Clinical Neurology and Neurosurgery 125 (2014) 189–193

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Validation assessment of risk tools to predict outcome after thrombolytic therapy for acute ischemic stroke Robbert-Jan Van Hooff a , Koenraad Nieboer b , Ann De Smedt a , Maarten Moens c , Peter Paul De Deyn d,e , Jacques De Keyser a,f , Raf Brouns a,∗ a

Department of Neurology, Universitair Ziekenhuis Brussel, Center for Neurosciences (C4N), Vrije Universiteit Brussel (VUB), Brussel, Belgium Department of Radiology, Universitair Ziekenhuis Brussel, Vrije Universiteit Brussel (VUB), Brussel, Belgium c Department of Neurosurgery, Universitair Ziekenhuis Brussel, Center for Neurosciences (C4N), Vrije Universiteit Brussel, Brussel, Belgium d Department of Neurology/Memory clinic, Middelheim General Hospital, ZNA, Antwerp, Belgium e Department of Neurology and Alzheimer Research Center, University Medical Center Groningen, Groningen, The Netherlands f Department of Neurology, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands b

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Article history: Received 19 June 2014 Received in revised form 25 July 2014 Accepted 7 August 2014 Available online 15 August 2014 Keywords: Stroke, acute Thrombolysis Stroke, outcome Prognosis

a b s t r a c t Objective: We evaluated the reliability of eight clinical prediction models for symptomatic intracerebral hemorrhage (sICH) and long-term functional outcome in stroke patients treated with thrombolytics according to clinical practice. Methods: In a cohort of 169 patients, 60 patients (35.5%) received IV rtPA according to the European license criteria. The remaining patients received off-label IV rtPA and/or were treated with intra-arterial thrombolysis. We used receiver operator characteristic curves to analyze the discriminative capacity of the MSS score, the HAT score, the SITS SICH score, the SEDAN score and the GRASPS score for sICH according to the NINDS and the ECASSII criteria. Similarly, the discriminative capacity of the s-TPI, the iScore and the DRAGON score were assessed for the modified Rankin Scale (mRS) score at 3 months poststroke. An area under the curve (c-statistic) >0.8 was considered to reflect good discriminative capacity. The reliability of the best performing prediction model was further examined with calibration curves. Separate analyses were performed for patients meeting the European license criteria for IV rtPA and patients outside these criteria. Results: For prediction of sICH c-statistics were 0.66–0.86 and the MMS yielded the best results. For functional outcome c-statistics ranged from 0.72 to 0.86 with s-TPI as best performer. The s-TPI had the lowest absolute error on the calibration curve for predicting excellent outcome (mRS 0–1) and catastrophic outcome (mRS 5–6). Conclusions: All eight clinical models for outcome prediction after thrombolysis for acute ischemic stroke showed fair predictive value in patients treated according daily practice. The s-TPI had the best discriminatory ability and was well calibrated in our study population. © 2014 Elsevier B.V. All rights reserved.

1. Introduction Thrombolytic therapy generally improves clinical outcome after acute ischemic stroke, but the decision to administer thrombolytics may be challenging because of hemorrhagic complications and poor response in some patients [1–4]. Many models for risk

∗ Corresponding author at: Department of Neurology, Universitair Ziekenhuis Brussel, Laarbeeklaan 101, 1090 Brussel, Belgium. Tel.: +32 2 4776410; fax: +32 2 4776800. E-mail address: [email protected] (R. Brouns). http://dx.doi.org/10.1016/j.clineuro.2014.08.011 0303-8467/© 2014 Elsevier B.V. All rights reserved.

stratification of thrombolytic treatment have been developed to support the clinician in this complex decision-making process. The Stroke-Thrombolytic Predictive Instrument (s-TPI) [5,6], the Multicenter Stroke Survey (MSS) score [7,8], the Hemorrhage After Thrombolysis (HAT) score [9], the Safe Implementation of Treatments in Stroke Symptomatic Intracerebral Hemorrhage (SITS SICH) score [10], the iScore [11], the Sugar Early infarct Dense cerebral artery Age National Institutes of Health Stroke Scale (NIHSS) (SEDAN) [12] score, the Dense cerebral artery prestroke modified Rankin scale Age Glucose Onset-to-treatment time NIHSS (DRAGON) [13] score and the Glucose Race Age Sex Pressure stroke Severity (GRASPS) score [14] are all based on parameters that are generally available before initiation of thrombolytic therapy and

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Table 1 Characteristics of the study corpus of 169 patients receiving thrombolytic therapy. Characteristic

Total study population (N = 169)

IV rtPA according to European license (N = 60)

Thrombolytic therapy not according European license (N = 109)

