a vascular distribution, negative radiography, and negative work up for mimic conditions were adjudicated as TIA. Each sample was evaluated for 262 biomarkers, which were a priori identified as being potentially relevant to metabolic cascades associated with cerebral ischemia, coagulation, endothelial dysfunction, neuronal injury, inflammation, gliosis, necrosis, or infection (Appendix).
Statistical Analysis Statistical analysis was performed using SAS 9.3(SAS, Cary, NC). Descriptive statistics were obtained for demographic and clinical variables in each pathologic group: ischemic stroke, ICH, TIA, and mimic. Variable distributions were compared across groups by 1-way analysis of variance tests if the variable was continuous and by c2 tests if categorical.
A BLOOD-BASED BIOMARKER PANEL TO DETECT ACUTE STROKE
Univariate logistic regressions were conducted with each biomarker to predict the likelihood of ischemic stroke versus mimic. This was considered the base model. To address the uncertainty of whether TIA was associated with cellular injury cascades, ischemic stroke versus TIA 1 mimic and ischemic stroke 1 TIA versus mimic models were created as well. Finally, we created an independent model to differentiate ischemic stroke 1 ICH versus mimic as this would reflect the clinical scenario in the prehospital setting before a CT could be obtained to rule out ICH. Biomarkers significant in univariate logistic regression in each group with a P value of .2 or less were retained and used to build multivariate logistic regression models. A stepwise selection method was employed to build the most parsimonious multivariate models maximizing predictive power and minimizing the number of covariates. Variables were entered and retained in the model if the chi-square statistic was significant at P less than .05. The final variables were tested for multicollinearity by testing linear correlation (r . .8) and variance inflation factor (.2.5). The area under the curve of the receiver operating characteristic (ROC) curve, captured by the concordance index or C statistic, was used as a measure of the overall discriminative capacity of each model. To internally validate this model, a SAS macro was adapted to bootstrap the parameter estimates and the C statistic of the base model by generating 50 balanced re-samples. To create 1 overall model, biomarkers significant in the multivariate models of ischemic stroke 1 TIA versus mimic, ischemic stroke versus TIA 1 mimic, ischemic stroke versus ICH, and ischemic stroke 1 ICH versus mimic were added to the base model incrementally. Biomarkers contributing at least a 1% increase in the C statistic discriminating ischemic stroke versus mimic were retained. The variables in the final model were used to generate bootstrapped C statistics for the following comparisons: ischemic stroke versus mimic, ischemic stroke versus ICH, and ischemic stroke 1 ICH versus mimic. Sensitivities, specificities, positive predictive values, and negative predictive values (NPVs) were determined for the probability of each outcome at which sensitivity is maximized with specificity of at least 50%. These statistics were calculated at an outcome probability of 30%, reflecting the approximate prevalence of stroke among the patients who present to emergency rooms with an acute neurologic deficit.15 Net reclassification improvement (NRI) is a measure used to quantify improvement in model performance after adding new variables to existing models. Increases in C indices with the addition of markers can be relatively small in magnitude if a very powerful factor already exists in the model, and NRI has been proposed as an additional, possibly more intuitive metric of added usefulness of additional biomarkers.16 NRI is expressed as difference in percent of patients with improved reclassification and
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percent of patients with worsened reclassification, where patients are reclassified ‘‘upward’’ (‘‘downward’’) if the predicted risk estimated using the new model is higher (lower) compared with the reference model. Values of NRI greater than 0 indicate the overall improvement in reclassification using the model with new biomarkers.17 Given that this is an exploratory study and meaningful risk categories have not been defined in the literature, a continuous (category less) NRI was undertaken to summarize the incremental value of biomarkers.17 In this analysis, a 11 was assigned to each patient with an event who moved up in estimated risk, 21 to those with an event who moved down, and 0 to those with no change in estimated risk. To test the robustness of this final model in face of the diagnostic uncertainty of the TIA group, the bootstrapped parameter estimates and intercept of the stroke versus mimic comparison were used to predict TIA 1 stroke versus mimic and stroke versus TIA 1 mimic. The model was also applied to test its ability to detect posterior circulation ischemic strokes versus mimics and its robustness in predicting ischemic stroke versus mimic in patients who presented within the clinically relevant time window of 4.5 hours.
Results Baseline Characteristics The sera of 167 patients were analyzed in this study. The adjudicated diagnoses included 57 ischemic strokes (34.1%), 32 ICH (19.2%), 41 TIA (24.6%), and 37 mimics (22.2%). All patient received at least a noncontrast head CT and further MRI testing if indicated. The major diagnoses on discharge among stroke mimic patients were migraines (27%), Bell’s palsy (10%), psychiatric disorder (10%), angina (10%), blood glucose derangement (10%), polypharmacy (7%), and peripheral neuropathy (7%). Table 1 depicts the demographic and health status characteristics of each pathology group. In this sample, the mean age was 64.4 6 15.2 years, and the average age among patients within each pathologic group was not significantly different at (P 5 .091). The proportion of patients of each gender did not vary as a function of pathology (P 5 .13). Among those with ICH, blacks and people of other races were over-represented. The proportion of patients with atrial fibrillation was higher in both the ischemic stroke and TIA groups (P 5 .044). Table 2 contains the most common presenting symptoms. A greater proportion of patients with ischemic strokes and TIAs was affected by speech/language difficulty (P 5 .0001). Patients with ischemic strokes were more likely to suffer from motor deficits than patients with other diagnoses (P 5 .0013). Patients with ischemic strokes and ICH were predominantly more likely to exhibit a change in mental status (P , .001). The average NIH stroke scale score was significantly higher in
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Table 1. Demographic and health status characteristics Characteristic
Ischemic stroke, N 5 56
ICH, N 5 32
TIA, N 5 41
Mimic, N 5 37
P value
Age (mean, SD) Males Race White Black 1 other Comorbidities Hypertension Diabetes mellitus Hyperlipidemia Atrial fibrillation Prior stroke/TIA Smoking Crack/cocaine
66.9 (15.6) 32 (56.1)
64.7 (13.9) 19 (59.4)
63.1 (15.9) 20 (48.8)
61.8 (15.0) 13 (35.1)
.0914 .1339 .0152
37 (64.9) 20 (35.1)
12 (37.5) 20 (62.5)
30 (73.2) 11 (26.8)
21 (56.8) 16 (43.2)
47 (82.5) 16 (28.1) 16 (28.1) 11 (19.3) 13 (22.8) 15 (26.3) 3 (5.3)
26 (81.3) 9 (28.1) 8 (25.0) 1 (3.1) 15 (46.9) 4 (12.5) 3 (9.4)
30 (73.2) 14 (34.2) 12 (29.3) 8 (19.5) 13 (31.7) 9 (22.0) 1 (2.4)
30 (81.1) 14 (37.8) 12 (32.4) 2 (5.4) 12 (32.4) 7 (18.9) 0 (.0)
.6972 .7310 .9226 .0443 .1392 .4777 .2371
Abbreviations: ICH, intracranial hemorrhage; TIA, transient ischemic attack. Significance was determined by c2 tests for categorical variables and 1-way analysis of variance for continuous variables.
