Disease-specific Information Research Articles

Unveiling diagnostic information for type 2 diabetes through interpretable machine learning

The interpretability of disease prediction models is often crucial for their trustworthiness and usability among medical practitioners. Existing methods in interpretable artificial intelligence improve model transparency but fall short in identifying precise, disease-specific primal information. In this work, an interpretable deep learning-based algorithm called the data space landmark refiner was developed, which not only enhances both global interpretability and local interpretability but also reveals the intrinsic information of the data distribution. Using the proposed method, a type 2 diabetes mellitus diagnostic model with high interpretability was constructed on the basis of the electronic health records from two hospitals. Moreover, effective diagnostic information was directly derived from the model’s internal parameters, demonstrating strong alignment with current clinical knowledge. Compared with conventional interpretable machine learning approaches, the proposed method offered more precise and specific interpretability, increasing clinical practitioners’ trust in machine learning-supported diagnostic models.

Integrative network analysis of miRNA-mRNA expression profiles during epileptogenesis in rats reveals therapeutic targets after emergence of first spontaneous seizure

Epileptogenesis is the process by which a normal brain becomes hyperexcitable and capable of generating spontaneous recurrent seizures. The extensive dysregulation of gene expression associated with epileptogenesis is shaped, in part, by microRNAs (miRNAs) – short, non-coding RNAs that negatively regulate protein levels. Functional miRNA-mediated regulation can, however, be difficult to elucidate due to the complexity of miRNA-mRNA interactions. Here, we integrated miRNA and mRNA expression profiles sampled over multiple time-points during and after epileptogenesis in rats, and applied bi-clustering and Bayesian modelling to construct temporal miRNA-mRNA-mRNA interaction networks. Network analysis and enrichment of network inference with sequence- and human disease-specific information identified key regulatory miRNAs with the strongest influence on the mRNA landscape, and miRNA-mRNA interactions closely associated with epileptogenesis and subsequent epilepsy. Our findings underscore the complexity of miRNA-mRNA regulation, can be used to prioritise miRNA targets in specific systems, and offer insights into key regulatory processes in epileptogenesis with therapeutic potential for further investigation.

An online, two-day educational seminar had no impact on disease-specific knowledge in patients with systemic sclerosis

Systemic sclerosis (SSc) is a multifaceted disease, and its diagnosis triggers substantial anxiety and uncertainty for those affected. Currently, there are no valid data describing the impact of disease-specific patient education on the disease knowledge available. We created a two-day, online educational seminar to provide SSc patients with disease-specific information. The primary objective of the study was to observe the change in the disease-specific knowledge of the patients. A total of 118 patients were randomized into an intervention group and a waiting list control group. The change in knowledge was assessed using a multiple-choice test. The intervention group completed the questionnaire before, directly after, and 3 months after the seminar, while the waiting list control group also took the test 3 months before the seminar to rule out nonspecific learning. The primary outcome measure was the score difference between baseline and 3 months after baseline. The study was registered in the German Clinical Trials Register (protocol code DRKS00024915). The educational seminar resulted in a small, but measurable, increase in knowledge. While the two tests in the waiting list control group prior to the seminar did not show a nonspecific increase in disease knowledge, the intervention led to a numerical increase in knowledge (mean ± sd score difference 0.34 ± 1.31, 95% CI (− 0.23; 0.86), p = 0.26) that did not reach statistical significance. Multiple linear regression analysis showed that being a member of a self-help group (β = 1.12; p = 0.03) is a positive predictor of a higher disease knowledge. Although highly appreciated by participants, a two-day online seminar may not be the most appropriate format to generate measurable disease-specific knowledge. Self-help group membership was a positive predictor of a higher level of disease-specific knowledge prior to the educational seminar and should be recommended to every affected person.