P-value

Mean age, yearsa Gender, malea Race, Asiana Mean weight, kilogramsb Median premorbid mRSc Median NIHSS at admissionc Median OTT, minutesc Mean SBP, mmHgb Mean DBP, mmHgb Mean glucose, mg/dLb Mean platelets, ×103 /mm3 b Diabetes mellitusa Prior stroke or TIAa Arterial hypertensiona Atrial fibrillationa Congestive heart failurea Cancera Renal dialysisa Antiplatelet therapy Aspirin monotherapya Aspirin + Clopidogrela Stroke subtype, lacunara EIS on admission CTa DAS on admission CTa Thrombolytic therapy IVT rtPAa IA UKa IVT rtPA and IA UKa mRS 0–1 at 3 months poststroke mRS 0–2 at 3 months poststroke mRS 5–6 at 3 months poststroke Mortality at 3 months poststroke sICH NINDSa sICH ECASSIIa

72.0 (±14.1) 81 (47.9%) 2 (1.2%) 74 (±18) 0 (0–1) 14 (7–20) 155 (100–202) 152 (±28) 82 (±17) 126 (±41) 269 (±103) 31 (18.3%) 37 (21.9%) 113 (66.9%) 52 (30.8%) 12 (7.1%) 19 (11.2%) 0 (0.0%)

65.8 (±11.4) 38 (63.3%) 2 (3.3%) 74 (±15) 0 (0–0) 9 (6–16) 164 (96–203) 145 (±23) 81 (±15) 116 (±94–138) 267 (±93) 5 (8.3%) 10 (16.7%) 34 (56.7%) 7 (11.7%) 2 (3.3%) 6 (10.0%) 0 (0.0%)

75.5 (±14.3) 43 (39.4%) 0 (0%) 75 (±19) 0 (0–1) 15 (10–23) 150 (104–202) 157 (±30) 82 (±19) 132 (±46) 271 (±109) 26 (23.9%) 27 (24.8%) 79 (72.5%) 45 (41.3%) 10 (9.2%) 13 (11.9%) 0 (0.0%)

0.05). 3.2. Prediction of functional outcome The median value for the s-TPI predicting excellent outcome was 0.44 (IQR, 0.10–0.67) and 0.20 (IQR, 0.07–0.39) for catastrophic outcome. The median iSCORE and DRAGON scores were 157 (IQR, 114–191) and 5 (IQR, 3–7), respectively. The areas under the ROC curves for prediction of excellent, good and catastrophic outcome at 3 months poststroke for the total cohort are described in Table 3. The c-statistics for prediction of excellent outcome were similar for the s-TPI and the DRAGON score (P > 0.05), which were significantly higher than for the iSCORE (P < 0.001). Prediction of catastrophic

Table 2 Areas under the ROC curves for prediction of SICH according to the NINDS criteria and the ECASS II criteria. Prediction model

SICH according to NINDS criteria

SICH according to ECASS II criteria

MMS HAT SITS SICH SEDAN GRASPS

0.70 (0.55–0.85) 0.67 (0.51–0.82) 0.68 (0.55–0.82) 0.70 (0.54–0.87) 0.66 (0.50–0.82)

0.86 (0.70–1.00) 0.79 (0.65–0.92) 0.76 (0.60–0.92) 0.69 (0.42–0.96) 0.83 (0.72–0.95)

Values are c-statistics (95% Confidence interval). GRASPS, Glucose Race Age Sex Pressure stroke Severity; HAT, Hemorrhage after Thrombolysis score; MMS, Multicenter Stroke Survey score; SITS SICH, Safe Implementation of Treatments in Stroke Symptomatic Intracerebral Hemorrhage score; SEDAN, Sugar Early infarct Dense cerebral artery Age NIHSS score.

Fig. 1. Calibration curves for the Stroke-Thrombolytic Predictive Instrument (s-TPI) predicting excellent (A) and catastrophic outcome (B) after thrombolytic therapy.

