Evaluation of Early Reperfusion Criteria in Acute Ischemic Stroke ¨ Thomalla, Salvador Pedraza, Brice Ozenne ∗ , Tae-Hee Cho ∗ , Irene Klærke Mikkelsen, Marc Hermier, Lars Ribe, Gotz ` Jean-Claude Baron, Pascal Roy, Yves Berthezene, Norbert Nighoghossian, Leif Østergaard, Delphine Maucort-Boulch From the Service de Biostatistique, Hospices Civils de Lyon, Lyon, France; Equipe Biostatistique Sante´ CNRS UMR 5558, Villeurbanne, France; Universite´ Lyon I, Lyon, France (BO, PR, DM-B); Department of Stroke Medicine and Department of Neuroradiology, Universite´ Lyon 1; CREATIS, CNRS UMR 5220-INSERM U1044, INSA-Lyon; Hospices Civils de Lyon, Lyon, France (T-HC, MH, YB, NN); Center of Functionally Integrative Neuroscience, Aarhus University, Aarhus, Denmark (IKM, LR, LØ); Department of Neurology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany (GT); Department of Radiology (IDI), Girona Biomedical Research Institute (IDIBGI), Hospital Universitari de Girona Dr Josep Trueta, Girona, Spain (SP); and INSERM U894, Hopital Sainte-Anne, Universite´ Paris Descartes, Sorbonne Paris Cite, ˆ ´ France (J-CB).
ABSTRACT BACKGROUND AND PURPOSE: Though still debated, early reperfusion is increasingly used as a biomarker for clinical outcome. However, the lack of a standard definition hinders the assessment of reperfusion therapies and study comparisons. The objective was to determine the optimal early reperfusion criteria that predicts clinical outcome in ischemic stroke. METHODS: Early reperfusion was assessed voxel-wise in 57 patients within 6 hours of symptom onset. The performance of the time to peak (TTP), the mean transit time (MTT), and the time to maximum of residue function (Tmax ) at various delays thresholds in predicting the neurological response (based on the National Institutes of Health Stroke Scale) and the functional outcome (modified Rankin scale ࣘ1) at 1 month were compared. A receiver operating characteristics (ROC) analysis determined the optimal extent of reperfusion. A novel unsupervised classification of reperfusion using group-based trajectory modeling (GBTM) was evaluated. RESULTS: MTT had a lower performance than TTP and Tmax in predicting the neurological response (P = .008 vs. TTP and P = .006 vs. Tmax ) or the functional outcome (P = .0006 vs. TTP; P = .002 vs. Tmax ). No delay threshold had a significantly higher predictive value than another. The optimal percentage of reperfusion was dependent on the outcome scale (P < .001). The GBTM-based classification of reperfusion was closely associated with the clinical outcome and had a similar accuracy compared to ROC-based classification. CONCLUSIONS: TTP and Tmax should be preferred to MTT in defining early reperfusion. GBTM provided a clinically relevant reperfusion classification that does not require prespecified delay thresholds or clinical outcomes.
Keywords: Acute stroke imaging, reperfusion, perfusion-weighted imaging (PWI), ROC analysis, group-based trajectory modeling (GBTM). Acceptance: Received September 30, 2014, and in revised form March 26, 2015. Accepted for publication March 27, 2015. Correspondence: Address correspondence to N. Nighoghossian, Hopital Neurologique Pierre Wertheimer, 59 Bd Pinel, F-69500, Bron, France. ˆ E-mail: [email protected]. ∗
Both authors contributed equally.
Sources of Funding: I-KNOW consortium was funded by the European Commission’s Sixth Framework Programme (Grant 027294). J Neuroimaging 2015;25:952-958. DOI: 10.1111/jon.12255
Introduction After acute ischemic stroke, early restoration of blood flow is the only therapy of proven benefit in reducing infarct growth and promoting clinical improvement. The effectiveness of intravenous recombinant tissue plasminogen activator (rt-PA) in clinical trials is likely driven by early reperfusion but with decreasing benefits in more delayed treatments.1 Currently, perfusion-weighted imaging (PWI) is used to estimate the extent of the tissue at the risk of infarction when early reperfusion is not achieved. MRI-based studies have suggested that the reperfusion might be a surrogate marker for clinical outcome.2–4 Furthermore, early reperfusion may be a useful biomarker of outcome in phase 2 trials assessing novel revascularization procedures or putative neuroprotectants.5 Despite its importance, there is currently no single consensus definition of reperfusion. Up to now, few studies have examined the progression of tissue reperfusion within the first hours after symptom onset. The available data show considerable
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variations, especially regarding the postprocessing methods of perfusion images.2–13 In fact, several MRI metrics were used to assess perfusion deficit without consensus on the best one to consider.8,11,14,15 Generally, a single threshold of a given perfusion parameter (eg, the time to the maximum of the residue function Tmax ࣙ 6 seconds) is used to define the tissue still at the risk of necrosis.12,13 Though the extent of early reperfusion is closely associated with the amount of clinical benefit, there is yet no widely accepted optimal reperfusion ratio to distinguish between reperfused and nonreperfused patients.4 The objectives were: (1) to determine the optimal criteria for early reperfusion predicting the National Institutes of Health Stroke Scale (NIHSS) and the modified Rankin Scale (mRS) based definitions of outcome. The predictive performance of several perfusion maps was tested over multiple delay thresholds; (2) to assess a novel method for classifying reperfusion that is independent from a priori clinical hypotheses: the groupbased trajectory modeling (GBTM).