ischemic strokes (mean 6.2) and ICH (mean 10.2) as compared with TIA or mimic (mean 1.5 and 2.1, respectively). The median time from symptom onset to serum biomarker draw was 6.7 hours for ischemic stroke, 7.7 hours for TIA, 7.1 hours for mimics, and 14.3 hours for ICH. Nearly 98% of patients received brain imaging that included CT, and 2% received MRI only. Among patients who had ischemic stroke or TIA, 74 patients (44.1%) had
both CT and MRI. Among patients who had ICH, 100% of patients had a CT. The average ABCD2 score for the TIA sample was 4.39 6 1.16. The Trial of Org 10172 in Acute Stroke Treatment classification system categorizes the etiology of a stroke.18 Most stroke events were because of an undetermined cause (40.4%) and secondary to atherosclerosis (35.1%), whereas 12.3% were because of a cardioembolic
Table 2. Clinical and radiographic characteristics Characteristic Symptoms Speech/language Motor deficits Sensory deficits Visual deficits Altered mental status Headache NIHSS at enrollment GCS (initial) t-PA administered ABCD2 score TOAST criteria 1. Atherosclerosis 2. Cardioembolic 3. Small-vessel occlusion 4. Other determined cause 5. Undetermined Vascular distribution Anterior Lacunar Posterior .1 Distribution
Ischemic stroke, N 5 56
ICH, N 5 32
TIA, N 5 41
Mimic, N 5 37
P value
41 (71.9) 46 (80.7) 21 (36.8) 12 (21.1) 15 (26.3) 12 (21.1) 6.8 (6.8) 13.9 (2.3) 21 (33.3) —
8 (25.0) 16 (50.0) 5 (15.6) 5 (15.6) 16 (50.0) 10 (31.3) 10.2 (7.9) 12.8 (3.1) — —
25 (61.0) 23 (56.1) 21 (51.2) 6 (14.6) 1 (2.4) 13 (31.7) 1.5 (2.5) 15 (.0) 2 (4.9) 4.4 (1.2)
16 (43.2) 22 (59.5) 14 (37.8) 9 (24.3) 5 (13.5) 13 (35.1) 2.1 (2.5) 14.8 (.6) 2 (5.4) —
.0001 .0113 ,.0001 .6664 ,.0001 .4474 ,.0001 .0025 ,.0001
20 (35.1) 7 (12.3) 1 (1.8) 6 (10.5) 23 (40.4)
— — — — —
— — — — —
— — — — —
36 (63.2) 6 (10.5) 9 (15.8) 2 (3.5)
— — — —
— — — —
— — — —
Abbreviations: GCS, Glasgow Coma Scale; ICH, intracranial hemorrhage; NIHSS, National Institutes of Health Stroke; TOAST, Trial of Org 10172 in Acute Stroke Treatment; t-PA, tissue plasminogen activator; TIA, transient ischemic attack. Significance was determined by c2 tests for categorical variables and 1-way analysis of variance for continuous variables.
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Table 3. Multivariate logistic regressions comparing ischemic stroke 1 TIA versus mimic, ischemic stroke versus TIA 1 mimic, ischemic stroke versus mimic, ischemic stroke 1 ICH versus mimic, and ischemic stroke versus ICH Biomarker Ischemic stroke 1 TIA vs. mimic Platelet endothelial cell adhesion molecule Interleukin-6 TNF receptor superfamily member 1B (v1) Overall model C statistic Ischemic stroke vs. TIA 1 mimic Granulocyte-colony stimulating factor Prostate-specific antigen Protein S100A12 Overall model C statistic Ischemic stroke vs. mimic Eotaxin EGFR (v1) Overall model C statistic Ischemic stroke 1 ICH vs. mimic Eotaxin EGFR (v1) Metalloproteinase inhibitor-4 Endothelial cell adhesion molecule Prolactin Overall model C statistic Ischemic stroke vs. ICH Calbindin Urokinase-type plasminogen activator Metalloproteinase inhibitor-2 Overall model C statistic
AUC
P value
.596
.0315
.596 .563
.0957 .1065
.857 .611
.0606
.603 .623 .868
.1467 .0376
.812 .818 .868
,.0001 .0123
.829 .732 .604 .540
,.0001 .0011 .0425 .1607
.666 .941
.0019
.628 .708
.0471 .0045
.724 .939
.0871
Abbreviations: AUC, area under the curve; EGFR, epidermal growth factor receptor; ICH, intracranial hemorrhage; TIA, transient ischemic attack; TNF, tumor necrosis factor. Biomarkers significant in multivariate logistic regressions derived by stepwise selection.
event. Among patients with ischemic strokes, nearly 63% occur in the anterior circulation, 11% comprise a lacunar infarct, and about 15% occur in the posterior circulation.
Model Selection A number of biomarkers predicted the likelihood of stroke versus mimic, stroke versus TIA 1 mimic, stroke 1 TIA versus mimic, stroke versus ICH, and stroke 1 ICH versus mimic in univariate logistic analyses at an alpha of .2 (Supplementary Tables 1 and 2). The logodds ratio of ischemic stroke versus mimic in multivariate analysis was predicted well by 2 biomarkers: eotaxin (P , .0001) and epidermal growth factor receptor (EGFR) v1 (P 5 .0123) with a C statistic of .868. Variables
Figure 1. Calibration curves. Observed versus predicted probabilities of each outcome predicted by the 5-variable model with corresponding linear best-fit curves and R2 values. Observed versus predicted probability of (A) ischemic stroke, (B) ischemic stroke or ICH, (C) ICH.
significant by multivariate regression in the TIA and ICH analyses are displayed in Table 3. The final model consisted of 5 variables that added the most incremental discriminative value: eotaxin, EGFR, S100A12, metalloproteinase inhibitor-4 (TIMP-4), and prolactin. Calibration curves are shown in Figure 1.
Measures of Discrimination Table 4 displays the parameter estimates, P values, and c statistics of the model when applied to differentiate ischemic stroke versus mimic (c 5 .90), ischemic stroke 1 ICH versus mimic (c 5 .91), and ischemic stroke
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Table 4. Characteristics of the 5-variable model predicting ischemic stroke vs. mimic, ischemic stroke 1 ICH vs. mimic, and ischemic stroke versus ICH Ischemic stroke vs. Mimic
Ischemic stroke 1 ICH vs. Mimic
Ischemic stroke vs. ICH
Biomarker
Parameter estimate
P
Parameter estimate
P
Parameter estimate
P
Eotaxin EGFR (v1) TIMP-4 Prolactin S100A12 Intercept C statistic
2.0024 2.0016 .0003 2.0001 .0872 3.5875 .901
.0003 .0781 .2520 .1658 .0310 .0054
2.0026 2.0016 .0003 2.0001 .0894 3.7229 .912
.0002 .0761 .2287 .1504 .0285 .0038
.0048 .0002 2.0003 .0003 .0120 .0762 .856
.2153 .8964 .1471 .1272 .6004 .9746
Abbreviations: EGFR, epidermal growth factor receptor; ICH, intracranial hemorrhage; Parameter estimates and P values associated with the c2 of the 5-variable logistic model in the setting of each comparison as well as each model’s C statistic.
versus ICH (c 5 .86). The model was internally validated by bootstrapping C statistics, yielding the following results: ischemic stroke versus mimic (c 5 .92), ischemic stroke 1 ICH versus mimic (c 5 .93), and ischemic stroke versus ICH (c 5 .82). Figure 2 presents the ROC curves of each of the 3 models in comparison with the ROC curves generated from patients’ age, gender, and race. In each analysis, the ROC curves created from the covariates of age, gender, and race generated areas under the curve less than models that included biomarkers and those that included demographic data plus biomarkers. Given the stroke prevalence of 38% in our population tested, the final panel was associated with a sensitivity of 90%, specificity of 84%, positive predictive value of 78%, and NPV of 93% in differentiating stroke from mimic.
Reclassification As seen in Table 5, continuous NRI metric was used to quantify the degree of correct reclassification from the reference model. When examining the ischemic stroke versus mimic comparison, the NRI associated with the 5 biomarkers plus age, race, and gender as opposed to age, race, and gender alone was significant (NRI 5 161%, P , .0001). Similar proportional impacts were seen when the 5 biomarkers plus age, race, and gender model was compared with the 5-biomarker model (NRI 5 113%, P , .0001). This pattern was seen again when the 5 biomarkers plus age, race, and gender were compared with the 3 demographic variable model in discriminating ischemic strokes 1 ICH versus mimics (NRI 5 171%, P ,.0001). However, when discriminating between ischemic strokes versus ICH, the NRI with biomarkers plus age, race, and gender as opposed to the demographic variables was no longer statistically significant (NRI 5 56%, P 5 .1419). When the biomarkers plus demographic variable model was compared with the model incorporating biomarkers
alone, the NRI was 65%, and it was marginally nonsignificant at a P value of .093.
