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Variations Between Countries in Outcome After
Stroke in the International Stroke Trial (IST)

NU Weir, MBChB*; PAG Sandercock, DM; SC Lewis, PhD;
DF Signorini, PhD; CP Warlow, MD on behalf of the
IST Collaborative Group

*Department of Neurology, Royal Hallamshire Hospital, Sheffield, UK
Department of Clinical Neurosciences, Western General Hospital, Edinburgh, UK

   Whether a patient who has had an acute stroke dies, survives in a disabled state or recovers completely is determined by many factors. Outcome after stroke varies from country to country and recent studies have sought to explain the variation (1-5). Some of the variation in outcome may be due to differences between countries in the process of stroke care (3;4;6-10). The International Stroke Trial (IST) was a multinational randomized controlled trial that investigated the impact of aspirin, heparin, both or neither on the outcome of patients admitted to hospital with an acute ischemic stroke. Preliminary analyses (with all treatment groups combined) of the nearly 20,000 patients entered revealed that among the 37 participating countries, six month case fatality ranged from 0 to 33% and the proportion dead or dependent ranged from 32 to 89% (11). In itself, this observation was perhaps not surprising, given the inevitable differences between countries in the baseline characteristics (casemix) of the patients entered. The IST employed a randomization procedure which anticipated this possibility and treatment allocation was prospectively balanced within countries to prevent any between country differences in casemix from biasing the assessment of the effects of treatment. In this paper, we describe the large variations in outcome between countries in the IST in detail and use the trial data set to determine how far these differences might be explained by adjusting for important differences in casemix and consider how far other factors, in particular international differences in the treatment of acute stroke, might account for any variation in outcome that remains.

Exclusion of data from countries recruiting less than 500 patients
   When comparing patients from different countries we wished to avoid the extreme variability in outcome that can occur with small sample sizes (12). We therefore arbitrarily chose to examine data from only those countries that had randomized more than 500 patients each. We combined data from the treatment and control patients in each country for all analyses and our findings therefore do not describe variation between countries in the efficacy of aspirin or heparin in patients with acute stroke.

Baseline characteristics and measures of the process of stroke care
   We extracted the following potential prognostic variables from the baseline dataset: age, gender, delay from symptom onset to randomization, conscious level, presence of atrial fibrillation (AF), systolic blood pressure on admission (sBP), presence of any infarction on CT scan, and presence or absence of eight different neurological deficits. We also extracted data on two potential markers of the quality of the process of care: whether patients had had a CT scan prior to randomization and whether patients who were discharged alive from the participating hospital were reported to be prescribed long-term aspirin.

Crude and adjusted outcome at six months
   We calculated the proportion who died from any cause (case fatality) and the proportion who were dead or needed help from another person for activities of daily living (death or dependency) at six months in each country (observed number). We used two previously described logistic regression models to adjust these data for important differences in casemix (13). The prognostic models were constructed and validated on two separate subsets of the IST data set and take the following variables into account: age, sex, sBP, AF, conscious level and the total number of neurological deficits (see statistical appendix). We used the models to predict the probability of an outcome event for each patient and then calculated the total number of predicted events for each country as the sum of these probabilities (predicted number).

   We expressed the adjusted outcome as a 'w-score', a method that measures the difference in the number of observed and predicted events per 1000 patients treated within each country (14;15). We calculated the w-score using the formula w =1000(o - p)/t, where o is the observed number of events, p the predicted number of events and t the total number of patients per country. For example, if 500 patients are treated and a total of 100 deaths are predicted but 150 deaths are observed, then the w-score is 1000 x (150-100)/500 = +100, that is, 100 more deaths than predicted per 1000 patients treated. However, we were principally interested in the difference in adjusted outcome between countries and calculated these simply by subtracting w-scores. For example, if the w-score for case fatality in country A was +30 and that in country B was -60, we estimated that the absolute difference in adjusted case fatality between countries to be 90 deaths per 1000 patients treated. In order to directly compare outcomes before and after adjusting for casemix, we also calculated crude w-scores by defining p in each country as tpo, where po is the proportion of patients that experienced the outcome in the entire study population (all countries combined).

Correction for multiple comparisons
   Because the study necessarily involved multiple comparisons between countries, we calculated 99% confidence intervals for the w-scores (according to Parry et al).(14) to reduce the possibility that any observed differences might be due to the play of chance.

