Literature DB >> 30157792

Re-evaluation of the stroke prognostication using age and NIH Stroke Scale index (SPAN-100 index) in IVT patients - the-SPAN 10065 index.

Cornelia Möbius1, Christian Blinzler1, Stefan Schwab1, Martin Köhrmann2, Lorenz Breuer3.   

Abstract

<span class="abstract_title">BACKGROUND: The <span class="Chemical">SPAN-100 index adds <span class="Species">patient age and baseline <span class="Chemical">NIHSS-score and was introduced to predict clinical outcome after <span class="Disease">acute ischemic stroke (AIS). Even with high <span class="Chemical">NIHSS-scores younger <span class="Species">patients cannot reach a <span class="Chemical">SPAN-100-positive status (index ≥100). We aimed to evaluate the <span class="Chemical">SPAN-100 index among a large, contemporary cohort of i.v.-thrombolysed AIS-<span class="Species">patients and exclusively among older <span class="Species">patients who can at least theoretically achieve <span class="Chemical">SPAN-100-positivity.
<span class="abstract_title">METHODS: The <span class="Chemical">SPAN-100 index was applied to AIS-<span class="Species">patients receiving i.v.-thrombolysis (IVT) in our institution between 01/2006 and 01/2013. Clinical outcome and symptomatic <span class="Disease">intracerebral hemorrhage rates were compared between <span class="Chemical">SPAN-100-positive and -negative <span class="Species">patients. Furthermore we excluded <span class="Species">patients < 65 years, without any theoretical chance to achieve <span class="Chemical">SPAN-100-positivity, and re-evaluated the index (SPAN65-100 index).
<span class="abstract_title">RESULTS: <span class="Chemical">SPAN-100-positive IVT-<span class="Species">patients (124/1002) had a 9-fold increased risk for unfavorable outcome compared to <span class="Chemical">SPAN-negative <span class="Species">patients (OR 9.39; 95% CI 5.87-15.02; p <  0.001). The odds ratio for mortality was 7.48 (95% CI 4.90-11.43; p <  0.001). No association was found between <span class="Chemical">SPAN-100-positivity and sICH-incidence (OR 0.88; 95% CI 0.31-2.53; p = 0.810). SPAN65-100-positivity (124/741) was associated with an 8-fold increased risk for unfavorable outcome (OR 7.6; 95% CI 4.71-12.22; p <  0.001) but not associated with higher sICH-rates (OR 0.86; 95% CI 0.29-2.53; p <  0.001).
<span class="abstract_title">CONCLUSIONS: Also for <span class="Species">patients ≥65 years the <span class="Chemical">SPAN-100 index can be a fast, easy method to predict clinical outcome of IVT-<span class="Species">patients in everyday practice. However, it should not be used to determine the risk of sICH after IVT. Based on a <span class="Chemical">SPAN-positive status IVT should not be withheld from AIS-<span class="Species">patients merely because of feared sICH-complications.

Entities:  

Keywords:  Acute ischemic stroke; Clinical outcome; I.V.-thrombolysis; SPAN-100 index; SPAN-10065 index; Symptomatic intracerebral hemorrhage

Mesh:

Substances:

Year:  2018        PMID: 30157792      PMCID: PMC6114699          DOI: 10.1186/s12883-018-1126-0

Source DB:  PubMed          Journal:  BMC Neurol        ISSN: 1471-2377            Impact factor:   2.474