Subgroup Analyses Diagnostic uncertainty is often greatest in the acute setting when CT is insensitive to ischemic changes and in patients with posterior circulation ischemia. To address this, we tested the 5-variable model in these patient subsets. Parameter estimates from the 5-variable model predicting ischemic stroke versus mimic were applied to a subgroup of patients to detect posterior circulation stroke versus mimic. It yielded a C statistic of .71 and eotaxin and S100A12 demonstrated Wald chi-squares with statistical significance of P 5 .0006 and .0331, respectively. The parameter estimates from the 5-variable model maintained discriminative significance when applied to a subgroup of patients who were enrolled within 4.5 hours of presentation (N 5 28) to predict ischemic stroke versus mimic (c 5 .79).
Discussion The need for a rapid, blood-based test for stroke is significant given the diagnostic uncertainty that surrounds patients presenting with acute neurologic deficits. In this study, appropriately adjudicated comparison groups of patients with similar symptoms and vascular comorbidities were included to evaluate a large number of potential biomarkers in a panel approach, allowing combinations of biomarkers with relatively high sensitivity and specificity. We found 5 biomarkers (eotaxin, EGFR, S100A12, TIMP-4, and prolactin) that accurately differentiated acute cerebrovascular injury from stroke mimics. To our knowledge, this is the first study to explore such a wide array of potential biomarker candidates to develop a multivariate model predicting ischemic strokes, ICHs, TIAs, and mimics. Biomarkers that had been previously identified as correlating with stroke,
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Figure 2. Receiver operating characterisitc curves. Discriminating capacity of 3 sets of predictors including (1) red: age, race, sex; (2) green: the 5 tested biomarkers; and (3) black: age, race, and sex plus the 5 tested biomarkers. The comparison groups were the following: (A) ischemic stroke versus mimic, (B) ischemic stroke 1 ICH versus mimic, and (C) ischemic stroke versus ICH. The c statistic for each model is overlaid. Abbreviation: ICH, intracerebral hemorrhage.
such as MMP-9, D-dimer, and BNP19 remained significant in univariate analyses, but in our current model, only S100 remained significant in multivariate analyses.19 Although several of these biomarkers have not been previously described in the context of stroke, they have known pathophysiologic roles that lend biologic plausibility to their statistical association with stroke. Eotaxin is a potent chemokine for eosinophils and other inflammatory cells.20 Treatment of vascular endothelial cells with tumor necrosis factor-a resulted in a 20-fold induc-
tion of smooth muscle expression of eotaxin in atheroma and an increase in macrophage and mast cell expression of CCR3, its receptor, suggesting that eotaxin recruits inflammatory cells in atheromas.21 Another study demonstrated an increase in eotaxin in ischemic stroke patients versus healthy controls.22 EGFR and its ligands may play a role in regulation of genes associated with reactive gliosis.23 After an ischemic event, proliferating glial cells abundant in the infarcted brain and astrocytes in the periphery of the infarct were highly immunoreactive to
Table 5. Continuous net reclassification criteria for models including demographic variables and biomarkers
Model
C index
NRI, %
95% CI for NRI
Event: ischemic stroke nonevent: mimic Age/race/sex .671 — — 1Biomarkers .970 161 (117, 205) Biomarkers .901 — — 1Age/race/sex .970 113 (69, 157) Event: ischemic stroke 1 ICH nonevent: mimic Age/race/sex .677 — — 1Biomarkers .973 171 (128, 214) Biomarkers .912 — — 1Age/race/sex .973 113 (69, 156) Event: ischemic stroke nonevent: ICH Age/race/sex .653 — — 1Biomarkers .905 056 (218, 131) Biomarkers .856 — — 1Age/race/sex .905 65 (29, 139)
P value
% Patients reclassified upward among nonevents
% Patients reclassified downward among nonevents
% Patients reclassified upward among events
% Patients reclassified downward among events
— ,.0001 — ,.0001
6.5 — 32.3
— 93.5 — 67.7
— 87.0 — 88.7
— 13.0 — 11.3
— ,.0001 — ,.0001
3.2 — 32.3
— 96.8 — 67.7
— 88.7 — 88.5
— 11.3 — 11.5
— .1419 — .093
— 62.5 — 25.0
— 37.5 — 75.0
— 90.7 — 57.4
— 9.3 — 42.6
Abbreviations: CI, confidence interval; ICH, intracranial hemorrhage; NRI, net reclassification improvement. C statistics, NRI, and proportions of patients among those with events and those with no events that were reclassified upward or downward, compared with the reference model. The biomarkers include all 5 biomarkers. The P values correspond to the test of NRI 5 0.
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EGFR. As a chemoattractant for monocytic cells, S100A12 is an inflammatory response mediator.24 Its receptor, RAGE (receptor for advanced glycation end products), activates transcription factors that are protective from oxidative stress. S100A12 induces neurite outgrowth from rat embryonic hippocampal cells.25 Plasma levels of S100A12 were higher in patients with carotid atherosclerosis and highest in patients with most recent symptoms.26 TIMP-4 irreversibly inactivates metalloproteinases and is expressed in astrocytes, monocytes, platelets, smooth muscle cells, and endothelial cells.27-29 It is involved in regulating platelet recruitment and aggregation.29 Prolactin is associated with platelet activation as P-selectin expression and platelet aggregation increase in its presence and in acute ischemic stroke patients.30,31 In this study, we categorized TIAs as a diagnosis of exclusion. The 5-variable model demonstrated robustness when TIAs were grouped with ischemic strokes, suggesting that the pathophysiologic effects of a TIA are more similar to that of strokes than mimics, even when there is no residual clinical deficit. This might be expected because the enrolled TIA patients were at high risk, as reflected by the high average ABCD2 score of 4.4 (8% corresponding 90-day risk of stroke).32 Thus, in future study design, high-risk TIAs may be grouped with stroke. Nearly 3%-5% of patients diagnosed with TIA in the emergency department (ED) will have strokes within the next 48 hours.33 Thus, a biomarker panel which diagnoses high-risk TIAs may guide the decision to hospitalize patients with high stroke risk. To be clinically useful in the ED setting, a biomarkerbased test would need to have a sufficiently high NPV to exclude cerebrovascular ischemia in a low-risk patient with atypical symptoms. Assuming that 30% of patients’ acute neurologic deficits being tested have a true diagnosis of stroke as has been suggested in several reports,15 the proportion of patients with a negative test who are correctly diagnosed by the biomarker test coupled with information about age, gender, and race is 93% when comparing ischemic stroke with mimics; the NPV increases to 95% if the overall prevalence of stroke is 10% in the population tested. There were a number of limitations to the present study that should be considered. The volume of plasma was less than 10 mL in some cases, and as a result, not all 262 biomarker levels were measured in all patients. Additionally, the sample size was not large enough to perform external validation to ensure that the parameter estimates were unbiased and generalizable. Of note, the median latency for enrollment after ICH was significantly longer than for stroke or TIA. This likely reflects the fact that the study was performed in a large academic medical center that received a significant number of ICH on transfer from smaller community hospitals, whereas most stroke and TIA patients presented primarily to the Duke ED. In the
current analysis, we only included the initial timepoint for analysis, thus precluding the ability to determine temporal profiles of biomarkers after stroke. There are a number of challenges associated with clinical implantation of a biomarker-based stroke diagnostic test. To guide early management decisions, a stroke diagnostic must be sensitive to early ischemia. Although our analysis of patients presenting within the 4.5-hour window suggested that the biomarker panel retained its diagnostic accuracy, this would need to be confirmed with larger patient numbers. Moreover, to achieve clinical utility, a biomarker-based test for stroke must be demonstrated to be cost effective and have rapid turnaround time. Although such a test might provide important adjunctive clinical information for early identification of ischemic stroke, given the ready availability of CT at most centers, it will not supplant initial imaging to exclude ICH, especially when administration of tPA is contemplated. Thus, although a test to identify ICH may be useful in the prehospital setting, it is extremely unlikely that the primary role of a biomarker-based test will be to differentiate ischemic from hemorrhagic stroke. Moreover, such a test will neither replace a careful clinical exam and history in determining eligibility for receiving a reperfusion intervention nor provide the detailed anatomical vascular information offered by conventional and noninvasive angiographic techniques. Nevertheless, when used in conjunction with clinical, historical, and radiographic data, a blood-based biomarker can inform decisions at several junctions. As a point-of-care device, paramedical personnel might use this to supplement information to expedite triage and transportation to a stroke center. In the ED, the panel’s high sensitivity may enhance data from a negative CT and conventional MRI to provide closer follow-up and care of a patient with atypical symptoms who might otherwise be sent home. The current data suggest the potential utility of a panel-based biomarker approach to aid in the diagnosis of acute cerebrovascular syndromes and the importance of performing future prospective validation studies in this area.