Relationship between measures of the process of care and adjusted outcome after stroke
   We ranked the countries by the two adjusted outcomes and by the two measured items of the process of stroke care. Better outcome and better process of care are represented by lower numbers and vice-versa. We investigated the relationship between rankings with Spearman's rank correlation coefficient.

The predictive properties of the prognostic models
   We determined the predictive properties of our prognostic models by estimating their calibration and discrimination (16). Calibration refers to the degree of bias of model predictions for groups of patients and can be estimated by plotting a calibration curve. To derive the calibration curves, we ordered the data-set by ascending predictions of risk and then divided it into 10 equal sized groups (deciles). For each decile, we plotted the mean observed risk against the mean predicted risk. A model is well calibrated if, within each decile, the proportion of patients predicted to have an event and the proportion observed to have done so is the same i.e. if the calibration curve follows a 45° line. Discrimination refers to the ability of the model to differentiate between individuals that do and do not experience an event and may be estimated by calculating the area under the Receiver Operator Characteristic (ROC) curve (a plot of the sensitivity against (1 - specificity) of the model predictions). Thus, a model that predicts case fatality with an area under the ROC of, say, 80% will, in 80% of cases, correctly assign a higher risk of death to a randomly selected patient with a fatal outcome than to a randomly selected survivor.

Analyses were performed using SAS (version 6.12).

Patients included in the analysis
   The IST recruited 19,435 patients from 37 countries. Nine countries recruited more than 500 patients: Argentina, Australia, Italy, the Netherlands, Norway, Poland, Sweden, Switzerland and the United Kingdom. These countries contributed a total of 15,116 patients (78% of the trial accrual); of these only 59 (0.4%) were lost to six month follow-up. The greatest losses to follow up occurred in Argentina and the Netherlands (5% and 2%, respectively); in all other countries outcome data were available in over 99% of cases.

Baseline characteristics and the process of care
   The proportion of patients with each prognostic variable at baseline varied highly significantly between countries (c2 tests, all p < 0.0001) (table 1), confirming our expectation that there would be marked variation between countries in the types of stroke patients considered eligible for the trial. Table 1 also shows substantial variation between countries in the process of stroke care, as judged by the proportion of patients who had a CT scan before randomization (range 42 to 98%) and the proportion of patients prescribed aspirin for long term secondary prevention at hospital discharge (range 53 to 73%).

Variations in outcome at six months (observed and predicted)
   Table 2 shows the variation between countries in the proportion of patients who had died by six months (range 12 to 30%) and in the proportion who were dead or dependent at six months (range 42 to 79%). The proportion of patients predicted dead and the proportion predicted dead or dependent can be taken to summarize the casemix of each cohort, and not surprisingly, these also varied considerably between countries (table 2).

Crude and adjusted case fatality at six months
   Figure 1 shows the w-scores for case fatality for each country both before and after adjusting for casemix. The figure clearly illustrates the substantial variation in crude case fatality between countries, the most extreme difference being between Sweden and Poland (171 more deaths per 1000 patients treated in Poland). After adjusting for casemix, some of the differences in case fatality between pairs of countries altered considerably. For example, adjustment for casemix reduced the differences in case fatality between Switzerland and Sweden and between the UK and the Netherlands by about 70%. However, the most striking finding shown in figure 1 is that, on the whole, adjusting for important differences in casemix had little influence on the variation in case fatality between countries, which remained very substantial. For example, at six months, the difference between Sweden and Italy was 69 deaths per 1000 patients and between the UK and Poland 57 deaths per 1000 patients; at its most extreme, 160 more patients were dead at six months per 1000 treated in Poland than in Sweden despite adjusting for casemix.

Crude and adjusted 'death or dependency' at six months
   Figure 2 shows the w-scores for death or dependency at six months before and after adjusting for casemix. As with case fatality, the crude number of patients dead or dependent at six months varied considerably between countries; the largest difference being between Sweden and the UK (375 more patients were dead or dependent per 1000 treated in the UK). Adjusting for casemix markedly reduced some of the between country differences, for example, that between Switzerland and Italy reduced by 86% and that between Argentina and Australia reduced by 60%. Again, however, despite adjusting for casemix, the overall variation in death or dependency between countries remained very substantial. For example, at six months, the difference between Sweden and Poland was 86 patients dead or dependent per 1000 treated and between Switzerland and the UK 146 patients dead or dependent per 1000 treated; at its most extreme, 311 more patients were dead or dependent per 1000 treated in the UK than in Sweden even after adjusting for casemix.

