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Table of Contents
Vol. 30, No. 2, 2010
Issue release date: July 2010
Cerebrovasc Dis 2010;30:120–126
(DOI:10.1159/000314715)

The Canadian Neurological Scale and the NIHSS: Development and Validation of a Simple Conversion Model

Nilanont Y.a · Komoltri C.b · Saposnik G.c · Côté R.d · Di Legge S.f · Jin Y.e · Prayoonwiwat N.a · Poungvarin N.a · Hachinski V.e
aDivision of Neurology, Department of Medicine, and bDepartment of Research Development, Faculty of Medicine, Siriraj Hospital, Mahidol University, Bangkok, Thailand; cDivision of Neurology, Department of Medicine, St Michael’s Hospital, University of Toronto, Toronto, Ont., dDivision of Neurology, Montreal General Hospital, McGill University, Montreal, Que., and eDepartment of Clinical Neurological Sciences, London Health Science Centre, University of Western Ontario, London, Ont., Canada; fDepartment of Clinical Neurological Sciences, University of Tor Vergata, Rome, Italy
email Corresponding Author

Abstract

Background: The Canadian Neurological Scale (CNS) and the National Institutes of Health Stroke Scale (NIHSS) are among the most reliable stroke severity assessment scales. The CNS requires less extensive neurological evaluation and is quicker and simpler to administer. Objective: Our aim was to develop and validate a simple conversion model from the CNS to the NIHSS. Methods: A conversion model was developed using data from a consecutive series of acute-stroke patients who were scored using both scales. The model was then validated in an external dataset in which all patients were prospectively assessed for stroke severity using both scales by different observers which consisted of neurology residents or stroke fellows. Results: In all, 168 patients were included in the model development, with a median age of 73 years (20–94). Men constituted 51.8%. The median NIHSS score was 6 (0–31). The median CNS score was 8.5 (1.5–11.5). The relationship between CNS and NIHSS could be expressed as the formula: NIHSS = 23 – 2 × CNS. A cohort of 350 acute-stroke patients with similar characteristics was used for model validation. There was a highly significant positive correlation between the observed and predicted NIHSS score (r = 0.87, p < 0.001). The predicted NIHSS score was on average 0.61 higher than the observed NIHSS score (95% CI = 0.31–0.91). Conclusions: The CNS can be reliably converted to the NIHSS using a simple conversion formula: NIHSS = 23 – 2 × CNS. This finding may have a practical impact by permitting reliable comparisons with NIHSS-based evaluations and simplifying the routine assessment of acute-stroke patients in more diverse settings.


 Outline


 goto top of outline Key Words

  • Acute stroke
  • National Institutes of Health Stroke Scale
  • Canadian Neurological Scale
  • Conversion model
  • Stroke severity assessment
  • Neurological scales

 goto top of outline Abstract

Background: The Canadian Neurological Scale (CNS) and the National Institutes of Health Stroke Scale (NIHSS) are among the most reliable stroke severity assessment scales. The CNS requires less extensive neurological evaluation and is quicker and simpler to administer. Objective: Our aim was to develop and validate a simple conversion model from the CNS to the NIHSS. Methods: A conversion model was developed using data from a consecutive series of acute-stroke patients who were scored using both scales. The model was then validated in an external dataset in which all patients were prospectively assessed for stroke severity using both scales by different observers which consisted of neurology residents or stroke fellows. Results: In all, 168 patients were included in the model development, with a median age of 73 years (20–94). Men constituted 51.8%. The median NIHSS score was 6 (0–31). The median CNS score was 8.5 (1.5–11.5). The relationship between CNS and NIHSS could be expressed as the formula: NIHSS = 23 – 2 × CNS. A cohort of 350 acute-stroke patients with similar characteristics was used for model validation. There was a highly significant positive correlation between the observed and predicted NIHSS score (r = 0.87, p < 0.001). The predicted NIHSS score was on average 0.61 higher than the observed NIHSS score (95% CI = 0.31–0.91). Conclusions: The CNS can be reliably converted to the NIHSS using a simple conversion formula: NIHSS = 23 – 2 × CNS. This finding may have a practical impact by permitting reliable comparisons with NIHSS-based evaluations and simplifying the routine assessment of acute-stroke patients in more diverse settings.

