The ability to treat acute stroke patients with thrombolytics has increased the need for rapid detection and evaluation of acute stroke. Increasingly, newer neuroprotective agents and recovery strategies are targeting the acute patients, raising demand for diagnostic and biomarker variables to assist with patient selection. Stroke imaging can contribute significantly by identifying patients likely to recover during the early, subacute phase due to restoration of arterial blood flow, either spontaneously or following thrombolytic treatment. It is important to not only weigh the risks versus benefits of aggressive treatment to restore cerebral perfusion, but also to support decisions regarding subsequent management.4
In this study, we developed a pilot model to predict early improvement of aphasia based on admission imaging and clinical data.
This model predicted clinical outcome with high accuracy (91%) based on data collected within 9 hours of stroke onset. In summary, aphasic patients with normal, near-normal, or hyperemic rCBF of the sub-insular ribbon and angular gyrus, and without proximal cerebral artery occlusion at admission, were most likely to show improvement of language function by hospital discharge (). Moreover, using only the four independent outcome predictors of our final model – specifically, admission NIHSS aphasia score, presence or absence of a proximal large vessel intracranial occlusion, and degree of rCBF derangement in the left subinsular ribbon and left angular gyrus - we developed a practical, simple-to-apply, pilot “aphasia improvement score” for predicting the likelihood of language improvement, which has the potential to be of value in patient triage pending validation by prospective studies. Notably, the scoring system was constructed based on the regression coefficients of our cohort population, thus requiring validation in larger independent studies.
Using the logistic regression equation of the final model (), one can more precisely estimates the probability of language improvement in a given patient based on the same four variables, as explained in the methods section. The value of our model may extend beyond the first few days following stroke onset, as the severity of aphasia within the first week post-ictus is one of the most important predictors of long-term language function, accounting for 47% of the variation in the aphasia score one year after stroke.13
Interestingly, the two brain regions we found to be independent predictors of aphasia improvement are not those most commonly thought of in association with language function (). However, left BA 39 (angular gyrus) has been reported to have a role in semantic processing,14
and hypoperfusion/infarction of this region contributes to error rate in oral naming.7
An analogous role of the insular ribbon in language function may apply. Although the brain regions identified in our study may directly contribute to language function - to some extent 7
- our results only reveal a correlation between aphasia outcome and perfusion status of these regions, and do not prove a direct association.
Of note, numerous abnormalities have been reported to be associated with insular infarction, including sympathetic activation,15
stroke-related myocardial damage,16
and positional vertigo.17
These findings, however, may be an epiphenomenon, and simply reflect the high frequency of insular damage in large MCA territory strokes. It is also plausible that the perfusion defect in the lower insular cortex reflects edematous damage or hypoperfusion of the adjacent inferior frontal gyrus Broca’s area, or the deep arcuate fasciculus.
Regardless, our findings suggest that ischemia of these two left hemisphere regions is a strong predictor of poor outcome in aphasic patients, albeit that these cerebral areas may not directly contribute to language function. Our data suggests that mean rCBF of these two regions better predicts language recovery than other cortical and sub-cortical areas known to be more directly involved in language production; therefore potentially reversible hypoperfusion (i.e. “ischemic penumbra”) of these specific regions reflects the overall perfusion status of a complex left hemispheric language neural network.
Clearly, the acute infarction volume has prognostic value; however, for optimal prediction of language outcome, it is valuable to know not only the size and degree of this “ischemic penumbra”, but also its spatial localization.9
Because the precise definition of “penumbra” remains controversial,18
and because the thresholds for distinguishing truly “ischemic” hypoperfused tissue from “benign oligemia” have not yet been validated and standardized,19– 21
our approach to determine the independent predictors of language improvement has been to directly
model the regional, normalized perfusion parameter values with a logistic regression, rather than segmenting these regions using arbitrary threshold values that have not yet been established. Having identified specific brain regions whose rCBF values served as independent predictors of functional improvement, we then integrated this CTP data with the admission CTA and clinical parameters to produce a more accurate, optimized model.
