Purpose of the measure. The Chedoke-McMaster Stroke Assessment measures physical impairment and disability in clients with stroke and other neurological. The Chedoke-McMaster Stroke Assessment measures physical impairment and disability in clients with stroke and other neurological impairment. The measure. The Chedoke-McMaster Stroke Assessment (Chedoke Assessment) is a reliable and valid measure used to assess physical impairment and disability in clients.

Author: Voodoonris Kinris
Country: Zambia
Language: English (Spanish)
Genre: Environment
Published (Last): 12 February 2018
Pages: 297
PDF File Size: 1.17 Mb
ePub File Size: 11.93 Mb
ISBN: 973-8-81617-987-8
Downloads: 35476
Price: Free* [*Free Regsitration Required]
Uploader: Kagajas

A longitudinal prognostic study using historical data obtained from patients admitted post cerebrovascular accident was undertaken. Data were abstracted for all patients undergoing rehabilitation post stroke who also had documented admission and discharge CMSA scores. Published predictive equations were used to determine predicted outcomes.

Complete data were available for 74 patients with a mean age of This study shows that the large error associated with the predictions as defined by the confidence band for the CMSA II and AI limits their clinical usefulness as a predictive measure. Further research to establish predictive models assessmenr alternative statistical procedures is warranted. More than 50, people have strokes in Canada each year, and there are currentlyCanadians living with the effects of stroke.

Measuring clinical outcomes is an integral part of evidence-based rehabilitation. The ability to predict outcomes following stroke yields several benefits: The Chedoke—McMaster Stroke Assessment CMSA is a highly reliable and valid outcome measure used by rehabilitation personnel to assess impairment and activity following stroke. The use of the CMSA to predict outcomes in patients with stroke undergoing rehabilitation was first reported by Gowland in the s.

The purpose of this investigation was to estimate the predictive accuracy and clinical usefulness of the CMSA predictive equations for patients with stroke undergoing rehabilitation. In the absence of previous cross-validation studies, determining the accuracy of the predictive equations will provide important information to health professionals working in stroke rehabilitation about the equations’ value in a clinical setting, and is in keeping with the mandate of evidence-based practice.

This longitudinal prognostic study used historical data obtained from the Rehabilitation Hospital Health Sciences Centre, a tertiary-care institution in Winnipeg, Manitoba. The data were analyzed at McMaster University. The University of Manitoba Health Research Ethics Board approved access to an anonymized database for this investigation. The database used in this study was originally developed for a previous study that gathered data retrospectively from patient charts. That study did not address predictive accuracy.

There was no change to usual assessment and intervention care over this period. Physiotherapists and student physiotherapists assessed patients with the CMSA according to the instructions outlined in the CMSA manual, 18 and all patients received conventional stroke rehabilitation during their stay in the rehabilitation unit. The original database consisted of patient demographic information, Impairment Inventory scores, and total Activity Inventory scores for patients.

All physiotherapists working in the stroke rehabilitation unit were involved in reviewing the patient charts and extracting the data. The purpose of the original study was to evaluate the functional outcomes of patients participating in a stroke rehabilitation program; specifically, the study reported on the extent to which change occurred during rehabilitation and the proportion of patients who met or exceeded the predicted discharge scores.

In Januaryfollowing confirmation of ethical approval for the present study, additional information was extracted from the same patients’ charts by staff from the hospital’s medical records department and added to the database by the current investigative team.

The newly retrieved information consisted of date of stroke, date of admission to and discharge from the rehabilitation unit, and scores on the 15 individual tasks of the Activity Inventory. For the current study, patients were excluded from the data analysis if they were admitted to the rehabilitation unit more than 45 days post stroke, if their data were incomplete, or if they had experienced a stroke event other than a unilateral stroke.

These criteria were chosen in an effort to achieve a more homogeneous sample. The CMSA comprises two components: Scores are determined by the quality of movement; a score of 1 indicates severe motor impairment, and a score of 7 indicates normal movement. The AI consists of two indices: The maximum total score of the AI is ; higher scores indicate greater functional independence.


The CMSA manual describes the administration and scoring guidelines for the measure.

Using the predictive equations and applying the patient’s CMSA scores cuedoke-mcmaster admission, predicted discharge scores can be determined for each II stage and for the total AI and each AI index. Next, we applied Gowland’s predictive equations to patients’ admission scores, as reported in the database, to obtain predicted discharge score estimates for the six II dimensions and for the AI and its two indices.

Chedoke-McMaster Stroke Assessment – Physiopedia

A predictive model is reliable to the extent that it generalizes to samples other than the one used to create it. The final aspect of our analysis was to consider whether a linear model—as assumed by Gowland—provided the best fit for the predictive models. This was accomplished in two steps. First we examined scatter plots of the actual discharge dependent variable and admission independent variable data with the line of best fit superimposed on the graph. When a curvilinear relationship was evident, we examined higher-order polynomials and tested whether the addition of higher-order terms contributed to the predictive ability of the revised model.

Our sample size was one of convenience, determined by the number of eligible patients in our database. Of the patients admitted to the rehabilitation unit between July and July who constituted the initial database, 30 were excluded from the present study owing to missing data, because they had had other than a unilateral stroke, or because they were admitted to the rehabilitation unit more than 45 days post stroke.