◦ 2015 by the American Society of Neuroimaging C
Materials and Methods Patients
Definitions of PWI lesion and reperfusion
The data derived from the database of the I-KNOW multicenter study.16 This study included prospective patients who underwent admission and follow-up MRIs in order to model voxel-wise probabilistic maps of infarction risk. The inclusion criteria were: (1) NIHSS ࣙ4; (2) diffusionweighted imaging (DWI) and PWI consistent with anteriorcirculation acute ischemic stroke; (3) admission MRI carried out within 6 hours in case of intravenous tPA or within 12 hours in case of conservative treatment. The study excluded all patients with lacunar or posterior circulation strokes, unknown time since symptom onset, or intracerebral hemorrhage on MRI. The same patient cohort was analyzed in four previous studies.15,17–19 Here, the analysis is the first detailed assessment of reperfusion in the I-KNOW database. Age, sex, time from symptom onset to imaging, use of thrombolysis, and stroke etiology were recorded. A regional ethics committee approved the protocol and informed consents were obtained from all patients or family members.
MRI Protocol At admission, all patients underwent: (1) a DWI (3 or 12 directions, repetition time >6000 ms, field of view 24 cm, matrix 128 × 128, slice thickness 3 or 5 mm); (2) a T2 Fluid-AttenuatedInversion-Recovery (FLAIR: repetition time 8690 ms, echo time 109 ms, inversion time 2500 ms, flip angle 150°, field of view 21 cm, matrix 224 × 256, 24 sections, section thickness 5 mm, slice gap 1 mm); (3) a T2*-weighted gradient-echo; (4) a Time-Of-Flight magnetic resonance angiography; and, (5) a PWI (echo time 30-50 ms, repetition time 1500 ms, field of view 24 cm, matrix 128 × 128, 18 slices, thickness 5 mm, gap = 1 mm, gadolinium contrast at .1 mmol/kg, intravenous injection 5 mL/s followed by 30 mL saline). An early follow-up MRI with the same sequences was performed 3 hours after the first scan (at H3) to assess early reperfusion.
Image Analysis Processing of PWI data
After motion correction, perfusion maps were computed by circular singular value decomposition of the tissue concentration curves with an arterial input function from the contralateral middle cerebral artery.20 Here, the analyses were restricted to the mean transit time (MTT) and the time to maximum of residue function (Tmax ) regarding deconvolution-based maps and to the time to peak (TTP) regarding the maps calculated by gamma-variate fitting. Using a reference region from the contralateral normal white matter, temporal parameters were normalized by subtracting the mean contralateral value and all further references to MTT, Tmax , and TTP refer to the relative parameters. Individual PWI maps at admission and 3 hours later were coregistered. All PWI data processing was performed with an in-house developed software using MATLAB 2010b (MathWorks Inc., Natick, MA, USA). Image coregistration was conducted with SPM8 (Wellcome Trust Centre for Neuroimaging, University College London, London, UK).
For TTP, MTT, and Tmax maps, the lower threshold defining the PWI lesion varied from 1 to 10 seconds per 1 second increments. For each patient, 60 PWI lesion masks were thus generated by admission and 3-hour MRIs. At each delay threshold, the reperfused tissue was defined as the set of voxels included in the initial PWI lesion but not in the 3-hour images. The reperfusion ratio (ie, the ratio of voxels showing reperfusion to those initially hypoperfused) was thus a voxel-based and coregistered measurement. Consequently, for each patient, several values of the reperfusion ratio were obtained because of the use of three perfusion parameter and ten delay thresholds. The final infarct sizes were measured with FLAIR at 1 month. The volume of DWI lesion reversal was defined as the set of voxels included in the baseline DWI lesion mask but not in the final FLAIR. This volume was expressed both as absolute volume and percentage of the baseline DWI lesion volume.
Outcome Assessment Two types of clinical outcomes were considered: (1) a “Favorable neurological response” defined as either an improvement in the NIHSS score of ࣙ8 points between admission (H0) and 1 month (M1) or a score of 0 or 1 at M1; and, (2) a “Favorable functional outcome” defined as a mRS ࣘ1 at M1. The relative change in the NIHSS score was assessed as follows: NIHSS% = (NIHSSM1 − NIHSSH0 ) × 100/NIHSSH0 . The relative change in lesion volume was assessed similarly using the DWI lesion volume at admission and the final FLAIR lesion volume at one month: ࢞Lesion% = (FLAIRM1 − DWIH0 ) × 100/DWIH0 .
Statistical Analysis Descriptive statistics are presented as medians [1st quartile; 3rd quartile] or percentages, as appropriate. Pearson correlation coefficients were used to examine the correlations between the reperfusion ratios and the delay thresholds. The reperfusion status was defined using a Receiver Operating Characteristics (ROC) analysis: for each PWI map and each delay threshold, the “optimal reperfusion ratio” (the value that ensures the best discrimination regarding the neurological response or the functional outcome) was determined using a utility function based on the empirical prevalence of positive outcome.21 An analysis of variance (ANOVA) was performed to assess the dependence of the optimal reperfusion ratio on the chosen perfusion parameter, the delay threshold, and the type of clinical outcome. The performances of TTP, MTT, and Tmax maps in predicting the neurological response were compared using crossvalidation (leave-one-out method). A Wilcoxon test was used to assess the association between the reperfusion status and recovery measures (NIHSS% and ࢞Lesion%). An ANOVA was used to investigate the influence of the choice of the perfusion parameter and its delay threshold in predicting correctly the clinical outcome.