Abstract PO5-19-04: EVOLVE: An Ambispective, Patient-Centered, Real-World Early-Stage Breast Cancer Study in the United States

Abstract Background: While survival for patients with early breast cancer (eBC) has greatly improved with earlier detection and modern treatments, the risk of recurrence and long-term/late treatment-related adverse events (AEs) remain concerns for many patients. Contemporary real-world data are needed to understand current diagnostic pathways, treatment patterns and toxicities, quality of life, and patient experience to better characterize eBC care and knowledge gaps and establish important hypotheses to test in prospective trials, with the ultimate goal to improve both treatment and outcomes for patients with eBC. Study design: PicnicHealth and AstraZeneca are collaborating to build a real-world observational eBC cohort composed of de-identified medical records data supplemented with patient-reported outcomes (PROs), including patient-reported social determinants of health (SDoH). Patients recruited from multiple channels, such as digital marketing and community partnerships, will consent to participate and sign HIPAA authorization for PicnicHealth to collect their medical records from all providers and sites of care in the United States. All available retrospective medical records in any format will be retrieved, and records will continue to be collected for patients prospectively following enrollment. Disease-specific information will be abstracted using PicnicHealth’s human-in-the-loop machine learning abstraction platform. Patients will have access to their records in the form of a searchable and shareable digital timeline. Additional data, including but not limited to symptoms, quality of life and treatment experience will be collected through surveys. The first patient was enrolled in May 2023. Patients will be followed for ≥8 years or until withdrawal or death, whichever occurs first. Eligibility criteria: Patients with stage I-III breast cancer diagnosed ≤3 years before enrollment, who onboard to the PicnicHealth platform, consent to participate, sign HIPAA authorization for medical record collection, and provide ≥1 provider/care site for record retrieval will be included in the cohort. Specific aims: The cohort aims to generate contemporary real-world data relevant to understanding demographic and clinical characteristics, SDoH, diagnostic/testing pathways, treatment patterns, and clinical outcomes, including AEs and AE management. Specifically, demographic and SDoH differences in management, outcomes, and the patient experience during and following eBC diagnosis and treatment will be evaluated. The cohort design and the data collected will evolve as the eBC landscape evolves to continuously address new research questions around diagnosis, management, outcomes, and patient experience. Statistical methods: The demographics, SDoH, clinical characteristics, treatments, healthcare resource utilization, eBC outcomes, such as recurrence and progression, and patient experience will be summarized for the cohort overall and by pre-determined subgroups using descriptive statistics. Additional analyses include, but are not limited to, evaluating the impact of key exposures, such as SDoH, on treatment and diagnostic pathways, treatment compliance, clinical outcomes, and PROs. All analyses will be prespecified in study-specific statistical analysis plans. Present accrual and target accrual: At least 3,000 patients will be enrolled in the PicnicHealth eBC Cohort. As of July 2023, 843 patients have onboarded to the PicnicHealth platform and will be screened for eligibility into the eBC registry cohort. Contact information for people with a specific interest in the study: For additional information, please contact breastcancer@picnichealth.com. Citation Format: Haley Friedler, Meghan Tierney, Melinda Baker, Gillian Hanson, Josefa Briceno, Karen Smith, Michele Baber, Kara Glover, Xiaoqing Xu, Kellie Ryan, Zulikhat Segunmaru, Clara Lam, Maryam Lustberg, N. Lynn Henry, Rachel Greenup, Marianna Chavez, Joseph Unger, Alice Ho, Deborah Collyar. EVOLVE: An Ambispective, Patient-Centered, Real-World Early-Stage Breast Cancer Study in the United States [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PO5-19-04.

BIOSKIN: A Protocol for the Copenhagen Translational Skin Immunology Biobank and Research Programme

IntroductionPsoriasis, atopic dermatitis and contact dermatitis are common chronic inflammatory skin diseases that have a significant impact on individuals and society.Methods and analysisThe Copenhagen Translational Skin Immunology Biobank and Research...

