Assessing the Validity of a Combined Healthy and Elderly Cohort in

A comparison of the relationship between participant age and the belief values B(NP) and B(OA) are plotted within Figure 4.27A and B respectively. There is no obvious relationship between either belief value and the age of the NP participant, however statistical analysis was continued for completeness. The B(OA), B(NP) and the age of participants were all tested for normality, and the age of participants was not normally distributed. This was, perhaps not surprising – many of the younger subjects recruited were University students and hence tend to be aged 18-26. A Spearman’s rank correlation test was performed. No significant correlation was observed found between age and the belief of OA (r=0.046, p=0.811) or NP (r=-0.146, p=0.44) for the non-

A

B

Figure 4.27 The relationship between participant age, in years, and

A) % belief of OA, B(OA), B) % belief in NP, B(NP).

pathological control cohort. It could be argued that the effects of ageing might only result in significant changes past a threshold age, therefore reducing the strength of any correlations when the group is treated as a whole. Data shown within Figure 4.27 seems to suggest that this is not the case, however, this would need to be explored with a larger dataset with equal subject numbers of subjects within specified age groups so that this could be further explored.

It is also worth noting that the oldest NP control subject within this study was 72 years. Of the 41 OA subjects, 14 were older than 72, and the oldest was 84 years. It therefore isn’t possible to deduce whether the classification might have been sensitive to age- related changes, had age-matched controls been used. In practice, it would be very difficult to recruit NP subjects within this age category who match the inclusion criteria within the study.

4.4 Conclusions

Aim 1: Assess the appropriateness of previously defined PC in representing variance between subjects collected with the updated methodology.

It was hypothesised that the changes to the methodology of calculating knee joint kinematics and kinetics would reduce the appropriateness of the previously defined PCs in representing biomechanical features. An analysis of 9OA and 9NP subjects calculated showed significant differences in several kinematic and kinetic measures by considering the linear correlation between PC scores calculated using the different methods, alongside the differences in means. It was hypothesised that there wouldn’t be any changes in the resultant PC scores from the GRF data, however there were still some differences within the AP and ML ground reaction forces. It was therefore decided the define and contextualise new PC vectors within this chapter.

Aim 2: Assess the validity of a combined young, middle-aged and elderly cohort in classifying OA subjects.

To assess biomechanical changes relating to OA it is necessary to compare OA biomechanics to that of NP subjects. The validity of using a combined young, middle- aged and elderly cohort to define NP function. It was hoped that having a heterogenous NP cohort would train the classifier to be less sensitive to biomechanical changes which might be related to ageing as opposed to specifically OA. This was confirmed by testing for linear correlations between B(OA), B(NP) and age within the NP cohort: no relationship appeared to exist between belief of osteoarthritic function and age of the NP subject.

The final classification of OA function within this chapter accurately distinguishes between OA gait biomechanics using only 18 biomechanical variables. These top-ranked variables were chosen in a way which was shown to reduce a positive bias in the resultant classification accuracy. One OA subject was misclassified, however upon

further review it appeared this person seemed to have a surprisingly high level of function for a TKR subject. It is possible that this person is a high-performing outlier within the dataset. This highlights difficulty of classifying the function of such a heterogenous cohort.

The objective classification of osteoarthritic gait biomechanics presented in this chapter forms the foundations in which pre and post-operative function shall be objectively quantified within the following chapter.

4.5 Clinical Summary

The application of PCA has again proved accurate at objectively describing differences in biomechanical gait parameters. When performing PCA, an ‘eigenvector’ is calculated which describes a prevalent feature of variance within the data. For each subject, a principal component value is then calculated for each subject. This method has been shown sensitive to changes in methodology – which was quantified by significant changes in PC values for subjects processed using two different techniques. PCs defined in Chapter 4 might therefore no longer be valid for objectively discerning between subjects within this chapter.

There are a whole host of challenges in comparing biomechanical information from subjects collected within different laboratories or processed using different approaches. Significant differences are likely to exist between datasets due to a plethora of factors, including biomechanical model definitions, hardware, and expertise in palpating anatomical landmarks. The same considerations should be considered when adopting a data reduction technique which has been defined or modelled on data which may have been collected or processed differently. Another example of this issue in literature is the findings of McMulkin and MacWilliams (2008), who found the Gillette Gait Index (introduced in Section 2.5.1) varied as much has 20% between multiple sites using the index.

The biomechanical features of OA presented within this chapter define the parameters for quantifying biomechanical function before and after TKR surgery. Interpretation of the biomechanical features can be found within Section 4.3.4 and are further discussed in Section 6.2. Some key findings are:

1. The magnitude of the double peak of the adduction moment discriminates severe knee OA better than the overall magnitude of the waveform. Many studies consider the magnitude of the EKAM peaks, however this discrete measure would include the effect of large magnitude differences in adduction moment peaks

throughout the gait cycle. A more discrete measure might be the value of the peaks of the adduction moment, as a percentage of the trough between the two peaks. This discrete metric might be a useful addition or alternative to solely considering EKAM peaks.

2. Hip ad/abduction can discriminate severe OA gait, but may be difficult to discriminate using traditional analytical techniques - The hip adduction angle was able to discriminate OA gait with 86.1% accuracy (Table 4.3), and the feature detected is shown later in Figure 5.7. In short, the feature appears to show ‘hip hiking’; perhaps a compensation strategy increase ground clearance and account for decreased knee flexion during swing phase. This relevant biomechanical feature, however, only represented 11% of the total variance between subjects. The vast majority of variance was accounted for in a feature which reconstructed large magnitude offsets throughout the gait cycle (see “PC1” within Figure 4.14). This is likely due to the difficulty in defining the coronal plane axis of both the hip and the pelvis. If may be of interest to calculate hip ad/abduction relative to the position at heel strike. Previous studies have, however, reported increased hip abduction throughout stance in severe OA subjects (Hunt et al., 2010), suggesting this feature is still detectable using traditional methods. The potentially enhanced ability of PCA to detect this feature over traditional methods warrants further investigation.

3. Transverse moments were again shown to discriminate severe OA gait, despite receiving less attention in the literature. It was discussed in the clinical summary of the previous chapter that the transverse (internal/external) knee moment was surprisingly useful in distinguishing the gait of severe OA subjects. The transverse moment of the hip, knee and ankle were all as highly accurate in classifying OA gait.

Chapter 5 - Quantifying Functional