Copyright © 2010 S. Karger AG, Basel


goto top of outline Introduction

The National Institutes of Health Stroke Scale (NIHSS) [1] and the Canadian Neurological Scale (CNS) [2,3] have been reported to be useful for the assessment of stroke severity. The NIHSS measures 15 items such as level of consciousness, vision, gaze, facial weakness, limb strength, ataxia, different sensory modalities including inattention, language and speech. Specific items are divided in 3 or 4 grades of severity with higher scores indicating greater deficit. The CNS is an 8-item scale which measures the level of consciousness, orientation, speech, motor function and facial weakness for a maximum score of 11.5 points in a normal individual. Both scales are among the most reliable clinical tools to assess acute-stroke patients and also in predicting stroke recovery [4,5,6,7].

The NIHSS is commonly used by stroke neurologists in the context of clinical research trials. However, its administration may be more problematic for emergency physicians, general practitioners, nurses and paramedics because of its level of complexity [8]. Underutilization of the NIHSS has also been documented in different settings [9,10]. When compared to the NIHSS, the CNS shows characteristics of a more practical scale, permitting simplicity and brevity of execution while retaining reproducibility and validity. A retrospective study has also shown that there are more missing items for the completion of the NIHSS when compared to the CNS, since it requires a more detailed neurological examination that may not be available in patients’ records [11]. This may be especially true in community hospitals where the majority of stroke patients are admitted and treated, or also in developing countries where neurological expertise might be limited.

Both the NIHSS and the CNS have been used as clinical tools to determine patient inclusion in clinical trials, to compare patient groups within or between trials, or as an outcome measure. Most of the world’s stroke patients are managed in settings where only a simpler scale such as the CNS may be used more reliably. It would be important for patient care to be able to express the CNS in terms of an NIHSS score for comparability purposes when considering interventions or establishing a prognosis. There are insufficient data available concerning the comparison between both scales [12]. The objective of this study was to develop and validate a simple conversion model which would predict the NIHSS from the CNS score. We hypothesized that there was an inverse relationship between the 2 scores. We then sought to test prospectively in a different population whether the NIHSS score can be reliably predicted by the CNS score.

 

goto top of outline Patients and Methods

goto top of outline Model Development

Subjects were recruited from consecutive acute-stroke patients admitted at the London Health Sciences Centre, London, Ont., Canada. Initial stroke severity assessment was performed prospectively in 75 patients using both the CNS and the NIHSS during the same visit by any of the 5 raters (on-call stroke fellows who were trained with both the CNS and the NIHSS videotraining tools). For 93 acute-stroke patients, data concerning stroke severity were collected retrospectively by chart review using a structured algorithm [13,14,15] by 1 rater (Y.N.). Total and subscores of the 2 scales, demographic data, and stroke subtypes as defined by the TOAST criteria were recorded as the derivation cohort for model development.

goto top of outline Model Validation

A validation cohort of 350 subjects was recruited from a consecutive series of acute-stroke patients participating in the Siriraj Acute Stroke Registry, Faculty of Medicine, Siriraj Hospital, Mahidol University, Bangkok, Thailand. Demographic data and stroke subtypes were prospectively collected as a part of the registry. Inter- and intrarater reliabilities of the scales were initially assessed in the first 38 patients. All raters composed of 2 stroke fellows, and 2 internal medicine residents (different from the raters in the first cohort) were trained to administer both scales prior to the study. Only 1 rater was assigned to administer both scales within 48 h after admission using computer-generated random selection. For the purpose of reliability testing, the initial assessment was recorded on videotape during the performance of the scale. After the examination, the tape was reviewed and scored independently by the rest of the raters (3 people). The first set of scores was then collected. After 3–5 weeks, all raters were assigned to review the videotape and independently provide a second set of scores. Total CNS and NIHSS scores from the first and second set of 38 patients were used in the analysis of the inter- and intrarater reliability. For the remaining 312 patients, a single assessment was performed by one of the raters who was on call. None of the authors were involved in the scoring process in the validation cohort. Total CNS and NIHSS scores from the initial 38 patients and the additional 312 patients were used for model validation.