Supplementary material Supplementary data related to this article can be found at http://dx.doi.org/10.1016/j.jstrokecerebrovasdis.2013. 07.034.
References 1. Roger VL, Go AS, Lloyd-Jones DM, et al. Heart disease and stroke statistics—2012 update: a report from the American Heart Association. Circulation 2012;125: e2-e220. 2. Kelly-Hayes M, Beiser A, Kase CS, et al. The influence of gender and age on disability following ischemic stroke: the Framingham Study. J Stroke Cerebrovasc Dis 2003; 12:119-126.
A BLOOD-BASED BIOMARKER PANEL TO DETECT ACUTE STROKE 3. Donnan GA, Fisher M, Macleod M, et al. Stroke. Lancet 2008;371:1612-1623. 4. Scott PA, Xu Z, Meurer WJ, et al. Attitudes and beliefs of michigan emergency physicians toward tissue plasminogen activator use in stroke: baseline survey results from the increasing stroke treatment through interactive behavioral change tactic (instinct) trial hospitals. Stroke 2010;41:2026-2032. 5. Nor AM, Davis J, Sen B, et al. The recognition of stroke in the emergency room (rosier) scale: development and validation of a stroke recognition instrument. Lancet Neurol 2005;4:727-734. 6. Harbison J, Hossain O, Jenkinson D, et al. Diagnostic accuracy of stroke referrals from primary care, emergency room physicians, and ambulance staff using the face arm speech test. Stroke 2003;34:71-76. 7. Mullins ME, Schaefer PW, Sorensen AG, et al. Ct and conventional and diffusion-weighted mr imaging in acute stroke: study in 691 patients at presentation to the emergency department. Radiology 2002;224:353-360. 8. Patel MR, Edelman RR, Warach S. Detection of hyperacute primary intraparenchymal hemorrhage by magnetic resonance imaging. Stroke 1996;27:2321-2324. 9. Libman RB, Wirkowski E, Alvir J, et al. Conditions that mimic stroke in the emergency department. Implications for acute stroke trials. Arch Neurol 1995;52:1119-1122. 10. Adams JE III, Bodor GS, Davila-Roman VG, et al. Cardiac troponin i. A marker with high specificity for cardiac injury. Circulation 1993;88:101-106. 11. Moser KM. Diagnosing pulmonary embolism. BMJ 1994; 309:1525-1526. 12. Maisel AS, Krishnaswamy P, Nowak RM, et al. Rapid measurement of b-type natriuretic peptide in the emergency diagnosis of heart failure. N Engl J Med 2002; 347:161-167. 13. Kernagis DN, Laskowitz DT. Evolving role of biomarkers in acute cerebrovascular disease. Ann Neurol 2012; 71:289-303. 14. Reynolds MA, Kirchick HJ, Dahlen JR, et al. Early biomarkers of stroke. Clin Chem 2003;49:1733-1739. 15. Moulin T, Sablot D, Vidry E, et al. Impact of emergency room neurologists on patient management and outcome. Eur Neurol 2003;50:207-214. 16. Pencina MJ, D’Agostino RB Sr, D’Agostino RB Jr, et al. Evaluating the added predictive ability of a new marker: from area under the roc curve to reclassification and beyond. Stat Med 2008;27:157-172. discussion 207-112. 17. Pencina MJ, D’Agostino RB, Vasan RS. Statistical methods for assessment of added usefulness of new biomarkers. Clin Chem Lab Med 2010;48:1703-1711. 18. Adams HP Jr, Bendixen BH, Kappelle LJ, et al. Classification of subtype of acute ischemic stroke. Definitions for use in a multicenter clinical trial. TOAST. Trial of Org 10172 in Acute Stroke Treatment. Stroke 1993;24:35-41.
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19. Laskowitz DT, Kasner SE, Saver J, et al. Clinical usefulness of a biomarker-based diagnostic test for acute stroke: the biomarker rapid assessment in ischemic injury (brain) study. Stroke 2009;40:77-85. 20. Graziano FM, Cook EB, Stahl JL. Cytokines, chemokines, rantes, and eotaxin. Allergy Asthma Proc 1999;20:141-146. 21. Haley KJ, Lilly CM, Yang JH, et al. Overexpression of eotaxin and the CCR3 receptor in human atherosclerosis: using genomic technology to identify a potential novel pathway of vascular inflammation. Circulation 2000; 102:2185-2189. 22. Canoui-Poitrine F, Luc G, Mallat Z, et al. Systemic chemokine levels, coronary heart disease, and ischemic stroke events: the prime study. Neurology 2011;77:1165-1173. 23. Planas AM, Justicia C, Soriano MA, et al. Epidermal growth factor receptor in proliferating reactive glia following transient focal ischemia in the rat brain. Glia 1998;23:120-129. 24. Miranda LP, Tao T, Jones A, et al. Total chemical synthesis and chemotactic activity of human s100a12 (en-rage). FEBS Lett 2001;488:85-90. 25. Mikkelsen SE, Novitskaya V, Kriajevska M, et al. S100A12 protein is a strong inducer of neurite outgrowth from primary hippocampal neurons. J Neurochem 2001;79:767-776. 26. Abbas A, Aukrust P, Dahl TB, et al. High levels of s100a12 are associated with recent plaque symptomatology in patients with carotid atherosclerosis. Stroke 2012;43:1347-1353. 27. Olson TM, Hirohata S, Ye J, et al. Cloning of the human tissue inhibitor of metalloproteinase-4 gene (timp4) and localization of the timp4 and timp4 genes to human chromosome 3p25 and mouse chromosome 6, respectively. Genomics 1998;51:148-151. 28. Rorive S, Maris C, Debeir O, et al. Exploring the distinctive biological characteristics of pilocytic and low-grade diffuse astrocytomas using microarray gene expression profiles. J Neuropathol Exp Neurol 2006;65:794-807. 29. Radomski A, Jurasz P, Sanders EJ, et al. Identification, regulation and role of tissue inhibitor of metalloproteinases-4 (timp-4) in human platelets. Br J Pharmacol 2002; 137:1330-1338. 30. Wallaschofski H, Donne M, Eigenthaler M, et al. PRL as a novel potent cofactor for platelet aggregation. J Clin Endocrinol Metabol 2001;86:5912-5919. 31. Marquardt L, Ruf A, Mansmann U, et al. Course of platelet activation markers after ischemic stroke. Stroke 2002; 33:2570-2574. 32. Johnston SC, Rothwell PM, Nguyen-Huynh MN, et al. Validation and refinement of scores to predict very early stroke risk after transient ischaemic attack. Lancet 2007; 369:283-292. 33. Shah KH, Kleckner K, Edlow JA. Short-term prognosis of stroke among patients diagnosed in the emergency department with a transient ischemic attack. Ann Emerg Med 2008;51:316-323.
