Preventive strategy of flatfoot deformity using fully automated procedure

A non-invasive, no radiation, out-of-hospital automated system is proposed to identify low arch integrated in the design and manufacturing of personalized orthoses using parametric modelling. The aim of the design process is to integrate assistive technology with assessment and prevent low arch progressing to a more serious case - flatfoot. In the automated procedure, we developed an assessment method including reliable thresholds of foot type classification and test protocol to reduce interferences due to preceding activities, an automation to translate scanned data into parametric design for orthotic customization, finite element model evaluating effectiveness of the personalized design, and a personalized comparative test to evaluate the long-term improvement of foot arch shape. Our low arch threshold established by subject-specific 3D models reduced the misclassification rate from 55%, as previously reported to 6.9%. Individuals who engaged in sedentary activity (i.e. sitting) had the greater change in arch height compared to active activity (i.e. standing and walking), which is more likely to affect the obtained measure. Therefore, a test protocol now states that participants are not allowed to sit over 100 min prior the measurement to reduce such interference. We have proposed and tested an automated algorithm to translate scanned data including seven foot's parameters into customised parametric design of the insert. The method decreases the required time of orthotic computer-aided design from over 3 h to less than 2 min. A finite element analysis procedure was additionally developed to assess the performance of geometries and material of designed orthotic based on the distribution of plantar pressure and internal stress. The personalized comparative assessment based on midfoot contact area was carried out periodically for follow-up and the orthotic could be re-designed if necessary. The proposed automated procedure develops a pre-screening system to distinguish low arch and provide preventatives before it becomes symptomatic. Furthermore, non-symptom flatfoot can be detected at early stages and referred to medics for further diagnosis or treatment.

A real-world single-centre analysis of alemtuzumab and cladribine for multiple sclerosis

BACKGROUND

Highly active MS may warrant higher efficacy treatments for disease control. However, these often confer more risk and have not been compared in head-to-head clinical trials, making relative efficacy and safety difficult to interpret. Alemtuzumab and cladribine are two high-efficacy treatments given as discrete courses separated by one year, followed by a durable response that potentially does not require ongoing treatment. Before the approval of oral cladribine, our centre had been treating patients with a bioequivalent intravenous (IV) regimen since 2010. The objective of this study is to report the safety and efficacy data of alemtuzumab and cladribine in a real-world, single centre setting.

METHODS

We retrospectively reviewed all patients treated with alemtuzumab or cladribine at the Ottawa Hospital MS Clinic with 2 or more years of follow-up. Information on baseline demographic variables, previous treatment, and prior disease activity was collected. Outcomes investigated were "no evidence of disease activity" (NEDA) and its constituents: new clinical relapse, new MRI activity, and Expanded Disability Status Scale (EDSS) progression; as well as any adverse events or treatment discontinuation. We performed univariate and multiple logistic regression to determine differences in 2-year NEDA and time-to-event analyses with Cox regression models to determine factors associated with each outcome through the study period.

RESULTS

Forty-six patients were treated with alemtuzumab and 65 with cladribine of whom 51 (78%) received the intravenous regimen, followed for a total of 420.1 person-years. The cladribine group was older (p=.0002), with higher baseline EDSS (p=.0015), and more likely secondary progressive (p<.0001). Alemtuzumab had a higher rate of 2-year NEDA than cladribine (OR 4.78, 95%CI: 1.57-14.50, p=.006), but beyond 2 years the difference was not statistically significant (HR 0.50, 95%CI: 0.25-1. 30, p=.061). More prior treatments were associated with lower likelihood of retaining NEDA (HR 1.26, 95%CI: 1.03-1.54, p=.027). Alemtuzumab had more infusion reactions (80% vs. 17%, p<.0001), shingles (22% vs. 2%, p=.005), and secondary autoimmunity (52% vs. 3%, p<.0001) than cladribine, but there was no difference in grade 3 or higher adverse events (21.7% vs. 18.5%, p=1.0).

CONCLUSION

In our cohort alemtuzumab and cladribine achieved similar rates of NEDA in long-term follow-up, with overall less adverse events with cladribine. Patient registries would allow more robust comparisons, detection of adverse events, and assessment of a durable response.

The correlation between baseline score and post-intervention score, and its implications for statistical analysis

Background

When using a continuous outcome measure in a randomised controlled trial (RCT), the baseline score should be measured in addition to the post-intervention score, and it should be analysed using the appropriate statistical analysis.

Methods

We derive the correlation between the change score and baseline score and show that there is always a correlation (usually negative) between the change score and baseline score. We discuss the following correlations and provide the mathematical derivations in the Appendix:

Correlation between change score and baseline score

Correlation between change score and post score

Correlation between change score and average score.

The setting here is a parallel, two-arm RCT, but the method discussed in this paper is applicable for any studies or trials that have a continuous outcome measure; it is not restricted to RCTs.

Results

We show that using the change score as the outcome measure does not address the problem of regression to the mean, nor does it take account of the baseline imbalance. Whether the outcome is change score or post score, one should always adjust for baseline using analysis of covariance (ANCOVA); otherwise, the estimated treat effect may be biased. We show that these correlations also apply when comparing two measurement methods using Bland-Altman plots.

Conclusions

The correlation between baseline and post-intervention scores can be derived using the variance sum law. We can then use the derived correlation to calculate the required sample size in the design stage. Baseline imbalance may occur in RCTs, and ANCOVA should be used to adjust for baseline in the analysis stage.