The association of the process of care with adjusted outcome
   The ranking of countries by adjusted outcome and by the proportion of patients that received each item of care is shown in table 3. Higher adjusted case fatality was strongly correlated with a lower rate of CT scanning before randomization (r 0.78) and also with a lower rate of prescription of long term aspirin (r 0.53). The correlation between adjusted death or dependency and the two process of care variables was weaker (r 0.40 in both cases).

The performance of the predictive models
   Figure 3 shows that both prognostic models were well calibrated (both calibration plots follow a 45° line). Both models also showed moderately good discrimination with an area under the ROC of 0.79 in each case. For both outcome states, however, a substantial proportion of the predicted probabilities (33% for death, 57% for death or dependency) lay between 25 and 75% i.e. the models failed to place substantial numbers of individuals into groups either very likely or very unlikely to have an outcome event.

   The principal aim of this study was to determine how far the large differences in crude outcome between countries participating in the IST might be explained by differences in the baseline characteristics of the patients entered into the trial. The answer to this question is clearly complex. For certain pairs of countries, it appears that the differences in crude outcome could be explained very largely by differences in casemix, in particular, by differences in the age, sex, proportion with atrial fibrillation and baseline stroke severity (measured by conscious level, number of neurological deficits and systolic blood pressure) of the patients entered into the trial. For example, most of the large differences in case fatality between Sweden and Switzerland and in death or dependency between Switzerland and Italy were explicable in this way. However, for the majority of countries, adjustment for the factors in our prognostic models led only to modest changes in outcome and, as a result, the overall variation in case fatality and in death or dependency between countries remained substantial. This finding was quite a surprise. We had anticipated that, with a prospective design, the availability of detailed and important prognostic data, virtually complete follow up and the use of a validated prognostic model, we would have been able to account for most of the overall variation in outcome. Clearly, this was not the case and factors other than those considered in our analysis must account for the majority of the variation in outcome between countries.

   Given the well described international differences in the treatment of acute stroke and speculation that these differences may contribute to differences in stroke outcome between countries (3;4;6-10), we considered whether differences in care between countries in the IST might explain their residual differences in outcome. Our observation that lower case fatality was strongly correlated with a higher proportion of patients with a CT head scan and with a higher proportion prescribed aspirin on discharge lends some support to this possibility. Similarly, it is notable that the Swedish and Norwegian cohorts had the lowest case fatality of all. The Scandinavian countries were early advocates of the organization and specialization of stroke care and, at the time of the IST, the majority of Swedish and Norwegian patients were likely to have been treated in a stroke unit (10). This is in sharp contrast to the UK, Argentina and Poland, the countries with the highest case fatality, where the majority of patients would have received conventional ward care (17). Such differences in care may well underlie at least some of the difference in outcome between the two groups of countries (18).

   On the whole, however, the evidence from these data that differences in care might explain some of the residual differences in outcome is weak. Stroke interventions, especially those relating to secondary prevention, may quite properly be withheld from patients who survive in a very poor functional state. Our simplistic measurements of the quality of care do not take this into account and portray all cases where the intervention is withheld as an error. Our finding that higher case fatality is strongly correlated with apparently worse provision of care was therefore perhaps inevitable. The weaker correlation between death or dependency and our measures of the process of care also argues against a significant impact of quality of care on the differences in outcome between countries. In particular, if a difference in the proportion of patients treated on a stroke unit were an important reason for the difference in outcome, how is one to reconcile Norway's apparently excellent 'performance' as measured by case fatality but its comparatively poor 'performance' when measured by the combined outcome of death or dependency? Similarly, how does one explain the opposite findings for Argentina and Poland? Perhaps, one might argue, where there is better care, patients otherwise destined to die are more likely to survive but do so in a dependent state e.g. Norway; and where there is inferior care patients with a poor prognosis die and so cannot be counted as dependent e.g. Argentina and Poland.

   However, if this were so, how would one explain the ranking of Sweden as the country with the best performance on both measures of outcome or the considerably worse performance of the UK when measured by death or dependency than when measured by case fatality alone?