Background: The aim of this study was to develop an adjunctive, peripheral biomarker test to differentiate ischemic strokes, intracranial hemorrhages (ICHs), and stroke mimics in the acute setting. Methods: Serum samples were collected from 167 patients who presented with an acute neurologic deficit within 24 hours of symptom onset. Patients were adjudicated to ischemic stroke, ICH, and mimic pathology groups based on clinical and radiographic findings. Samples were tested for levels of 262 potential markers. A multivariate Cox proportional hazards regression model of 5 biomarkers was built by stepwise selection and validated by bootstrapping. Its discriminative capacity was quantified by C index and net reclassification improvement (NRI). Results: The final model consisted of eotaxin, epidermal growth factor receptor, S100A12, metalloproteinase inhibitor-4, and prolactin. It demonstrated a discriminative capacity for ischemic stroke versus mimic (C 5 .92), ischemic stroke and ICH versus mimic (C 5 .93), and ischemic stroke versus ICH (C 5 .82). The inclusion of biomarkers to a model consisting of age, race, and gender resulted in an NRI of 161% when detecting ischemic stroke versus mimic (P ,.0001), an improvement of 171% when detecting ischemic strokes plus ICH versus mimic (P ,.0001), and an improvement of 56% when detecting ischemic strokes versus ICH (P 5.1419). Conclusions: These results suggest that information obtained from a 5-biomarker panel may add valuable information in the early evaluation and management of patients with stroke-like symptoms. Key Words: Ischemic stroke— diagnostic biomarkers—stroke mimic—intracranial hemorrhage. Ó 2013 by National Stroke Association
Introduction Stroke is the fourth leading cause of death in the United States and remains the sixth most common cause of lost From the *Duke University School of Medicine, Durham, North Carolina; †Department of Medicine (Neurology), Duke University Medical Center, Durham, North Carolina; and ‡Duke Clinical Research Institute, Durham, North Carolina. Received June 4, 2013; revision received July 17, 2013; accepted July 25, 2013. Funding: The study was subsidized by a research grant from Astute Medical, Inc. The Duke University Stead Scholarship was also a funding source. Disclosures: The authors served as site investigators in this study ,and D.T.L. has served as a scientific consultant for Astute Medical, Inc., which performed the antibody-based assays used in this study. Address correspondence to Daniel T. Laskowitz, MD, MHS, Department of Medicine, Duke University School of Medicine, Box 2900, Durham, NC 27710. E-mail: [email protected]. 1052-3057/$ - see front matter Ó 2013 by National Stroke Association http://dx.doi.org/10.1016/j.jstrokecerebrovasdis.2013.07.034
disability-adjusted life-years globally.1,2 A number of conditions present with stroke-like symptoms and timely diagnosis of the etiology of an acute neurologic deficit is critical to make appropriate management decisions. The only approved pharmacologic intervention for acute ischemic stroke is recombinant tissue plasminogen activator (rt-PA).3 However, administration of r-tPA is time limited, and diagnostic uncertainty may contribute to underutilization of this therapy.4 Although 99% of emergency physicians believe rt-PA should be the standard of care for acute stroke, two thirds feel uncomfortable making the diagnosis and administering rt-PA without a neurologic consultation.4 In addition to ischemic stroke, there are a number of neurologic conditions that may present with similar clinical presentations, include transient ischemic attack (TIA), intracranial hemorrhage (ICH), and subarachnoid hemorrhage (SAH), and nonvascular mimics such as postictal paresis, complex migraine, toxic–metabolic conditions, peripheral neuropathy, mass lesions, and conversion disorder.
Journal of Stroke and Cerebrovascular Diseases, Vol. -, No. - (---), 2013: pp 1-9
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Once in the hospital, there are established diagnostic algorithms for ICH and SAH as computed tomography (CT) is quite sensitive for the detection of acute intracranial blood. However, the diagnosis of acute stroke is still largely clinical. Standardized diagnostic scales are helpful in the preclinical setting and in hospitals without a neurologist on site. For example, the Recognition of Stroke in the Emergency Room Scale and the Face Arm Speech Test5,6 have utility in these settings; however, these tools demonstrate suboptimal specificity.5,6 Adjunctive imaging also plays an important role in stroke diagnosis. However, radiographic studies such as noncontrasted CT and magnetic resonance imaging (MRI) vary in terms of discriminative capacity and interobserver reliability.7,8 Furthermore, the technology and expertise necessary to operate and interpret sophisticated multimodal imaging may not be universally available in the emergent setting. Ultimately, a significant number of patients initially diagnosed with stroke suffer from stroke mimics such as seizures, systemic infection, brain tumors, and toxic– metabolic syndromes, for which thrombolytic therapy is not appropriate and may potentially be harmful.9 Given these limitations, there is a need for a rapid test to expedite the diagnosis and guide early management decisions in suspected acute cerebrovascular ischemia. One possibility is a blood-based test measuring biomarkers associated with stroke and cerebral injury. Precedents for this approach exist for emergent medical conditions such as the use of troponin for myocardial infarction, D-dimer for pulmonary embolism, and B-type natriuretic factor for congestive heart failure.10-12 To date, no single stroke biomarker with clinically useful accuracy has been identified in the setting of acute stroke. Challenges involve the heterogeneity of central nervous system cell populations and their abilities to withstand ischemia, the complexity of inflammatory and secondary injury cascades, and the presence of the blood–brain barrier.13 Thus, one approach has been to simultaneously evaluate a number of analytes associated with acute stroke. These panels incorporate biomarkers that are reflective of different aspects of the ischemic cascade. Although early work in this area has been promising and has demonstrated the feasibility of this approach, one of the few large multicenter stroke biomarker trials evaluating the use of matrix metalloproteinase-9, brain natriuretic protein, D-dimer, and S100B failed to establish a discriminative capacity that was optimal for clinical use.14 Thus, the aim of this study is to identify a panel of biomarkers differentiating ischemic strokes, ICHs, TIAs, and mimics within 24 hours of symptom onset. We hypothesize that because acute stroke is associated with inflammation, oxidative stress, primary and secondary neuronal damage, and compromise of vascular integrity, markers of these processes will be differentially represented in patients with ischemic strokes, ICH, TIAs, and mimics.
Methods Study Design and Participants This study was approved by the Duke Institutional Review Board. Patients were enrolled from August 2007 to February 2012 at the Duke University Medical Center. Inclusion criteria consisted of age 18 years or older, no recent history of trauma, documented brain imaging, and having blood draw within 24 hours of symptom onset. As per Duke IRB policy, exclusion criteria included hemoglobin less than 12.5 g/dL for women and 13.5 g/dL for men, untreated systolic blood pressure less than 90 mm Hg, and untreated diastolic blood pressure less than 50 mm Hg. Consent forms were signed by the patient or a legal representative. Blood samples were obtained from venous puncture or from an existing vascular catheter using sterile technique, collected in EDTAcontaining tubes, and centrifuged within 1 hour of collection at 10,000 g. The supernatant serum was frozen at 280 C and sent to Astute Medical, Inc., San Diego, CA, to determine the biomarker levels. Demographic, clinical, laboratory, and radiographic data were extracted from electronic medical records into case report forms. All cases were adjudicated by 2 physicians (D.T.L. and R.S.) who were blinded to biomarker results. Stroke was defined as a persistent neurologic deficit in a vascular distribution lasting 24 hours or more or less than 24 hours but with CT and/or MRI confirmation. The diagnosis of ICH (excluding SAH) was based on CT findings. All radiology studies were performed by a clinical neuroradiologist blinded to biomarker values as part of standard clinical practice. National Institutes of Health Stroke Scale scores and pertinent demographic information were collected by the clinical co-ordinator at study entry. Patients with a demonstrated nonvascular condition as a source of their symptoms and negative imaging were adjudicated as stroke mimics. Patients with neurologic findings lasting less than 24 hours in a vascular distribution, negative radiography, and negative work up for mimic conditions were adjudicated as TIA. Each sample was evaluated for 262 biomarkers, which were a priori identified as being potentially relevant to metabolic cascades associated with cerebral ischemia, coagulation, endothelial dysfunction, neuronal injury, inflammation, gliosis, necrosis, or infection (Appendix).
Statistical Analysis Statistical analysis was performed using SAS 9.3(SAS, Cary, NC). Descriptive statistics were obtained for demographic and clinical variables in each pathologic group: ischemic stroke, ICH, TIA, and mimic. Variable distributions were compared across groups by 1-way analysis of variance tests if the variable was continuous and by c2 tests if categorical.
