Kinematic and clinical outcomes to evaluate the efficacy of a multidisciplinary intervention on functional mobility in Parkinson's disease

Copyright © 2021 Bouça-Machado, Branco, Fonseca, Fernandes, Abreu, Guerreiro, Ferreira and The CNS Physiotherapy Study Group. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.Introduction: Functional mobility (FM) is a concept that incorporates the capacity of a person to move independently and safely to accomplish tasks. It has been proposed as a Parkinson's disease (PD) functional and global health outcome. In this study, we aimed to identify which kinematic and clinical outcomes changes better predict FM changes when PD patients are submitted to a specialized multidisciplinary program. Methods: PD patients engaged in a pre-defined specialized multidisciplinary program were assessed at admission and discharge. Change from baseline was calculated for all kinematic and clinical outcomes, and Timed Up and Go (TUG) was defined as the primary outcome for FM. A stepwise multivariate linear regression was performed to identify which outcome measures better predict TUG changes. Results: Twenty-four patients were included in the study. The changes in TUG Cognitive test, supervised step length, and free-living (FL) step time asymmetry were identified as the best predictors of TUG changes. The supervised step length and FL step time asymmetry were able to detect a small to moderate effect of the intervention (d values ranging from -0.26 to 0.42). Conclusions: Our results support the use of kinematic outcome measures to evaluate the efficacy of multidisciplinary interventions on PD FM. The TUG Cognitive, step length, and FL step time asymmetry were identified as having the ability to predict TUG changes. More studies are needed to identify the minimal clinically important difference for step length and FL step time asymmetry in response to a multidisciplinary intervention for PD FM.The authors would like to acknowledge the support of Fundação para a Ciência e a Tecnologia (FCT) (SFRH/BD/120773/2016 to RB-M; SFRH/BD/144242/2019 to DB, UIDB/00408/2020 and UIDP/00408/2020 to LASIGE).info:eu-repo/semantics/publishedVersio

Clube Português do Pâncreas Recommendations for Chronic Pancreatitis: Medical, Endoscopic, and Surgical Treatment (Part II)

Chronic pancreatitis (CP) is a complex disease that should be treated by experienced teams of gastroenterologists, radiologists, surgeons, and nutritionists in a multidisciplinary environment. Medical treatment includes lifestyle modification, nutrition, exocrine and endocrine pancreatic insufficiency correction, and pain management. Up to 60% of patients will ultimately require some type of endoscopic or surgical intervention for treatment. However, regardless of the modality, they are often ineffective unless smoking and alcohol cessation is achieved. Surgery retains a major role in the treatment of CP patients with intractable chronic pain or suspected pancreatic mass. For other complications like biliary or gastroduodenal obstruction, pseudocyst drainage can be performed endoscopically. The recommendations for CP were developed by Clube Português do Pâncreas (CPP), based on literature review to answer predefined topics, subsequently discussed and approved by all members of CPP. Recommendations are separated in two parts: "chronic pancreatitis etiology, natural history, and diagnosis," and "chronic pancreatitis medical, endoscopic, and surgical treatment." This abstract pertains to part II.info:eu-repo/semantics/publishedVersio

Clube Português do Pâncreas Recommendations for Chronic Pancreatitis: Etiology, Natural History, and Diagnosis (Part I)

Chronic pancreatitis (CP) is a heterogeneous disease, with different causes and often a long delay between onset and full classic presentation. Clinical presentation depends on the stage of the disease. In earlier stages, recurrent episodes of acute pancreatitis are the major signs dominating clinical presentation. As the inflammatory process goes on, less acute episodes occur, and pain adopts different aspects or may even disappear. After 10–15 years from onset, functional insufficiency occurs. Then, a classic presentation with pain and pancreatic exocrine and endocrine insufficiency appears. Diagnosis remains challenging in the early stages of the disease, as its initial presentation is usually ill-defined and overlaps with other digestive disorders. Computed tomography and magnetic resonance cholangiopancreatography should be the first choice in patients with suspected CP. If the results are normal or equivocal but still there is a high suspicion of CP, the next option should be endoscopic ultrasound. Endoscopic retrograde cholangiopancreatography is mainly a therapeutic technique, and for the diagnostic purpose should only be used when all other imaging modalities and pancreatic function tests have been exhausted. Indirect tests are used to quantify the degree of insufficiency in already-established late CP. Recommendations on CP were developed by Clube Português do Pâncreas (CPP), based on literature review to answer predefined topics, subsequently discussed and approved by all members of CPP. Recommendations are separated in two parts: “chronic pancreatitis etiology, natural history, and diagnosis,” and “chronic pancreatitis medical, endoscopic, and surgical treatment.” This abstract pertains to part I

Personalised Gait Recognition for People with Neurological Conditions

There is growing interest in monitoring gait patterns in people with neurological conditions. The democratisation of wearable inertial sensors has enabled the study of gait in free living environments. One pivotal aspect of gait assessment in uncontrolled environments is the ability to accurately recognise gait instances. Previous work has focused on wavelet transform methods or general machine learning models to detect gait; the former assume a comparable gait pattern between people and the latter assume training datasets that represent a diverse population. In this paper, we argue that these approaches are unsuitable for people with severe motor impairments and their distinct gait patterns, and make the case for a lightweight personalised alternative. We propose an approach that builds on top of a general model, fine-tuning it with personalised data. A comparative proof-of-concept evaluation with general machine learning (NN and CNN) approaches and personalised counterparts showed that the latter improved the overall accuracy in 3.5% for the NN and 5.3% for the CNN. More importantly, participants that were ill-represented by the general model (the most extreme cases) had the recognition of gait instances improved by up to 16.9% for NN and 20.5% for CNN with the personalised approaches. It is common to say that people with neurological conditions, such as Parkinson’s disease, present very individual motor patterns, and that in a sense they are all outliers; we expect that our results will motivate researchers to explore alternative approaches that value personalisation rather than harvesting datasets that are may be able to represent these differences

Personalised Gait Recognition for People with Neurological Conditions

There is growing interest in monitoring gait patterns in people with neurological conditions. The democratisation of wearable inertial sensors has enabled the study of gait in free living environments. One pivotal aspect of gait assessment in uncontrolled environments is the ability to accurately recognise gait instances. Previous work has focused on wavelet transform methods or general machine learning models to detect gait; the former assume a comparable gait pattern between people and the latter assume training datasets that represent a diverse population. In this paper, we argue that these approaches are unsuitable for people with severe motor impairments and their distinct gait patterns, and make the case for a lightweight personalised alternative. We propose an approach that builds on top of a general model, fine-tuning it with personalised data. A comparative proof-of-concept evaluation with general machine learning (NN and CNN) approaches and personalised counterparts showed that the latter improved the overall accuracy in 3.5% for the NN and 5.3% for the CNN. More importantly, participants that were ill-represented by the general model (the most extreme cases) had the recognition of gait instances improved by up to 16.9% for NN and 20.5% for CNN with the personalised approaches. It is common to say that people with neurological conditions, such as Parkinson’s disease, present very individual motor patterns, and that in a sense they are all outliers; we expect that our results will motivate researchers to explore alternative approaches that value personalisation rather than harvesting datasets that are may be able to represent these differences

Neuromelanin magnetic resonance imaging of the Substantia Nigra in LRRK2-related Parkinson’s Disease

info:eu-repo/semantics/publishedVersio