The Role of Visualization in Estimating Cardiovascular Disease Risk: Scoping Review.

BACKGROUND: Supporting and understanding the health of patients with chronic diseases and cardiovascular disease (CVD) risk is often a major challenge. Health data are often used in providing feedback to patients, and visualization plays an important role in facilitating the interpretation and understanding of data and, thus, influencing patients' behavior. Visual analytics enable efficient analysis and understanding of large datasets in real time. Digital health technologies can promote healthy lifestyle choices and assist in estimating CVD risk. OBJECTIVE: This review aims to present the most-used visualization techniques to estimate CVD risk. METHODS: In this scoping review, we followed the Joanna Briggs Institute PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) guidelines. The search strategy involved searching databases, including PubMed, CINAHL Ultimate, MEDLINE, and Web of Science, and gray literature from Google Scholar. This review included English-language articles on digital health, mobile health, mobile apps, images, charts, and decision support systems for estimating CVD risk, as well as empirical studies, excluding irrelevant studies and commentaries, editorials, and systematic reviews. RESULTS: We found 774 articles and screened them against the inclusion and exclusion criteria. The final scoping review included 17 studies that used different methodologies, including descriptive, quantitative, and population-based studies. Some prognostic models, such as the Framingham Risk Profile, World Health Organization and International Society of Hypertension risk prediction charts, Cardiovascular Risk Score, and a simplified Persian atherosclerotic CVD risk stratification, were simpler and did not require laboratory tests, whereas others, including the Joint British Societies recommendations on the prevention of CVD, Systematic Coronary Risk Evaluation, and Framingham-Registre Gironí del COR, were more complex and required laboratory testing-related results. The most frequently used prognostic risk factors were age, sex, and blood pressure (16/17, 94% of the studies); smoking status (14/17, 82%); diabetes status (11/17, 65%); family history (10/17, 59%); high-density lipoprotein and total cholesterol (9/17, 53%); and triglycerides and low-density lipoprotein cholesterol (6/17, 35%). The most frequently used visualization techniques in the studies were visual cues (10/17, 59%), followed by bar charts (5/17, 29%) and graphs (4/17, 24%). CONCLUSIONS: On the basis of the scoping review, we found that visualization is very rarely included in the prognostic models themselves even though technology-based interventions improve health care worker performance, knowledge, motivation, and compliance by integrating machine learning and visual analytics into applications to identify and respond to estimation of CVD risk. Visualization aids in understanding risk factors and disease outcomes, improving bioinformatics and biomedicine. However, evidence on mobile health's effectiveness in improving CVD outcomes is limited.

Visualization Techniques for Summarizing Single Patient Health Data to Support Physicians' Clinical Decisions - A Scoping Review.

INTRODUCTION: User-centered data visualizations can reduce physician cognitive load and support clinical decision making. To facilitate the selection of appropriate visualizations for single patient health data summaries, this scoping review provides a literature overview of possible visualization techniques and the corresponding reported user-centered design phases. METHODS: The publication databases PubMed, Web of Science, IEEE Xplore and ACM Digital Library were searched for relevant articles from 2017 to 2022. RESULTS: Of the 777 articles screened, 78 articles were included in the final analysis. The most commonly used visualization techniques are table, scatterplot-line timeline, text and event timelines, with 24 other visualization techniques identified. The testing phase of the user centered design process is reported most frequently. CONCLUSION: This scoping review can support developers in the selection of suitable visualizations for single patient health data by revealing the design space of possible visualization techniques.

Epid data explorer: A visualization tool for exploring and comparing spatio-temporal epidemiological data.

The analysis of large sets of spatio-temporal data is a fundamental challenge in epidemiological research. As the quantity and the complexity of such kind of data increases, automatic analysis approaches, such as statistics, data mining, machine learning, etc., can be used to extract useful information. While these approaches have proven effective, they require a priori knowledge of the information being sought, and some interesting insights into the data may be missed. To bridge this gap, information visualization offers a set of techniques for not only presenting known information, but also exploring data without having a hypothesis formulated beforehand. In this paper, we introduce Epid Data Explorer (EDE), a visualization tool that enables exploration of spatio-temporal epidemiological data. EDE allows easy comparisons of indicators and trends across different geographical areas and times. It facilitates this exploration through ready-to-use pre-loaded datasets as well as user-chosen datasets. The tool also provides a secure architecture for easily importing new datasets while ensuring confidentiality. In two use cases using data associated with the COVID-19 epidemic, we demonstrate the substantial impact of implemented lockdown measures on mobility and how EDE allows assessing correlations between the spread of COVID-19 and weather conditions.