Background

Age and <span class="Disease">stroke severity are known major risk factors and predictors for unfavorable outcome in <span class="Disease">acute ischemic stroke (AIS) <span class="Species">patients [1-3]. However, impact of higher age and higher initial National Institutes of Health <span class="Disease">Stroke Scale (<span class="Chemical">NIHSS)-scores on clinical outcome after AIS and the way how these two factors interact with each other still remain not entirely clear. In particular data showing which individual <span class="Species">patient will achieve favorable or unfavorable outcome is sparse. Different scores to predict clinical outcome after AIS have been introduced [4-8]. Most of them are rather complex and may therefore not be extensively used in daily routine. In order to create a fast and practical tool to predict clinical outcome of AIS <span class="Species">patients the <span class="Disease">Stroke Prognostication using Age and <span class="Disease">NIH Stroke Scale index (<span class="Chemical">SPAN-100 index) was introduced [9]. It simply combines <span class="Species">patient age (years) and the <span class="Chemical">NIHSS-Score at <span class="Disease">stroke onset. <span class="Species">Patients with a score ≥ 100 are classified as <span class="Chemical">SPAN-100–positive and those with a score < 100 are designated as <span class="Chemical">SPAN-100-negative. The index was applied to the dataset of the National Institute of <span class="Disease">Neurological Disorders and <span class="Disease">Stroke (NINDS)-tPA trials [10] and found to be of good value to predict clinical outcome and risk of <span class="Disease">intracerebral hemorrhage [9]. Since its introduction the <span class="Chemical">SPAN-100-index has been evaluated several times in different <span class="Species">patient cohorts [11-15]. However, previous investigations have in common that they included <span class="Species">patients who could not achieve a <span class="Chemical">SPAN-100-positve status at all, e.g. the original <span class="Chemical">SPAN-100-evaluation [9] with a considerable proportion of younger <span class="Species">patients. Even with very high <span class="Chemical">NIHSS-scores they were not able to reach a <span class="Chemical">SPAN-Index of ≥100. Including such <span class="Species">patients might have increased selectivity of the score potentially leading to an overestimation of its benefit. The first aim of our study was to re-evaluate reliability of the original <span class="Chemical">SPAN-100 index in an independent, more recent cohort of 1002 i.v.-thrombolysed AIS-<span class="Species">patients. The second objective was to exclude younger <span class="Species">patients from our <span class="Species">patient cohort to exclusively evaluate the <span class="Chemical">SPAN-index among the subgroup of <span class="Species">patients who could at least theoretically achieve <span class="Chemical">SPAN-100-positivity.

Methods

The Erlangen <span class="Disease">Stroke and Thrombolysis Database is a prospective database of all AIS-<span class="Species">patients at the University Hospital Erlangen, Germany. It contains baseline demographic and <span class="Disease">stroke related data as well as treatment specifics, imaging information and outcome parameters for each <span class="Disease">stroke <span class="Species">patient. Outcome at day 90 was assessed using the mRS (modified Rankin Scale) evaluated by a neurologist as part of the general database independently from the present study using a semi-structured interview either in <span class="Species">person or by telephone. Favorable clinical outcome was defined as mRS: 0–2 and/or clinical recovery to the pre-<span class="Disease">stroke mRS. Unfavorable outcome was defined as a mRS of 4–6. The study was approved by the local ethics committee. 17/1002 <span class="Species">patients were lost to follow-up at day 90. Symptomatic <span class="Disease">intracerebral hemorrhage (sICH) was defined according to the European Cooperative <span class="Disease">Acute Stroke Study (ECASS) III criteria (sICH-definition 1) [16]. To assure comparability to the derivation cohort we additionally applied the sICH-definition (any documented decline in the neurologic status) used in the original publication [9] (sICH-definition 2). Our institutional guidelines are less restrictive than the European Medicines Agency -licence for tPA, therefore more than half of our <span class="Species">patients receive off-label IVT. Besides that all <span class="Species">patients were treated and monitored on our <span class="Disease">stroke unit in accordance with European guidelines [17]. Standard CT-based treatment was performed within the 4.5-h-window. For <span class="Species">patients within an extended or unknown time window our institution uses an MRI mismatch-based algorithm as described elsewhere [18]. Follow-up imaging was performed after 24 to 36 h to evaluate for <span class="Disease">intracerebral hemorrhage and <span class="Disease">infarct distribution. <span class="Chemical">NIHSS-scores were documented by <span class="Chemical">NIHSS-certified <span class="Disease">stroke neurologists.