   A stronger argument that differences in stroke care are not the major cause of the residual differences in outcome is the sheer size of the absolute differences between countries. The differences in the proportion of patients dead or dependent between the UK and the other eight countries lay between 150 to 300 events per 1000 patients treated. These absolute differences in outcome are two to four times larger than the treatment effect of stroke unit care (18) and twice as large as the benefit of giving thrombolysis within 3 hours of the onset of stroke (19). That is to say, the differences in outcome are much larger than might plausibly be explained by the differential use of even the most efficacious of known interventions. Indeed, at the time of the IST, thrombolysis was not in routine use. Thus, while it remains plausible that differences in medical treatment may account for some of the residual differences in outcome, other factors are likely to account for much more.

   As with all observational studies, these other factors are the play of chance, bias and residual variation in casemix (confounding). Given our use of large samples and the narrow 99% confidence intervals, chance is highly unlikely to explain much of the residual variation in outcome between countries. Similarly, given the near complete follow up and the unambiguous nature of death, biased measurement cannot explain the residual variation in case fatality. However, measurement error may underlie some of the residual variation in death or dependency. Functional status is difficult to define and measure, and it is known that international comparisons are prone to bias (20;21). The fact that the range of differences between countries was greater for death or dependency than for death alone suggests that such bias might have operated here. In non-English speaking countries, the follow-up questions were translated into the local language without back-translation to check for alterations, and subtle but important differences in interpretation may have been introduced (22;23). The method used to collect outcome varied between countries. In some it was by postal questionnaire, in others by telephone, and in others still by a combination of the two methods. These differences might also have influenced response. Cultural differences in the perception of disability and dependence may also have lead patients in different countries to report dependency differently despite similar degrees of impairment in function (21;22;24;25). In general, however, the impact of these second order biases is modest. They would also be unlikely to explain the marked difference in death or dependency between patients in the UK and Australia, countries who used exactly the same outcome questionnaire and method of follow up and who experience reasonably similar cultures. Further, empirical research suggests that people in Sweden and the UK, the countries with the greatest difference in the proportions dead or dependent, value health states very similarly (26). It seems likely, therefore, that most of the residual variation in outcome between countries in the IST must be due to unmeasured variation in casemix.

   The reason for the marked variation in casemix between countries in the IST is that the fundamental entry criterion for the trial was simply that the clinician had to be substantially uncertain whether or not to treat a given patient with aspirin, heparin, both or neither. Variation between countries in 'uncertainty', in the types of physician participating in the trial and in the types of stroke patients routinely admitted to hospital (9) are therefore all likely to have played their part. Although detailed, our prognostic models might have better accounted for the marked differences in casemix if they had adjusted for other recognized prognostic variables, such as pre-stroke functional status and living arrangements, co-morbid conditions, such as heart failure and diabetes mellitus, and post stroke urinary incontinence, hyperglycemia, and the size of the stroke lesion on brain imaging.(27) However, they certainly could not have accounted for the (probably many) important differences in casemix that are not currently understood. The limitations of our models are illustrated by the fact that for large numbers of patients the predictions of risk are in the middle, non-confident range i.e. they are not particularly good at separating patients into high and low risk groups. This in turn implies that they explain only a small part of the total variability in outcome (analogous to having a low r2 statistic in linear regression). This is the case despite the fact that both models show excellent calibration and moderately good discrimination, two widely quoted measures of model 'performance'. This observation highlights the important difference between a prognostic model having a good 'fit' and it providing clinically useful predictions (28).

   Regardless of their explanation, our findings have a number of implications. First, they emphasize the potential limitations of adjusting non-randomized comparisons of stroke outcomes for differences in casemix, even with high quality and complete data from large numbers of patients and especially when the differences in unadjusted outcome are very large. These observations may be of particular relevance to those attempting to draw inferences about the quality of care from non-randomized comparisons of stroke outcome, whether between hospitals, regions or countries, and especially if their data are retrospective, incomplete or less completely adjusted than our data. Second, investigators need to be aware that simply because casemix adjustment has been performed using models that show excellent calibration and moderately good discrimination, a considerable amount of variation in outcome may remain to be explained. Therefore, and thirdly, before reaching conclusions about the quality of stroke care, the plausibility of ascribing any residual differences in outcome to variation in the use of currently understood stroke interventions should be carefully considered. Fourthly, those wishing to measure between country differences in functional outcome after stroke should recognize that this task is prone to various subtle forms of bias.

