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      Inflammatory Markers and Poor Outcome after Stroke: A Prospective Cohort Study and Systematic Review of Interleukin-6

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          Abstract

          In a prospective cohort study of patient outcomes following stroke, William Whiteley and colleagues find that markers of inflammatory response are associated with poor outcomes. However, addition of these markers to existing prognostic models does not improve outcome prediction.

          Abstract

          Background

          The objective of this study was to determine whether: (a) markers of acute inflammation (white cell count, glucose, interleukin-6, C-reactive protein, and fibrinogen) are associated with poor outcome after stroke and (b) the addition of markers to previously validated prognostic models improves prediction of poor outcome.

          Methods and Findings

          We prospectively recruited patients between 2002 and 2005. Clinicians assessed patients and drew blood for inflammatory markers. Patients were followed up by postal questionnaire for poor outcome (a score of>2 on the modified Rankin Scale) and death through the General Register Office (Scotland) at 6 mo. We performed a systematic review of the literature and meta-analysis of the association between interleukin-6 and poor outcome after stroke to place our study in the context of previous research. We recruited 844 patients; mortality data were available in 844 (100%) and functional outcome in 750 (89%). After appropriate adjustment, the odds ratios for the association of markers and poor outcome (comparing the upper and the lower third) were interleukin-6, 3.1 (95% CI: 1.9–5.0); C-reactive protein, 1.9 (95% CI: 1.2–3.1); fibrinogen, 1.5 (95% CI: 1.0–2.36); white cell count, 2.1 (95% CI: 1.3–3.4); and glucose 1.3 (95% CI: 0.8–2.1). The results for interleukin-6 were similar to other studies. However, the addition of inflammatory marker levels to validated prognostic models did not materially improve model discrimination, calibration, or reclassification for prediction of poor outcome after stroke.

          Conclusions

          Raised levels of markers of the acute inflammatory response after stroke are associated with poor outcomes. However, the addition of these markers to a previously validated stroke prognostic model did not improve the prediction of poor outcome. Whether inflammatory markers are useful in prediction of recurrent stroke or other vascular events is a separate question, which requires further study.

          Please see later in the article for the Editors' Summary

          Editors' Summary

          Background

          Every year, 15 million people have a stroke. In the US alone, someone has a stroke every 40 seconds and someone dies from a stroke every 3–4 minutes. Stroke occurs when the blood supply to the brain is suddenly interrupted by a blood clot blocking a blood vessel in the brain (ischemic stroke, the commonest type of stroke) or by a blood vessel in the brain bursting (hemorrhagic stroke). Deprived of the oxygen normally carried to them by the blood, the brain cells near the blockage die. The symptoms of stroke depend on which part of the brain is damaged but include sudden weakness or paralysis along one side of the body, vision loss in one or both eyes, and confusion or trouble speaking or understanding speech. Anyone experiencing these symptoms should seek medical assistance immediately because prompt treatment can limit the damage to the brain. Risk factors for stroke include age (three-quarters of strokes occur in people over 65 years old), high blood pressure, and heart disease.

          Why Was This Study Done?

          Many people are left with permanent disabilities after a stroke. An accurate way to predict the likely long-term outcome (prognosis) for individual patients would help clinicians manage their patients and help relatives and patients come to terms with their changed circumstances. Clinicians can get some idea of their patients' likely outcomes by assessing six simple clinical variables. These include the ability to lift both arms and awareness of the present situation. But could the inclusion of additional variables improve the predictive power of this simple prognostic model? There is some evidence that high levels in the blood of inflammatory markers (for example, interleukin-6 and C-reactive protein) are associated with poor outcomes after stroke—inflammation is the body's response to infection and to damage. In this prospective cohort study, the researchers investigate whether inflammatory markers are associated with poor outcome after stroke and whether the addition of these markers to the six-variable prognostic model improves its predictive power. Prospective cohort studies enroll a group of participants and follow their subsequent progress.

          What Did the Researchers Do and Find?