Resilience and Overcoming Experiences of Coronavirus Disease 2019 Patients Hospitalized in a Single-Room Isolation Ward: A Mixed-Methods Study

PurposeThe COVID-19 pandemic has caused patients to suffer from various physical and psychological symptoms and social challenges, but the impact was harder for those COVID-19 patients receiving treatment in single-room isolation wards in South Korea. This study aimed to investigate resilience, anxiety, depression, and sleep quality of those patients and the relationships between those variables, and explore the patients’ experience of resilience during the COVID-19 treatment in single-room isolation wards. MethodsThis study employed a mixed-methods approach, collecting quantitative data through surveys and qualitative data through semi-structured interviews conducted between May and October 2022. Quantitative surveys encompassing disease-specific and demographic information, visual analog scales to assess anxiety, depression, and sleep quality, and the Connor–Davidson resilience scale were administered to patients (N=153). Qualitative thematic analyses were conducted following interviews with a subset of patients (N=13) belonging to the high-resilience group. ResultsResilience exhibited a negative correlation with anxiety and depression, while showing a positive correlation with sleep quality. Factors affecting resilience include having a spouse, educational attainment, and depression. The qualitative thematic analysis results were categorized into (1) facing the reality of being isolated in a room, (2) struggling to accept and adapt to isolation, and (3) seeking connections in isolation. ConclusionsThis research sheds light on the challenges faced by individuals in isolation and underscores the crucial role of resilience in overcoming such challenges. The resilience observed in these patients is grounded in both interpersonal and profound spiritual connections. These findings underscore the necessity for nurses to develop customized strategies to alleviate the impacts of social isolation.

ShapeMed-Knee: A Dataset and Neural Shape Model Benchmark for Modeling 3D Femurs.

Analyzing anatomic shapes of tissues and organs is pivotal for accurate disease diagnostics and clinical decision-making. One prominent disease that depends on anatomic shape analysis is osteoarthritis, which affects 30 million Americans. To advance osteoarthritis diagnostics and prognostics, we introduce ShapeMed-Knee, a 3D shape dataset with 9,376 high-resolution, medical-imaging-based 3D shapes of both femur bone and cartilage. Besides data, ShapeMed-Knee includes two benchmarks for assessing reconstruction accuracy and five clinical prediction tasks that assess the utility of learned shape representations. Leveraging ShapeMed-Knee, we develop and evaluate a novel hybrid explicit-implicit neural shape model which achieves up to 40% better reconstruction accuracy than a statistical shape model and implicit neural shape model. Our hybrid models achieve state-of-the-art performance for preserving cartilage biomarkers; they're also the first models to successfully predict localized structural features of osteoarthritis, outperforming shape models and convolutional neural networks applied to raw magnetic resonance images and segmentations. The ShapeMed-Knee dataset provides medical evaluations to reconstruct multiple anatomic surfaces and embed meaningful disease-specific information. ShapeMed-Knee reduces barriers to applying 3D modeling in medicine, and our benchmarks highlight that advancements in 3D modeling can enhance the diagnosis and risk stratification for complex diseases. The dataset, code, and benchmarks will be made freely accessible.

A Qualitative Study of Patients’ Perspectives regarding Digital Health Technology to Support Self-management and to Improve Integrated Stroke Care: The ValueCare study.

Background: Digital technologies, such as mobile apps and robotics, provide opportunities to involve stroke patients better in the care process and to promote self-management. Previous pilot studies suggest that technology could be a meaningful tool for post-acute stroke care. However, barriers exists constraining the adoption and acceptance of technology in clinical practice. Examples of barriers are privacy concerns, challenges regarding usability, and the perception that there is no need for technology. Co-design enables patients to reflect on their experiences of a service and to identify improvement priorities. This study uses data from co-design sessions with stroke patients conducted as part of the ValueCare study.
 Objective: The aim of this study is to explore what stroke patients’ perspectives are towards how digital health technology could support self-management regarding health and well-being, and integrated stroke care.
 Methods: A qualitative study was conducted to understand patient perspectives. Data was collected in co-design sessions of the ValueCare study. Patients from a Dutch hospital who experienced an ischemic stroke (n=36) within the past 18 months were invited to participate. Data collection took place between December 2020 and April 2021 via one-to-one telephone interviews. A short self-report questionnaire was used to collect data on socio-demographics, disease-specific information, and technology use. All interviews were audio-taped and transcribed verbatim. The interview data were analyzed using a thematic approach.
 Results: Patients held mixed attitudes towards how digital health technologies could support their self-management and the care they receive. Suggested digital features by stroke patients included (i) an easily accessible online library with relevant information regarding stroke-related health and care issues, (ii) a personal health record (PHR) by which patients can retrieve and manage their own health information, and (iii) a physical exercise module to empower patients to organize rehabilitation support at home. Regarding the user interface of future digital health technology, patients emphasized the need for a very easy-to-use and simple design.
 Conclusions: Stroke patients mentioned easy-to-find and credible health information, a PHR, and online-rehabilitation support as the main features to include in future digital health technologies. We recommend that developers and designers of digital health for stroke care listen to the ‘voice of the stroke patients’, with regard to both functionality and the characteristics of the interface. The findings of this study provide insight in the needs and preferences of stroke patients for using ICT to manage their health and care. These findings serve as touch points that can be explored further in co-design sessions. 
 Keywords: stroke patients, digital health technology, self-management, co-design, user-requirements, user-centered design, qualitative research