Lessons Learned From Developing Dashboards to Support Decision-Making for Community Opioid Response by Community Stakeholders: Mixed Methods and Multisite Study.

BACKGROUND: Data dashboards are published tools that present visualizations; they are increasingly used to display data about behavioral health, social determinants of health, and chronic and infectious disease risks to inform or support public health endeavors. Dashboards can be an evidence-based approach used by communities to influence decision-making in health care for specific populations. Despite widespread use, evidence on how to best design and use dashboards in the public health realm is limited. There is also a notable dearth of studies that examine and document the complexity and heterogeneity of dashboards in community settings. OBJECTIVE: Community stakeholders engaged in the community response to the opioid overdose crisis could benefit from the use of data dashboards for decision-making. As part of the Communities That HEAL (CTH) intervention, community data dashboards were created for stakeholders to support decision-making. We assessed stakeholders' perceptions of the usability and use of the CTH dashboards for decision-making. METHODS: We conducted a mixed methods assessment between June and July 2021 on the use of CTH dashboards. We administered the System Usability Scale (SUS) and conducted semistructured group interviews with users in 33 communities across 4 states of the United States. The SUS comprises 10 five-point Likert-scale questions measuring usability, each scored from 0 to 4. The interview guides were informed by the technology adoption model (TAM) and focused on perceived usefulness, perceived ease of use, intention to use, and contextual factors. RESULTS: Overall, 62 users of the CTH dashboards completed the SUS and interviews. SUS scores (grand mean 73, SD 4.6) indicated that CTH dashboards were within the acceptable range for usability. From the qualitative interview data, we inductively created subthemes within the 4 dimensions of the TAM to contextualize stakeholders' perceptions of the dashboard's usefulness and ease of use, their intention to use, and contextual factors. These data also highlighted gaps in knowledge, design, and use, which could help focus efforts to improve the use and comprehension of dashboards by stakeholders. CONCLUSIONS: We present a set of prioritized gaps identified by our national group and list a set of lessons learned for improved data dashboard design and use for community stakeholders. Findings from our novel application of both the SUS and TAM provide insights and highlight important gaps and lessons learned to inform the design of data dashboards for use by decision-making community stakeholders. TRIAL REGISTRATION: ClinicalTrials.gov NCT04111939; https://clinicaltrials.gov/study/NCT04111939.

Transforming Digital Phenotyping Raw Data Into Actionable Biomarkers, Quality Metrics, and Data Visualizations Using Cortex Software Package: Tutorial.

As digital phenotyping, the capture of active and passive data from consumer devices such as smartphones, becomes more common, the need to properly process the data and derive replicable features from it has become paramount. Cortex is an open-source data processing pipeline for digital phenotyping data, optimized for use with the mindLAMP apps, which is used by nearly 100 research teams across the world. Cortex is designed to help teams (1) assess digital phenotyping data quality in real time, (2) derive replicable clinical features from the data, and (3) enable easy-to-share data visualizations. Cortex offers many options to work with digital phenotyping data, although some common approaches are likely of value to all teams using it. This paper highlights the reasoning, code, and example steps necessary to fully work with digital phenotyping data in a streamlined manner. Covering how to work with the data, assess its quality, derive features, and visualize findings, this paper is designed to offer the reader the knowledge and skills to apply toward analyzing any digital phenotyping data set. More specifically, the paper will teach the reader the ins and outs of the Cortex Python package. This includes background information on its interaction with the mindLAMP platform, some basic commands to learn what data can be pulled and how, and more advanced use of the package mixed with basic Python with the goal of creating a correlation matrix. After the tutorial, different use cases of Cortex are discussed, along with limitations. Toward highlighting clinical applications, this paper also provides 3 easy ways to implement examples of Cortex use in real-world settings. By understanding how to work with digital phenotyping data and providing ready-to-deploy code with Cortex, the paper aims to show how the new field of digital phenotyping can be both accessible to all and rigorous in methodology.

