scholarly journals Is Obesity Related to Processing Speed Impairment in Patients with Multiple Sclerosis: Results of a Large-Scale, Multicenter Study

2020 ◽  
Vol 35 (5) ◽  
pp. 506-510
Author(s):  
Rachel Galioto ◽  
Devon S Conway ◽  
Sarah M Planchon ◽  
Stephen M Rao
Keyword(s):  
Multiple Sclerosis ◽  
Processing Speed ◽  
Large Scale ◽  
Clinical Care ◽  
Patient Characteristics ◽  
Normal Weight ◽  
Specific Patient ◽  
Speed Test ◽  
Electronic Implementation ◽  
One Way Anova

Abstract Background Obesity is linked to greater physical disability and increased comorbidities among patients with multiple sclerosis (MS). Its contribution to cognition in this group is unclear. This observational study examines the link between obesity and processing speed in a large sample of patients with MS (PwMS). Methods As part of routine clinical care at our center, PwMS completed the Processing Speed Test (PST), an electronic implementation of the Symbol Digit Modalities Test (SDMT). Height and weight were used to calculate body mass index (BMI). Bivariate correlations were conducted to examine the association between PST and BMI in the group overall and in subgroups based on demographic and clinical variables. A one-way ANOVA examined differences in PST by BMI categories (normal weight, overweight, obese). Results The sample included 8,713 patients. No association between PST and BMI was found in the entire sample (r = .01), nor within subgroups based on demographic and disease variables. No difference in PST score was found between BMI categories. Conclusions No association between BMI and processing speed was found among PwMS regardless of demographic or disease-specific patient characteristics.

scholarly journals Smartphone-Based Symbol-Digit Modalities Test Reliably Measures Cognitive Function in Multiple Sclerosis Patients

2020 ◽  
Author(s):  
Linh Pham ◽  
Thomas Harris ◽  
Mihael Varosanec ◽  
Peter Kosa ◽  
Bibiana Bielekova
Keyword(s):  
Multiple Sclerosis ◽  
Cognitive Processing ◽  
Processing Speed ◽  
Test Performance ◽  
Relevant Information ◽  
Imaging Features ◽  
Speed Test ◽  
Brain Parenchymal Fraction ◽  
Brain Parenchymal ◽  
Multiple Sclerosis Patients

AbstractLimited time for patient encounters prevents reliable evaluation of all neurological functions in routine clinical practice. Quantifying neurological disability in a patient-autonomous manner via smartphones may remedy this problem, if such tests provide reliable, disease-relevant information.We developed a smartphone version of the cognitive processing speed test, the Symbol-Digit Modalities Test (SDMT), and assessed its clinical utility. The traditional SDMT uses identical symbol-number codes, allowing memorization after repeated trials. In the phone app, the symbol-number codes are randomly generated.In 154 multiple sclerosis (MS) patients and 39 healthy volunteers (HV), traditional and smartphone SDMT have good agreement (Lin’s coefficient of concordance [CCC] = 0.84) and comparable test-retest variance. In subjects with available volumetric MRI and digitalized neurological examinations (112 MS, 12 HV), the SDMT scores were highly associated with T2 lesion load and brain parenchymal fraction, when controlled for relevant clinical characteristics. The smartphone SDMT association with clinical/imaging features was stronger (R2 = 0.75, p < 0.0001) than traditional SDMT (R2 = 0.65, p < 0.0001). In the longitudinal subcohort, improvements from testing repetition (learning effects), were identifiable using non-linear regression in 14/16 subjects and, on average, peaked after 8 trials. Averaging several post-learning SDMT results significantly lowers the threshold for detecting true decline in test performance.In conclusion, smartphone, self-administered SDMT is a reliable substitute of the traditional SDMT for measuring processing speed in MS patients. Granular measurements at home increase sensitivity to detect true performance decline in comparison to sporadic assessments in the clinic.

Comorbidity effect on processing speed test and MRI measures in multiple sclerosis patients

2020 ◽  
Vol 46 ◽  
pp. 102593
Author(s):  
Justin R. Abbatemarco ◽  
Daniel Ontaneda ◽  
Kunio Nakamura ◽  
Scott Husak ◽  
Zhini Wang ◽  
...  
Keyword(s):  
Multiple Sclerosis ◽  
Processing Speed ◽  
Speed Test ◽  
Multiple Sclerosis Patients

scholarly journals Stratified Management for Bacterial Infections in Late Preterm and Term Neonates: Current Strategies and Future Opportunities Toward Precision Medicine