Quality and Reliability Analysis of YouTube as a Source of Patient Information on de Quervain's Tenosynovitis

Abstract Purpose This study seeks to evaluate the quality and reliability of information regarding de Quervain's tenosynovitis on YouTube. Methods A search on the YouTube was performed using the keywords de Quervain's tenosynovitis, and the first 50 videos were evaluated. Video characteristics including views, content type, and video upload source were recorded. Video reliability was assessed using the Journal of the American Medical Association (JAMA) benchmark criteria. Video quality was assessed using the Global Quality Score (GQS) and a novel de Quervain's Tenosynovitis-Specific Score (DQT-SS). Results The total number of views for all videos evaluated was 5,508,498 (mean, 110,169.96 ± 155,667.07). Video reliability and quality metrics were low, with a mean JAMA score of 2.17 ± 0.82 out of 4, a mean GQS of 2.49 ± 1.28 out of 5, and a mean DQT-SS of 4.53 ± 2.35 out of 11. Significant between-group effects were found for the video source and DQT-SS (p = 0.027), as well as between content type and JAMA score (p = 0.027), GQS (p = 0.003), and DQT-SS (p = 0.003). Positive independent predictors of DQT-SS included video duration in seconds (β = 0.391) and disease-specific information content type (β = 0.648). Conclusion Videos on YouTube regarding de Quervain's tenosynovitis were frequently viewed; however, the information present was of low quality and reliability. Physician-uploaded videos had the highest mean JAMA scores, GQS, and DQT-SS, but had the second-lowest mean number of views of video sources. Patients should receive proper in-office education and be directed toward reputable resources for their orthopaedic conditions.

Evaluating Multidisciplinary Bone Marrow Failure Care (EMBRACE) - a Hybrid Implementation-Effectiveness Study

The bone marrow failure syndromes (BMFS) are clinically diverse, with both inherited and acquired etiologies. Patients with BMFS may present unique, complex management issues and frequently experience poor outcomes due to multiple factors, including the rarity of the individual conditions. The Evaluating Multidisciplinary Bone maRrow fAilure CarE (EMBRACE) study is a multi-stage hybrid implementation-effectiveness study that aims to address these challenges by understanding the nature and scale of issues faced by these patients and their physicians, and then developing, implementing, and evaluating a comprehensive 10-component model of care (MoC10). Herein we report the results of the first stage of the EMBRACE study of issue identification and MoC10 development. To identify barriers to optimal care, we performed structured interviews with patients with BMFS and patient advocate groups, followed by a nation-wide physician survey. Thematic analysis of interviews (n = 9) revealed multiple challenges with patient care, including (i) a lack of access to genomic testing to provide a definitive diagnosis, (ii) widespread difficulty in accessing genetic counselling and fertility advice, (iii) a lack of disease-specific patient information at the time of diagnosis, and (iv) perceived failings in care when transitioning from pediatric to adult services. Patients also described anxiety resulting from frequent encounters with health professionals unfamiliar with their disease, leading to significant anxiety and demonstrating a clear need for education both at the time of initial presentation, in considering whether an underlying inherited condition could be present and also following diagnosis, permitting relevant disease specific surveillance and care. As part of this patient-facing analysis, there was a desire on behalf of patients and patient advocates for the opportunity to participate in research and clinical trials. A nation-wide physician survey was then performed targeting clinicians that treat patients with BMFS which received 74 responses across hematologists, oncologists, pediatricians and geneticists who cared for 267 patients with BMFS. The median number of patients cared for per physician was 2 (range 0 - 25), demonstrating a wide range of subspecialty experience and non-centralized care. Responses indicated a desire for affordable access to genomic testing, assistance with result interpretation and genetic counselling/cascade testing of extended kindred, a forum for complex case discussion and advice regarding patient management strategies. Responses from patients, patient advocates and physicians, along with existing evidence-based and best-practice guidelines, were then used to develop a MoC. The MoC10 (Figure 1) incorporates 10 pillars directed at optimising care for patients with BMFS including funded access to genomic testing, access to expert genetic and fertility counselling, standardized hematological and solid organ cancer surveillance, and opportunity for multidisciplinary case discussion including expert opinion regarding allogeneic stem cell transplant. In addition, uncertain and novel germline variants are presented alongside de-identified case presentations at a national forum attended by physicians, scientists, and researchers to promote education, collaboration and information sharing with treating teams. After institutional approval, the MoC10 has been implemented and is undergoing structured evaluation to determine patient and physician acceptability and adherence. Embracing the well-known ‘Plan, Do, Study, Act’ cycle, our implementation approach facilitates iterative learning for continuous improvement. In summary, after extensive stakeholder engagement, we have developed an evidence based comprehensive MoC addressing the current challenges facing patients with BMFS and their care teams. This comprehensive care program continues to undergo evaluation and audit consistent with the principles of an implementation-effectiveness study to determine both patient and physician satisfaction with the model, and iterative refinement in order to provide optimum care.