A BLOOD-BASED BIOMARKER PANEL TO DETECT ACUTE STROKE
Univariate logistic regressions were conducted with each biomarker to predict the likelihood of ischemic stroke versus mimic. This was considered the base model. To address the uncertainty of whether TIA was associated with cellular injury cascades, ischemic stroke versus TIA 1 mimic and ischemic stroke 1 TIA versus mimic models were created as well. Finally, we created an independent model to differentiate ischemic stroke 1 ICH versus mimic as this would reflect the clinical scenario in the prehospital setting before a CT could be obtained to rule out ICH. Biomarkers significant in univariate logistic regression in each group with a P value of .2 or less were retained and used to build multivariate logistic regression models. A stepwise selection method was employed to build the most parsimonious multivariate models maximizing predictive power and minimizing the number of covariates. Variables were entered and retained in the model if the chi-square statistic was significant at P less than .05. The final variables were tested for multicollinearity by testing linear correlation (r . .8) and variance inflation factor (.2.5). The area under the curve of the receiver operating characteristic (ROC) curve, captured by the concordance index or C statistic, was used as a measure of the overall discriminative capacity of each model. To internally validate this model, a SAS macro was adapted to bootstrap the parameter estimates and the C statistic of the base model by generating 50 balanced re-samples. To create 1 overall model, biomarkers significant in the multivariate models of ischemic stroke 1 TIA versus mimic, ischemic stroke versus TIA 1 mimic, ischemic stroke versus ICH, and ischemic stroke 1 ICH versus mimic were added to the base model incrementally. Biomarkers contributing at least a 1% increase in the C statistic discriminating ischemic stroke versus mimic were retained. The variables in the final model were used to generate bootstrapped C statistics for the following comparisons: ischemic stroke versus mimic, ischemic stroke versus ICH, and ischemic stroke 1 ICH versus mimic. Sensitivities, specificities, positive predictive values, and negative predictive values (NPVs) were determined for the probability of each outcome at which sensitivity is maximized with specificity of at least 50%. These statistics were calculated at an outcome probability of 30%, reflecting the approximate prevalence of stroke among the patients who present to emergency rooms with an acute neurologic deficit.15 Net reclassification improvement (NRI) is a measure used to quantify improvement in model performance after adding new variables to existing models. Increases in C indices with the addition of markers can be relatively small in magnitude if a very powerful factor already exists in the model, and NRI has been proposed as an additional, possibly more intuitive metric of added usefulness of additional biomarkers.16 NRI is expressed as difference in percent of patients with improved reclassification and
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percent of patients with worsened reclassification, where patients are reclassified ‘‘upward’’ (‘‘downward’’) if the predicted risk estimated using the new model is higher (lower) compared with the reference model. Values of NRI greater than 0 indicate the overall improvement in reclassification using the model with new biomarkers.17 Given that this is an exploratory study and meaningful risk categories have not been defined in the literature, a continuous (category less) NRI was undertaken to summarize the incremental value of biomarkers.17 In this analysis, a 11 was assigned to each patient with an event who moved up in estimated risk, 21 to those with an event who moved down, and 0 to those with no change in estimated risk. To test the robustness of this final model in face of the diagnostic uncertainty of the TIA group, the bootstrapped parameter estimates and intercept of the stroke versus mimic comparison were used to predict TIA 1 stroke versus mimic and stroke versus TIA 1 mimic. The model was also applied to test its ability to detect posterior circulation ischemic strokes versus mimics and its robustness in predicting ischemic stroke versus mimic in patients who presented within the clinically relevant time window of 4.5 hours.
Results Baseline Characteristics The sera of 167 patients were analyzed in this study. The adjudicated diagnoses included 57 ischemic strokes (34.1%), 32 ICH (19.2%), 41 TIA (24.6%), and 37 mimics (22.2%). All patient received at least a noncontrast head CT and further MRI testing if indicated. The major diagnoses on discharge among stroke mimic patients were migraines (27%), Bell’s palsy (10%), psychiatric disorder (10%), angina (10%), blood glucose derangement (10%), polypharmacy (7%), and peripheral neuropathy (7%). Table 1 depicts the demographic and health status characteristics of each pathology group. In this sample, the mean age was 64.4 6 15.2 years, and the average age among patients within each pathologic group was not significantly different at (P 5 .091). The proportion of patients of each gender did not vary as a function of pathology (P 5 .13). Among those with ICH, blacks and people of other races were over-represented. The proportion of patients with atrial fibrillation was higher in both the ischemic stroke and TIA groups (P 5 .044). Table 2 contains the most common presenting symptoms. A greater proportion of patients with ischemic strokes and TIAs was affected by speech/language difficulty (P 5 .0001). Patients with ischemic strokes were more likely to suffer from motor deficits than patients with other diagnoses (P 5 .0013). Patients with ischemic strokes and ICH were predominantly more likely to exhibit a change in mental status (P , .001). The average NIH stroke scale score was significantly higher in
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Table 1. Demographic and health status characteristics Characteristic
Ischemic stroke, N 5 56
ICH, N 5 32
TIA, N 5 41
Mimic, N 5 37
P value
Age (mean, SD) Males Race White Black 1 other Comorbidities Hypertension Diabetes mellitus Hyperlipidemia Atrial fibrillation Prior stroke/TIA Smoking Crack/cocaine
66.9 (15.6) 32 (56.1)
64.7 (13.9) 19 (59.4)
63.1 (15.9) 20 (48.8)
61.8 (15.0) 13 (35.1)
.0914 .1339 .0152
37 (64.9) 20 (35.1)
12 (37.5) 20 (62.5)
30 (73.2) 11 (26.8)
21 (56.8) 16 (43.2)
47 (82.5) 16 (28.1) 16 (28.1) 11 (19.3) 13 (22.8) 15 (26.3) 3 (5.3)
26 (81.3) 9 (28.1) 8 (25.0) 1 (3.1) 15 (46.9) 4 (12.5) 3 (9.4)
30 (73.2) 14 (34.2) 12 (29.3) 8 (19.5) 13 (31.7) 9 (22.0) 1 (2.4)
30 (81.1) 14 (37.8) 12 (32.4) 2 (5.4) 12 (32.4) 7 (18.9) 0 (.0)
.6972 .7310 .9226 .0443 .1392 .4777 .2371
Abbreviations: ICH, intracranial hemorrhage; TIA, transient ischemic attack. Significance was determined by c2 tests for categorical variables and 1-way analysis of variance for continuous variables.
ischemic strokes (mean 6.2) and ICH (mean 10.2) as compared with TIA or mimic (mean 1.5 and 2.1, respectively). The median time from symptom onset to serum biomarker draw was 6.7 hours for ischemic stroke, 7.7 hours for TIA, 7.1 hours for mimics, and 14.3 hours for ICH. Nearly 98% of patients received brain imaging that included CT, and 2% received MRI only. Among patients who had ischemic stroke or TIA, 74 patients (44.1%) had
both CT and MRI. Among patients who had ICH, 100% of patients had a CT. The average ABCD2 score for the TIA sample was 4.39 6 1.16. The Trial of Org 10172 in Acute Stroke Treatment classification system categorizes the etiology of a stroke.18 Most stroke events were because of an undetermined cause (40.4%) and secondary to atherosclerosis (35.1%), whereas 12.3% were because of a cardioembolic
Table 2. Clinical and radiographic characteristics Characteristic Symptoms Speech/language Motor deficits Sensory deficits Visual deficits Altered mental status Headache NIHSS at enrollment GCS (initial) t-PA administered ABCD2 score TOAST criteria 1. Atherosclerosis 2. Cardioembolic 3. Small-vessel occlusion 4. Other determined cause 5. Undetermined Vascular distribution Anterior Lacunar Posterior .1 Distribution
Ischemic stroke, N 5 56
ICH, N 5 32
TIA, N 5 41
Mimic, N 5 37
P value
41 (71.9) 46 (80.7) 21 (36.8) 12 (21.1) 15 (26.3) 12 (21.1) 6.8 (6.8) 13.9 (2.3) 21 (33.3) —
8 (25.0) 16 (50.0) 5 (15.6) 5 (15.6) 16 (50.0) 10 (31.3) 10.2 (7.9) 12.8 (3.1) — —
25 (61.0) 23 (56.1) 21 (51.2) 6 (14.6) 1 (2.4) 13 (31.7) 1.5 (2.5) 15 (.0) 2 (4.9) 4.4 (1.2)
16 (43.2) 22 (59.5) 14 (37.8) 9 (24.3) 5 (13.5) 13 (35.1) 2.1 (2.5) 14.8 (.6) 2 (5.4) —
.0001 .0113 ,.0001 .6664 ,.0001 .4474 ,.0001 .0025 ,.0001
20 (35.1) 7 (12.3) 1 (1.8) 6 (10.5) 23 (40.4)
— — — — —
— — — — —
— — — — —
36 (63.2) 6 (10.5) 9 (15.8) 2 (3.5)
— — — —
— — — —
— — — —
Abbreviations: GCS, Glasgow Coma Scale; ICH, intracranial hemorrhage; NIHSS, National Institutes of Health Stroke; TOAST, Trial of Org 10172 in Acute Stroke Treatment; t-PA, tissue plasminogen activator; TIA, transient ischemic attack. Significance was determined by c2 tests for categorical variables and 1-way analysis of variance for continuous variables.