2021 ◽  
Vol 9 ◽  
Author(s):  
Fleur M. Keij ◽  
Niek B. Achten ◽  
Gerdien A. Tramper-Stranders ◽  
Karel Allegaert ◽  
Annemarie M. C. van Rossum ◽  
...  
Keyword(s):  
Precision Medicine ◽  
Antibiotic Treatment ◽  
Bacterial Infections ◽  
Large Scale ◽  
Patient Characteristics ◽  
Late Preterm ◽  
Specific Patient ◽  
Term Neonates ◽  
Treatment Regimens ◽  
Disease Signatures

Bacterial infections remain a major cause of morbidity and mortality in the neonatal period. Therefore, many neonates, including late preterm and term neonates, are exposed to antibiotics in the first weeks of life. Data on the importance of inter-individual differences and disease signatures are accumulating. Differences that may potentially influence treatment requirement and success rate. However, currently, many neonates are treated following a “one size fits all” approach, based on general protocols and standard antibiotic treatment regimens. Precision medicine has emerged in the last years and is perceived as a new, holistic, way of stratifying patients based on large-scale data including patient characteristics and disease specific features. Specific to sepsis, differences in disease susceptibility, disease severity, immune response and pharmacokinetics and -dynamics can be used for the development of treatment algorithms helping clinicians decide when and how to treat a specific patient or a specific subpopulation. In this review, we highlight the current and future developments that could allow transition to a more precise manner of antibiotic treatment in late preterm and term neonates, and propose a research agenda toward precision medicine for neonatal bacterial infections.

Fundamental study on clarification of brain molecular pathology of Nasu-Hakola disease

Impact ◽  
2019 ◽  
Vol 2019 (8) ◽  
pp. 24-26
Author(s):  
Jun-ichi Satoh
Keyword(s):  
Multiple Sclerosis ◽  
Human Brain ◽  
Clinical Research ◽  
Neurodegenerative Diseases ◽  
Molecular Pathology ◽  
Large Scale ◽  
Molecular Pathogenesis ◽  
Novel Therapies ◽  
Brain Pathology ◽  
Fundamental Study

Brain pathology expert Dr Jun-ichi Satoh, from the Department of Bioinformatics and Molecular Neuropathology of Meiji Pharmaceutical University in Tokyo, is drawing on his expertise on neurology and neuroimmunology to delve into some of the more complex diseases impacting the human brain. His knowledge and expertise have allowed him to direct his research interests to study neurodegenerative diseases, such as Alzheimer's disease (AD), and neuroinflammatory diseases, such as multiple sclerosis (MS), and the analysis of their molecular pathogenesis by using a bioinformatics approach. His current focus is on Nasu-Hakola disease (NHD), a disease whose rarity has posed significant barriers towards performing large-scale clinical research in order to understand what exactly causes this disease and develop effective novel therapies.

scholarly journals Patient Experience in Home Respiratory Therapies: Where We Are and Where to Go

2019 ◽  
Vol 8 (4) ◽  
pp. 555 ◽  
Author(s):  
Cátia Caneiras ◽  
Cristina Jácome ◽  
Sagrario Mayoralas-Alises ◽  
José Ramon Calvo ◽  
João Almeida Fonseca ◽  
...  
Keyword(s):  
Clinical Research ◽  
Patient Experience ◽  
Clinical Care ◽  
Research Priorities ◽  
Specific Patient ◽  
Quality Healthcare ◽  
Body Of Knowledge ◽  
Number Of Patients ◽  
Patient Reported ◽  
System Sustainability

The increasing number of patients receiving home respiratory therapy (HRT) is imposing a major impact on routine clinical care and healthcare system sustainability. The current challenge is to continue to guarantee access to HRT while maintaining the quality of care. The patient experience is a cornerstone of high-quality healthcare and an emergent area of clinical research. This review approaches the assessment of the patient experience in the context of HRT while highlighting the European contribution to this body of knowledge. This review demonstrates that research in this area is still limited, with no example of a prescription model that incorporates the patient experience as an outcome and no specific patient-reported experience measures (PREMs) available. This work also shows that Europe is leading the research on HRT provision. The development of a specific PREM and the integration of PREMs into the assessment of prescription models should be clinical research priorities in the next several years.

Evaluation and cost estimation of laboratory tests overuse through a CCDSS in a university hospital

2020 ◽  
Vol 30 (Supplement_5) ◽  
Author(s):  
D Vallone ◽  
A Tamburrano ◽  
C Carrozza ◽  
A Urbani ◽  
A Cambieri ◽  
...  
Keyword(s):  
Cost Estimation ◽  
Laboratory Tests ◽  
Quality Care ◽  
Clinical Decision Support Systems ◽  
Patient Characteristics ◽  
Clinical Decision ◽  
University Hospital ◽  
Specific Patient ◽  
Global Rate ◽  
Common Laboratory