CIViCutils: Matching and downstream processing of clinical annotations from CIViC

Background: With the advent of next-generation sequencing, profiling the genetic landscape of tumors entered clinical diagnostics, bringing the resolution of precision oncology to unprecedented levels. However, the wealth of information generated in a sequencing experiment can be difficult to manage, especially if hundreds of mutations need to be interpreted in a clinical context. Dedicated methods and databases are required that assist in interpreting the importance of a mutation for disease progression, prognosis, and with respect to therapy. Here, the CIViC knowledgebase is a valuable curated resource, however, utilizing CIViC in an efficient way for querying a large number of mutations needs sophisticated downstream methods. Methods: To this end, we have developed CIViCutils, a Python package to query, annotate, prioritize, and summarize information from the CIViC database. Our package provides functionality for performing high-throughput searches in CIViC, automatically matching clinical evidence to input variants, evaluating the accuracy of the extracted variant matches, fully exploiting the available disease-specific information according to cancer types of interest, and in-silico predicting drug-target interactions tailored to individual patients. Results: CIViCutils allows the simultaneous query of hundreds of mutations and is able to harmonize input across different nomenclatures. Moreover, it supports gene expression data, single nucleotide mutations, as well as copy number alterations as input. We utilized CIViCutils in a study on the bladder cancer cohort from The Cancer Genome Atlas (TCGA-BLCA), where it helped to extract clinically relevant mutations for personalized therapy recommendation. Conclusions: CIViCutils is an easy-to-use Python package that can be integrated into workflows for profiling the genetic landscape of tumor samples. It streamlines interpreting large numbers of variants with retrieving and processing curated CIViC information.

Global prioritization of endemic zoonotic diseases for conducting surveillance in domestic animals to protect public health.

Zoonotic diseases (zoonoses) originating from domestic animals pose a significant risk to people's health and livelihoods, in addition to jeopardizing animal health and production. Effective surveillance of endemic zoonoses at the animal level is crucial to assessing the disease burden and risk, and providing early warning to prevent epidemics in animals and spillover to humans. Here we aimed to prioritize and characterize zoonoses for which surveillance in domestic animals is important to prevent human infections at a global scale. A multi-criteria qualitative approach was used, where disease-specific information was obtained across literature of the leading international health organizations. Thirty-two zoonoses were prioritized, all of which have multi-regional spread, cause unexceptional human infections and have domestic animal hosts as important sources or sentinels of zoonotic infections. Most diseases involve multiple animal hosts and/or modes of zoonotic transmission, where a lack of specific clinical signs in animals further complicates surveillance. We discuss the challenges of animal health surveillance in endemic and resource-limited settings, as well as potential avenues for improvement such as the multi-disease, multi-sectoral and digital surveillance approaches. Our study will support global capacity-building efforts to strengthen the surveillance and control of endemic zoonoses at their animal sources. This article is part of the theme issue 'Challenges and opportunities in the fight against neglected tropical diseases: a decade from the London Declaration on NTDs'.

