Plan de ordenación y manejo de la cuenca hidrográfica del río Otún

There are many outcome scores that reflect aspects o f recovery, but not all are

necessarily correlated with one another. The consensus view is that a range o f

measures should be used to build an overall picture o f degree o f recovery,

(Duncan et al., 2000; Turton and Fraser, 1986). This issue has been neglected by previous studies, so that patients have been described as fully recovered on the

basis o f a single measure. Com bining a num ber o f outcome m easures in order to

account of the complex nature of the recovery process, but allows patients to be compared in terms of recovery or outcome.

4.2.1 Outcome scores

The following outcome scores were used in all the patient studies described. Details of how these tests are administered and scored are given in Appendix I.

4.2.1.1 Rankin Scale

The Rankin Scale claims to be a measure of handicap, but mixes impairment and disability. It has a strong emphasis on mobility. It is relatively insensitive but is

very easy to use and extremely reliable (Van Swieten et aL, 1988).

4.2.1.2 Barthel Activities of Daily Living Index

The Barthel ADL index is the most widely used activities of daily living measure. It covers elements such as walking, dressing, going to the toilet and continence. Its validity and reliability have been demonstrated in many studies (Wade and Collin,

1989).

4.2.1.3 Orpington Prognostic Scale

The Orpington Prognostic Scale (OPS) is a stroke impairment scale, comparable to another well known scale, the National Institutes of Health Stroke Scale (NIHSS). Both have been found to be valid and reliable. The OPS is easier to use

and more sensitive to change at higher levels of physical function (Lai et al.,

4.2.1.4 Motricity Index

The arm and leg sections of the Motricity Index were used in the assessments performed as part of this thesis. It is a short simple measure of motor loss with

good validity and reliability (Sunderland et aL, 1989; Collen et aL, 1990).

4.2.1.5 Nine Hole Peg Test

The nine hole peg test (NHPT) is a simple and short test of manual dexterity. It is

both valid and reliable (Mathiowetz et aL, 1985; Heller et aL, 1987; Sunderland et

aL, 1989). It is particularly sensitive to changes in performance in the upper

ranges, but is less useful in severe impairment as patients are not able to pick up the pegs. In addition it is not sensitive at detecting proximal weakness.

4.2.1.6 Grip strength

Measurement of maximum grip strength with the affected hand is a good

prognostic indicator after stroke (Heller et aL, 1987). It has good validity and

reliability and is sensitive to changes over a wide range of impairment

(Sunderland et aL, 1989)

4.2.1.7 Action Research Arm Test

The Action Research Arm Test (ARAT) is a modification of a battery of tests first introduced in 1965 (Carol, 1965). It is designed to assess proximal and distal strength, as well as dexterity. The original test has been shortened (Lyle, 1981), but retains good validity and reliability.

4.2.1.8 Timed Ten-Metre Walk

Measuring the time it takes a subject to walk 10 metres is a simple, reliable, valid

measure of mobility (Holden et aL, 1984; Wade et aL, 1987). It has been criticised

for not considering the quality of the gait, but it is likely that good quality is

associated with greater speed (Wade et aL, 1987).

The above measures cover a variety of aspects of the recovery process, including focal upper limb impairment (grip strength, and arm section of Motricity Index), focal upper limb disability (nine hole peg test), general upper limb ability (Action Research Arm Test), focal lower limb impairment (leg section of Motricity Index), mobility (timed ten metre walk, and to a certain extent the Rankin Scale), activities of daily living (Barthel ADL Index), some aspects of handicap (Rankin Scale) and a general stroke impairment score including a basic cognitive assessment (Orpington Prognostic Scale). The selection of a wide variety of outcome scores is made with a specific experimental question in mind. My intention is to investigate whether task (motor) related brain activation in stroke

patients is related to their overall recovery, not specifically upper limb recovery.

However, the lower limb tests are not independent of either ADL scores or upper

limb scores. Walking speed for example is affected by both upper limb function and truncal stability and lower limb Motricity Index scores correlate with hand grip scores (Cameron and Bohannon, 2000) suggesting that processes that facilitate upper limb recovery also facilitate lower limb recovery.

These scores were used to provide a quantification of different aspects of the recovery process. In addition patients were also assessed for the presence of

sensory deficits, apraxia, and cognitive deficits, particularly neglect syndromes (see Appendix I).

4.2.2 Combining outcome scores: principal component analysis

Thus I scored a number of individuals on a variety of outcome measures, or individuals were scored at a number of time points. Several different measures were used, because no single measurement encompasses overall recovery. In order to determine how many trends are present in the data set I used a multivariate analysis technique called principal component analysis.

Suppose that p observations {xi, X2, ... Xp) are made on each of n individuals. It is possible to combine these variables in the form of a number of different

independent variables {yi, y2, ... yp)- The new variables are defined,

y i = diiXi + ai2X2 + ... + aipXp,

y i = a2lX i + U22X2 + ... + tt2pXp,

The new variable yi explains the highest possible variance, y2 the next largest

variance, ys the third largest and so on. Each variable is uncorrelated, and so

accounts for different trends in the original data set. Thus yi represents the linear

combination of the xs that best explains the differences between the individual

subjects (or time points), and is termed the first principal component.

h > h > h > ... > V

where, 1% = var(v/).

The total variance in the original data set is preserved, such that, L v a r ( x j = L var(v/) = L

Because alterations in the scale of the original variables will alter the result, it is important that the original variables are normalized (i.e. mean corrected and divided by the standard deviation) to give zero mean and unit variance. From the above equation it therefore follows that,

4- ^2 + ^3 + ... +

Thus, the total variance of the original data set explained by the first principal

component is Ip.

In summaiy, this process reparameterises the original data set, producing a new set of uncorrelated variables. Because the new variables have exactly the same total variance, principal component analysis is only useful if the majority of the variance is accounted for by a reduced number of new variables. I have used this method in the expectation that the first principal component will account for a large proportion of the variance and will be used to compare overall recovery or outcome between different patients, or across different time points in the same patient.