A BLOOD-BASED BIOMARKER PANEL TO DETECT ACUTE STROKE
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Table 3. Multivariate logistic regressions comparing ischemic stroke 1 TIA versus mimic, ischemic stroke versus TIA 1 mimic, ischemic stroke versus mimic, ischemic stroke 1 ICH versus mimic, and ischemic stroke versus ICH Biomarker Ischemic stroke 1 TIA vs. mimic Platelet endothelial cell adhesion molecule Interleukin-6 TNF receptor superfamily member 1B (v1) Overall model C statistic Ischemic stroke vs. TIA 1 mimic Granulocyte-colony stimulating factor Prostate-specific antigen Protein S100A12 Overall model C statistic Ischemic stroke vs. mimic Eotaxin EGFR (v1) Overall model C statistic Ischemic stroke 1 ICH vs. mimic Eotaxin EGFR (v1) Metalloproteinase inhibitor-4 Endothelial cell adhesion molecule Prolactin Overall model C statistic Ischemic stroke vs. ICH Calbindin Urokinase-type plasminogen activator Metalloproteinase inhibitor-2 Overall model C statistic
AUC
P value
.596
.0315
.596 .563
.0957 .1065
.857 .611
.0606
.603 .623 .868
.1467 .0376
.812 .818 .868
,.0001 .0123
.829 .732 .604 .540
,.0001 .0011 .0425 .1607
.666 .941
.0019
.628 .708
.0471 .0045
.724 .939
.0871
Abbreviations: AUC, area under the curve; EGFR, epidermal growth factor receptor; ICH, intracranial hemorrhage; TIA, transient ischemic attack; TNF, tumor necrosis factor. Biomarkers significant in multivariate logistic regressions derived by stepwise selection.
event. Among patients with ischemic strokes, nearly 63% occur in the anterior circulation, 11% comprise a lacunar infarct, and about 15% occur in the posterior circulation.
Model Selection A number of biomarkers predicted the likelihood of stroke versus mimic, stroke versus TIA 1 mimic, stroke 1 TIA versus mimic, stroke versus ICH, and stroke 1 ICH versus mimic in univariate logistic analyses at an alpha of .2 (Supplementary Tables 1 and 2). The logodds ratio of ischemic stroke versus mimic in multivariate analysis was predicted well by 2 biomarkers: eotaxin (P , .0001) and epidermal growth factor receptor (EGFR) v1 (P 5 .0123) with a C statistic of .868. Variables
Figure 1. Calibration curves. Observed versus predicted probabilities of each outcome predicted by the 5-variable model with corresponding linear best-fit curves and R2 values. Observed versus predicted probability of (A) ischemic stroke, (B) ischemic stroke or ICH, (C) ICH.
significant by multivariate regression in the TIA and ICH analyses are displayed in Table 3. The final model consisted of 5 variables that added the most incremental discriminative value: eotaxin, EGFR, S100A12, metalloproteinase inhibitor-4 (TIMP-4), and prolactin. Calibration curves are shown in Figure 1.
Measures of Discrimination Table 4 displays the parameter estimates, P values, and c statistics of the model when applied to differentiate ischemic stroke versus mimic (c 5 .90), ischemic stroke 1 ICH versus mimic (c 5 .91), and ischemic stroke
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Table 4. Characteristics of the 5-variable model predicting ischemic stroke vs. mimic, ischemic stroke 1 ICH vs. mimic, and ischemic stroke versus ICH Ischemic stroke vs. Mimic
Ischemic stroke 1 ICH vs. Mimic
Ischemic stroke vs. ICH
Biomarker
Parameter estimate
P
Parameter estimate
P
Parameter estimate
P
Eotaxin EGFR (v1) TIMP-4 Prolactin S100A12 Intercept C statistic
2.0024 2.0016 .0003 2.0001 .0872 3.5875 .901
.0003 .0781 .2520 .1658 .0310 .0054
2.0026 2.0016 .0003 2.0001 .0894 3.7229 .912
.0002 .0761 .2287 .1504 .0285 .0038
.0048 .0002 2.0003 .0003 .0120 .0762 .856
.2153 .8964 .1471 .1272 .6004 .9746
Abbreviations: EGFR, epidermal growth factor receptor; ICH, intracranial hemorrhage; Parameter estimates and P values associated with the c2 of the 5-variable logistic model in the setting of each comparison as well as each model’s C statistic.
versus ICH (c 5 .86). The model was internally validated by bootstrapping C statistics, yielding the following results: ischemic stroke versus mimic (c 5 .92), ischemic stroke 1 ICH versus mimic (c 5 .93), and ischemic stroke versus ICH (c 5 .82). Figure 2 presents the ROC curves of each of the 3 models in comparison with the ROC curves generated from patients’ age, gender, and race. In each analysis, the ROC curves created from the covariates of age, gender, and race generated areas under the curve less than models that included biomarkers and those that included demographic data plus biomarkers. Given the stroke prevalence of 38% in our population tested, the final panel was associated with a sensitivity of 90%, specificity of 84%, positive predictive value of 78%, and NPV of 93% in differentiating stroke from mimic.
Reclassification As seen in Table 5, continuous NRI metric was used to quantify the degree of correct reclassification from the reference model. When examining the ischemic stroke versus mimic comparison, the NRI associated with the 5 biomarkers plus age, race, and gender as opposed to age, race, and gender alone was significant (NRI 5 161%, P , .0001). Similar proportional impacts were seen when the 5 biomarkers plus age, race, and gender model was compared with the 5-biomarker model (NRI 5 113%, P , .0001). This pattern was seen again when the 5 biomarkers plus age, race, and gender were compared with the 3 demographic variable model in discriminating ischemic strokes 1 ICH versus mimics (NRI 5 171%, P ,.0001). However, when discriminating between ischemic strokes versus ICH, the NRI with biomarkers plus age, race, and gender as opposed to the demographic variables was no longer statistically significant (NRI 5 56%, P 5 .1419). When the biomarkers plus demographic variable model was compared with the model incorporating biomarkers
alone, the NRI was 65%, and it was marginally nonsignificant at a P value of .093.
Subgroup Analyses Diagnostic uncertainty is often greatest in the acute setting when CT is insensitive to ischemic changes and in patients with posterior circulation ischemia. To address this, we tested the 5-variable model in these patient subsets. Parameter estimates from the 5-variable model predicting ischemic stroke versus mimic were applied to a subgroup of patients to detect posterior circulation stroke versus mimic. It yielded a C statistic of .71 and eotaxin and S100A12 demonstrated Wald chi-squares with statistical significance of P 5 .0006 and .0331, respectively. The parameter estimates from the 5-variable model maintained discriminative significance when applied to a subgroup of patients who were enrolled within 4.5 hours of presentation (N 5 28) to predict ischemic stroke versus mimic (c 5 .79).