The Group-Based Trajectory Modeling A group-based trajectory modeling (GBTM) was used to identify the criteria of reperfusion independently of the clinical outcome. GBTM is an unsupervised approach to identify groups of individuals with distinct clinical courses or responses to
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Table 1. Characteristics of Included Patients According to the Neurological Response
Number of patients Age (years) Females Thrombolysis Time from symptom onset to imaging (min) Baseline NIHSS Acute DWI lesion volume (mL) Acute hypoperfusion volume (mL, Tmax > 6 seconds) H3 hypoperfusion volume (mL, Tmax > 6 seconds)
Whole Population
Favorable Neurological Response*
Unfavorable Neurological Response
P Value
57 (100%) 70 24 (42%) 39 (68%) 126 10 10 26 8
36 (63%) 69 18 (50%) 26 (72%) 124 10 7 13 4
21 (37%) 70 6 (29%) 13 (62%) 142 11 26 52 25
– .68 .17 .56 .41 .29 .06 .95): distinct reperfusion groups could thus be formed. Considering Tmax maps, 57.9% of the patients were classified as “reperfused” and 42.1% as “nonreperfused” (Fig 3). A concordant classification throughout all three perfusion parameters was reached in 80.7% of patients. The classifications based on TTP and Tmax maps were highly concordant (96.5% agreement). No significant differences in age, sex, baseline NIHSS score, time since symptom onset to MRI, or use of thrombolysis were found between reperfused and nonreperfused patients. The reperfused group (vs. the nonreperfused group) had smaller DWI lesion volumes at baseline (9.4 mL [5.2; 13.6] vs.
47.0 mL [27.4; 66.7]; P < .001), nonsignificantly smaller DWI reversal volumes (3.03 mL [1.70; 7.81] vs. 5.00 mL [3.34; 11.73]; P = .08) but significantly higher percentages of DWI reversals versus the baseline DWI lesion volume (71.6% [44.6; 91.8] vs. 25.9% [17.6; 51.8]; P = .008). Higher rates of favorable neurological response as well as greater improvements of NIHSS scores and lesion reduction were observed in reperfused than in non-reperfused patients (Table 2). There was no significant difference in performance between the best ROC criteria and the GBTM criteria in predicting favorable neurological response or favorable functional outcome (P > .1).
Discussion This study sought to determine the criteria of early reperfusion that have the strongest association with clinical improvement. It focused on TTP, MTT, and Tmax , the most frequently used perfusion parameters that share uniform values across gray and white matter and offer suitable lesion conspicuity on thresholdbuilt maps. The results showed that TTP- and Tmax -based definitions of reperfusion had similar predictive abilities and tended to be superior to the MTT-based definition. This is in line with a
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Fig 2. Optimal reperfusion ratios according to various thresholds for time to peak (TTP), mean transit time (MTT), and time to maximum of residue function (Tmax ) maps. ***P < .001, **P between .001 and .01, *P between .01 and .05. previous study, where Tmax ࣙ 6 seconds and MTT ࣙ 7 seconds had AUC values circa .70 and .60, respectively.13 The absence of significant difference in relative infarct reduction between MTT reperfusion groups corroborates this result. However, large confidence intervals were found both for the AUCs and the proportions of correct classification. A larger study allowing pairwise comparison is required to confirm these results. In this study, the choice of the delay threshold had no significant impact on the predictive performance of the reperfusion criteria, although there were some discrepancies between delay thresholds (Fig 1, lower panel). This may be imputed to a lack of power; as expected very high thresholds were too restrictive and very low ones too permissive. The optimal reperfusion ratio was found to be dependent on the threshold chosen to delimit the PWI lesion. Increasing percentages of reperfusion were required with increasing thresholds of TTP, MTT, and Tmax . Indeed, severely hypoperfused voxels were found to reperfuse more often than mildly hypoperfused ones. This may be explained by the methods currently used to measure reperfusion. As in previous studies, a single
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delay threshold defined the perfusion deficit, and hence reperfusion: a voxel was deemed reperfused if its value decreased below a given threshold. This method does not consider that the magnitude of the perfusion increase, which may have rendered reperfusion less specific with higher delay thresholds. For example, a voxel with a Tmax delay of 7 seconds and a subsequent decrease down to 5 seconds would be classified as reperfused at the threshold of 6 seconds, but not at the threshold of 2 seconds. Future researches should thus address the amplitude and the volumetric extent of reperfusion in voxel-based analyses. Previous studies used various criteria for reperfusion and provided limited information regarding their choices. In DEFUSE2 and EPITHET3 studies, the tissue at risk was defined as voxels with Tmax ࣙ 2 seconds and reperfusion required ࣙ30% and ࣙ10 mL reduction in the PWI lesion volume over 3-6 hours in DEFUSE2 but >90% reduction between baseline and day 3-5 in EPITHET.3 In the recently published DEFUSE-2,4 a Tmax > 6 seconds defined the PWI lesion because this threshold might be more specific in defining the
Journal of Neuroimaging Vol 25 No 6 November/December 2015
Fig 3. GBTM-based classification of reperfusion using Tmax (A), MTT (B), and TTP (C). The bold lines represent the group trajectories. The thin lines represent the individual trajectories, in black for reperfused patients (green color online) and gray for nonreperfused ones (red color online). Table 2. Clinical and Imaging Outcomes in the Tmax -based GBTM Outcome Measure
Favorable neurological response, % Favorable functional outcome, % Median ࢞NIHSS% Median ࢞Lesion%*
Early Reperfusion
No Early Reperfusion
P Value
81.8
37.5
.002
60.6
20.8
.007
−100 −44
−59 29
.002 .03
*Two missing values, ࢞NIHSS%: relative change in the NIHSS score from admission to 1 month and ࢞Lesion%: relative change in lesion volume.