Abstract Computerized Clinical Decision Support Systems (CCDSS) are information technology-based systems that use specific patient characteristics and combine them with rule-based algorithms. The aim of this study is to conduct a survey to measure and assess the over-utilization rates of laboratory requests and to estimate the monthly cost of inappropriate requests in inpatients of the “Fondazione Policlinico Universitario A. Gemelli IRCCS” Care Units. This observational study is based on the count of rules violations for 43 different types of laboratory tests requested by the Hospital physicians, for a total of 5,716,370 requests, over a continuous period of 20 months (from 1 July 2016 to 28 February 2018). Requests from all the hospital internal departments (except for Emergency, Intensive Care Units and Urgent requests) were monitored. The software intercepted and counted, in silent mode for the operator, all requests and violations for each laboratory test among those identified. During the observation period a mean of 285,819 requests per month were analyzed and 40,462 violations were counted. The global rate of overuse was 15.2% ± 3.0%. The overall difference among sub-groups was significant (p &lt; 0.001). The most inappropriate exams were Alpha Fetoprotein (85.8% ± 30.5%), Chlamydia trachomatis PCR (48.7% ± 8.8%) and Alkaline Phosphatase (20.3% ± 6.5%). All the exams, globally considered, generated an estimated avoidable cost of 1,719,337€ (85,967€ per month) for the hospital. This study reports rates (15.2%) similar to other works. The real impact of inappropriateness is difficult to assess, but the generated costs for patients, hospitals and health systems are certainly high and not negligible. Key messages It would be desirable for international medical communities to produce a complete panel of prescriptive rules for all the most common laboratory exam. That is useful not only to reduce costs, but also to ensure standardization and high-quality care.

scholarly journals Challenges and opportunities for ovarian cancer management in the epidemic of Covid-19: lessons learned from Wuhan, China

2021 ◽  
Vol 14 (1) ◽  
Author(s):  
Zhilan Chen ◽  
Chun Zhang ◽  
Jiu Yin ◽  
Xin Xin ◽  
Hemei Li ◽  
...  
Keyword(s):  
Ovarian Cancer ◽  
Large Scale ◽  
Treatment Plan ◽  
Clinical Care ◽  
Gynecologic Cancer ◽  
Treatment Phase ◽  
Tumor Stage ◽  
Current Treatment ◽  
Lessons Learned ◽  
Cancer Management

AbstractChina and the rest of the world are experiencing an outbreak of the 2019 novel coronavirus disease (COVID-19). Patients with cancer are more susceptible to viral infection and are more likely to develop severe complications, as compared to healthy individuals. The growing spread of COVID-19 presents challenges for the clinical care of patients with gynecological malignancies. Ovarian debulking surgery combined with the frequent need for chemotherapy is most likely why ovarian cancer was rated as the gynecologic cancer most affected by COVID-19. Therefore, ovarian cancer presents a particular challenging task. Concerning the ovarian cancer studies with confirmed COVID-19 reported from large-scale general hospitals in Wuhan, we hold that the treatment plan was adjusted appropriately and an individualized remedy was implemented. The recommendations discussed here were developed mainly based on the experience from Wuhan. We advise that the management strategy for ovarian cancer patients should be adjusted in the light of the local epidemic situation and formulated according to the pathological type, tumor stage and the current treatment phase. Online medical service is an effective and convenient communication platform during the pandemic.

scholarly journals EyeHealer: A large-scale anterior eye segment dataset with eye structure and lesion annotations

2021 ◽  
Author(s):  
Wenjia Cai ◽  
Jie Xu ◽  
Ke Wang ◽  
Xiaohong Liu ◽  
Wenqin Xu ◽  
...  
Keyword(s):  
Deep Learning ◽  
Large Scale ◽  
Image Annotation ◽  
Clinical Care ◽  
Significant Proportion ◽  
Anterior Segment ◽  
Future Research ◽  
Learning Models ◽  
Anterior Eye Segment ◽  
Segmentation Models

Abstract Anterior segment eye diseases account for a significant proportion of presentations to eye clinics worldwide, including diseases associated with corneal pathologies, anterior chamber abnormalities (e.g. blood or inflammation) and lens diseases. The construction of an automatic tool for the segmentation of anterior segment eye lesions will greatly improve the efficiency of clinical care. With research on artificial intelligence progressing in recent years, deep learning models have shown their superiority in image classification and segmentation. The training and evaluation of deep learning models should be based on a large amount of data annotated with expertise, however, such data are relatively scarce in the domain of medicine. Herein, the authors developed a new medical image annotation system, called EyeHealer. It is a large-scale anterior eye segment dataset with both eye structures and lesions annotated at the pixel level. Comprehensive experiments were conducted to verify its performance in disease classification and eye lesion segmentation. The results showed that semantic segmentation models outperformed medical segmentation models. This paper describes the establishment of the system for automated classification and segmentation tasks. The dataset will be made publicly available to encourage future research in this area.

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