   Lastly, our findings also have implications for the design of multinational randomized controlled trials. The variations in outcome between groups of patients in different countries in the IST do not affect the validity of the overall trial results because the trial used a method of allocation that ensured balance within countries (minimization). This design enabled the trial to detect the effect of a treatment (a reduction in death or dependency of 10 cases per 1000 treated) that was 30 times smaller than the largest difference in outcome between countries. If, however, a multinational study did not ensure balanced allocation within countries, then treatment effects might be spuriously generated or obscured. For instance, in an imaginary trial of a truly ineffective treatment using the same study population as the IST, if by chance the proportion of patients randomized to drug or to placebo happened to be 2:1 in Sweden and, for the same number of patients, 1:2 in the UK, then an apparent benefit of the truly ineffective drug would be observed even though the trial would have allocated equal numbers to each intervention.

   In summary, this study demonstrates the potential limitations of using analyses of observational data to explain international differences in outcome after stroke. Differences in the quality of care, chance, measurement error and cultural bias may account for some of the residual variation in outcome between countries in the IST, but most of the unexplained variation is likely to reflect the difficulty of achieving perfect casemix adjustment. To avoid these biases, multinational randomized controlled trials in stroke must ensure a balance of treatment and control within each country as well as in the overall trial. Furthermore, those wishing to draw inferences from non-randomized comparisons of outcome after stroke should consider the issues raised in this paper carefully.

Background and purpose: This study describes the large variations in outcome after stroke between countries that participated in the International Stroke Trial and sought to define whether they could be explained by variations in casemix or by other factors.
Methods: We analyzed data from the 15,116 patients recruited in Argentina, Australia, Italy, the Netherlands, Norway, Poland, Sweden, Switzerland and United Kingdom. We compared crude case fatality and the proportion of patients 'dead or dependent' at six months; we used logistic regression to adjust for age, sex, AF, systolic BP, conscious level and number of neurological deficits. We used the frequency of pre-randomization CT head scan and prescription of aspirin at discharge to indicate quality of care.
Results: The differences in outcome (all treatment groups combined) between the 'best' and 'worst' countries were very large for death (171 cases per 1000 patients) and for death or dependency (375 cases per1000 patients). The differences were somewhat smaller after adjusting for casemix (160 and 311 cases per 1000 patients, respectively). Process of care may have accounted for some but not all of the residual variation in outcome.
   Conclusion: Adjusting for casemix explained only some of the variation in outcome between countries. The residual differences in outcome were too large to be explained by variations in care, and are most likely to reflect differences in unmeasured baseline factors. These findings demonstrate the need to achieve balance of treatment and control within each country in multinational randomized controlled stroke trials; and for caution in the interpretation of non-randomized comparisons of outcome after stroke between countries.

   We calculated the predicted risk of an outcome event (P) for each patient using the logistic regression models as follows: P = ey / ( 1 + ey) where y is the linear predictor of the model and e is the exponential constant. We derived the linear predictor y of each model as follows:

Case fatality at six months:
y = -7.3529 + (0.0603 x age) - (0.1637 x sex) + (0.5130 x AF) + (0.9533 x conscious level) + (0.3272 x number of neurological deficits).
Dead or dependent at six months:
y = -1.5288 + (1.059 x age) + (0.2988 x age2) + (0.3066 x sex) + (0.1963 x AF) + (1.0634 x conscious level) - (0.1012 x sBP) + (0.2291 x sBP2) + (0.4249 x number of neurological deficits)
Coding of variables
Age = (age - 70)/20; age2 = (age variable)2
Sex: female = 1, male = 0
Atrial fibrillation (AF): present = 1, absent = 0
Conscious level: drowsy/unconscious = 1, fully conscious = 0
Systolic BP (sBP) = (sBP - 160)/60; sBP2 = (sBP variable)2
Number of neurological deficits = observed number except 0 coded 1; and 8 coded 7 (co-variate treated as a continuous variable because it has a linear relationship with each outcome)

   We are grateful to the trial collaborators and to the patients that participated in the IST.
   We thank Dr Eduardo Szwarcer (Argentina) for his help in shaping this paper for the Vitrual Congress on Cardiology.

   The IST was supported by grants from the Medical Research Council, the Stroke Association and the European Commission. NU Weir was supported by a Wellcome Trust Research Training Fellowship.



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