ischemic penumbra.29 Reperfusion was assessed within 12 hours of endovascular treatment and required ࣙ50% reduction of the PWI lesion volume. These differences in time points of imaging follow-up hamper study comparisons. Nevertheless, considering the same outcome measure as in DEFUSE or DEFUSE-2, similar optimal values for the reperfusion ratio were found: 34% for the ROC model built on Tmax ࣙ 2 seconds and 44% for that built on Tmax ࣙ 6 seconds. Delayed examinations, especially beyond 6 hours, may lack specificity because they may be confounded by late reperfusion that is less likely to influence the clinical outcome. More stringent levels of reperfusion should thus be applied in studies that choose later time points for imaging follow-up. In this study, the optimal reperfusion ratio depended on the chosen type of clinical outcome; this complicates its use in studies with other outcome measures. Unlike ROC-based analyses that require a priori definitions of outcome, GBTM is an unsupervised method for reperfusion characterization and classification. GBTM provided clinically relevant results: it classified the patients into two groups with distinctive clinical courses. Because no predefined clinical outcome or delay threshold is required, GBTM may help comparing results from studies with different protocols. This study has some limitations. Its aim was not to build a complete predictive model for clinical outcome, nor to validate reperfusion as a marker for patient outcome or patient selection for revascularization. In addition to early reperfusion, such a model would require other factors such as the baseline clinical status, the DWI and PWI lesion volumes, the time since symptom onset, the treatment, the percentage of salvaged penumbra,
and the extent of the de novo hypoperfusion.9,17 As perfusion metrics depend on the postprocessing methods, specific reperfusion criteria may be required with other imaging techniques (eg, CT perfusion). The results suggest that GBTM-based analyses may assist cross-platform comparisons. The study included patients treated with intravenous rt-PA and others managed without thrombolysis. The I-KNOW study was not designed to assess the efficacy of intravenous thrombolysis or the merits of MRI in guiding therapy and the treatment decisions were not randomized or controlled otherwise. Here, the aim was to better characterize early reperfusion as a biomarker of clinical progression. Thrombolysis may or may not induce reperfusion; thus, treatment allocation is unlikely to have biased the analyses. Finally, most included patients had distal (>M1 segment) occlusions; thus, small baseline DWI lesion volumes (median: 10 mL). The present results require therefore confirmations in cohorts with higher prevalences of proximal occlusions and treatments by endovascular techniques. In conclusion, reperfusion criteria based on TTP or Tmax maps were more strongly associated with patient recovery than MTT maps. In addition, contrarily to the GBTM approach that successfully defined reperfusion criteria without the need for prespecified delay thresholds or clinical outcomes, the ROCbased reperfusion criteria were found to depend on the chosen clinical outcome.
References 1. Lees K, Bluhmki E, von Kummer R, et al. Time to treatment with intravenous alteplase and outcome in stroke: an updated pooled analysis of ECASS, ATLANTIS, NINDS, and EPITHET trials. Lancet 2010;375:1695-703. 2. Albers GW, Thijs VN, Wechsler L, et al. Magnetic resonance imaging profiles predict clinical response to early reperfusion: the DEFUSE study. Ann Neurol 2006;60:508-17. 3. Davis SM, Donnan GA, Parsons MW, et al. Effects of alteplase beyond 3 h after stroke in the Echoplanar Imaging Thrombolytic Evaluation Trial (EPITHET): a placebo-controlled randomised trial. Lancet Neurol 2008;7:299-309. 4. Lansberg MG, Straka M, Kemp S, et al. MRI profile and response to endovascular reperfusion after stroke (DEFUSE 2): a prospective cohort study. Lancet Neurol 2012;11:860-7. 5. Parsons M, Spratt N, Bivard A, et al. A randomized trial of tenecteplase versus alteplase for acute ischemic stroke. N Engl J Med 2012;366:1099-107.
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6. Grandin CB, Duprez TP, Smith AM, et al. Which MR-derived perfusion parameters are the best predictors of infarct growth in hyperacute stroke? Comparative study between relative and quantitative measurements. Radiology 2002;223:361-70. 7. Shimosegawa E, Hatazawa J, Ibaraki M, et al. Metabolic penumbra of acute brain infarction: a correlation with infarct growth. Ann Neurol 2005;57:495-504. 8. Takasawa M1, Jones PS, Guadagno JV, et al. How reliable is perfusion MR in acute stroke? Validation and Determination of the penumbra threshold against quantitative PET. Stroke 2008;39:8707. 9. An H, Ford AL, Vo K, Eldeniz C, et al. Early changes of tissue perfusion after tissue plasminogen activator in hyperacute ischemic stroke. Stroke 2011;42:65-72. 10. Kane I, Carpenter T, Chappell F, et al. Comparison of 10 different magnetic resonance perfusion imaging processing methods in acute ischemic stroke: effect on lesion size, proportion of patients with diffusion/perfusion mismatch, clinical scores, and radiologic outcomes. Stroke 2007;38:3158-64. 11. Christensen S, Mouridsen K, Wu O, et al. Comparison of 10 perfusion MRI parameters in 97 sub-6-hour stroke patients using voxel-based receiver operating characteristics analysis. Stroke 2009;40:2055-61. 12. Inoue M, Mlynash M, Straka M, et al. Clinical outcomes strongly associated with the degree of reperfusion achieved in target mismatch patients: pooled data from the diffusion and perfusion imaging evaluation for understanding stroke evolution studies. Stroke 2013;44:1885-90. 13. Eilaghi A, Brooks J, d’Esterre C, et al. Reperfusion is a stronger predictor of good clinical outcome than recanalization in ischemic stroke. Radiology 2013;269:240-8. 14. Zaro-Weber O, Moeller-Hartmann W, Heiss W-D, et al. Maps of time to maximum and time to peak for mismatch definition in clinical stroke studies validated with positron emission tomography. Stroke 2010;41:2817-21. 15. Carrera E, Jones P, Alawneh J, et al. Predicting infarction within the diffusion-weighted imaging lesion: does the mean transit time have added value? Stroke 2011;42:1602-7. 16. I-know Integration Information from Molecule to Man. European Commision’s Sixth Framework Programme, Aarhus, 2006.