Study population

For this study we extracted all AIS-<span class="Species">patients from the database who consecutively received IVT between 01/2006 and 01/2013. We applied the <span class="Chemical">SPAN-100 index to this cohort to create two groups by adding age (years) to the <span class="Chemical">NIHSS-score on admission. <span class="Species">Patients with a score ≥ 100 were classified as <span class="Chemical">SPAN-100-positive, whereas all <span class="Species">patients with a score < 100 were designated as <span class="Chemical">SPAN-100-negative. We compared clinical outcomes and sICH-rates of <span class="Chemical">SPAN-100-negative and <span class="Chemical">SPAN-100-positive <span class="Species">patients. To identify <span class="Species">patients who were eligible for the second part of our study, we subtracted the highest <span class="Chemical">NIHSS-score documented in our <span class="Species">patient cohort from the <span class="Chemical">SPAN-100 cut off score of ≥100. Referring to the <span class="Disease">NIHSS-Coma Scoring Rules (as stated in the original scoring manual developed for the NINDS Trial of tPA for <span class="Disease">Acute Stroke) this was a <span class="Chemical">NIHSS-score of 35 resulting in a second <span class="Species">patient-cohort aged ≥65 years. We again compared clinical outcomes at day 90 and sICH-rates in both groups (SPAN65–100-positive and SPAN65–100-negative <span class="Species">patients).

Statistical analysis

Statistical analysis was performed using PASW Statistics 19 (SPSS Inc., Chicago, Ill., USA). All data were tested for normality. Categorical variables are presented as frequencies and percentages, whereas continuous data are expressed as mean and standard deviation or as median and interquartile range as appropriate. Intergroup differences were assessed using analysis of variance for normally distributed items, the Kruskal-Wallis test for non-normal data, and the x2-test for dichotomous variables. Binary logistic regression was performed with <span class="Chemical">SPAN-100-status as independent variable. p-values ≤0.05 were considered significant.

Results

1002 AIS-<span class="Species">patients received IVT at our institution between 01/2006 and 01/2013. After applying the <span class="Chemical">SPAN-100 index 124 (12.4%) were classified as <span class="Chemical">SPAN-100-positive.

Baseline characteristics

Mean age was 87 years for <span class="Chemical">SPAN-100-positive and 72 years in <span class="Chemical">SPAN-100-negative <span class="Species">patients. Median <span class="Chemical">NIHSS-scores on admission were 19 for <span class="Chemical">SPAN-100-positive and 8 for <span class="Chemical">SPAN-100-negative <span class="Species">patients respectively. Door-to-needle-time did not differ in both groups. Compared to <span class="Chemical">SPAN-100-negative <span class="Species">patients, those in the <span class="Chemical">SPAN-100-positive group were more likely to be <span class="Species">women and to suffer from <span class="Disease">coronary artery disease and <span class="Disease">atrial fibrillation. <span class="Chemical">SPAN-100-positive <span class="Species">patients had worse pre-<span class="Disease">stroke-mRS scores than <span class="Chemical">SPAN-100-negative <span class="Species">patients. Baseline characteristics are summarized in Table 1.
Table 1

