Chronic Inflammatory Demyelinating Polyneuropathy (CIDP) is considered a rare autoimmune disorder which makes it difficult to accurately diagnose and creates a lot of opportunity for active research. With its wide array of presentations and even similarities to other neurological disorders, improvement must be made in the field of diagnostic methods in order to offer concise and effective treatments. As with many other neurological disorders, CIDP patients are at a higher risk for falls. The dual effect of gait impairment due to neuropathy and general effects of older age create a dangerous combination and increase the risk for falls. By increasing the accuracy with which physicians and health professionals predict falls in patients, they can effectively prevent serious injury and improve quality of life. Consequently, in order to predict the risk of falls, and therefore prevent severe injury, the ability to accurately access the specific qualities of the patient’s gait is critical. Without the ability to precisely identify patients’ particular gait impairment presentation it is very difficult to establish their risk for falls.
The current methods of diagnoses focuses mainly on PROs (patient reported outcomes) which are often gathered through patient questionnaires. Previous research has shown that such methods are simply not detailed and patient specific enough to offer a complete picture of a patient’s condition. We believe ProtoKinetics Movement Analysis Software (PKMAS) is an objective, examiner independent measure of patients’ gait, and offers a method of quantifying patients’ functional gait outcomes in a way that is superior to the current standard of care procedures. Therefore, in this study we aim to reveal the shortcomings of current standard of care procedures in the diagnosis and treatment of CIDP, while also demonstrating the superior value of PKMAS in providing a detailed patient disease profile for CIDP afflicted individuals. Specifically, we demonstrate PKMAS’ increased ability to predict fall risk in CIDP patients, as compared with currently used methods.
In this study PKMAS data was collected under two conditions: Dual Task and PWS (preferred walking speed). During each condition the patient was asked to walk across the Gait Map/Zeno Walkway as the ProtoKinetics Software collected detailed information about the patients’ gait.
For PWS, patients were asked to walk across the map at a speed they were most comfortable at as PKMAS data was collected. For Dual Task, patients are asked to walk at their preferred walking speed while simultaneously doing a simple cognitive task, for instance, counting backwards from a hundred. This second condition is particularly important. The point of such a task is to mimic real-life walking. When we walk on a daily basis we are usually thinking or doing something else simultaneously, even if we may not be consciously aware of this. As such, comparing the results for PWS and Dual Task for patients can shine light onto their real-life gait experience. In order to do so, we measured the percent change in abilities between PWS and Dual Task walking. A greater change signifies greater gait impairment, and a change of greater than 15% places the patient at risk for falls.
Among the PROs, INCAT is the one most often referred to in neurological standard of care and as such we focused on this particular questionnaire separately as well. To do so, t-tests were completed to demonstrate the lack of validity in scoring, by looking at the PKMAS data as compared between two INCAT scores.
In this study we seek to demonstrate the superiority of ProtoKinetics Movement Analysis Software (PKMAS) over the current standard of care for CIDP patients. Specifically, in accurately evaluating patients’ gait and future fall risk. The ability to do so is vitally important for elderly patients who already suffer from decreased gait stability and the additional impact of CIDP can accentuate that risk.
| Identifer | oai:union.ndltd.org:bu.edu/oai:open.bu.edu:2144/43538 |
| Date | 10 December 2021 |
| Creators | Rosenfeld, Yulia |
| Contributors | Gudesblatt, Mark, Moussavi, Mina |
| Source Sets | Boston University |
| Language | en_US |
| Detected Language | English |
| Type | Thesis/Dissertation |
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