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17. Alawneh JA, Jones PS, Mikkelsen IK, et al. Infarction of ‘noncore-non-penumbral’ tissue after stroke: multivariate modelling of clinical impact. Brain 2011;134:1765-76. 18. Hermitte L, Cho T, Ozenne B, et al. Very low cerebral blood volume predicts parenchymal hematoma in acute ischemic stroke. Stroke 2013;44:2318-20. 19. Cheng B, Forkert ND, Zavaglia M, et al. Influence of stroke infarct location on functional outcome measured by the modified rankin scale. Stroke 2014;45:1695-702. 20. Wu O, Østergaard L, Weisskoff RM, et al. Tracer arrival timinginsensitive technique for estimating flow in MR perfusion-weighted imaging using singular value decomposition with a block-circulant deconvolution matrix. Magn Reson Med 2003;50:164-74. 21. Irwin RJ, Irwin TC. A principled approach to setting optimal diagnostic thresholds: where ROC and indifference curves meet. Eur J Intern Med 2011;22:230-4. 22. Nagin DS, Tremblay RE. Analyzing developmental trajectories of distinct but related behaviors: a group-based method. Psychol Methods 2001;6:18-34. 23. Nagin D. Group-based modeling of development. Cambridge MA, Harvard University Press, 2005. 24. Mayo NE, Fellows LK, Scott SC, et al. A longitudinal view of apathy and its impact after stroke. Stroke 2009;40:3299-307. 25. Dempster AP, Laird NM, Rubin DB. Maximum likelihood from incomplete data via the EM algorithm. J R Stat Soc Series B (Methodological) 1977;39:1-38. 26. Calinski T, Harabasz J. A dendrite method for cluster analysis. Commun Stat 1974;3:1-27. 27. Davies DL, Bouldin DW. A cluster separation measure. IEEE Trans Pattern Anal Mach Intell 1979;1:224-7. 28. Ray S, Turi R. Determination of number of clusters in K-means clustering and application in colour image segmentation. 4th International Conference on Advances in Pattern Recognition and Digital Techniques 1999;1:137-43. 29. Olivot JM, Mlynash M, Thijs VN, et al. Optimal Tmax threshold for predicting penumbral tissue in acute stroke. Stroke 2009;40:46975. 30. Zaro-Weber O, Moeller-Hartmann W, Heiss WD, et al. MRI perfusion maps in acute stroke validated with 15O-water positron emission tomography. Stroke 2010;41:443-9.
Journal of Neuroimaging Vol 25 No 6 November/December 2015
ABSTRACT BACKGROUND AND PURPOSE: Though still debated, early reperfusion is increasingly used as a biomarker for clinical outcome. However, the lack of a standard definition hinders the assessment of reperfusion therapies and study comparisons. The objective was to determine the optimal early reperfusion criteria that predicts clinical outcome in ischemic stroke. METHODS: Early reperfusion was assessed voxel-wise in 57 patients within 6 hours of symptom onset. The performance of the time to peak (TTP), the mean transit time (MTT), and the time to maximum of residue function (Tmax ) at various delays thresholds in predicting the neurological response (based on the National Institutes of Health Stroke Scale) and the functional outcome (modified Rankin scale ࣘ1) at 1 month were compared. A receiver operating characteristics (ROC) analysis determined the optimal extent of reperfusion. A novel unsupervised classification of reperfusion using group-based trajectory modeling (GBTM) was evaluated. RESULTS: MTT had a lower performance than TTP and Tmax in predicting the neurological response (P = .008 vs. TTP and P = .006 vs. Tmax ) or the functional outcome (P = .0006 vs. TTP; P = .002 vs. Tmax ). No delay threshold had a significantly higher predictive value than another. The optimal percentage of reperfusion was dependent on the outcome scale (P < .001). The GBTM-based classification of reperfusion was closely associated with the clinical outcome and had a similar accuracy compared to ROC-based classification. CONCLUSIONS: TTP and Tmax should be preferred to MTT in defining early reperfusion. GBTM provided a clinically relevant reperfusion classification that does not require prespecified delay thresholds or clinical outcomes.
