scholarly journals Accurate prediction of clinical stroke scales and improved biomarkers of motor impairment from robotic measurements

PLoS ONE ◽  
2021 ◽  
Vol 16 (1) ◽  
pp. e0245874
Author(s):  
Dimitris K. Agrafiotis ◽  
Eric Yang ◽  
Gary S. Littman ◽  
Geert Byttebier ◽  
Laura Dipietro ◽  
...  
Keyword(s):  
Clinical Trials ◽  
Motor Impairment ◽  
Clinical Trial Design ◽  
Arm Movement ◽  
Ant Colonies ◽  
Clinical Scales ◽  
Human Movements ◽  
Complete Measurement ◽  
Neural Network Ensembles ◽  
Network Ensembles

Objective One of the greatest challenges in clinical trial design is dealing with the subjectivity and variability introduced by human raters when measuring clinical end-points. We hypothesized that robotic measures that capture the kinematics of human movements collected longitudinally in patients after stroke would bear a significant relationship to the ordinal clinical scales and potentially lead to the development of more sensitive motor biomarkers that could improve the efficiency and cost of clinical trials. Materials and methods We used clinical scales and a robotic assay to measure arm movement in 208 patients 7, 14, 21, 30 and 90 days after acute ischemic stroke at two separate clinical sites. The robots are low impedance and low friction interactive devices that precisely measure speed, position and force, so that even a hemiparetic patient can generate a complete measurement profile. These profiles were used to develop predictive models of the clinical assessments employing a combination of artificial ant colonies and neural network ensembles. Results The resulting models replicated commonly used clinical scales to a cross-validated R2 of 0.73, 0.75, 0.63 and 0.60 for the Fugl-Meyer, Motor Power, NIH stroke and modified Rankin scales, respectively. Moreover, when suitably scaled and combined, the robotic measures demonstrated a significant increase in effect size from day 7 to 90 over historical data (1.47 versus 0.67). Discussion and conclusion These results suggest that it is possible to derive surrogate biomarkers that can significantly reduce the sample size required to power future stroke clinical trials.

Outcomes assessment in multiple sclerosis clinical trials: a critical analysis

1995 ◽  
Vol 1 (1) ◽  
pp. 37-47 ◽  
Author(s):  
JN Whitaker ◽  
HF McFarland ◽  
P Rudge ◽  
SC Reingold
Keyword(s):  
Clinical Trials ◽  
International Workshop ◽  
Clinical Trial Design ◽  
Evaluation Study ◽  
The United States ◽  
Clinical Scales ◽  
Task Forces ◽  
Therapeutic Trials ◽  
History Of ◽  
The Many

The feasibility and precision of clinical trials for the treatment of MS must be improved Subsequent to the approval by the Food and Drug Administration of the United States of interferon beta-1b as a safe and effective, though not curative, treatment for relapsing-remitting MS, the testing of other agents in this disease has been undertaken or is anticipated. This report summarises the discussions and recommendations of an international workshop held to review critically the elements of current MS therapeutic trials and to identify the most important aspects of clinical evaluation, study design and data analysis that would allow agents for MS to be tested as accurately, rapidly and economically as possible. While acknowledging the many uncertainties about the pathophysiology and natural history of MS, the workshop participants made recommendations about the preferred components to be used in the design of trials which may be different depending on the treatment goal and agent studied. It was concluded that the formulation of a useful clinical trial design must be based on specific guidelines for clinical scales and imaging for which task forces were recommended and subsequently appointed.

Recent Updates in the Alzheimer’s Disease Etiopathology and Possible Treatment Approaches: A Narrative Review of Current Clinical Trials

2020 ◽  
Vol 13 (4) ◽  
pp. 273-294 ◽  
Author(s):  
Elahe Zarini-Gakiye ◽  
Javad Amini ◽  
Nima Sanadgol ◽  
Gholamhassan Vaezi ◽  
Kazem Parivar
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Clinical Trials ◽  
Small Molecules ◽  
Clinical Trial Design ◽  
Treatment Approaches ◽  
Drug Candidates ◽  
Novel Treatments ◽  
Approved Drugs ◽  
Tau Aggregation

Background: Alzheimer’s disease (AD) is the most frequent subtype of incurable neurodegenerative dementias and its etiopathology is still not clearly elucidated. Objective: Outline the ongoing clinical trials (CTs) in the field of AD, in order to find novel master regulators. Methods: We strictly reviewed all scientific reports from Clinicaltrials.gov and PubMed databases from January 2010 to January 2019. The search terms were “Alzheimer's disease” or “dementia” and “medicine” or “drug” or “treatment” and “clinical trials” and “interventions”. Manuscripts that met the objective of this study were included for further evaluations. Results: Drug candidates have been categorized into two main groups including antibodies, peptides or hormones (such as Ponezumab, Interferon β-1a, Solanezumab, Filgrastim, Levemir, Apidra, and Estrogen), and naturally-derived ingredients or small molecules (such as Paracetamol, Ginkgo, Escitalopram, Simvastatin, Cilostazo, and Ritalin-SR). The majority of natural candidates acted as anti-inflammatory or/and anti-oxidant and antibodies exert their actions via increasing amyloid-beta (Aβ) clearance or decreasing Tau aggregation. Among small molecules, most of them that are present in the last phases act as specific antagonists (Suvorexant, Idalopirdine, Intepirdine, Trazodone, Carvedilol, and Risperidone) or agonists (Dextromethorphan, Resveratrol, Brexpiprazole) and frequently ameliorate cognitive dysfunctions. Conclusion: The presences of a small number of candidates in the last phase suggest that a large number of candidates have had an undesirable side effect or were unable to pass essential eligibility for future phases. Among successful treatment approaches, clearance of Aβ, recovery of cognitive deficits, and control of acute neuroinflammation are widely chosen. It is predicted that some FDA-approved drugs, such as Paracetamol, Risperidone, Escitalopram, Simvastatin, Cilostazoand, and Ritalin-SR, could also be used in off-label ways for AD. This review improves our ability to recognize novel treatments for AD and suggests approaches for the clinical trial design for this devastating disease in the near future.

