1. University of Thessaly, Larissa, Greece
The ASTRAL score to predict functional outcome in acute ischemic stroke
George Ntaios
University of Thessaly, Larissa, Greece

2. Dear colleagues, I would like first to thank the organizing committee for the kind invitation to be here today and discuss with you about the ASTRAL score.
I am sure that it happened to all of us that a patient who was just admitted with stroke or his relatives ask us if he will be able walk again, if he will be able to go back to work again, and generally if he is going to be independent again. I don’ t know how you respond to these questions, but until recently I used to answer that it is not possible at admission to make a safe prediction about the future of the patient, and that we need at least a few more days. So, actually, this is the question that we tried to answer with this study, that is to see if it possible to predict patient’s outcome right away from the very first minutes after stroke onset.

3. Let’s design the ideal prognostic score… not too many variables
readily available variables
simple to calculate
applicable as early as possible
widely-used scale for outcome assessment
validated externally
validated in large population
validated in multiple populations
So, when we started designing this study, we spent a few days thinking how should the ideal stroke prognostic score look like. And, we came down to the following characteristics: first of all, the ideal score should not include too many variables. When a score includes many variables, it is not very user-friendly and as a result, doctors are relatively reluctant to use it. Secondly, the ideal score should include variables which are easy to assess. For example, imagine a stroke prognostic score which includes the penumbra volume. How many centers around the world do you think that would be able to use such a score in everyday clinical practice? Then, the ideal prognostic score should be calculated easily without complex mathematical formulas or the use of calculators. Also, the ideal prognostic score should be applied as early as possible after stroke onset. I mean, we can all make a decent guess about the patient’s future at discharge, so we would not really need a prognostic score at discharge. Also, it is important that to use a widely-used scale is used for the assessment of outcome. There are several indices of outcome like the modified Rankin scale score, the Barthel index, the Glasgow outcome scale, the Oxford Handicap scale and many other. Among them, probably the modified Rankin Scale score is the most widely used. Also, the ideal prognostic score should be externally –not internally- validated (we will discuss a bit more about this in a few minutes). Finally, the ideal prognostic score should be validated in a large population and if possible, in multiple populations.
Modified Rankin Scale score
Barthel index
Glasgow outcome scale
Oxford Handicap Scale

4. ASTRAL registry (Lausanne/Switzerland) n=1645
Athens Stroke Registry
n=1659
Vienna Stroke registry
n=653
Development of the prognostic score
Double external validation of the prognostic score
Prognostic point-based score
Color chart
So, keeping these things in mind, this is how we designed our study in order to develop a prognostic score for the prediction of stroke outcome. We used data from the ASTRAL registry, which is the registry of all consecutive patients admitted in CHUV hospital in Lausanne, Switzerland since Several parameters are registered in this registry such as demographics, patient history, clinical examination, imaging findings, laboratory data and treatment modalities. We included all these parameters in a multivariate analysis using poor outcome as the endpoint, defined as modified Rankin Scale score of 3 or more. The parameters which were shown to be independent predictors of outcome in this multivariate analysis were used to construct the prognostic score.
Then, the next step would be to validate the performance of our score. A validation can be either internal of external. In internal validation, what you actually do is split your dataset in two randomly chosen groups, you use the first group to develop your score, and then you use the second group to validate the performance of the score. However, taken into consideration that actually both groups come from the same mother population, an internal validation is not so reliable. On the contrary, the external validation means that you use your full dataset to develop your score and then you use an external independent population to validate the performance of the score. So, we decided to validate our score externally, and for this reason we used the data from the Athens Stroke registry. But we wanted to be strict with our score, so we decided to look for a second external validation in another population and so, we used also the data from the Vienna Stroke registry.
Finally, we wanted to make this score as user-friendly as possible, so we aimed to draw a color chart which would help predict stroke outcome in daily clinical practice easily without any need for mathematical formulas or calculators.

5. If we have a look at the baseline characteristics of the 16 hundred patients from the ASTRAL registry who were used to develop the score, we can see that their median age was 68 years, their mean NIHSS was 9, 32% of them had a visual defect, 90% of them were fully alert, and their mean BP was 160 over 96.