Tumor Board Visualization: Integrating Clinical and Laboratory Insights.

We present a data visualization tool for tumor boards, merging clinical with molecular and multi-omics data to refine precision oncology decisions. The tool offers a holistic patient perspective, facilitating personalized treatment strategies. By integrating clinical and laboratory datasets, it enables intuitive navigation through complex information. Clinicians are supported in their decision-making by user-friendly visualizations. Future studies are needed to evaluate its real-world impact and usability in precision oncology settings.

Intuitive Data Visualization for Enhanced Patient Comprehension.

The pilot study explores how data visualization influences patient comprehension and engagement in understanding hyperlipidemia test results across diverse patient groups. Employing Gestalt theory and the Relational Information Display (RID) framework, intuitive visual tools were developed using Google Sheets, QlikView®, and Microsoft® Excel®. The survey conducted with patients used a Likert scale to evaluate six different line and bar graphs, each presenting the same LDL cholesterol data. The study emphasized the creation of graphs that were easily interpretable. The survey aimed to assess preferences for various data visualization formats. The survey results indicated that patients preferred stacked area charts, while healthcare providers favored line charts. The results highlight the importance of user-centric design and the effective application of theoretical frameworks in creating visualizations that enhance patient engagement and comprehension. The study highlights the role of tailored data visualizations in healthcare, emphasizing the need for such tools in user-centered health technology.

A User-Centered Data Visualization of Atherosclerotic Heart Disease Risk Factors.

High cholesterol levels significantly contribute to the risk of atherosclerotic cardiovascular disease (ACVD), with a notable portion of ischemic heart disease cases linked to elevated cholesterol levels. Effective graphical displays of lipid panel tests and other cardiac risk factors are crucial for quick and accurate data interpretation, enabling early intervention for individuals with hyperlipidemia. Applying design theories such as Gestalt and distributed cognitive theories is essential for creating user-centered graphical data displays in the context of cardiovascular (CV) risk factors. The proposed dashboard informed by these theories is expected to help healthcare providers better address cardiovascular disease (CVD), enhancing diagnosis, treatment, and prevention. Moreover, this approach may help alleviate clinical provider burnout, improve patient outcomes, and reduce provider stress, thus contributing to safer and more effective healthcare systems.

Durga: an R package for effect size estimation and visualization.

Statistical analysis and data visualization are integral parts of science communication. One of the major issues in current data analysis practice is an overdependency on-and misuse of-p-values. Researchers have been advocating for the estimation and reporting of effect sizes for quantitative research to enhance the clarity and effectiveness of data analysis. Reporting effect sizes in scientific publications has until now been mainly limited to numeric tables, even though effect size plotting is a more effective means of communicating results. We have developed the Durga R package for estimating and plotting effect sizes for paired and unpaired group comparisons. Durga allows users to estimate unstandardized and standardized effect sizes and bootstrapped confidence intervals of the effect sizes. The central functionality of Durga is to combine effect size visualizations with traditional plotting methods. Durga is a powerful statistical and data visualization package that is easy to use, providing the flexibility to estimate effect sizes of paired and unpaired data using different statistical methods. Durga provides a plethora of options for plotting effect size, which allows users to plot data in the most informative and aesthetic way. Here, we introduce the package and its various functions. We further describe a workflow for estimating and plotting effect sizes using example data sets.

LipidSig 2.0: integrating lipid characteristic insights into advanced lipidomics data analysis.

In the field of lipidomics, where the complexity of lipid structures and functions presents significant analytical challenges, LipidSig stands out as the first web-based platform providing integrated, comprehensive analysis for efficient data mining of lipidomic datasets. The upgraded LipidSig 2.0 (https://lipidsig.bioinfomics.org/) simplifies the process and empowers researchers to decipher the complex nature of lipids and link lipidomic data to specific characteristics and biological contexts. This tool markedly enhances the efficiency and depth of lipidomic research by autonomously identifying lipid species and assigning 29 comprehensive characteristics upon data entry. LipidSig 2.0 accommodates 24 data processing methods, streamlining diverse lipidomic datasets. The tool's expertise in automating intricate analytical processes, including data preprocessing, lipid ID annotation, differential expression, enrichment analysis, and network analysis, allows researchers to profoundly investigate lipid properties and their biological implications. Additional innovative features, such as the 'Network' function, offer a system biology perspective on lipid interactions, and the 'Multiple Group' analysis aids in examining complex experimental designs. With its comprehensive suite of features for analyzing and visualizing lipid properties, LipidSig 2.0 positions itself as an indispensable tool for advanced lipidomics research, paving the way for new insights into the role of lipids in cellular processes and disease development.