Rebuilding a Reproductive Future Informed by Disability and Reproductive Justice.

Rebuilding a Reproductive Future Informed by Disability and Reproductive Justice.

Development of complemented comprehensive networks for rapid screening of repurposable drugs applicable to new emerging disease outbreaks

BackgroundComputational drug repurposing is crucial for identifying candidate therapeutic medications to address the urgent need for developing treatments for newly emerging infectious diseases. The recent COVID-19 pandemic has taught us the importance of rapidly discovering candidate drugs and providing them to medical and pharmaceutical experts for further investigation. Network-based approaches can provide repurposable drugs quickly by leveraging comprehensive relationships among biological components. However, in a case of newly emerging disease, applying a repurposing methods with only pre-existing knowledge networks may prove inadequate due to the insufficiency of information flow caused by the novel nature of the disease.MethodsWe proposed a network-based complementary linkage method for drug repurposing to solve the lack of incoming new disease-specific information in knowledge networks. We simulate our method under the controlled repurposing scenario that we faced in the early stage of the COVID-19 pandemic. First, the disease-gene-drug multi-layered network was constructed as the backbone network by fusing comprehensive knowledge database. Then, complementary information for COVID-19, containing data on 18 comorbid diseases and 17 relevant proteins, was collected from publications or preprint servers as of May 2020. We estimated connections between the novel COVID-19 node and the backbone network to construct a complemented network. Network-based drug scoring for COVID-19 was performed by applying graph-based semi-supervised learning, and the resulting scores were used to validate prioritized drugs for population-scale electronic health records-based medication analyses.ResultsThe backbone networks consisted of 591 diseases, 26,681 proteins, and 2,173 drug nodes based on pre-pandemic knowledge. After incorporating the 35 entities comprised of complemented information into the backbone network, drug scoring screened top 30 potential repurposable drugs for COVID-19. The prioritized drugs were subsequently analyzed in electronic health records obtained from patients in the Penn Medicine COVID-19 Registry as of October 2021 and 8 of these were found to be statistically associated with a COVID-19 phenotype.ConclusionWe found that 8 of the 30 drugs identified by graph-based scoring on complemented networks as potential candidates for COVID-19 repurposing were additionally supported by real-world patient data in follow-up analyses. These results show that our network-based complementary linkage method and drug scoring algorithm are promising strategies for identifying candidate repurposable drugs when new emerging disease outbreaks.

Modeling the sequence dependence of differential antibody binding in the immune response to infectious disease.

Past studies have shown that incubation of human serum samples on high density peptide arrays followed by measurement of total antibody bound to each peptide sequence allows detection and discrimination of humoral immune responses to a variety of infectious diseases. This is true even though these arrays consist of peptides with near-random amino acid sequences that were not designed to mimic biological antigens. This "immunosignature" approach, is based on a statistical evaluation of the binding pattern for each sample but it ignores the information contained in the amino acid sequences that the antibodies are binding to. Here, similar array-based antibody profiles are instead used to train a neural network to model the sequence dependence of molecular recognition involved in the immune response of each sample. The binding profiles used resulted from incubating serum from 5 infectious disease cohorts (Hepatitis B and C, Dengue Fever, West Nile Virus and Chagas disease) and an uninfected cohort with 122,926 peptide sequences on an array. These sequences were selected quasi-randomly to represent an even but sparse sample of the entire possible combinatorial sequence space (~1012). This very sparse sampling of combinatorial sequence space was sufficient to capture a statistically accurate representation of the humoral immune response across the entire space. Processing array data using the neural network not only captures the disease-specific sequence-binding information but aggregates binding information with respect to sequence, removing sequence-independent noise and improving the accuracy of array-based classification of disease compared with the raw binding data. Because the neural network model is trained on all samples simultaneously, a highly condensed representation of the differential information between samples resides in the output layer of the model, and the column vectors from this layer can be used to represent each sample for classification or unsupervised clustering applications.