6. So, as we already mentioned, we performed a multivariate analysis of all available parameters using poor outcome as the dependent variable, defined as Rankin score of 3 or more. The results showed six independent predictors of outcome which were age, stroke severity assessed with the NIHSS score, the time between stroke onset and hospital admission, the range of vision, glucose value at admission and level of consciousness. And as you can see, the acronym ASTRAL comes from the first letters of these parameters. Now, from these analysis, we used the beta-coefficients, which actually show the relative contribution of each parameter in the prediction of outcome, and we multiplied them with the number four. Then we rounded them to the closest digit.

7. So, this is how the ASTRAL score was developed
So, this is how the ASTRAL score was developed. And as we can see, a patient gets 1 point for every 5 years of age, 1 point for every 1 point in the NIHSS score, 2 points if admitted later than 3 hours after stroke onset, two points if there is a visual field defect, 1 point if his admission glucose is higher than 7.3 or less than 3.7 mmol/lt and 3 points if his level of concsiousness is impaired. The sum of all these points gives the overall score.

8. Now, the blue bars in this diagram show the number of patients in the Lausanne registry for every given value of the ASTRAL score. So, for example, the ASTRAL score was 17 in approximately 100 patients, and 25 in almost 50 patients. As we can see, most patients had an ASTRAL score between 15 and 25.
Now, the S-curve, the sigmoid curve shows the probability of poor outcome for every given value of the ASTRAL score. In general, the higher the ASTRAL score, the worse the outcome is. So for example, a patient with an ASTRAL score of 22 has only 20% probability of bad outcome, a patient with a score of 30 has a 50% possibility of bad outcome, and a patient with 38 has more than 80% probability of bad outcome.

9. P=0.43
Now, for any prognostic score, in order to see if this score is reliable, we can use two statistical tools. The first one is called calibration, that is the agreement between predicted and actual outcome. And this graph shows that the internal calibration for our score was very good. The S-curve shows again the predicted possibility of bad outcome for any given value of the ASTRAL score, as this is calculated by the mathematical model. The yellow dots show the actual outcome of the registered patients. As we see, the dots fall very very close to the S-curve, which means that the actual outcome of the patients was very similar to the predicted outcome.
Ίσως κάποιος σχολιάσει ότι στα δύο άκρα της καμπύλης τα όρια εμπιστοσύνης είναι πιο μεγάλα, αλλά αυτό δεν οφείλεται σε μειωμένη αξιοπιστία του σκορ σε εκείνες τις περιοχές, αλλά σε μικρότερο πλήθος ασθενών με αντίστοιχα σκορ.
Calibration, i.e., the agreement between predicted and actual outcome, was assessed in the derivation and validation cohorts with the use of the Hosmer-Lemeshow goodness-of-fit test with 10 groups

10. The second statistical tool to see if a prognostic score is reliable is called discrimination, that is the efficiency of the score to discriminate between patients with good and bad outcome. This is assessed by calculating the area under the curve or else, AUC. So, in the internal discrimination of our score, the AUC was 0.85 which is a really excellent result.

11. ASTRAL registry (Lausanne/Switzerland) n=1645
Athens Stroke registry
n=1659
Vienna Stroke registry
n=653
Development of the prognostic score
Double external validation of the prognostic score
Prognostic point-based score
Color chart
So, as soon as the score was developed, the next step was to validate it. As we already mentioned, we chose to validate it externally, in two independent stroke registries: the Athens stroke registry and the Vienna stroke registry.

12. As we can see, the basic characteristics of the three cohorts were similar, with the main difference being that the Austrian strokes were less severe.

13. P=0.22
The external validation of the score is again assessed using the tools of calibration and discrimination. We see that the calibration in the Athens cohort is really excellent as the dots –which show the actual outcome- fall very close to the S-curve –which shows the predicted probability of outcome.

14. P=0.49
The calibration of the score in the Vienna registry is not so excellent like in the Athens cohort , but still is very good and of course statistically significant.