goto top of outline Statistical Analysis

Patients’ characteristics were described using medians for continuous variables and by frequencies and proportions for categorical variables. As for the derivation cohort, significant median differences between prospective and retrospective data were assessed statistically by the Wilcoxon test and by the χ2 test for differences between proportions. The Pearson correlation coefficient and linear regression analysis were used to assess the relationship between the CNS and the NIHSS. The linear regression equation between the NIHSS and CNS was then tested against the validation cohort dataset. The correlation between observed and predicted NIHSS was assessed using Pearson’s correlation. The difference between observed and predicted NIHSS scores was reported with its 95% confidence interval (CI). Inter- and intrarater reliabilities of both scales were determined using the intraclass correlation coefficient (ICC). Data were analyzed with SPSS for Windows 15.0 software (SPSS Inc., Chicago, Ill., USA). This study was approved by the research ethics committee of the University of Western Ontario, London Health Sciences Centre, Ont., Canada, and the Siriraj Hospital, Mahidol University, Bangkok, Thailand.

 

goto top of outline Results

In all, 172 acute-stroke patients were recruited in the derivation dataset for model development. Four comatose patients were excluded due to restrictions related to the use of the CNS. Data from the remaining 168 patients were analyzed. The CNS and NIHSS were prospectively scored in 75 patients. In 93 patients, both scores were determined retrospectively from medical records. The median age at onset as well as the median NIHSS and CNS scores were similar between subjects assessed prospectively and retrospectively, as shown in table 1. More men were evaluated by the retrospective method (p < 0.01).

TAB01
Table 1. Characteristics of acute-stroke patients in the derivation and validation cohorts

goto top of outline CNS and NIHSS Relationship and Model Development

A strong inverse relationship between the CNS score and the NIHSS score was observed among patients assessed both prospectively and retrospectively (upper panel of fig. 1; r = –0.91 and –0.94, respectively, with p < 0.001 for both). Simple linear regression equations of NIHSS on CNS for prospective and retrospective data were NIHSS = 23.96 – 2.07 × CNS and NIHSS = 22.41 – 1.95 × CNS, respectively. Multiple linear regression analysis showed no significant difference in y-intercept (p = 0.147) and slope (p = 0.346) between the prospective and the retrospective datasets. Therefore, we pooled together the scores collected from the 2 sources and obtained an equivalent formula for the relationship between the CNS score and the NIHSS score: NIHSS = 23.02 – 2.0 × CNS (r = –0.93 with p < 0.001). Sex and age had no significant effect on this association.

FIG01
Fig. 1. The relationship between the CNS score and the NIHSS score of prospective, retrospective and pool datasets from the derivation cohort.

For the validation cohort, 350 acute-stroke patients were enrolled, with a median age lower than that of the derivation cohort. The initial stroke severity as measured by the NIHSS and the CNS was similar between the 2 cohorts. The patients’ characteristics are shown in table 1. Mean observed NIHSS and CNS scores were used for model validation.

goto top of outline Inter- and Intrarater Reliability

The interobserver reliability between the 4 raters was excellent with an ICC of 0.90 (95% CI = 0.85–0.94) and 0.87 (95% CI = 0.81–0.92) for the NIHSS and CNS score, respectively.

Intrarater reliability showed a high agreement with an ICC of 0.97, 0.96, 0.97 and 0.96 for the NIHSS and 0.99, 0.97, 0.98 and 0.96 for the CNS for the 2 stroke fellows and 2 internal medicine residents, respectively.

goto top of outline Model Validation

The previously obtained equation represented by NIHSS = 23 – 2 × CNS was used to compute the predicted NIHSS score for the validation cohort dataset. The observed and predicted NIHSS score had a very high positive correlation of 0.87 (p < 0.001), as shown in figure 2. The average difference between the predicted NIHSS and the observed NIHSS is 0.61 (95% CI = 0.31–0.91) with a standard deviation of 2.86. Eighty percent of the differences were spread between –3.5 and 2.0. When the direction of the differences was removed, the mean difference was 2.0 (95% CI = 1.8–2.2) with a standard deviation of 2.13. Eighty percent of the absolute differences were distributed between 0.0 and 4.5. Distribution of differences between the observed and predicted NIHSS scores are shown in table 2 and figure 3.

