Magnetic resonance imaging in the diagnosis of white matter signal abnormalities.

Background White matter abnormalities (WMAs) pose a diagnostic challenge when trying to establish etiologic diagnoses. During childhood and adult years, genetic disorders, metabolic disorders and acquired conditions are included in differential diagnoses. To assist clinicians and radiologists, a structured algorithm using cranial magnetic resonance imaging (MRI) has been recommended to aid in establishing working diagnoses that facilitate appropriate biochemical and genetic investigations. This retrospective pilot study investigated the validity and diagnostic utility of this algorithm when applied to white matter signal abnormalities (WMSAs) reported on imaging studies of patients seen in our clinics. Methods The MRI algorithm was applied to 31 patients selected from patients attending the neurometabolic/neurogenetic/metabolic/neurology clinics at a tertiary care hospital. These patients varied in age from 5 months to 79 years old, and were reported to have WMSAs on cranial MRI scans. Twenty-one patients had confirmed WMA diagnoses and 10 patients had non-specific WMA diagnoses (etiology unknown). Two radiologists, blinded to confirmed diagnoses, used clinical abstracts and the WMSAs present on patient MRI scans to classify possible WMA diagnoses utilizing the algorithm. Results The MRI algorithm displayed a sensitivity of 100%, a specificity of 30.0% and a positive predicted value of 74.1%. Cohen\u27s kappa statistic for inter-radiologist agreement was 0.733, suggesting good agreement between radiologists. Conclusions Although a high diagnostic utility was not observed, results suggest that this MRI algorithm has promise as a clinical tool for clinicians and radiologists. We discuss the benefits and limitations of this approach

Toward shared decision-making in degenerative cervical myelopathy: Protocol for a mixed methods study

Health care decisions are a critical determinant in the evolution of chronic illness. In shared decision-making (SDM), patients and clinicians work collaboratively to reach evidence-based health decisions that align with individual circumstances, values, and preferences. This personalized approach to clinical care likely has substantial benefits in the oversight of degenerative cervical myelopathy (DCM), a type of nontraumatic spinal cord injury. Its chronicity, heterogeneous clinical presentation, complex management, and variable disease course engenders an imperative for a patient-centric approach that accounts for each patient's unique needs and priorities. Inadequate patient knowledge about the condition and an incomplete understanding of the critical decision points that arise during the course of care currently hinder the fruitful participation of health care providers and patients in SDM. This study protocol presents the rationale for deploying SDM for DCM and delineates the groundwork required to achieve this. The study's primary outcome is the development of a comprehensive checklist to be implemented upon diagnosis that provides patients with essential information necessary to support their informed decision-making. This is known as a core information set (CIS). The secondary outcome is the creation of a detailed process map that provides a diagrammatic representation of the global care workflows and cognitive processes involved in DCM care. Characterizing the critical decision points along a patient's journey will allow for an effective exploration of SDM tools for routine clinical practice to enhance patient-centered care and improve clinical outcomes. Both CISs and process maps are coproduced iteratively through a collaborative process involving the input and consensus of key stakeholders. This will be facilitated by Myelopathy.org, a global DCM charity, through its Research Objectives and Common Data Elements for Degenerative Cervical Myelopathy community. To develop the CIS, a 3-round, web-based Delphi process will be used, starting with a baseline list of information items derived from a recent scoping review of educational materials in DCM, patient interviews, and a qualitative survey of professionals. A priori criteria for achieving consensus are specified. The process map will be developed iteratively using semistructured interviews with patients and professionals and validated by key stakeholders. Recruitment for the Delphi consensus study began in April 2023. The pilot-testing of process map interview participants started simultaneously, with the formulation of an initial baseline map underway. This protocol marks the first attempt to provide a starting point for investigating SDM in DCM. The primary work centers on developing an educational tool for use in diagnosis to enable enhanced onward decision-making. The wider objective is to aid stakeholders in developing SDM tools by identifying critical decision junctures in DCM care. Through these approaches, we aim to provide an exhaustive launchpad for formulating SDM tools in the wider DCM community. DERR1-10.2196/46809. [Abstract copyright: ©Irina Sangeorzan, Grazia Antonacci, Anne Martin, Ben Grodzinski, Carl M Zipser, Rory K J Murphy, Panoraia Andriopoulou, Chad E Cook, David B Anderson, James Guest, Julio C Furlan, Mark R N Kotter, Timothy F Boerger, Iwan Sadler, Elizabeth A Roberts, Helen Wood, Christine Fraser, Michael G Fehlings, Vishal Kumar, Josephine Jung, James Milligan, Aria Nouri, Allan R Martin, Tammy Blizzard, Luiz Roberto Vialle, Lindsay Tetreault, Sukhvinder Kalsi-Ryan, Anna MacDowall, Esther Martin-Moore, Martin Burwood, Lianne Wood, Abdul Lalkhen, Manabu Ito, Nicky Wilson, Caroline Treanor, Sheila Dugan, Benjamin M Davies. Originally published in JMIR Research Protocols (https://www.researchprotocols.org), 09.10.2023.

Risk factors associated with Trypanosoma cruziexposure in domestic dogs from a rural community in Panama

Chagas disease, caused by Trypanosoma cruzi infection, is a zoonosis of humans, wild and domestic mammals,including dogs. In Panama, the main T. cruzi vector is Rhodnius pallescens, a triatomine bug whose main naturalhabitat is the royal palm, Attalea butyracea. In this paper, we present results from three T. cruzi serological tests(immunochromatographic dipstick, indirect immunofluorescence and ELISA) performed in 51 dogs from 24 housesin Trinidad de Las Minas, western Panama. We found that nine dogs were seropositive (17.6% prevalence). Dogswere 1.6 times more likely to become T. cruzi seropositive with each year of age and 11.6 times if royal palms wherepresent in the peridomiciliary area of the dog’s household or its two nearest neighbours. Mouse-baited-adhesivetraps were employed to evaluate 12 peridomestic royal palms. All palms were found infested with R. pallescens withan average of 25.50 triatomines captured per palm. Of 35 adult bugs analysed, 88.6% showed protozoa flagellates intheir intestinal contents. In addition, dogs were five times more likely to be infected by the presence of an additionaldomestic animal species in the dog’s peridomiciliary environment. Our results suggest that interventions focused onroyal palms might reduce the exposure to T. cruzi infection

eWound-PRIOR: An Ensemble Framework for Cases Prioritization After Orthopedic Surgeries

© 2021, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG. Patient follow-up appointments are an imperative part of the healthcare model to ensure safe patient recovery and proper course of treatment. The use of mobile devices can help patient monitoring and predictive approaches can provide computational support to identify deteriorating cases. Aiming to aggregate the data produced by those devices with the power of predictive approaches, this paper proposes the eWound-PRIOR framework to provide a remote assessment of postoperative orthopedic wounds. Our approach uses Artificial Intelligence (AI) techniques to process patients’ data related to postoperative wound healing and makes predictions as to whether the patient requires an in-person assessment or not. The experiment results showed that the predictions are promising and adherent to the application context, even if the on-line questionnaire had impaired the training model and the performance