Digital Technology in Clinical Trials for Multiple Sclerosis: a systematic review. (Preprint)

2020 ◽  
Author(s):  
Marcello De Angelis ◽  
Luigi Lavorgna ◽  
Antonio Carotenuto ◽  
Martina Petruzzo ◽  
Roberta Lanzillo ◽  
...  
Keyword(s):  
Multiple Sclerosis ◽  
Clinical Trial ◽  
Clinical Trials ◽  
Outcome Measures ◽  
Digital Technology ◽  
Clinical Trial Design ◽  
Motor Rehabilitation ◽  
Robot Assisted ◽  
Future Directions ◽  
Treatment Side Effects

BACKGROUND Clinical trials in multiple sclerosis (MS) have leveraged the use of digital technology to overcome limitations in treatment and disease monitoring. OBJECTIVE To review the use of digital technology in concluded and ongoing MS clinical trials. METHODS In March 2020, we searched for “multiple sclerosis” and “trial” on pubmed.gov and clinicaltrials.gov using “app”, “digital”, “electronic”, “internet” and “mobile” as additional search words, separately. Overall, we included thirty-five studies. RESULTS Digital technology is part of clinical trial interventions to deliver psychotherapy and motor rehabilitation, with exergames, e-training, and robot-assisted exercises. Also, digital technology has become increasingly used to standardise previously existing outcome measures, with automatic acquisitions, reduced inconsistencies, and improved detection of symptoms. Some trials have been developing new patient-centred outcome measures for the detection of symptoms and of treatment side effects and adherence. CONCLUSIONS We will discuss how digital technology has been changing MS clinical trial design, and possible future directions for MS and neurology research.

scholarly journals Deep Convolutional Neural Network Ensembles Using ECOC

IEEE Access ◽  
2021 ◽  
pp. 1-1
Author(s):  
Sara Atito Ali Ahmed ◽  
Cemre Zor ◽  
Muhammad Awais ◽  
Berrin Yanikoglu ◽  
Josef Kittler
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Deep Convolutional Neural Network ◽  
Neural Network Ensembles ◽  
Network Ensembles

scholarly journals Conceptual Framework to Support Clinical Trial Optimization and End-to-End Enrollment Workflow

2019 ◽  
pp. 1-10 ◽  
Author(s):  
Neha M. Jain ◽  
Alison Culley ◽  
Teresa Knoop ◽  
Christine Micheel ◽  
Travis Osterman ◽  
...  
Keyword(s):  
Clinical Trial ◽  
Clinical Trials ◽  
Knowledge Representation ◽  
Conceptual Framework ◽  
Trial Design ◽  
Clinical Trial Design ◽  
Prospective Clinical Trial ◽  
Future Trends ◽  
Current State ◽  
Evaluation Strategies

In this work, we present a conceptual framework to support clinical trial optimization and enrollment workflows and review the current state, limitations, and future trends in this space. This framework includes knowledge representation of clinical trials, clinical trial optimization, clinical trial design, enrollment workflows for prospective clinical trial matching, waitlist management, and, finally, evaluation strategies for assessing improvement.

scholarly journals Opportunities and challenges of incorporating clinical outcome assessments in brain tumor clinical trials

2018 ◽  
Vol 6 (2) ◽  
pp. 81-92 ◽  
Author(s):  
Emanuela Molinari ◽  
Tito R Mendoza ◽  
Mark R Gilbert
Keyword(s):  
Clinical Trials ◽  
Brain Tumor ◽  
Clinical Outcome ◽  
Clinical Trial Design ◽  
Well Being ◽  
Successful Implementation ◽  
Outcome Assessments ◽  
Tumor Studies ◽  
Clinical Outcome Assessments ◽  
The Impact

Abstract Regulatory agencies have progressively emphasized the importance of assessing broader aspects of patient well-being to better define therapeutic gain. As a result, clinical outcome assessments (COAs) are increasingly used to evaluate the impact, both positive and negative, of cancer treatments and in some instances have played a major factor in the regulatory approval of drugs. Challenges remain, however, in the routine incorporation of these measures in cancer clinical trials, particularly in brain tumor studies. Factors unique to brain tumor patients such as cognitive decline and language dysfunction may hamper their successful implementation. Study designs often relegated these outcome measures to exploratory endpoints, further compromising data completion. New strategies are needed to maximize the complementary information that COAs could add to clinical trials alongside more traditional measures such as progression-free and overall survival. The routine incorporation of COAs as either primary or secondary objectives with attention to minimizing missing data should define a novel clinical trial design. We provide a review of the approaches, challenges, and opportunities for incorporating COAs into brain tumor clinical research, providing a perspective for integrating these measures into clinical trials.

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