TAB02
Table 2. Differences between observed and predicted NIHSS scores

FIG02
Fig. 2. The relationship between observed and predicted NIHSS scores (r = 0.87) in the validation cohort.

FIG03
Fig. 3. Distribution of the absolute difference between observed and predicted NIHSS scores in percent in the validation cohort.

 

goto top of outline Discussion

Our study demonstrated a strong inverse relationship between the NIHSS and the CNS. Furthermore, the findings suggest that the NIHSS score can be reliably predicted from the CNS score using the formula: NIHSS = 23 – 2 × CNS.

Our results were obtained in 2 different university hospitals from patients who were separately evaluated by different raters trained with both the CNS and the NIHSS scales. The derivation dataset was developed from both prospective and retrospective sources. The absence of a significant difference in terms of correlation and regression of the modeling data obtained from 2 different sources strengthened the compatibility and reliability of both approaches either with trained raters or a structured algorithm. In the validation cohort, both the NIHSS and CNS scores were generated by a different group of raters who were not aware of the model development data. In addition, the test-retest reproducibility of both scales was excellent with an ICC of >0.8 in both groups of raters, thus reducing the likelihood of a significant bias.

Although the validity study of the CNS-NIHSS conversion model revealed a statistically significant difference between the predicted and observed NIHSS scores, the absolute difference between the mean of both scores was equal to 2.0 (95% CI = 1.8–2.2), which is not clinically relevant. If an absolute difference of ≧4 on the NIHSS score is considered clinically meaningful [16], approximately 90% of our validation cohort would fall below this threshold, as is shown in table 2 and figure 3.

Previous studies demonstrated that the degree of neurological deficit during the initial stroke presentation is a strong predictor of stroke outcome [17,18,19]. Standardized stroke severity assessment scales are increasingly important in clinical practice. Because of the availability of numerous and diverse stroke scales such as the NIHSS, the CNS, the Scandinavian Stroke Scale [20] and the Orgogozo Scale [21], comparison of stroke outcomes between trials can be difficult to interpret. The possibilities of converting scores obtained with the NIHSS to the CNS and Orgogozo Scale was first studied by Muir et al. [12]. An unacceptably large error was found in the prediction of the CNS score from the NIHSS score because of the ceiling effect of the NIHSS and the differences in weighting between the 2 scales. However, our results demonstrate a much smaller prediction error when the NIHSS score was predicted from the CNS score. Another study by Ali et al. [22] demonstrated a strong correlation between changes in the Scandinavian Stroke Scale and the NIHSS scores and also that both scales can be converted into each other by using a simple equation.

It may be difficult to decide which stroke scale is the most suitable clinical instrument to assess stroke severity. In settings where a neurology consultation service is not available, as is the case in many community hospitals where most stroke patients are admitted or in developing countries where neurological expertise might be limited, a less complicated but still reliable method of assessment (i.e. the CNS) may be more practical to use. In this situation, the CNS may be selected as the clinical scale for follow-up assessments and evaluate the extent of recovery after stroke in general medical wards. Alternatively, the more comprehensive NIHSS may be more appropriate for use in a more specialized center or in clinical trials where more detailed examinations are required.

The results from our study also confirm the potential for a reliable comparison between the CNS and NIHSS, which may be useful especially for retrospective research where a greater number of missing items have been observed for the NIHSS than for the CNS [11,13,23]. Furthermore, this conversion model may be used to facilitate decisions concerning potential interventions and determination of prognosis in different clinical settings where there is limited neurological expertise.