Baseline characteristics of SPAN-100-positive and SPAN-100-negative patients

VariableTotal(n = 1002)SPAN pos.(n = 124)SPAN neg.(n = 878)p-value
 Age (years), median (IQR)73 (18)87 (8)72 (17)<  0.001
 Sex (female), n (%)473 (47.2%)81 (65.3%)392 (44.6%)<  0.001
Risk factors, n (%)
 Hypertension872 (87.0%)111 (89.5%)761 (86.7%)0.475
 Hypercholesterolemia571 (57.3%)47 (37.9%)524 (60.0%)<  0.001
 Nicotine145 (14.5%)7 (5.6%)138 (15.7%)0.002
 Diabetes330 (32.9%)40 (32.3%)290 (33.0%)0.919
 Coronary artery disease283 (28.2%)46 (37.1%)237 (27.0%)0.025
 Previous myocardial infarction112 (11.2%)19 (15.3%)93 (10.6%)0.128
 Previous stroke/TIA241 (24.1%)33 (26.6%)208 (23.7%)0.501
 Atrial fibrillation412 (41.1%)86 (69.4%)326 (37.1%)< 0.001
 door-to-needle time [min.] Median (IQR)32 (26)34,5 (24)32 (26)0.543
 NIHSS-score on admission Median (IQR)10 (10)19 (6)8 (8)< 0.001
 Pre-stroke mRS: 0–1, n (%)753 (75.1%)57 (46.0%)696 (79.3%< 0.001
Vital signs/laboratory findings
 Temperature on admission [°C] median (IQR)36.6 (1.0)36.4 (1.0)36.7 (0.9)0.004
 Blood glucose on admission [mg/dl] median (IQR)117 (41)118 (34)116 (43)0.509
 Systolic blood pressure on admission [mmHg], median (IQR)160 (36)160 (37)160 (35)0.748
 Diastolic blood pressure on admission [mmHg], median (IQR)88.5 (23)85 (19)89 (23)0.028
 Leucocytes on admission [× 103/μl] median (IQR)8.40 (3.73)8.63 (3.61)8.31 (3.79)0.073
 CRP on admission [mg/l],median (IQR)4.3 (8.1)6.85 (13.2)4.0 (7.8)0.001
 Platelet count on admission [×103] median (IQR)234 (98)243.5 (102)232 (97))0.205
 Triglycerides [mg/dl] median (IQR)115 (71)106 (49)118 (78)0.055
Stroke subtype0.001
 Large artery disease92 (9.3%)8 (6.5%)84 (9.7%)
 Cardioembolic440 (44.3%)88 (71%)352 (40.5%)
 Small-vessel disease36 (3.6%)3 (2.4%)33 (3.8%)
 Other36 (3.6%)036 (4.1%)
 Unknown389 (39.2%)25 (20.2%)364 (41.9%)

Abbreviations SPAN = Stroke Prognostication using Age and NIH Stroke Scale, NIHSS = NIH Stroke Scale, TIA = transient ischemic attack, CRP = C-reactive protein

Baseline characteristics of <span class="Chemical">SPAN-100-positive and <span class="Chemical">SPAN-100-negative <span class="Species">patients Abbreviations <span class="Chemical">SPAN = <span class="Disease">Stroke Prognostication using Age and <span class="Disease">NIH Stroke Scale, <span class="Chemical">NIHSS = <span class="Disease">NIH Stroke Scale, TIA = transient <span class="Disease">ischemic attack, CRP = C-reactive protein

Outcome comparison of SPAN-100-positive and SPAN-100-negative patients

<span class="Chemical">SPAN-100-positive <span class="Species">patients were more likely to have unfavorable outcomes (80.1% vs. 30.1%; p <  0.001), and only 13 of them achieved favorable outcome at day 90 (10.7% vs. 36.9% in the <span class="Chemical">SPAN-100-negative group; p <  0.001) (Fig. 1). Mortality during hospital stay (29.8% vs. 6.4%; p <  0.001) and after three months (44.6% vs. 9.7%; p <  0.001) was significantly higher in <span class="Chemical">SPAN-100-positive <span class="Species">patients. Unadjusted binary regression analysis showed a 9-fold increase in the odds of unfavorable outcome for <span class="Chemical">SPAN-100-positive <span class="Species">patients and a 7.5-fold increase for mortality after three months (Table 2).
Fig. 1

Comparison of functional outcome according to the mRS between SPAN-100-positive and SPAN-100-negative patients as well as between SPAN-10065-positive and SPAN-10065-negative patients. Abbreviations: SPAN = Stroke Prognostication using Age and NIH Stroke Scale; mRS = modified Rankin scale

Table 2

Association between SPAN-100-positivity and outcome

variableOR95% CIp-value
 mRS 4–69.395.869–15.021<  0.001
 mortality at 3 months7.484.901–11.427<  0.001
 sICH-Def. 10.880.305–2.5270.810
 sICH-Def. 21.010.389–2.6380.978

Abbreviations SPAN = Stroke Prognostication using Age and NIH Stroke Scale, mRS = modified Rankin scale, sICH = symptomatic intracranial hemorrhage, OR = odds ratio, CI = confidence interval