          The researchers recruited 844 patients who had had a stroke (mainly mild ischemic strokes) in Edinburgh. Each patient was assessed soon after the stroke by a clinician and blood was taken for the measurement of inflammatory markers. Six months after the stroke, the patient or their relatives completed a postal questionnaire that assessed their progress. Information about patient deaths was obtained from the General Register Office for Scotland. Dependency on others for the activities of daily life or dying was recorded as a poor outcome. In their statistical analysis of these data, the researchers found that raised levels of several inflammatory markers increased the likelihood of a poor outcome. For example, after allowing for age and other factors, individuals with interleukin-6 levels in the upper third of the measured range were three times as likely to have a poor outcome as patients with interleukin-6 levels in the bottom third of the range. A systematic search of the literature revealed that previous studies that had looked at the potential association between interleukin-6 levels and outcome after stroke had found similar results. Finally, the researchers found that the addition of inflammatory marker levels to the six-variable prognostic model did not substantially improve its ability to predict outcome after stroke for this cohort of patients.

          What Do These Findings Mean?

          These findings provide additional support for the idea that increased levels of inflammatory markers are associated with a poor outcome after stroke. However, because patients with infections were not excluded from the study, infection may be responsible for part of the observed association. Importantly, these findings also show that although the inclusion of inflammatory markers in the six variable prognostic model slightly improves its ability to predict outcome, the magnitude of this improvement is too small to warrant the use of these markers in routine practice. Whether the measurement of inflammatory markers might be useful in the prediction of recurrent stroke—at least a quarter of people who survive a stroke will have another one within 5 years—requires further study.

          Additional Information

          Please access these Web sites via the online version of this summary at http://dx.doi.org/10.1371/journal.pmed.1000145.

          • This study is further discussed in a PLoS Medicine Perspective by Len Kritharides

          • The US National Institute of Neurological Disorders and Stroke provides information about all aspects of stroke (in English and Spanish); the Know Stroke site provides educational materials about stroke prevention, treatment, and rehabilitation (in English and Spanish)

          • The Internet Stroke Center provides detailed information about stroke for patients, families and health professionals (in English and Spanish)

          • The UK National Health Service also provides information for patients and their families about stroke (in several languages)

          • MedlinePlus provides links to further resources and advice about stroke (in English and Spanish)

          • The six simple variable model for prediction of death or disability after stroke is available here: http://dcnapp1.dcn.ed.ac.uk/scope/

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          Most cited references29

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          Use and misuse of the receiver operating characteristic curve in risk prediction.

          The c statistic, or area under the receiver operating characteristic (ROC) curve, achieved popularity in diagnostic testing, in which the test characteristics of sensitivity and specificity are relevant to discriminating diseased versus nondiseased patients. The c statistic, however, may not be optimal in assessing models that predict future risk or stratify individuals into risk categories. In this setting, calibration is as important to the accurate assessment of risk. For example, a biomarker with an odds ratio of 3 may have little effect on the c statistic, yet an increased level could shift estimated 10-year cardiovascular risk for an individual patient from 8% to 24%, which would lead to different treatment recommendations under current Adult Treatment Panel III guidelines. Accepted risk factors such as lipids, hypertension, and smoking have only marginal impact on the c statistic individually yet lead to more accurate reclassification of large proportions of patients into higher-risk or lower-risk categories. Perfectly calibrated models for complex disease can, in fact, only achieve values for the c statistic well below the theoretical maximum of 1. Use of the c statistic for model selection could thus naively eliminate established risk factors from cardiovascular risk prediction scores. As novel risk factors are discovered, sole reliance on the c statistic to evaluate their utility as risk predictors thus seems ill-advised.
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            Limitations of the odds ratio in gauging the performance of a diagnostic, prognostic, or screening marker.