Patients' Perspectives Regarding Digital Health Technology to Support Self-management and Improve Integrated Stroke Care: Qualitative Interview Study.

Digital technologies such as mobile apps and robotics have the potential to involve stroke patients better in the care process and to promote self-management. However, barriers exist that constrain the adoption and acceptance of technology in clinical practice. Examples of barriers are privacy concerns, challenges regarding usability, and the perception that there is no need for health-related technology. To address these barriers, co-design can be used to enable patients to reflect on their experiences of a service and to tailor digital technologies to the needs and preferences of end users regarding content and usability. This study aims to explore the perspectives of stroke patients toward how digital health technology could support self-management regarding health and well-being, as well as integrated stroke care. A qualitative study was conducted to understand patient perspectives. Data were collected in co-design sessions during the ValueCare study. Patients from a Dutch hospital who experienced an ischemic stroke (n=36) within the past 18 months were invited to participate. Data collection took place between December 2020 and April 2021 via one-to-one telephone interviews. A short self-report questionnaire was used to collect data on sociodemographics, disease-specific information, and technology use. All interviews were audio-taped and transcribed verbatim. The interview data were analyzed using a thematic approach. Patients held mixed attitudes toward digital health technologies. Some patients viewed digital technology as a convenient product or service, while others expressed no desire or need to use technology for self-management or care. Digital features suggested by stroke patients included (1) information about the causes of stroke, medication, prognosis, and follow-up care; (2) an online library with information regarding stroke-related health and care issues; (3) a personal health record by which patients can retrieve and manage their own health information; and (4) online rehabilitation support to empower patients to exercise at home. Regarding the user interface of future digital health technology, patients emphasized the need for easy-to-use and simple designs. Stroke patients mentioned credible health information, an online library with stroke-related health and care information, a personal health record, and online rehabilitation support as the main features to include in future digital health technologies. We recommend that developers and designers of digital health for stroke care listen to the "voice of the stroke patients" regarding both functionality and the characteristics of the interface. RR2-10.1186/s12877-022-03333-8.

Identifying diseases associated with Post-COVID syndrome through an integrated network biology approach

A growing body of research shows that COVID-19 is now recognized as a multi-organ disease with a wide range of manifestations that can have long-lasting repercussions, referred to as post-COVID-19 syndrome. It is unknown why the vast majority of COVID-19 patients develop post-COVID-19 syndrome, or why patients with pre-existing disorders are more likely to experience severe COVID-19. This study used an integrated network biology approach to obtain a comprehensive understanding of the relationship between COVID-19 and other disorders. The approach involved building a PPI network with COVID-19 genes and identifying highly interconnected regions. The molecular information contained within these subnetworks, as well as the pathway annotations, were used to reveal the link between COVID-19 and other disorders. Using Fisher’s exact test and disease-specific gene information, significant COVID-19-disease associations were discovered. The study discovered diseases that affect multiple organs and organ systems, thus proving the theory of multiple organ damage caused by COVID-19. Cancers, neurological disorders, hepatic diseases, cardiac disorders, pulmonary diseases, and hypertensive diseases are just a few of the conditions linked to COVID-19. Pathway enrichment analysis of shared proteins revealed the shared molecular mechanism of COVID-19 and these diseases. The findings of the study shed new light on the major COVID-19-associated disease conditions and how their molecular mechanisms interact with COVID-19. The novelty of studying disease associations in the context of COVID-19 provides new insights into the management of rapidly evolving long-COVID and post-COVID syndromes, which have significant global implications. Communicated by Ramaswamy H. Sarma

Development, Validation, and Impact of Patient Information Booklet for Gamma Knife Radiosurgery.