Our study has certain limitations. Firstly, most patients had mild to moderate strokes and comatosed patients were excluded because of the inherent restrictions of the CNS, which was not designed for assessment of coma. For comatosed patients, the Glasgow Coma Scale is generally recommended. Thus, based on the characteristics of our stroke population, the conversion model is expected to be valid only if some level of consciousness is retained. Further evaluation of the proposed conversion equation in patients presenting with severer deficits would be interesting to confirm our findings. Secondly, bias in the model derivation might be introduced because of the use of a retrospective approach in scoring by a single rater. However, the possibility of such a bias was minimized by the use of a structured algorithm [13,14,15]. Moreover, the derivation dataset was highly homogeneous. There was no significant difference in the intercept and slope between data collected prospectively and retrospectively, as shown in figure 1. Thirdly, we have tested the model which was developed in an English-speaking North American environment within a different cultural environment and non-English-speaking population. One might have predicted that language and cultural barriers might have affected the results negatively. However, this was not the case. We consider this a strong positive point, since the results were still valid in a population with a different language and cultural milieu.

In summary, our results suggest that there is a strong inverse linear relationship between the CNS and the NIHSS. The CNS can be reliably converted to the NIHSS using the formula: NIHSS = 23 – 2 × CNS. This conversion model may have a practical impact by simplifying and abbreviating the assessment of stroke severity and allowing reliable comparisons with NIHSS-based evaluations, where required. The fact that the CNS can be reliably converted to the NIHSS suggests that it can be used in setting with limited neurological expertise, which is the case in most of the world.

 

goto top of outline Acknowledgements

The authors thank all patients who participated in this study and Ms. Pimpa Thepphawan for selecting participants, collecting and monitoring the data and for technical and logistic support.


 goto top of outline References
  1. Brott T, Adams HP Jr, Olinger CP, Marler JR, Barsan WG, Biller J, Spilker J, Holleran R, Eberle R, Hertzberg V: Measurements of acute cerebral infarction: a clinical examination scale. Stroke 1989;20:864–870.
  2. Cote R, Hachinski VC, Shurvell BL, Norris JW, Wolfson C: The Canadian Neurological Scale: a preliminary study in acute stroke. Stroke 1986;17:731–737.
  3. Cote R, Battista RN, Wolfson C, Boucher J, Adam J, Hachinski V: The Canadian Neurological Scale: validation and reliability assessment. Neurology 1989;39:638–643.
  4. Lyden PD, Lau GT: A critical appraisal of stroke evaluation and rating scales. Stroke 1991;22:1345–1352.
  5. Adams HP Jr, Davis PH, Leira EC, Chang KC, Bendixen BH, Clarke WR, Woolson RF, Hansen MD: Baseline NIH Stroke Scale score strongly predicts outcome after stroke: a report of the Trial of Org 10172 in Acute Stroke Treatment (TOAST). Neurology 1999;53:126–131.
  6. Appelros P, Terent A: Characteristics of the National Institute of Health Stroke Scale: results from a population-based stroke cohort at baseline and after one year. Cerebrovasc Dis 2004;17:21–27.
  7. Dewey HM, Donnan GA, Freeman EJ, Sharples CM, Macdonell RA, McNeil JJ, Thrift AG: Interrater reliability of the National Institutes of Health Stroke Scale: rating by neurologists and nurses in a community-based stroke incidence study. Cerebrovasc Dis 1999;9:323–327.
  8. Whelley-Wilson CM, Newman GC: A stroke scale for emergency triage. J Stroke Cerebrovasc Dis 2004;13:247–253.
  9. Burgin WS, Staub L, Chan W, Wein TH, Felberg RA, Grotta JC, Demchuk AM, Hickenbottom SL, Morgenstern LB: Acute stroke care in non-urban emergency departments. Neurology 2001;57:2006–2012.
  10. Leira EC, Pary JK, Davis PH, Grimsman KJ, Adams HP Jr: Slow progressive acceptance of intravenous thrombolysis for patients with stroke by rural primary care physicians. Arch Neurol 2007;64:518–521.
  11. Bushnell CD, Johnston DC, Goldstein LB: Retrospective assessment of initial stroke severity: comparison of the NIH Stroke Scale and the Canadian Neurological Scale. Stroke 2001;32:656–660.
  12. Muir KW, Grosset DG, Lees KR: Interconversion of stroke scales: implications for therapeutic trials. Stroke 1994;25:1366–1370.
  13. Williams LS, Yilmaz EY, Lopez-Yunez AM: Retrospective assessment of initial stroke severity with the NIH Stroke Scale. Stroke 2000;31:858–862.
  14. Stavem K, Lossius M, Ronning OM: Reliability and validity of the Canadian Neurological Scale in retrospective assessment of initial stroke severity. Cerebrovasc Dis 2003;16:286–291.
  15. Goldstein LB, Chilukuri V: Retrospective assessment of initial stroke severity with the Canadian Neurological Scale. Stroke 1997;28:1181–1184.
  16. National Institute of Neurological Disorders and Stroke rt-PA Stroke Study Group: Tissue plasminogen activator for acute ischemic stroke. N Engl J Med 1995;333:1581–1587.
  17. Adams HP Jr, Leclerc JR, Bluhmki E, Clarke W, Hansen MD, Hacke W: Measuring outcomes as a function of baseline severity of ischemic stroke. Cerebrovasc Dis 2004;18:124–129.
  18. Christensen H, Boysen G, Truelsen T: The Scandinavian stroke scale predicts outcome in patients with mild ischemic stroke. Cerebrovasc Dis 2005;20:46–48.
  19. Fullerton KJ, Mackenzie G, Stout RW: Prognostic indices in stroke. Q J Med 1988;66:147–162.
  20. Scandinavian Stroke Study Group: Multicenter trial of hemodilution in ischemic stroke – background and study protocol. Stroke 1985;16:885–890.
  21. Orgogozo JM, Capildeo R, Anagnostou CN, Juge O, Pere JJ, Dartigues JF, Steiner TJ, Yotis A, Rose FC: Development of a neurological score for the clinical evaluation of sylvian infarctions. Presse Med 1983;12:3039–3044.
  22. Ali K, Cheek E, Sills S, Crome P, Roffe C: Development of a conversion factor to facilitate comparison of National Institute of Health Stroke Scale scores with Scandinavian Stroke Scale scores. Cerebrovasc Dis 2007;24:509–515.
  23. Kasner SE, Cucchiara BL, McGarvey ML, Luciano JM, Liebeskind DS, Chalela JA: Modified National Institutes of Health Stroke Scale can be estimated from medical records. Stroke 2003;34:568–570.