Comparison of functional outcome according to the mRS between <span class="Chemical">SPAN-100-positive and <span class="Chemical">SPAN-100-negative <span class="Species">patients as well as between SPAN-10065-positive and SPAN-10065-negative <span class="Species">patients. Abbreviations: <span class="Chemical">SPAN = <span class="Disease">Stroke Prognostication using Age and <span class="Disease">NIH Stroke Scale; mRS = modified Rankin scale Association between <span class="Chemical">SPAN-100-positivity and outcome Abbreviations <span class="Chemical">SPAN = <span class="Disease">Stroke Prognostication using Age and <span class="Disease">NIH Stroke Scale, mRS = modified Rankin scale, sICH = symptomatic <span class="Disease">intracranial hemorrhage, OR = odds ratio, CI = confidence interval Area under the curve (AUC) for the <span class="Chemical">SPAN-100 concerning mRS 4–6 was 0.74; 95%; p <  0.0001; 95% CI (0.71–0.77). Sensitivity and specificity for the cut-off of 100 was 0.27 and 0.96 respectively.

SICH comparison between SPAN-100-positive and SPAN-100-negative patients

Of 36 sICH (sICH-definition 1) 4 (3.2%) occurred in <span class="Chemical">SPAN-100-positive and 32 (3.7%) in <span class="Chemical">SPAN-100-negative <span class="Species">patients, respectively (p = 1.000). The binary logistic regression analysis showed that <span class="Chemical">SPAN-100-positive status was not predictive for sICH-incidence regardless if sICH-definition 1 or 2 were applied (Table 2).

Evaluation of the SPAN-100 index in patients ≥65 years (SPAN-10065 index)

741/1002 (74%) <span class="Species">patients were ≥ 65 years old. After applying the SPAN65–100 index, we identified 617 (83.3%) SPAN65–100-negative and 124 (16.7%) SPAN65–100-positive <span class="Species">patients. Selected baseline characteristics for both subgroups are shown in Table 3.
Table 3

Baseline characteristics of SPAN65–100-positive and SPAN65–100-negative patients

VariableTotal(n = 741)SPAN65 pos.(n = 124)SPAN65 neg.(n = 617)p-value
 Age (years), median (IQR)78 (12)87 (8)76 (10)<  0.001
 Sex (female), n (%)385 (52%)81 (65.3%)304 (49.3%)< 0.001
 NIHSS-score on admission,median (IQR)10 (11)19 (6)8 (8)< 0.001

Abbreviations SPAN = Stroke Prognostication using Age and NIH Stroke Scale, NIHSS = NIH Stroke Scale, IQR inter quartile Range

Baseline characteristics of SPAN65–100-positive and SPAN65–100-negative <span class="Species">patients Abbreviations <span class="Chemical">SPAN = <span class="Disease">Stroke Prognostication using Age and <span class="Disease">NIH Stroke Scale, <span class="Chemical">NIHSS = <span class="Disease">NIH Stroke Scale, IQR inter quartile Range Outcome results of the SPAN65-cohort did not substantially differ from the findings of the total cohort. Mortality during hospital stay (29.8% vs. 7.1%; p <  0.001) and after three months (44.6% vs. 11.5%; p <  0.001) was higher for SPAN65–100-positive <span class="Species">patients. <span class="Species">Patients in the SPAN65–100-positive group were more likely to achieve unfavorable outcomes (80.1% vs. 34.8%; p <  0.001) and only 13 <span class="Species">patients in the <span class="Chemical">SPAN-100-positive group achieved a favorable outcome at 90 days (10.7% vs. 42,8% in the <span class="Chemical">SPAN 100 negative group; p <  0.001) (Fig. 1). SICH (sICH-definition 1) occurred in 4 (3.2%) and 23 (2.7%) of the SPAN65–100-positive and SPAN65–100-negative <span class="Species">patients, respectively (p = 1.000). Binary regression analysis revealed <span class="Species">patients with SPAN65–100-positive status to have an 8-fold increased risk in the odds for unfavorable outcome. AUC for the SPAN65–100 was 0.73; 95%; p <  0.0001; 95% CI (0.69–0.76). Sensitivity and specificity for the cut-off of 100 was 0.32 and 0.94 respectively. No significant association was found between SPAN65–100-status and sICH-incidence regardless which sICH-definition was used. (Table 4).
Table 4

Association between SPAN-10065-positivity and outcome

variableOR95% CIp-value
 mRS 4–67.584.708–12.222< 0.001
 mortality at 3 months6.183.996–3.996< 0.001
 sICH-Def. 10.860.291–2.5260.781
 sICH-Def. 21.220.488–3.0310.674