15. P=0.82
Now, if we combine the Athens and Vienna registries, we can see again that the score has an excellent calibration in this pooled dataset.

16. The second statistic tool to assess external validity of the score is the discrimination. And as we see, the discrimination of the score in the Athens cohort is really excellent, since the area under the curve is really impressive: 0.93.

17. The area under the curve in the Vienna registry is not so impressive like in the Athens registry, but still it is very good: 0.77

18. Now if we pool the Athens and the Vienna registries together, the area under the curve has again an excellent, a very convincing value of 0.90.

19. ASTRAL registry (Lausanne/Switzerland) n=1645
Athens Stroke Registry
n=1659
Vienna Stroke Registry
n=653
Development of the prognostic score
Double external validation of the prognostic score
Prognostic point-based score
Color chart
So, we just saw that the ASTRAL score was externally validated in a very convincing way. So, the final step in our project was to draw a color chart to make our score even more user friendly.

20. So, this is the graph that we have drawn
So, this is the graph that we have drawn. In this graph, the possibility of poor outcome is pre-calculated according to the values that each parameter may have and is represented in a color palette. So, let’s see how to use this graph. The first parameter that we examine is if there is visual field defect. And let’ s consider a patient with no visual field defect, so we focus on the upper part of the graph. Then, we examine his admission glucose, so if it between 3.7 and 7.3, for example 5.1, then we focus on the left part of the graph. Then, we examine his level of consciousness, and if it is good we focus on the left part. Then we examine how soon after stroke onset was he admitted, and if he arrived within less than three hours we focus on the lower part of the graph. Then, we examine the patient’s age and if for example it is 68 years we focus on a specific line. And finally, we examine stroke severity, so if it is 5 for example, we focus to this little square. The color of this little square corresponds to the possibility of bad outcome, as described in the color palette at the lower part of the graph. So, for example, the patient that we just described has a 10-20% possibility of bad outcome.

21. the ASTRAL score is a very reliable prognostic score
Take-home messages
the ASTRAL score is a very reliable prognostic score
it calculates the probability of poor outcome at three months
it can be used already at admission
it is very simple to use, especially with the help of the color chart
So, to conclude, the ASTRAL score is…….
Before I close, I would like to thank these people for being so nice to work with: Dr Patrik Michel and Mohamed Faouzi from Lausanne, Dr Kostas Vemmos from Athens, and Dr Julia Ferrari and Prof Wilfried Lang from Vienna. Thank you.

22. Spare slides

23. Oxford Handicap Scale score < 3
Now, let’s see some other prognostic scores that can be used to predict stroke outcome. And allow me to start with a prognostic score which was introduced and published in 2002 in Stroke. From my point of view, this score has two major weaknesses which limit the wide applicability of the score in clinical practice. Firstly, the outcome scale is the Oxford Handicap Scale score, which as far as I know, is not used very often. Actually, how many of us use this scale to assess our patient’s outcome?......(pause)….. Oh, I see, not so many…. The second major weakness is that it is too complex to calculate. As is described in the paper, you need to use this following formula taken into consideration these numbers. Well, you certainly need a calculator for this!
Stroke 2002; 33:

24. logit(BI<95) = -5.782 + (0.049*age) + (0.272*NIHSS)
This is another score introduced in 2004 in Stroke again. The advantage of this score is that it includes only two parameters, the age and the NIHSS. On the other hand, a little weakness is that it uses the Barthel index to assess stroke outcome, which is not so widely used as the Rankin score. At the beginning, there was also another major weakness, which again was the complexity of the score. As you see, you had to calculate the possibility of bad outcome using this complex formula.
Stroke 2004;35:

25. Fortunately, after a while, the authors introduced a normogram which helps to assess the possibility of good outcome without any complex calculations. So, how it works? First you find the age of the patient, which corresponds to some points at the first axis. Then, you assess stroke severity which corresponds to some more points at the first axis. Then, you add these points, and you get a sum which corresponds to the possibility of good outcome.
Stroke 2008;39;