            M. S. Pepe (2004)
            A marker strongly associated with outcome (or disease) is often assumed to be effective for classifying persons according to their current or future outcome. However, for this assumption to be true, the associated odds ratio must be of a magnitude rarely seen in epidemiologic studies. In this paper, an illustration of the relation between odds ratios and receiver operating characteristic curves shows, for example, that a marker with an odds ratio of as high as 3 is in fact a very poor classification tool. If a marker identifies 10% of controls as positive (false positives) and has an odds ratio of 3, then it will correctly identify only 25% of cases as positive (true positives). The authors illustrate that a single measure of association such as an odds ratio does not meaningfully describe a marker's ability to classify subjects. Appropriate statistical methods for assessing and reporting the classification power of a marker are described. In addition, the serious pitfalls of using more traditional methods based on parameters in logistic regression models are illustrated.
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              Classification and natural history of clinically identifiable subtypes of cerebral infarction.

              We describe the incidence and natural history of four clinically identifiable subgroups of cerebral infarction in a community-based study of 675 patients with first-ever stroke. Of 543 patients with a cerebral infarct, 92 (17%) had large anterior circulation infarcts with both cortical and subcortical involvement (total anterior circulation infarcts, TACI); 185 (34%) had more restricted and predominantly cortical infarcts (partial anterior circulation infarcts, PACI); 129 (24%) had infarcts clearly associated with the vertebrobasilar arterial territory (posterior circulation infarcts, POCI); and 137 (25%) had infarcts confined to the territory of the deep perforating arteries (lacunar infarcts, LACI). There were striking differences in natural history between the groups. The TACI group had a negligible chance of good functional outcome and mortality was high. More than twice as many deaths were due to the complications of immobility than to direct neurological sequelae of the infarct. Patients in the PACI group were much more likely to have an early recurrent stroke than were patients in other groups. Those in the POCI group were at greater risk of a recurrent stroke later in the first year after the index event but had the best chance of a good functional outcome. Despite the small anatomical size of the infarcts in the LACI group, many patients remained substantially handicapped. The findings have important implications for the planning of stroke treatment trials and suggest that various therapies could be directed specifically at the subgroups.
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                Author and article information

                Contributors
                Role: Academic Editor
                Journal
                PLoS Med
                PLoS
                plosmed
                PLoS Medicine
                Public Library of Science (San Francisco, USA )
                1549-1277
                1549-1676
                September 2009
                September 2009
                8 September 2009
                : 6
                : 9
                : e1000145
                Affiliations
                [1 ]Division of Clinical Neurosciences, Western General Hospital, University of Edinburgh, Edinburgh, United Kingdom
                [2 ]Division of Cardiovascular and Medical Sciences, Royal Infirmary, University of Glasgow, Glasgow, Scotland, United Kingdom
                [3 ]SFC Brain Imaging Research Centre, SINAPSE Collaboration, University of Edinburgh, United Kingdom
                [4 ]Institute of Genetics and Molecular Medicine, University of Edinburgh, United Kingdom
                The George Institute, Australia
                Author notes

                ICMJE criteria for authorship read and met: WW CJ SL GL AR PS JW MD CS. Agree with the manuscript's results and conclusions: WW CJ SL GL AR PS JW MD CS. Designed the experiments/the study: WW CJ JW CS. Analyzed the data: WW CJ CS. Collected data/did experiments for the study: WW CJ GL AR JW CS. Enrolled patients: WW PS MD CS. Wrote the first draft of the paper: WW. Contributed to the writing of the paper: WW CJ SL GL AR PS JW MD CS. Provided statistical advice: SL. Contributed to the design of the analyses and the interpretation of the data: PS. Contributed to patient characterization: JW. Principal investigator for this study: CS.

                Article
                09-PLME-RA-0909R2
                10.1371/journal.pmed.1000145
                2730573
                19901973
                97ff5b8a-d364-40ef-836f-bb46a92edc91
                Whiteley et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
                History
                : 3 April 2009
                : 31 July 2009
                Page count
                Pages: 12
                Categories
                Research Article
                Evidence-Based Healthcare/Clinical Decision-Making
                Neurological Disorders/Cerebrovascular Disease

                Medicine
                Medicine

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