Keywords: Acute stroke imaging, reperfusion, perfusion-weighted imaging (PWI), ROC analysis, group-based trajectory modeling (GBTM). Acceptance: Received September 30, 2014, and in revised form March 26, 2015. Accepted for publication March 27, 2015. Correspondence: Address correspondence to N. Nighoghossian, Hopital Neurologique Pierre Wertheimer, 59 Bd Pinel, F-69500, Bron, France. ˆ E-mail: [email protected]. ∗
Both authors contributed equally.
Sources of Funding: I-KNOW consortium was funded by the European Commission’s Sixth Framework Programme (Grant 027294). J Neuroimaging 2015;25:952-958. DOI: 10.1111/jon.12255
Introduction After acute ischemic stroke, early restoration of blood flow is the only therapy of proven benefit in reducing infarct growth and promoting clinical improvement. The effectiveness of intravenous recombinant tissue plasminogen activator (rt-PA) in clinical trials is likely driven by early reperfusion but with decreasing benefits in more delayed treatments.1 Currently, perfusion-weighted imaging (PWI) is used to estimate the extent of the tissue at the risk of infarction when early reperfusion is not achieved. MRI-based studies have suggested that the reperfusion might be a surrogate marker for clinical outcome.2–4 Furthermore, early reperfusion may be a useful biomarker of outcome in phase 2 trials assessing novel revascularization procedures or putative neuroprotectants.5 Despite its importance, there is currently no single consensus definition of reperfusion. Up to now, few studies have examined the progression of tissue reperfusion within the first hours after symptom onset. The available data show considerable
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variations, especially regarding the postprocessing methods of perfusion images.2–13 In fact, several MRI metrics were used to assess perfusion deficit without consensus on the best one to consider.8,11,14,15 Generally, a single threshold of a given perfusion parameter (eg, the time to the maximum of the residue function Tmax ࣙ 6 seconds) is used to define the tissue still at the risk of necrosis.12,13 Though the extent of early reperfusion is closely associated with the amount of clinical benefit, there is yet no widely accepted optimal reperfusion ratio to distinguish between reperfused and nonreperfused patients.4 The objectives were: (1) to determine the optimal criteria for early reperfusion predicting the National Institutes of Health Stroke Scale (NIHSS) and the modified Rankin Scale (mRS) based definitions of outcome. The predictive performance of several perfusion maps was tested over multiple delay thresholds; (2) to assess a novel method for classifying reperfusion that is independent from a priori clinical hypotheses: the groupbased trajectory modeling (GBTM).
◦ 2015 by the American Society of Neuroimaging C
Materials and Methods Patients
Definitions of PWI lesion and reperfusion
The data derived from the database of the I-KNOW multicenter study.16 This study included prospective patients who underwent admission and follow-up MRIs in order to model voxel-wise probabilistic maps of infarction risk. The inclusion criteria were: (1) NIHSS ࣙ4; (2) diffusionweighted imaging (DWI) and PWI consistent with anteriorcirculation acute ischemic stroke; (3) admission MRI carried out within 6 hours in case of intravenous tPA or within 12 hours in case of conservative treatment. The study excluded all patients with lacunar or posterior circulation strokes, unknown time since symptom onset, or intracerebral hemorrhage on MRI. The same patient cohort was analyzed in four previous studies.15,17–19 Here, the analysis is the first detailed assessment of reperfusion in the I-KNOW database. Age, sex, time from symptom onset to imaging, use of thrombolysis, and stroke etiology were recorded. A regional ethics committee approved the protocol and informed consents were obtained from all patients or family members.
MRI Protocol At admission, all patients underwent: (1) a DWI (3 or 12 directions, repetition time >6000 ms, field of view 24 cm, matrix 128 × 128, slice thickness 3 or 5 mm); (2) a T2 Fluid-AttenuatedInversion-Recovery (FLAIR: repetition time 8690 ms, echo time 109 ms, inversion time 2500 ms, flip angle 150°, field of view 21 cm, matrix 224 × 256, 24 sections, section thickness 5 mm, slice gap 1 mm); (3) a T2*-weighted gradient-echo; (4) a Time-Of-Flight magnetic resonance angiography; and, (5) a PWI (echo time 30-50 ms, repetition time 1500 ms, field of view 24 cm, matrix 128 × 128, 18 slices, thickness 5 mm, gap = 1 mm, gadolinium contrast at .1 mmol/kg, intravenous injection 5 mL/s followed by 30 mL saline). An early follow-up MRI with the same sequences was performed 3 hours after the first scan (at H3) to assess early reperfusion.
Image Analysis Processing of PWI data
After motion correction, perfusion maps were computed by circular singular value decomposition of the tissue concentration curves with an arterial input function from the contralateral middle cerebral artery.20 Here, the analyses were restricted to the mean transit time (MTT) and the time to maximum of residue function (Tmax ) regarding deconvolution-based maps and to the time to peak (TTP) regarding the maps calculated by gamma-variate fitting. Using a reference region from the contralateral normal white matter, temporal parameters were normalized by subtracting the mean contralateral value and all further references to MTT, Tmax , and TTP refer to the relative parameters. Individual PWI maps at admission and 3 hours later were coregistered. All PWI data processing was performed with an in-house developed software using MATLAB 2010b (MathWorks Inc., Natick, MA, USA). Image coregistration was conducted with SPM8 (Wellcome Trust Centre for Neuroimaging, University College London, London, UK).