26. - Validation in the VISTA dataset ASTRAL score
Score by Weimar et al.
- Validation in the VISTA dataset
ASTRAL score
- Validation in pooled Athens/Vienna dataset
This score was externally validated in the VISTA dataset. The area under the curve was 0.80, which is a very good result showing that this prognostic score is reliable. However, please allow me to notice that this result is still far from the result of the validation of our score, which was 0.90 in a double external validation.
Stroke 2008;39;
Neurology 2012;78:1916–1922

27. Recently, another prognostic score was introduced, the iSCORE.
Stroke 2011;42:

28. It was recently shown that the iSCORE can predict stroke outcome
It was recently shown that the iSCORE can predict stroke outcome. As can be seen in the figure, the higher the iSCORE, the higher the probability of mortality or disability at 30 days.
Stroke 2011;42:

29. The iSCORE can be calculated easily taken into consideration several parameters such as age, sex, stroke severity, stroke subtype, atrial fibrillation, heart failure, cancer, renal dialysis, preadmission disability and admission glucose. However, we need to point out that perhaps the major weakness of the iSCORE is that stroke severity needs to be assessed using the Canadian Neurological Scale which is again not so widely used. I wonder, how many of us do we regularly use the Canadian Neurological Scale to assess stroke severity in your patients….(pause)….. I see, not many…
Stroke 2011;42:

30. Συνοψίζοντας λοιπόν, προτείνουμε ένα νέο εύχρηστο και αξιόπιστο τρόπο για την πρόγνωση της έκβασης στα ισχαιμικά αγγειακά εγκεφαλικά επεισόδια. Ευχαριστώ!

33. P=0.43
P=0.22
P=0.49
P=0.82
Το πάνω αριστερό διάγραμα το είδαμε και προηγουμένως και δείχνει την σύμπτωση μεταξύ της προβλεπόμενης και της πραγματικης έκβασης του επεισοδίου στην ομάδα της Λωζάννης. Πάνω δεξιά βλέπουμε το ίδιο διάγραμμα ια την ομάδα της Αθήνας όπου και πάλι βλέπουμε πόσο πολύ συμπίπτουν. Κάτω δεξιά βλέπουμε την ομάδα της Αυστρίας όπου η σύμπτωση λιγότερο εντυπωσιακή, ενώ στο τέταρτο διάγραμμα όπου έχουν συνδυαστεί οι ασθενείς της Αθήνας και της Αυστρίας εη σύμπτωση είναι επίσης εξαιρετική.
Hosmer-Lemeshow test

34. The
Τα παραπάνω αντικατοπτρίζονται και στις areas under the curve, οι οποίες ήταν 0.85 όπως είδαμε στην ομάδα της Λωζάννης, ένα εξαιρετικά εντυπωσιακό 0.93 στην ομάδα της Αθήνας, 0.77 στην ομάδα της Αυστρίας, και επίσης ένα εντυπωσιακό 0.90 στο συνδυασμό της Αθήνας και της Αυστρίας.

35. Τα παραπάνω αντικατοπτρίζονται και στις areas under the curve, οι οποίες ήταν 0.85 όπως είδαμε στην ομάδα της Λωζάννης, ένα εξαιρετικά εντυπωσιακό 0.93 στην ομάδα της Αθήνας, 0.77 στην ομάδα της Αυστρίας, και επίσης ένα εντυπωσιακό 0.90 στο συνδυασμό της Αθήνας και της Αυστρίας.

36. Predictors of long-term outcome
5-year unfavourable outcome (mRS=3-6)
5-year
mortality
ASTRAL score
(per 1 point increase)
1.09 (1.08 – 1.10)*
Atrial fibrillation
1.53 (1.14 – 2.04)†
Heart Failure
1.56 (1.14 – 2.13)†
Stroke subtypes 1
Lacunar
1.71 (1.10 – 2.66)‡
1.14 (0.67 – 1.94)
Atherosclerotic
1.65 (1.03 – 2.63)‡
1.74 (1.02 – 2.97)‡
Cardioembolic
2.82 (1.84 – 4.34)*
2.39 (1.46 – 3.93)*
Undetermined
0.74 (0.58 – 0.95)‡