The other 74 patients were included in the data analysis. Patients had a mean SD age of The five most prevalent comorbidities were hypertension, diabetes mellitus type I or type IIhyperlipidemia, musculoskeletal disorders, and a previous stroke or transient ischemic attack TIA. Our lack-of-fit analysis revealed that a second-degree polynomial provided better predictive models for CMSA hand and arm impairment data.

Measuring physical impairment and disability with the Chedoke-McMaster Stroke Assessment.

The additional predictive ability of models that included a squared term were as follows: The goal of our study was to estimate the predictive etroke and clinical usefulness of reported CMSA equations. To meet this objective, we applied statistical procedures to examine the relationships between the actual results in our sample of patients undergoing rehabilitation following stroke and the outcomes predicted using Gowland’s predictive equations.

The shrinkage coefficients for the stages of the II were all less than 0.

One possible explanation for the large shrinkage value is that there were differences between the stroks samples of patients e. Unfortunately, information on the patients in sttroke sample used by Gowland to generate the predictive equations was not available to explore these potential explanations. While the results of this analysis suggest that the shrinkage for the II scores is acceptable, we further considered the confidence bands of the predicted outcomes to gain additional information.

The clinical usefulness of the predictive equations is directly related to the width of the prediction bands: The width of prediction bands depends on the variability in the data, the mean score, and the distance of a particular value aszessment the mean score: This width is likely too large to provide a confident prediction of a patient’s outcome.

We offer the following clinical vignette to assist in the interpretation of our findings. A year-old man was admitted to the rehabilitation unit after a right cerebral vascular accident CVA 16 days ago.

The physiotherapist assesses the patient using the CMSA within the first week of admission. The CMSA stage for the hand at admission is 3. The physiotherapist calculates expected discharge scores using Gowland’s predictive equations.

The predicted discharge score for the hand is stage 3. Stage 1 indicates that the hand will be flaccid and that there will be no evidence of any reflexive or active movement, while Stage 6 indicates that coordination and patterns of movement for the hand will be near normal. We also considered whether a more complex model in this case, higher-degree polynomial might provide a better fit than the models proposed by Gowland et al. We wish to stress that our study focused on the predictive ability of the CMSA and not on its properties in assessing patient outcomes.


Proficient outcome measures must display high levels of discrimination and the ability to detect change, and previous investigations have provided support for these measurement properties as they apply to the CMSA when applied to patients with stroke. Our study has several limitations, many of which arise from the use of a historical database. Despite efforts to chefoke-mcmaster a large sample size, the database was missing data for a number of patients, who were therefore excluded from the analysis.

Because there was no standard protocol for data collection, patients were assessed at different time points including time post stroke and length of time between initial and discharge assessmentproducing a non-homogenous sample.

In addition, at the time of assessment the patients’ CMSA scores were recorded on paper score sheets, which were stored in the patient files; for the purpose of the current study, these scores were then recorded on a new data-collection sheet by staff in medical records and subsequently added to the database by members of our research team.

The transferring of data on several occasions by different individuals may have resulted in errors in the database used for the study. Furthermore, we have limited information about the graduate and student physiotherapists who collected data for the original study.

For example, there are no data on their experience in working with patients with stroke or on their formal training or lack thereof in administering and scoring the CMSA. The accuracy of the data they collected may also have influenced our results. The results of this study suggest that statistical procedures other than linear regression can be considered to develop new and more accurate prediction models. Future research to develop predictive equations should involve a prospective study with a larger sample.

Methodological considerations would include specifying assessment times related to time post stroke, rather than using admission and discharge dates, to increase accuracy of predictions. For approximately 20 years, clinicians in rehabilitation settings have been using the CMSA predictive equations developed by Gowland to enhance clinical decision making. Furthermore, the confidence bands associated with both II and AI scores are too large to be considered clinically useful.

Future research to identify models that rehabilitation professionals can use in predicting outcomes for individual clients with stroke would facilitate evidence-based practice.

The importance of the CMSA’s role as a discriminative and evaluative measure for persons with stroke remains unchanged. The CMSA is a highly valid and reliable measure to discriminate and detect change in persons post stroke. There has been no research to evaluate the accuracy of the predictive equations developed for use with clients undergoing rehabilitation for stroke.

The results of this study call into question the clinical usefulness of the predictive equations developed for stroke rehabilitation.

Chedoke-McMaster Stroke Assessment

Future research using alternative predictive models is warranted. Estimating the accuracy of the Chedoke—McMaster Stroke Assessment predictive equations for stroke chedok-emcmaster. National Center for Biotechnology InformationU. Journal List Physiother Can v. Published online Aug Mia DangKalinda D. RamsaranMelissa E. Stratfordand Patricia A. Author information Copyright and License information Disclaimer.

Miller is an instructor of training workshops on the administration, scoring, and clinical application of the CMSA. A portion of this manuscript was presented at the annual Canadian Physiotherapy Association Congress held in St.

John’s, Newfoundland, on July 23, Address correspondence to Paul W. See commentary ” Clinician’s Commentary on Dang et al.

This article has been cited by chedoke-mcmaser articles in PMC. Participants and Procedure The database used in this study was originally developed for a previous study that gathered data retrospectively from patient charts.

Author: admin