 goto top of outline Author Contacts

Yongchai Nilanont, MD
Division of Neurology, Department of Medicine, Faculty of Medicine
Siriraj Hospital, Mahidol University
2 Prannok Road, Bangkoknoi, Bangkok 10700 (Thailand)
Tel. +66 2 419 7101, Fax +66 2 412 3009, E-Mail siysl@mahidol.ac.th


 goto top of outline Article Information

Received: July 21, 2009
Accepted: March 12, 2010
Published online: May 22, 2010
Number of Print Pages : 7
Number of Figures : 3, Number of Tables : 2, Number of References : 23


 goto top of outline Publication Details

Cerebrovascular Diseases

Vol. 30, No. 2, Year 2010 (Cover Date: July 2010)

Journal Editor: Hennerici M.G. (Mannheim)
ISSN: 1015-9770 (Print), eISSN: 1421-9786 (Online)

For additional information: http://www.karger.com/CED


Copyright / Drug Dosage / Disclaimer

Copyright: All rights reserved. No part of this publication may be translated into other languages, reproduced or utilized in any form or by any means, electronic or mechanical, including photocopying, recording, microcopying, or by any information storage and retrieval system, without permission in writing from the publisher or, in the case of photocopying, direct payment of a specified fee to the Copyright Clearance Center.
Drug Dosage: The authors and the publisher have exerted every effort to ensure that drug selection and dosage set forth in this text are in accord with current recommendations and practice at the time of publication. However, in view of ongoing research, changes in goverment regulations, and the constant flow of information relating to drug therapy and drug reactions, the reader is urged to check the package insert for each drug for any changes in indications and dosage and for added warnings and precautions. This is particularly important when the recommended agent is a new and/or infrequently employed drug.
Disclaimer: The statements, opinions and data contained in this publication are solely those of the individual authors and contributors and not of the publishers and the editor(s). The appearance of advertisements or/and product references in the publication is not a warranty, endorsement, or approval of the products or services advertised or of their effectiveness, quality or safety. The publisher and the editor(s) disclaim responsibility for any injury to persons or property resulting from any ideas, methods, instructions or products referred to in the content or advertisements.