Admission aphasia score and cerebral arterial occlusion were also independent predictors of aphasia improvement. This is in agreement with previous reports.13
Due to the very high co-linearity between the total NIHSS score and the NIHSS aphasia score, we included only the more relevant NIHSS admission aphasia score in our model, although it is noteworthy that Pedesen et al found that the inclusion of an overall admission stroke severity score, in addition to the admission aphasia quotient, incrementally improved the accuracy of their 1-year aphasia outcome prognosis.13
The application of multivariate model using imaging variables only () could be especially valuable in circumstances when clinical exam is unavailable or unreliable, such as the presence of a language barrier, or in an obtunded patient. Admission CTA findings were also important in this regard. We categorized CTA findings, based on the Boston Acute Stroke Imaging Scale (BASIS),12
into those patients with versus without proximal large vessel cerebral artery occlusion (intracranial ICA, M1 or M2MCA). Patients with major stroke by BASIS classification are reported to have higher mortality rates, longer durations of hospital stay, and higher rates of discharge to rehabilitation.12
Based on our results, one can approximate the probability of language improvement based on the admission CTA and CTP findings alone, albeit that the addition of admission NIHSS aphasia score improves the accuracy of this prediction.
In addition to our clinical results, our methodological approach is noteworthy, in that it has the potential to be generalized to the prediction of other clinical outcomes, such as motor function. We developed an automated method for image analysis, with minimal operator-dependent bias, and highly reproducible results. All CTP images were automatically co-registered to standard MNI-152 brain space using highly robust and effective registration software (FLIRT 5.5).11
For parcellation of the brain, a pre-set atlas based on widely available Talairach and JHU atlases supplied with FSL freeware was used.22
Thus, our image analysis was totally operator-independent, representing a major advantage over the typical methodology for such studies, which relies on manual registration/overlay for segmentation of the brain.20
In addition, this tool can be used to create a same-language forum for comparison of results across different centers. This elimination of operator dependent bias underscores the predictive strength of the resulting regression models.
Among patients receiving thrombolytic therapy, there was no significant difference between those with and without language improvement. Due to the small number of cases in our cohort, we were unable to construct separate blocks for our multivariate model sub-stratified by treatment. Moreover, many patients received IV thrombolytic therapy prior to CTP acquisition. An ideal model would be able to predict the probability that a particular treatment, at a given time after stroke onset, improves clinical outcome.
The major limitation of our study was that the same cohort was used for both model development and estimation of test characteristics; in an ideal situation, different samples should be used for model development and validation to avoid bias towards an overestimation of the test-characteristics. Another important limitation of our study was the lack of patient evaluation with a specialized “language test” battery.7, 9, 13
Using a specialized battery not only provides a more reliable measure of outcome, but also provides the ability to quantitatively rate the degree of improvement.7, 9
Moreover, the correlation between specific brain regions and functional outcome cannot be assessed when voxels in those regions, due to artifact or insufficient coverage of the inferior MCA division territory, are insufficiently scanned in every patient. As noted in the results, it was for this reason that we excluded some brain regions (n=5) from our model; importantly, these exclusions did not include Brocca’s and Wernicke’s areas. Of note, , MR perfusion or CTP with 256 and 320 multi-detector CT scanners can potentially improve spatial coverage of the brain.
An additional caveat is the hypotheses relating special functional outcomes to particular regions of brain, whether one identifies areas by Brodmann’s areas, gyri, or groups of voxels on scans registered to spatial coordinates of an “idealized” brain image (MNI-152): even the most robust and effective methods of co-registration or normalization of brain images are imperfect. Each individual patient has a unique pattern of sulci, gyri, and a variable degree of structural deformity that may interfere with co-registration to MNI-152 young brain template. However, visual inspection of co-registered CTP images in our series confirmed adequate cortical co-registration. Moreover, exclusion of patients with previous ischemic stroke and inclusion of cased with acute ischemic stroke in our study minimized asymmetrical brain abnormalities that might interfere with effective co-registration.