Development and Assessment of a Low Literacy, Pictographic Nausea Action Plan

Objective Chemotherapy-induced nausea and vomiting (CINV) is characterized by disabling nausea and emesis that can recur throughout the treatment of cancer and affects approximately 59% of pediatric and young adult patients. CINV can be associated with significant clinical morbidity, frequent hospital admissions, negative effects on health care related quality-of-life and can exhibit downstream effects such as weight loss that can worsen overall outcomes. Despite the vast number and potential combinations of pharmacotherapies and lifestyle modifications available to manage CINV in children, there are currently no clinical action tools offered to manage this condition better at home. We aimed to develop and assess an evidence-based, personalized pictogram-based nausea action plan (NAP) to aid providers, parents, and patients in the management of CINV. Methods The USNAP (Figure 1) facilitates the management of CINV by using a health literacy-informed approach to provide instructions for pharmacotherapies and lifestyle modifications. This study included Part 1 (Pictogram Validation) and Part 2 (Assessment). For Part 1, Pictogram transparency, translucency, and recall were assessed by parent survey with transparency ≥85%, mean translucency score ≥5, recall ≥85% required for validation. For Part 2, the USNAP was assessed by parents, clinical librarians, and clinicians. Patient/caregiver perceptions (n=27) were assessed using the Consumer Information Rating Form (17 questions) to gauge comprehension, design quality and usefulness. Readability was assessed by 5 formulas and a Readability Consensus Score was calculated. Clinical Librarians (n=2) used the Patient Education Materials Assessment Tool to measure the understandability (19 questions) and actionability (7 questions) of the plan and audiovisual educational content (>80% acceptable.) Suitability was assessed by clinicians (n=16) using Doaks' Suitability Assessment of Materials (superior≥70% rating). Results All 15 pictograms demonstrated appropriate transparency, translucency, and recall. Patient/caregiver perceptions reflected appropriate comprehension, design quality, and usefulness. The Readability Composite Score measured at a fourth-grade level. Clinical librarians reported acceptable understandability and actionability. Clinicians reported superior suitability. Conclusion The Uniformed Services Nausea Action Plan (USNAP) is the first clinical action tool designed to assist in managing CINV at home. Although recent clinical practice updates provide guidance on the therapeutic management of CINV in pediatric and young adult patients with cancer, these recommendations do not fully address the needs of the patient at home or adequately inform in instances of low health literacy. The USNAP seeks to mitigate this by making the clinical practice guidance useful to patients and caregivers at home. In addition, the USNAP is poised to identify other urgent clinical developments for patients with cancer that could masquerade as nausea and may serve as an early warning sign in these instances. The USNAP met all criteria for clinical implementation. The USNAP has potential to become an important tool in the care of patients with CINV, improving both quality-of-care and clinical outcomes. Future study of USNAP implementation for treating children with chronic CINV is needed. Figure 1 Figure 1. Disclosures No relevant conflicts of interest to declare.

Acromegaly disease activity according to ACRODAT®, a cross-sectional study in Spain: ACROVAL study

Objectives To evaluate disease activity status using the Acromegaly Disease Activity Tool (ACRODAT®) in a cohort of Spanish acromegaly patients, to assess the relationship between the level of disease activity according to both ACRODAT® and the physicians’ clinical evaluation, and to study the potential discrepancies in the perception of symptoms between physicians and patients. Design Multicenter, observational, descriptive and cross-sectional study. Methods Disease activity was assessed in adult patients with acromegaly under pharmacological treatment during at least 6 months using ACRODAT®. Results According to ACRODAT®, 48.2%, 31.8% and 20.0% of a total of 111 patients were classified as having a stable disease (S), mild disease activity (M-DA) and significant disease activity (S-DA) respectively. ACRODAT® classification of disease activity significantly correlated with physicians’ opinion, with a moderate inter-rater agreement and a specificity of 92.45% (PPV = 86.21%). No correlation was found between IGF-I levels and severity of symptoms or quality of life (QoL). A decision to take clinical action was significantly more frequent in S-DA and M-DA patients than S patients but no action was taken on 5 (22.7%) and 27 (77.1%) S-DA and M-DA patients, respectively Conclusions ACRODAT® detected disease activity in 51.8% of patients. Interestingly, although M-DA and S-DA patients were likely to be in the process of being controlled, action was not always taken on these patients. ACRODAT® is a validated and highly specific tool that may be useful to routinely monitor acromegaly and to identify patients with non-obvious disease activity by incorporating “patient-centred” parameters like symptoms and QoL to the clinical evaluation of acromegaly.