Abstract

Background: The Canadian Neurological Scale (CNS) and the National Institutes of Health Stroke Scale (NIHSS) are among the most reliable stroke severity assessment scales. The CNS requires less extensive neurological evaluation and is quicker and simpler to administer. Objective: Our aim was to develop and validate a simple conversion model from the CNS to the NIHSS. Methods: A conversion model was developed using data from a consecutive series of acute-stroke patients who were scored using both scales. The model was then validated in an external dataset in which all patients were prospectively assessed for stroke severity using both scales by different observers which consisted of neurology residents or stroke fellows. Results: In all, 168 patients were included in the model development, with a median age of 73 years (20–94). Men constituted 51.8%. The median NIHSS score was 6 (0–31). The median CNS score was 8.5 (1.5–11.5). The relationship between CNS and NIHSS could be expressed as the formula: NIHSS = 23 – 2 × CNS. A cohort of 350 acute-stroke patients with similar characteristics was used for model validation. There was a highly significant positive correlation between the observed and predicted NIHSS score (r = 0.87, p < 0.001). The predicted NIHSS score was on average 0.61 higher than the observed NIHSS score (95% CI = 0.31–0.91). Conclusions: The CNS can be reliably converted to the NIHSS using a simple conversion formula: NIHSS = 23 – 2 × CNS. This finding may have a practical impact by permitting reliable comparisons with NIHSS-based evaluations and simplifying the routine assessment of acute-stroke patients in more diverse settings.



 goto top of outline Author Contacts

Yongchai Nilanont, MD
Division of Neurology, Department of Medicine, Faculty of Medicine
Siriraj Hospital, Mahidol University
2 Prannok Road, Bangkoknoi, Bangkok 10700 (Thailand)
Tel. +66 2 419 7101, Fax +66 2 412 3009, E-Mail siysl@mahidol.ac.th


 goto top of outline Article Information

Received: July 21, 2009
Accepted: March 12, 2010
Published online: May 22, 2010
Number of Print Pages : 7
Number of Figures : 3, Number of Tables : 2, Number of References : 23


 goto top of outline Publication Details

Cerebrovascular Diseases

Vol. 30, No. 2, Year 2010 (Cover Date: July 2010)

Journal Editor: Hennerici M.G. (Mannheim)
ISSN: 1015-9770 (Print), eISSN: 1421-9786 (Online)

For additional information: http://www.karger.com/CED


Copyright / Drug Dosage

Copyright: All rights reserved. No part of this publication may be translated into other languages, reproduced or utilized in any form or by any means, electronic or mechanical, including photocopying, recording, microcopying, or by any information storage and retrieval system, without permission in writing from the publisher or, in the case of photocopying, direct payment of a specified fee to the Copyright Clearance Center.
Drug Dosage: The authors and the publisher have exerted every effort to ensure that drug selection and dosage set forth in this text are in accord with current recommendations and practice at the time of publication. However, in view of ongoing research, changes in goverment regulations, and the constant flow of information relating to drug therapy and drug reactions, the reader is urged to check the package insert for each drug for any changes in indications and dosage and for added warnings and precautions. This is particularly important when the recommended agent is a new and/or infrequently employed drug.
Disclaimer: The statements, opinions and data contained in this publication are solely those of the individual authors and contributors and not of the publishers and the editor(s). The appearance of advertisements or/and product references in the publication is not a warranty, endorsement, or approval of the products or services advertised or of their effectiveness, quality or safety. The publisher and the editor(s) disclaim responsibility for any injury to persons or property resulting from any ideas, methods, instructions or products referred to in the content or advertisements.