Abbreviations SPAN = Stroke Prognostication using Age and NIH Stroke Scale, mRS = modified Rankin scale, sICH = symptomatic intracranial hemorrhage, OR = odds ratio, CI = confidence interval

Association between SPAN-10065-positivity and outcome Abbreviations <span class="Chemical">SPAN = <span class="Disease">Stroke Prognostication using Age and <span class="Disease">NIH Stroke Scale, mRS = modified Rankin scale, sICH = symptomatic <span class="Disease">intracranial hemorrhage, OR = odds ratio, CI = confidence interval

Discussion

The <span class="Chemical">SPAN-100 index was introduced by Saposnik et al. to facilitate treatment decisions in <span class="Disease">stroke <span class="Species">patients [9]. With age and <span class="Disease">stroke severity it combines two main predictors for unfavorable <span class="Disease">stroke outcome. After applying the index to the <span class="Species">patients in the NINDS-tPA trials [10] <span class="Chemical">SPAN-100-positivity was found to be associated with worse outcomes. Besides that higher ICH-rates were seen in <span class="Chemical">SPAN-100-positive compared to <span class="Chemical">SPAN-negative <span class="Species">patients whether they had received IVT before or not. An IVT-benefit was described for <span class="Chemical">SPAN-100-negative, but not for <span class="Chemical">SPAN-100-positive <span class="Species">patients [9]. However, several conditions may have influenced these results. First, the number of <span class="Chemical">SPAN-positive <span class="Species">patients who received IVT was rather small (n = 36). Second, the population of the NINDS-trials does not reflect current treatment standards, e.g. <span class="Species">patients were randomized to tPA or placebo within 3-h after symptom onset [10]. Several additional studies evaluated the <span class="Chemical">SPAN-100 index after its first introduction [11–15, 19]. Overall they confirmed that <span class="Chemical">SPAN-100-positive <span class="Species">patients are less likely to achieve favorable outcome compared to <span class="Chemical">SPAN-negative <span class="Species">patients. Only one Chinese study found a low prediction power of the <span class="Chemical">SPAN-100 index for 3- and 12-month outcome [19]. However, data concerning the predictive power of the <span class="Chemical">SPAN-100 index for sICH are inconsistent. Krishnan et al. found higher <span class="Disease">intracerebral hemorrhage-rates in <span class="Chemical">SPAN-100-positive <span class="Species">patients compared to the <span class="Chemical">SPAN-negative <span class="Species">patients [9, 13] while Abilleira et al. observed similar sICH-rates [11]. In contrast two other studies comparing performance of different sICH-scores found a poor predictive power of the <span class="Chemical">SPAN-100 index [14, 20]. In our study, we initially re-evaluated the original <span class="Chemical">SPAN-100 index among a larger, more contemporary cohort of 1002 IVT-<span class="Species">patients, including off-label treated <span class="Species">patients. In terms of outcome our results are in line with previous studies. <span class="Species">Patients with <span class="Chemical">SPAN-100-positive status had a 9-fold increased risk of unfavorable outcome at three months. Irrespective of the used sICH-definition <span class="Chemical">SPAN-100-positive status was not associated with a higher risk of sICH. Therefore our data do not support the hypothesis by Saposnik et al. who suggested that the <span class="Chemical">SPAN-100 index might help to evaluate the risk of ICH after IVT [9]. This discrepancy might possibly be explained by the fact that advances concerning diagnostic and treatment infrastructure were achieved since the NINDS trials [10]. Continued development of imaging techniques might account for lower sICH-rates despite inclusion of off-label treated <span class="Species">patients, e.g. <span class="Species">patients with prolonged or unknown time window. Previous evaluations of the <span class="Chemical">SPAN-100-Index did not differentiate between <span class="Species">patients who could a priori reach a <span class="Chemical">SPAN-positive status and those who could not. Even with very high <span class="Chemical">NIHSS-scores, younger <span class="Species">patients are unable to reach the <span class="Chemical">SPAN-100 index cut off score of 100. In the analysis by Saposnik et al. 219/624 (35.1%) <span class="Species">patients in the <span class="Chemical">SPAN-negative group were younger than 65 years [9]. This might have biased the <span class="Chemical">SPAN-negative group towards a better outcome potentially increasing selectivity of the index and leading to an overestimation of its benefit. In the second part of our study we addressed this issue by removing <span class="Species">patients younger than 65 years from our data set and applying the SPAN-10065 index to the resulting new <span class="Species">patient-cohort aged ≥65 years. Similar to the results of the total cohort, SPAN65–100-positive <span class="Species">patients had an 8–fold higher risk to reach unfavorable outcome. But even in IVT-<span class="Species">patients ≥65 years sICH-rates did not differ between SPAN65–100-positive and SPAN65–100-negative <span class="Species">patients. Hence, unfavorable outcome in our <span class="Chemical">SPAN-100-positive <span class="Species">patients was not related to <span class="Disease">hemorrhagic complications of thrombolytic therapy suggesting that IVT should not be withheld from older <span class="Disease">stroke <span class="Species">patients with high <span class="Chemical">NIHSS-scores merely because of safety reasons. This is in line with results from the SITS-ISTR Registry which show no excess risk of <span class="Disease">cerebral hemorrhage in <span class="Species">patients with <span class="Chemical">NIHSS-score > 25 [21]. Besides that several observational studies indicate safety and efficacy of tPA-treatment in <span class="Disease">stroke <span class="Species">patients > 80 years [22-25] and the results of the third international <span class="Disease">stroke trial (IST-3) suggest that age should not be a barrier to IVT [26]. Our study has several limitations, primarily the single-center approach and the use of our institutional diagnostic and treatment guidelines as well as the retrospective design. However, including off-label <span class="Species">patients might increasingly reflect common daily routine.