Discussion The need for a rapid, blood-based test for stroke is significant given the diagnostic uncertainty that surrounds patients presenting with acute neurologic deficits. In this study, appropriately adjudicated comparison groups of patients with similar symptoms and vascular comorbidities were included to evaluate a large number of potential biomarkers in a panel approach, allowing combinations of biomarkers with relatively high sensitivity and specificity. We found 5 biomarkers (eotaxin, EGFR, S100A12, TIMP-4, and prolactin) that accurately differentiated acute cerebrovascular injury from stroke mimics. To our knowledge, this is the first study to explore such a wide array of potential biomarker candidates to develop a multivariate model predicting ischemic strokes, ICHs, TIAs, and mimics. Biomarkers that had been previously identified as correlating with stroke,
A BLOOD-BASED BIOMARKER PANEL TO DETECT ACUTE STROKE
7
Figure 2. Receiver operating characterisitc curves. Discriminating capacity of 3 sets of predictors including (1) red: age, race, sex; (2) green: the 5 tested biomarkers; and (3) black: age, race, and sex plus the 5 tested biomarkers. The comparison groups were the following: (A) ischemic stroke versus mimic, (B) ischemic stroke 1 ICH versus mimic, and (C) ischemic stroke versus ICH. The c statistic for each model is overlaid. Abbreviation: ICH, intracerebral hemorrhage.
such as MMP-9, D-dimer, and BNP19 remained significant in univariate analyses, but in our current model, only S100 remained significant in multivariate analyses.19 Although several of these biomarkers have not been previously described in the context of stroke, they have known pathophysiologic roles that lend biologic plausibility to their statistical association with stroke. Eotaxin is a potent chemokine for eosinophils and other inflammatory cells.20 Treatment of vascular endothelial cells with tumor necrosis factor-a resulted in a 20-fold induc-
tion of smooth muscle expression of eotaxin in atheroma and an increase in macrophage and mast cell expression of CCR3, its receptor, suggesting that eotaxin recruits inflammatory cells in atheromas.21 Another study demonstrated an increase in eotaxin in ischemic stroke patients versus healthy controls.22 EGFR and its ligands may play a role in regulation of genes associated with reactive gliosis.23 After an ischemic event, proliferating glial cells abundant in the infarcted brain and astrocytes in the periphery of the infarct were highly immunoreactive to
Table 5. Continuous net reclassification criteria for models including demographic variables and biomarkers
Model
C index
NRI, %
95% CI for NRI
Event: ischemic stroke nonevent: mimic Age/race/sex .671 — — 1Biomarkers .970 161 (117, 205) Biomarkers .901 — — 1Age/race/sex .970 113 (69, 157) Event: ischemic stroke 1 ICH nonevent: mimic Age/race/sex .677 — — 1Biomarkers .973 171 (128, 214) Biomarkers .912 — — 1Age/race/sex .973 113 (69, 156) Event: ischemic stroke nonevent: ICH Age/race/sex .653 — — 1Biomarkers .905 056 (218, 131) Biomarkers .856 — — 1Age/race/sex .905 65 (29, 139)
P value
% Patients reclassified upward among nonevents
% Patients reclassified downward among nonevents
% Patients reclassified upward among events
% Patients reclassified downward among events
— ,.0001 — ,.0001
6.5 — 32.3
— 93.5 — 67.7
— 87.0 — 88.7
— 13.0 — 11.3
— ,.0001 — ,.0001
3.2 — 32.3
— 96.8 — 67.7
— 88.7 — 88.5
— 11.3 — 11.5
— .1419 — .093
— 62.5 — 25.0
— 37.5 — 75.0
— 90.7 — 57.4
— 9.3 — 42.6
Abbreviations: CI, confidence interval; ICH, intracranial hemorrhage; NRI, net reclassification improvement. C statistics, NRI, and proportions of patients among those with events and those with no events that were reclassified upward or downward, compared with the reference model. The biomarkers include all 5 biomarkers. The P values correspond to the test of NRI 5 0.
R. SHARMA ET AL.
8 23
EGFR. As a chemoattractant for monocytic cells, S100A12 is an inflammatory response mediator.24 Its receptor, RAGE (receptor for advanced glycation end products), activates transcription factors that are protective from oxidative stress. S100A12 induces neurite outgrowth from rat embryonic hippocampal cells.25 Plasma levels of S100A12 were higher in patients with carotid atherosclerosis and highest in patients with most recent symptoms.26 TIMP-4 irreversibly inactivates metalloproteinases and is expressed in astrocytes, monocytes, platelets, smooth muscle cells, and endothelial cells.27-29 It is involved in regulating platelet recruitment and aggregation.29 Prolactin is associated with platelet activation as P-selectin expression and platelet aggregation increase in its presence and in acute ischemic stroke patients.30,31 In this study, we categorized TIAs as a diagnosis of exclusion. The 5-variable model demonstrated robustness when TIAs were grouped with ischemic strokes, suggesting that the pathophysiologic effects of a TIA are more similar to that of strokes than mimics, even when there is no residual clinical deficit. This might be expected because the enrolled TIA patients were at high risk, as reflected by the high average ABCD2 score of 4.4 (8% corresponding 90-day risk of stroke).32 Thus, in future study design, high-risk TIAs may be grouped with stroke. Nearly 3%-5% of patients diagnosed with TIA in the emergency department (ED) will have strokes within the next 48 hours.33 Thus, a biomarker panel which diagnoses high-risk TIAs may guide the decision to hospitalize patients with high stroke risk. To be clinically useful in the ED setting, a biomarkerbased test would need to have a sufficiently high NPV to exclude cerebrovascular ischemia in a low-risk patient with atypical symptoms. Assuming that 30% of patients’ acute neurologic deficits being tested have a true diagnosis of stroke as has been suggested in several reports,15 the proportion of patients with a negative test who are correctly diagnosed by the biomarker test coupled with information about age, gender, and race is 93% when comparing ischemic stroke with mimics; the NPV increases to 95% if the overall prevalence of stroke is 10% in the population tested. There were a number of limitations to the present study that should be considered. The volume of plasma was less than 10 mL in some cases, and as a result, not all 262 biomarker levels were measured in all patients. Additionally, the sample size was not large enough to perform external validation to ensure that the parameter estimates were unbiased and generalizable. Of note, the median latency for enrollment after ICH was significantly longer than for stroke or TIA. This likely reflects the fact that the study was performed in a large academic medical center that received a significant number of ICH on transfer from smaller community hospitals, whereas most stroke and TIA patients presented primarily to the Duke ED. In the
current analysis, we only included the initial timepoint for analysis, thus precluding the ability to determine temporal profiles of biomarkers after stroke. There are a number of challenges associated with clinical implantation of a biomarker-based stroke diagnostic test. To guide early management decisions, a stroke diagnostic must be sensitive to early ischemia. Although our analysis of patients presenting within the 4.5-hour window suggested that the biomarker panel retained its diagnostic accuracy, this would need to be confirmed with larger patient numbers. Moreover, to achieve clinical utility, a biomarker-based test for stroke must be demonstrated to be cost effective and have rapid turnaround time. Although such a test might provide important adjunctive clinical information for early identification of ischemic stroke, given the ready availability of CT at most centers, it will not supplant initial imaging to exclude ICH, especially when administration of tPA is contemplated. Thus, although a test to identify ICH may be useful in the prehospital setting, it is extremely unlikely that the primary role of a biomarker-based test will be to differentiate ischemic from hemorrhagic stroke. Moreover, such a test will neither replace a careful clinical exam and history in determining eligibility for receiving a reperfusion intervention nor provide the detailed anatomical vascular information offered by conventional and noninvasive angiographic techniques. Nevertheless, when used in conjunction with clinical, historical, and radiographic data, a blood-based biomarker can inform decisions at several junctions. As a point-of-care device, paramedical personnel might use this to supplement information to expedite triage and transportation to a stroke center. In the ED, the panel’s high sensitivity may enhance data from a negative CT and conventional MRI to provide closer follow-up and care of a patient with atypical symptoms who might otherwise be sent home. The current data suggest the potential utility of a panel-based biomarker approach to aid in the diagnosis of acute cerebrovascular syndromes and the importance of performing future prospective validation studies in this area.
Supplementary material Supplementary data related to this article can be found at http://dx.doi.org/10.1016/j.jstrokecerebrovasdis.2013. 07.034.
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