References

  1. Brott T, Adams HP Jr, Olinger CP, Marler JR, Barsan WG, Biller J, Spilker J, Holleran R, Eberle R, Hertzberg V: Measurements of acute cerebral infarction: a clinical examination scale. Stroke 1989;20:864–870.
  2. Cote R, Hachinski VC, Shurvell BL, Norris JW, Wolfson C: The Canadian Neurological Scale: a preliminary study in acute stroke. Stroke 1986;17:731–737.
  3. Cote R, Battista RN, Wolfson C, Boucher J, Adam J, Hachinski V: The Canadian Neurological Scale: validation and reliability assessment. Neurology 1989;39:638–643.
  4. Lyden PD, Lau GT: A critical appraisal of stroke evaluation and rating scales. Stroke 1991;22:1345–1352.
  5. Adams HP Jr, Davis PH, Leira EC, Chang KC, Bendixen BH, Clarke WR, Woolson RF, Hansen MD: Baseline NIH Stroke Scale score strongly predicts outcome after stroke: a report of the Trial of Org 10172 in Acute Stroke Treatment (TOAST). Neurology 1999;53:126–131.
  6. Appelros P, Terent A: Characteristics of the National Institute of Health Stroke Scale: results from a population-based stroke cohort at baseline and after one year. Cerebrovasc Dis 2004;17:21–27.
  7. Dewey HM, Donnan GA, Freeman EJ, Sharples CM, Macdonell RA, McNeil JJ, Thrift AG: Interrater reliability of the National Institutes of Health Stroke Scale: rating by neurologists and nurses in a community-based stroke incidence study. Cerebrovasc Dis 1999;9:323–327.
  8. Whelley-Wilson CM, Newman GC: A stroke scale for emergency triage. J Stroke Cerebrovasc Dis 2004;13:247–253.
  9. Burgin WS, Staub L, Chan W, Wein TH, Felberg RA, Grotta JC, Demchuk AM, Hickenbottom SL, Morgenstern LB: Acute stroke care in non-urban emergency departments. Neurology 2001;57:2006–2012.
  10. Leira EC, Pary JK, Davis PH, Grimsman KJ, Adams HP Jr: Slow progressive acceptance of intravenous thrombolysis for patients with stroke by rural primary care physicians. Arch Neurol 2007;64:518–521.
  11. Bushnell CD, Johnston DC, Goldstein LB: Retrospective assessment of initial stroke severity: comparison of the NIH Stroke Scale and the Canadian Neurological Scale. Stroke 2001;32:656–660.
  12. Muir KW, Grosset DG, Lees KR: Interconversion of stroke scales: implications for therapeutic trials. Stroke 1994;25:1366–1370.
  13. Williams LS, Yilmaz EY, Lopez-Yunez AM: Retrospective assessment of initial stroke severity with the NIH Stroke Scale. Stroke 2000;31:858–862.
  14. Stavem K, Lossius M, Ronning OM: Reliability and validity of the Canadian Neurological Scale in retrospective assessment of initial stroke severity. Cerebrovasc Dis 2003;16:286–291.
  15. Goldstein LB, Chilukuri V: Retrospective assessment of initial stroke severity with the Canadian Neurological Scale. Stroke 1997;28:1181–1184.
  16. National Institute of Neurological Disorders and Stroke rt-PA Stroke Study Group: Tissue plasminogen activator for acute ischemic stroke. N Engl J Med 1995;333:1581–1587.
  17. Adams HP Jr, Leclerc JR, Bluhmki E, Clarke W, Hansen MD, Hacke W: Measuring outcomes as a function of baseline severity of ischemic stroke. Cerebrovasc Dis 2004;18:124–129.
  18. Christensen H, Boysen G, Truelsen T: The Scandinavian stroke scale predicts outcome in patients with mild ischemic stroke. Cerebrovasc Dis 2005;20:46–48.
  19. Fullerton KJ, Mackenzie G, Stout RW: Prognostic indices in stroke. Q J Med 1988;66:147–162.
  20. Scandinavian Stroke Study Group: Multicenter trial of hemodilution in ischemic stroke – background and study protocol. Stroke 1985;16:885–890.
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