For TTP, MTT, and Tmax maps, the lower threshold defining the PWI lesion varied from 1 to 10 seconds per 1 second increments. For each patient, 60 PWI lesion masks were thus generated by admission and 3-hour MRIs. At each delay threshold, the reperfused tissue was defined as the set of voxels included in the initial PWI lesion but not in the 3-hour images. The reperfusion ratio (ie, the ratio of voxels showing reperfusion to those initially hypoperfused) was thus a voxel-based and coregistered measurement. Consequently, for each patient, several values of the reperfusion ratio were obtained because of the use of three perfusion parameter and ten delay thresholds. The final infarct sizes were measured with FLAIR at 1 month. The volume of DWI lesion reversal was defined as the set of voxels included in the baseline DWI lesion mask but not in the final FLAIR. This volume was expressed both as absolute volume and percentage of the baseline DWI lesion volume.
Outcome Assessment Two types of clinical outcomes were considered: (1) a “Favorable neurological response” defined as either an improvement in the NIHSS score of ࣙ8 points between admission (H0) and 1 month (M1) or a score of 0 or 1 at M1; and, (2) a “Favorable functional outcome” defined as a mRS ࣘ1 at M1. The relative change in the NIHSS score was assessed as follows: NIHSS% = (NIHSSM1 − NIHSSH0 ) × 100/NIHSSH0 . The relative change in lesion volume was assessed similarly using the DWI lesion volume at admission and the final FLAIR lesion volume at one month: ࢞Lesion% = (FLAIRM1 − DWIH0 ) × 100/DWIH0 .
Statistical Analysis Descriptive statistics are presented as medians [1st quartile; 3rd quartile] or percentages, as appropriate. Pearson correlation coefficients were used to examine the correlations between the reperfusion ratios and the delay thresholds. The reperfusion status was defined using a Receiver Operating Characteristics (ROC) analysis: for each PWI map and each delay threshold, the “optimal reperfusion ratio” (the value that ensures the best discrimination regarding the neurological response or the functional outcome) was determined using a utility function based on the empirical prevalence of positive outcome.21 An analysis of variance (ANOVA) was performed to assess the dependence of the optimal reperfusion ratio on the chosen perfusion parameter, the delay threshold, and the type of clinical outcome. The performances of TTP, MTT, and Tmax maps in predicting the neurological response were compared using crossvalidation (leave-one-out method). A Wilcoxon test was used to assess the association between the reperfusion status and recovery measures (NIHSS% and ࢞Lesion%). An ANOVA was used to investigate the influence of the choice of the perfusion parameter and its delay threshold in predicting correctly the clinical outcome.
The Group-Based Trajectory Modeling A group-based trajectory modeling (GBTM) was used to identify the criteria of reperfusion independently of the clinical outcome. GBTM is an unsupervised approach to identify groups of individuals with distinct clinical courses or responses to
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Table 1. Characteristics of Included Patients According to the Neurological Response
Number of patients Age (years) Females Thrombolysis Time from symptom onset to imaging (min) Baseline NIHSS Acute DWI lesion volume (mL) Acute hypoperfusion volume (mL, Tmax > 6 seconds) H3 hypoperfusion volume (mL, Tmax > 6 seconds)
Whole Population
Favorable Neurological Response*
Unfavorable Neurological Response
P Value
57 (100%) 70 24 (42%) 39 (68%) 126 10 10 26 8
36 (63%) 69 18 (50%) 26 (72%) 124 10 7 13 4
21 (37%) 70 6 (29%) 13 (62%) 142 11 26 52 25
– .68 .17 .56 .41 .29 .06 .95): distinct reperfusion groups could thus be formed. Considering Tmax maps, 57.9% of the patients were classified as “reperfused” and 42.1% as “nonreperfused” (Fig 3). A concordant classification throughout all three perfusion parameters was reached in 80.7% of patients. The classifications based on TTP and Tmax maps were highly concordant (96.5% agreement). No significant differences in age, sex, baseline NIHSS score, time since symptom onset to MRI, or use of thrombolysis were found between reperfused and nonreperfused patients. The reperfused group (vs. the nonreperfused group) had smaller DWI lesion volumes at baseline (9.4 mL [5.2; 13.6] vs.
47.0 mL [27.4; 66.7]; P < .001), nonsignificantly smaller DWI reversal volumes (3.03 mL [1.70; 7.81] vs. 5.00 mL [3.34; 11.73]; P = .08) but significantly higher percentages of DWI reversals versus the baseline DWI lesion volume (71.6% [44.6; 91.8] vs. 25.9% [17.6; 51.8]; P = .008). Higher rates of favorable neurological response as well as greater improvements of NIHSS scores and lesion reduction were observed in reperfused than in non-reperfused patients (Table 2). There was no significant difference in performance between the best ROC criteria and the GBTM criteria in predicting favorable neurological response or favorable functional outcome (P > .1).