Protocol for enhancing visualization clarity for categorical spatial datasets using Spaco.

In categorical data visualization, appropriate color arrangements can avoid perceptual ambiguity and help perceive underlying data patterns. We introduce a protocol to assign contrastive colors to neighboring categories using both Python and R packages. We describe steps for calculating the interlacement between clusters and generating a proper color palette and calculating color contrast. We then detail procedures for aligning cluster interlacement and color contrast to get an optimized cluster-color assignment, achieving clear categorical visualization. For complete details on the use and execution of this protocol, please refer to Jing et al.1.

Visualization strategies to aid interpretation of high-dimensional genotoxicity data.

This article describes a range of high-dimensional data visualization strategies that we have explored for their ability to complement machine learning algorithm predictions derived from MultiFlow® assay results. For this exercise, we focused on seven biomarker responses resulting from the exposure of TK6 cells to each of 126 diverse chemicals over a range of concentrations. Obviously, challenges associated with visualizing seven biomarker responses were further complicated whenever there was a desire to represent the entire 126 chemical data set as opposed to results from a single chemical. Scatter plots, spider plots, parallel coordinate plots, hierarchical clustering, principal component analysis, toxicological prioritization index, multidimensional scaling, t-distributed stochastic neighbor embedding, and uniform manifold approximation and projection are each considered in turn. Our report provides a comparative analysis of these techniques. In an era where multiplexed assays and machine learning algorithms are becoming the norm, stakeholders should find some of these visualization strategies useful for efficiently and effectively interpreting their high-dimensional data.

Novel data visualization and risk analytic algorithm in a low-middle income country.

To discuss the use of T3™, a data aggregation, visualization, and risk analytic platform in a single centre and its framework for implementation of such a tool in clinical care. We share experience of a tool implemented in a tertiary care Intensive Care Unit (ICU) with limited resources. Superusers were identified and trained. Implementation involved monitoring, evaluation, and user engagement data for continuous emphasis on the use of this tool. Persistent display of T3 data enhanced nursing operational efficiency. Its use was expanded to use in nurses rounds and handover, mortality and morbidity meetings, clinical team teaching through selected teaching cases and analysis of stored data with different research questions. However, lack of infrastructure and technological comprehension, paucity of multidisciplinary teams makes it a challenge in its implementation. Clear framework of implantation and pre-designed studies to determine the clinical usage and effectiveness are important for wide-spread use of such tools.

Evaluation of different landing pages on behavioural engagement with the CARA dashboard: A user research protocol.

BACKGROUND: CARA set out to develop a data-visualisation platform to facilitate general practitioners to develop a deeper understanding of their patient population, disease management and prescribing through dashboards. To support the continued use and sustainability of the CARA dashboards, dashboard performance and user engagement have to be optimised. User research places people at the centre of the design process and aims to evaluate the needs, behaviours and attitudes of users to inform the design, development and impact of a product. OBJECTIVE: To explore how different initial key messages impact the level of behavioural engagement with a CARA dashboard. METHODS: Participating general practices can upload their practice data for analysis and visualisation in CARA dashboards. Practices will be randomised to one of three different initial landing pages: the full dashboard or one of two key messages: a between comparison (their practice prescribing with the average of all other practices) or within comparison (with practice data of the same month the previous year) with subsequent continuation to the full dashboard. Analysis will determine which of the three landing pages encourages user interaction, as measured by the number of 'clicks', 'viewings' and 'sessions'. Dashboard usage data will be collected through Google analytics. DISCUSSION: This study will provide evidence of behavioural engagement and its metrics during the implementation of the CARA dashboards to optimise and sustain interaction. TRIAL REGISTRATION: ISRCTN32783644 (Registration date: 02/01/2024).