The inadequate awareness in the general population is a primary concern for the management of various neurosurgical ailments treated with gamma knife radiosurgery (GKRS). Our study aimed to assess the written patient information by focusing on readability, recall, communication, compliance, and patient satisfaction. The senior author formulated disease-specific patient information booklets. The booklets comprised two segments: general information about GKRS and disease-specific information. The common themes for discussion were "What is your disease?", "What is gamma knife radiosurgery?", "What are the alternatives to gamma knife radiosurgery," "The benefits of gamma knife radiosurgery," "About gamma knife radiosurgery," "Recovering from gamma knife radiosurgery," "Follow up," "What are the risks," and "Contact." The booklet was emailed after the first consultation to 102 patients. Patients' socioeconomic status and comprehensibility were assessed on validated scoring. Post-GKRS, we emailed a custom-made Google feedback survey of 10 leading questions about the role of patient information booklet in educating and decision-making process. We tried to assess if the booklet helped the patient understand the disease and treatment options. In total, 94% of patients read it thoroughly and understood it to their satisfaction. They also shared and discussed the information booklet with their family members and relatives (92%). Furthermore, 96% of patients found the disease-specific information informative. For 83% of patients, the information brochure cleared the doubts regarding the GKRS completely. For 66% of patients, their expectations met the reality. In addition, 94% of patients still recommended giving the booklet to the patients. All high, upper- and middle-class responders were happy and content with the patient information booklet. In contrast, 18 (90%) of the lower middle class and 2 (66.7%) of the lower class considered the information useful to the patients. Also, 90% patients found the language of the patient information booklet comprehensible and not too technical to understand. An essential component of disease management is to relieve the anxiety and confusion in the patient's mind and help one choose a treatment modality among the available options. A patient-centric booklet helps impart knowledge, clears doubts, and provides an opportunity to discuss options with family members.

Improving the Hospital Transfer Process for Acute Type A Aortic Dissections.

Patients with acute type A aortic dissection who arrive athospitals that lack the facilities to treat them must be transferred to a tertiary care facility to receive treatment. The transfer process involves a checkpoint at which the transfer is accepted or denied. Delays in making this decision may lead to suboptimal health outcomes. In light of this, the goal of this project was to devise a way to reduce the time to decision of transfer requests for patients with an acute type A aortic dissection. The project followed the Define-Measure-Analyze-Improve-Control (DMAIC) approach. To better understand the process, data were obtained from the University of Texas Southwestern Medical Center regarding reasons for patient transfer cancellation and the average time until a transfer was denied or accepted. After data analysis, a fishbone diagram was used to display 23 root causes of the delays in time to decision of the transfer request. These were narrowed down to the following four significant causes using a nominal voting technique: (1) no standard on disease-specific information for the handoff, (2) lack of a real-time database, (3) incompatible electronic health record system between facilities, and (4) multiple communication handoffs causing confusion. Solutions to each root cause were evaluated using a solution selection matrix. The final two solutions proposed for implementation were as follows: (1) to establish checklists of required documents and patient transfer criteria and (2) to create a regional database to provide real-time information on hospital capacity.

Anxieties, age and motivation influence physical activity in patients with myeloproliferative neoplasms - a multicenter survey from the East German study group for hematology and oncology (OSHO #97).

Physical activity (PA) is a non-pharmacological approach to alleviate symptom burden and improve health-related quality of life (HrQoL) in cancer patients (pts). Whether pts with myeloproliferative neoplasms (MPN) PA behavior changes due to symptom burden and/or knowledge of the putative beneficial effects of PA has not yet been investigated. We performed a large questionnaire study in MPN pts. Self-reported PA behavior and potential influencing factors of 634 MPN pts were analyzed. Questionnaires were used to assess demographics, anxiety, severity of symptoms, HrQoL, current level of everyday and sports activities, and the level of information regarding the importance/possibilities of PA. According to their PA, the pts were assigned to the three groups: "inactive", "non-targeted active", and "sporty active" and compared with each other. Key findings are that in 73% of the pts, the disease had an impact on PA, with 30% of pts reducing their PA. The prevalence of anxieties (e.g., occurrence of thrombosis and bleeding) regarding PA was 45%. Sporty active pts had a lower symptom burden and better HrQoL (p ≤ 0.001) compared to the other groups. Inactive pts were significantly older and had a higher body mass index than sporty active pts. Inactive and non-targeted active pts felt less informed about the importance/possibilities of PA (p = 0.002). Our results suggest that especially older and non-sporty MPN pts could benefit from motivational as well as disease-specific PA information. This study was registered at the German Registry of Clinical Trials, DRKS00023698.