Social isolation is linked to classical risk factors of Alzheimer's disease-related dementias

Alzheimer's disease and related dementias is a major public health burden - compounding over upcoming years due to longevity. Recently, clinical evidence hinted at the experience of social isolation in expediting dementia onset. In 502,506 UK Biobank participants and 30,097 participants from the Canadian Longitudinal Study of Aging, we revisited traditional risk factors for developing dementia in the context of loneliness and lacking social support. Across these measures of subjective and objective social deprivation, we have identified strong links between individuals' social capital and various indicators of Alzheimer's disease and related dementias risk, which replicated across both population cohorts. The quality and quantity of daily social encounters had deep connections with key aetiopathological factors, which represent 1) personal habits and lifestyle factors, 2) physical health, 3) mental health, and 4) societal and external factors. Our population-scale assessment suggest that social lifestyle determinants are linked to most neurodegeneration risk factors, highlighting them promising targets for preventive clinical action.

Metabolic and immune markers for precise monitoring of COVID-19 severity and treatment

Deep understanding of the SARS-CoV-2 effects on host molecular pathways is paramount for the discovery of early biomarkers of outcome of coronavirus disease 2019 (COVID-19) and the identification of novel therapeutic targets. In that light, we generated metabolomic data from COVID-19 patient blood using high-throughput targeted nuclear magnetic resonance (NMR) spectroscopy and high-dimensional flow cytometry. We find considerable changes in serum metabolome composition of COVID-19 patients associated with disease severity, and response to tocilizumab treatment. We built a clinically annotated, biologically-interpretable space for precise time-resolved disease monitoring and characterize the temporal dynamics of metabolomic change along the clinical course of COVID-19 patients and in response to therapy. Finally, we leverage joint immuno-metabolic measurements to provide a novel approach for patient stratification and early prediction of severe disease. Our results show that high-dimensional metabolomic and joint immune-metabolic readouts provide rich information content for elucidation of the host’s response to infection and empower discovery of novel metabolic-driven therapies, as well as precise and efficient clinical action.

Evaluating Classification Consistency of Oral Lesion Images for Use in an Image Classification Teaching Tool

A web-based image classification tool (DiLearn) was developed to facilitate active learning in the oral health profession. Students engage with oral lesion images using swipe gestures to classify each image into pre-determined categories (e.g., left for refer and right for no intervention). To assemble the training modules and to provide feedback to students, DiLearn requires each oral lesion image to be classified, with various features displayed in the image. The collection of accurate meta-information is a crucial step for enabling the self-directed active learning approach taken in DiLearn. The purpose of this study is to evaluate the classification consistency of features in oral lesion images by experts and students for use in the learning tool. Twenty oral lesion images from DiLearn’s image bank were classified by three oral lesion experts and two senior dental hygiene students using the same rubric containing eight features. Classification agreement among and between raters were evaluated using Fleiss’ and Cohen’s Kappa. Classification agreement among the three experts ranged from identical (Fleiss’ Kappa = 1) for “clinical action”, to slight agreement for “border regularity” (Fleiss’ Kappa = 0.136), with the majority of categories having fair to moderate agreement (Fleiss’ Kappa = 0.332–0.545). Inclusion of the two student raters with the experts yielded fair to moderate overall classification agreement (Fleiss’ Kappa = 0.224–0.554), with the exception of “morphology”. The feature of clinical action could be accurately classified, while other anatomical features indirectly related to diagnosis had a lower classification consistency. The findings suggest that one oral lesion expert or two student raters can provide fairly consistent meta-information for selected categories of features implicated in the creation of image classification tasks in DiLearn.