Conclusions

Our findings confirm the value of the <span class="Chemical">SPAN-100 index. Also after excluding younger <span class="Species">patients who a priori cannot reach a <span class="Chemical">SPAN-positive status the <span class="Chemical">SPAN-100 index can be an easy to use, readily available calculator in everyday clinical practice to predict clinical outcome in IVT <span class="Species">patients. However, in our cohort <span class="Chemical">SPAN-100-positive status was not predictive for the risk of sICH after IVT. Based on a positive <span class="Chemical">SPAN-100 status IVT should not be withheld from <span class="Species">patients merely because of feared sICH complications.
  26 in total

1.  The stroke-thrombolytic predictive instrument: a predictive instrument for intravenous thrombolysis in acute ischemic stroke.

Authors:  David M Kent; Harry P Selker; Robin Ruthazer; Erich Bluhmki; Werner Hacke
Journal:  Stroke       Date:  2006-10-26       Impact factor: 7.914

2.  Symptomatic intracerebral hemorrhage after intravenous thrombolysis in Chinese patients: comparison of prediction models.

Authors:  Mu Li; Run-Qi Wang-Qin; Yi-Long Wang; Li-Bin Liu; Yue-Song Pan; Xiao-Ling Liao; Yong-Jun Wang; An-Ding Xu
Journal:  J Stroke Cerebrovasc Dis       Date:  2015-04-16       Impact factor: 2.136

3.  A simple risk index and thrombolytic treatment response in acute ischemic stroke.

Authors:  Bruce Ovbiagele; Mathew J Reeves; Mojdeh Nasiri; S Claiborne Johnston; Philip M Bath; Gustavo Saposnik
Journal:  JAMA Neurol       Date:  2014-07-01       Impact factor: 18.302

4.  Tissue plasminogen activator for acute ischemic stroke.

Authors: 
Journal:  N Engl J Med       Date:  1995-12-14       Impact factor: 91.245

5.  The HAT Score: a simple grading scale for predicting hemorrhage after thrombolysis.

Authors:  M Lou; A Safdar; M Mehdiratta; S Kumar; G Schlaug; L Caplan; D Searls; M Selim
Journal:  Neurology       Date:  2008-10-28       Impact factor: 9.910

6.  Thrombolysis with alteplase 3 to 4.5 hours after acute ischemic stroke.

Authors:  Werner Hacke; Markku Kaste; Erich Bluhmki; Miroslav Brozman; Antoni Dávalos; Donata Guidetti; Vincent Larrue; Kennedy R Lees; Zakaria Medeghri; Thomas Machnig; Dietmar Schneider; Rüdiger von Kummer; Nils Wahlgren; Danilo Toni
Journal:  N Engl J Med       Date:  2008-09-25       Impact factor: 91.245

7.  Predicting long-term outcome after acute ischemic stroke: a simple index works in patients from controlled clinical trials.