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for lower scores (lowest two quintiles), but the risk of catastrophic outcome was overestimated for higher scores (Fig. 1B). The mean absolute error for predicting excellent and catastrophic outcome by the s-TPI was 0.16 and 0.10. 3.4. Subgroup analysis c-statistics of the s-TPI for prediction of excellent, good and catastrophic outcome were similar in the MISS cohort and the UZB registry (P > 0.05). The areas under the ROC curves for the s-TPI in patients receiving IV rtPA according to the European license criteria were also comparable to patients who did not meet these criteria (P > 0.05). Similar results were obtained in patients receiving IA UK. 4. Discussion Balancing risks and benefits of thrombolytic therapy is difficult and physician uncertainty about the impact of thrombolytic therapy in acute ischemic stroke is an important barrier to thrombolysis [24]. Prediction of postthrombolytic outcome is not synonymous with determining whether thrombolysis is beneficial or harmful in individual patients, but it can help align patient and family expectations and may facilitate rational decision-making with regard to the use of IV rt-PA and rescue therapies. A variety of risk prediction tools exist for estimation of functional outcome and the risk of sICH after thrombolytic therapy for acute ischemic stroke [6–15]. Most of the proposed predictive models, however, are based on cohorts with restrictive criteria for thrombolytic therapy. Information on their predictive value is lacking for off-label IV rtPA and intra-arterial thrombolysis. The use of thrombolytic therapy in our population differs greatly from the original derivation cohorts and validation studies. Our institutional stroke guidelines stipulate that intra-arterial thrombolysis may be considered in patients with acute large artery stroke who are ineligible for IV rt-PA or in whom such therapy has failed, as was suggested by Ellis et al. [16]. About two third of patients treated with thrombolytics did not meet the European license criteria [18]. It should be noted that off-label thrombolysis was not associated with poor outcome in the Helsinki Stroke Thrombolysis Registry and that many of the numerous contraindications for IV rtPA are based on expert opinion without support by scientific evidence [16]. In our study cohort, patients receiving thrombolytic therapy according to the European license criteria had better long-term outcome than those not meeting the license criteria. This, however, can be explained by the older age, more severe strokes, higher serum glucose levels at admission, and more important comorbidity in off-label treated patients (Table 1). Moreover, our off-label treated patients did not have poorer outcome compared with literature data [24]. We demonstrate that all eight clinical tools for outcome prediction after IV rtPA [6–15] have an acceptable discriminatory performance for prediction of functional outcome and sICH after thrombolytic therapy in contemporary clinical practice. Our results confirm the predictive value in the subgroup of patients receiving IV rtPA according to the European license criteria and suggest that the predictive models can also be applied in patients who do not meet these criteria, including those treated with intra-arterial thrombolysis. Functional outcome prediction was overall most accurate by the s-TPI. With mean absolute errors far below the threshold of 0.4, it can be concluded that this instrument is well calibrated for prediction of excellent and catastrophic outcome in our population. In accordance with literature data, the s-TPI tended to overestimate the chance of excellent outcome [7]. The risk for over catastrophic outcome was slightly underestimated in patients with low probability of poor outcome and was overestimated in those with high probability of poor outcome.

This study is the first to compare eight available clinical predictive models for outcome after thrombolytic therapy for acute ischemic stroke. The study cohort is representative for the general stroke population and treatment was administered according to contemporary clinical practice rather than the restrictive criteria of clinical trials. Prediction of both long-term functional outcome and short-term risk of hemorrhagic complications according to two prevailing definitions of sICH is another strength of this study. The rather small study sample is the main limitation and underscores the need for prospective testing in a larger multicenter study. Finally, all eight predictive models have not been exclusively developed for outcome after intra-arterial thrombolysis; distinctive models for this category should be developed and validated. Summing-up, our study compares eight currently available clinical models for outcome prediction after thrombolysis for acute ischemic stroke. We show that the s-TPI, MMS, HAT, SITS SICH, iSCORE, SEDAN, DRAGON and GRASPS scores have adequate predictive value in contemporary daily practice including off-license IV rt-PA and intra-arterial thrombolysis. Overall, the s-TPI had the best discriminatory ability for long-term functional outcome and this instrument was well calibrated in our study population.

Funding This research was supported by the Brussels Institute for Research and Innovation, the King Baudouin Foundation, and the Scientific Fund Willy Gepts.

Author contributions Robbert-Jan Van Hooff: Drafting and revising the manuscript, study concept and design, analysis and interpretation of data, statistical analysis, acquisition of data. Ann De Smedt and Koenraad Nieboer: Revising the manuscript, acquisition of data. Maarten Moens and Peter Paul De Deyn: Revising the manuscript, study concept and design. Jacques De Keyser: Revising the manuscript, study concept and design, study supervision and coordination. Raf Brouns: Drafting and revising the manuscript, study concept and design, analysis and interpretation of data, statistical analysis, acquisition of data, study supervision and coordination, obtaining funding.

Disclosures Robbert-Jan Van Hooff, Ann De Smedt, Peter Paul De Deyn, and Jacques De Keyser report no disclosures. Maarten Moens is clinical investigator of The Research Foundation Flanders (FWO) and received the Lyrica Independent Investigator Research Award (LIIRA). He received consultancy or speaker honoraria from Medtronic and Pfizer. Koenraad Nieboer is member of the medical advisory board iodinated contrast products of GE Healthcare Medical diagnostics. He received speaker honoraria from GE Healthcare Medical Imaging. Raf Brouns received consultancy or speaker honoraria from Pfizer, Medtronic, Shire Human Genetics Therapies, Sanofi-Aventis and Bayer.

Acknowledgment A.D. is a research assistant of the Fund for Scientific research Flanders (FWO-Vlaanderen).

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Appendix A. Supplementary data Supplementary data associated with this article can be found, in the online version, at http://dx.doi.org/ 10.1016/j.clineuro.2014.08.011.

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Validation assessment of risk tools to predict outcome after thrombolytic therapy for acute ischemic stroke.

We evaluated the reliability of eight clinical prediction models for symptomatic intracerebral hemorrhage (sICH) and long-term functional outcome in s...
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