Principles for Real-World Implementation of Bedside Predictive Analytics Monitoring

A new development in the practice of medicine is Artificial Intelligence-based predictive analytics that forewarn clinicians of future deterioration of their patients. This proactive opportunity, though, is different from the reactive stance that clinicians traditionally take. Implementing these tools requires new ideas about how to educate clinician users to facilitate trust and adoption and to promote sustained use. Our real-world hospital experience implementing a predictive analytics monitoring system that uses electronic health record and continuous monitoring data has taught us principles that we believe to be applicable to the implementation of other such analytics systems within the health care environment. These principles are mentioned below:• To promote trust, the science must be understandable.• To enhance uptake, the workflow should not be impacted greatly.• To maximize buy-in, engagement at all levels is important.• To ensure relevance, the education must be tailored to the clinical role and hospital culture.• To lead to clinical action, the information must integrate into clinical care.• To promote sustainability, there should be periodic support interactions after formal implementation.

Characterization and Investigation of Stigmasterol Isolated from Rodent Tuber Mutant Plant (Typhonium flagelliforme), Its Molecular Docking as Anticancer on MF-7 Cells

Typhonium flagelliforme is an Indonesian herbal plant used and applied traditionally to treat cancer diseases. Gamma rays have irradiated rodent tuber mutant plant at six doses gray to in-crease the chemical compounds of anticancer activity. The effect of isolated compounds from ro-dent tuber mutant plants has never been studied and published yet. Our study unveiled the poten-tial of stigmasterol as a remarkable cytotoxic agent and the significant contribution of NMR spectroscopy, IR, Mass spectra, QTOF MS towards the isolation and identification of this anti-cancer agent. Stigmasterol was isolated from T. flagelliforme mutant plant. Stigmasterol was more effective against MCF-7 cells with an IC50 value of 0.1623 µM than Cisplatin with IC50 value 13.2 µM. It is the most potential and active fraction in the human breast cancer cell line. The mo-lecular docking study analyzed the chemical profile of stigmasterol to confirm the receptor in agonist binding sites. The prediction of the toxicity of stigmasterol compounds using in silico and analysis of its interaction with the receptor can act as a competitive regulator with a high-affinity binding site on FXR. Stigmasterol has potential as a candidate for an anticancer drug that pro-moting further clinical action.

Acromegaly Disease Activity According to ACRODAT®, in Spain: ACROVAL Study

Objectives: To evaluate disease activity status using the Acromegaly Disease Activity Tool (ACRODAT®) in a cohort of Spanish acromegaly patients, to assess the relationship between the level of disease activity according to both ACRODAT® and the physicians’ clinical evaluation, and to study the potential discrepancies in the perception of symptoms between physicians and patients.Design: Multicenter, observational, descriptive and cross-sectional study. Methods: Disease activity was assessed in adult patients with acromegaly under pharmacological treatment during at least 6 months using ACRODAT®.Results: According to ACRODAT®, 48.2%, 31.8% and 20.0% of a total of 111 patients were classified as having a stable disease (S), mild disease activity (M-DA) and significant disease activity (S-DA) respectively. ACRODAT® classification of disease activity significantly correlated with physicians’ opinion, with a moderate inter-rater agreement and a specificity of 92.45% (PPV=86.21%). No correlation was found between IGF-1 levels and severity of symptoms or quality of life (QoL). A decision to take clinical action was significantly more frequent in S-DA and M-DA patients than S patients but no action was taken on 5 (22.7%) and 27 (77.1%) S-DA and M-DA patients, respectivelyConclusions: ACRODAT® detected disease activity in 51.8% of patients. Interestingly, although M-DA and S-DA patients were likely to be in the process of being controlled, action was not always taken on these patients. ACRODAT® is a validated and highly specific tool that may be useful to routinely monitor acromegaly and to identify patients with non-obvious disease activity by incorporating “patient-centered” parameters like symptoms and QoL to the clinical evaluation of acromegaly.