Authors:  Inke R König; Andreas Ziegler; Erich Bluhmki; Werner Hacke; Philip M W Bath; Ralph L Sacco; Hans C Diener; Christian Weimar
Journal:  Stroke       Date:  2008-04-10       Impact factor: 7.914

8.  IV thrombolysis in very severe and severe ischemic stroke: Results from the SITS-ISTR Registry.

Authors:  Michael V Mazya; Kennedy R Lees; David Collas; Viiu-Marika Rand; Robert Mikulik; Danilo Toni; Nils Wahlgren; Niaz Ahmed
Journal:  Neurology       Date:  2015-11-06       Impact factor: 9.910

9.  The benefits and harms of intravenous thrombolysis with recombinant tissue plasminogen activator within 6 h of acute ischaemic stroke (the third international stroke trial [IST-3]): a randomised controlled trial.

Authors:  Peter Sandercock; Joanna M Wardlaw; Richard I Lindley; Martin Dennis; Geoff Cohen; Gordon Murray; Karen Innes; Graham Venables; Anna Czlonkowska; Adam Kobayashi; Stefano Ricci; Veronica Murray; Eivind Berge; Karsten Bruins Slot; Graeme J Hankey; Manuel Correia; Andre Peeters; Karl Matz; Phillippe Lyrer; Gord Gubitz; Stephen J Phillips; Antonio Arauz
Journal:  Lancet       Date:  2012-05-23       Impact factor: 79.321

10.  Thrombolysis in very elderly people: controlled comparison of SITS International Stroke Thrombolysis Registry and Virtual International Stroke Trials Archive.

Authors:  Nishant K Mishra; Niaz Ahmed; Grethe Andersen; José A Egido; Perttu J Lindsberg; Peter A Ringleb; Nils G Wahlgren; Kennedy R Lees
Journal:  BMJ       Date:  2010-11-23
View more
  5 in total

1.  Prediction of Clinical Outcome in Patients with Large-Vessel Acute Ischemic Stroke: Performance of Machine Learning versus SPAN-100.

Authors:  B Jiang; G Zhu; Y Xie; J J Heit; H Chen; Y Li; V Ding; A Eskandari; P Michel; G Zaharchuk; M Wintermark
Journal:  AJNR Am J Neuroradiol       Date:  2021-01-07       Impact factor: 3.825

2.  Outcome prediction for patients with anterior circulation acute ischemic stroke following endovascular treatment: A single-center study.

Authors:  Xiao Wu; Guoqing Liu; Wu Zhou; Aihua Ou; Xian Liu; Yuhan Wang; Sifan Zhou; Wenting Luo; Bo Liu
Journal:  Exp Ther Med       Date:  2019-09-25       Impact factor: 2.447

3.  Artificial neural network based prediction of postthrombolysis intracerebral hemorrhage and death.

Authors:  Chen-Chih Chung; Lung Chan; Oluwaseun Adebayo Bamodu; Chien-Tai Hong; Hung-Wen Chiu
Journal:  Sci Rep       Date:  2020-11-25       Impact factor: 4.379

4.  Nomogram to predict hemorrhagic transformation for acute ischemic stroke in Western China: a retrospective analysis.

Authors:  Keming Zhang; Jianfang Luan; Changqing Li; Mingli Chen
Journal:  BMC Neurol       Date:  2022-04-26       Impact factor: 2.903

5.  Age Is Only a Number Also in Hyperacute Stroke Care-But Not an Irrelevant One.

Authors:  Jussi O T Sipilä
Journal:  J Clin Med       Date:  2022-08-13       Impact factor: 4.964

  5 in total

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