Discussion This study sought to determine the criteria of early reperfusion that have the strongest association with clinical improvement. It focused on TTP, MTT, and Tmax , the most frequently used perfusion parameters that share uniform values across gray and white matter and offer suitable lesion conspicuity on thresholdbuilt maps. The results showed that TTP- and Tmax -based definitions of reperfusion had similar predictive abilities and tended to be superior to the MTT-based definition. This is in line with a
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Fig 2. Optimal reperfusion ratios according to various thresholds for time to peak (TTP), mean transit time (MTT), and time to maximum of residue function (Tmax ) maps. ***P < .001, **P between .001 and .01, *P between .01 and .05. previous study, where Tmax ࣙ 6 seconds and MTT ࣙ 7 seconds had AUC values circa .70 and .60, respectively.13 The absence of significant difference in relative infarct reduction between MTT reperfusion groups corroborates this result. However, large confidence intervals were found both for the AUCs and the proportions of correct classification. A larger study allowing pairwise comparison is required to confirm these results. In this study, the choice of the delay threshold had no significant impact on the predictive performance of the reperfusion criteria, although there were some discrepancies between delay thresholds (Fig 1, lower panel). This may be imputed to a lack of power; as expected very high thresholds were too restrictive and very low ones too permissive. The optimal reperfusion ratio was found to be dependent on the threshold chosen to delimit the PWI lesion. Increasing percentages of reperfusion were required with increasing thresholds of TTP, MTT, and Tmax . Indeed, severely hypoperfused voxels were found to reperfuse more often than mildly hypoperfused ones. This may be explained by the methods currently used to measure reperfusion. As in previous studies, a single
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delay threshold defined the perfusion deficit, and hence reperfusion: a voxel was deemed reperfused if its value decreased below a given threshold. This method does not consider that the magnitude of the perfusion increase, which may have rendered reperfusion less specific with higher delay thresholds. For example, a voxel with a Tmax delay of 7 seconds and a subsequent decrease down to 5 seconds would be classified as reperfused at the threshold of 6 seconds, but not at the threshold of 2 seconds. Future researches should thus address the amplitude and the volumetric extent of reperfusion in voxel-based analyses. Previous studies used various criteria for reperfusion and provided limited information regarding their choices. In DEFUSE2 and EPITHET3 studies, the tissue at risk was defined as voxels with Tmax ࣙ 2 seconds and reperfusion required ࣙ30% and ࣙ10 mL reduction in the PWI lesion volume over 3-6 hours in DEFUSE2 but >90% reduction between baseline and day 3-5 in EPITHET.3 In the recently published DEFUSE-2,4 a Tmax > 6 seconds defined the PWI lesion because this threshold might be more specific in defining the
Journal of Neuroimaging Vol 25 No 6 November/December 2015
Fig 3. GBTM-based classification of reperfusion using Tmax (A), MTT (B), and TTP (C). The bold lines represent the group trajectories. The thin lines represent the individual trajectories, in black for reperfused patients (green color online) and gray for nonreperfused ones (red color online). Table 2. Clinical and Imaging Outcomes in the Tmax -based GBTM Outcome Measure
Favorable neurological response, % Favorable functional outcome, % Median ࢞NIHSS% Median ࢞Lesion%*
Early Reperfusion
No Early Reperfusion
P Value
81.8
37.5
.002
60.6
20.8
.007
−100 −44
−59 29
.002 .03
*Two missing values, ࢞NIHSS%: relative change in the NIHSS score from admission to 1 month and ࢞Lesion%: relative change in lesion volume.
ischemic penumbra.29 Reperfusion was assessed within 12 hours of endovascular treatment and required ࣙ50% reduction of the PWI lesion volume. These differences in time points of imaging follow-up hamper study comparisons. Nevertheless, considering the same outcome measure as in DEFUSE or DEFUSE-2, similar optimal values for the reperfusion ratio were found: 34% for the ROC model built on Tmax ࣙ 2 seconds and 44% for that built on Tmax ࣙ 6 seconds. Delayed examinations, especially beyond 6 hours, may lack specificity because they may be confounded by late reperfusion that is less likely to influence the clinical outcome. More stringent levels of reperfusion should thus be applied in studies that choose later time points for imaging follow-up. In this study, the optimal reperfusion ratio depended on the chosen type of clinical outcome; this complicates its use in studies with other outcome measures. Unlike ROC-based analyses that require a priori definitions of outcome, GBTM is an unsupervised method for reperfusion characterization and classification. GBTM provided clinically relevant results: it classified the patients into two groups with distinctive clinical courses. Because no predefined clinical outcome or delay threshold is required, GBTM may help comparing results from studies with different protocols. This study has some limitations. Its aim was not to build a complete predictive model for clinical outcome, nor to validate reperfusion as a marker for patient outcome or patient selection for revascularization. In addition to early reperfusion, such a model would require other factors such as the baseline clinical status, the DWI and PWI lesion volumes, the time since symptom onset, the treatment, the percentage of salvaged penumbra,
and the extent of the de novo hypoperfusion.9,17 As perfusion metrics depend on the postprocessing methods, specific reperfusion criteria may be required with other imaging techniques (eg, CT perfusion). The results suggest that GBTM-based analyses may assist cross-platform comparisons. The study included patients treated with intravenous rt-PA and others managed without thrombolysis. The I-KNOW study was not designed to assess the efficacy of intravenous thrombolysis or the merits of MRI in guiding therapy and the treatment decisions were not randomized or controlled otherwise. Here, the aim was to better characterize early reperfusion as a biomarker of clinical progression. Thrombolysis may or may not induce reperfusion; thus, treatment allocation is unlikely to have biased the analyses. Finally, most included patients had distal (>M1 segment) occlusions; thus, small baseline DWI lesion volumes (median: 10 mL). The present results require therefore confirmations in cohorts with higher prevalences of proximal occlusions and treatments by endovascular techniques. In conclusion, reperfusion criteria based on TTP or Tmax maps were more strongly associated with patient recovery than MTT maps. In addition, contrarily to the GBTM approach that successfully defined reperfusion criteria without the need for prespecified delay thresholds or clinical outcomes, the ROCbased reperfusion criteria were found to depend on the chosen clinical outcome.
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Journal of Neuroimaging Vol 25 No 6 November/December 2015
