Characteristics, Symptom Severity, and Experiences of Patients Reporting Chronic Kidney Disease in the PatientsLikeMe Online Health Community: Retrospective and Qualitative Study

Perspectives of Chronic Kidney Disease Patients on Readiness for Advance Care Planning at Komfo Anokye Teaching Hospital, Ghana

This study examined the impact of advance care planning (ACP) on the quality of life for patients with chronic kidney disease (CKD) at Komfo Anokye Teaching Hospital in Ghana. It specifically investigated patients' perspectives on their readiness for ACP. Utilizing a qualitative descriptive design, one-on-one interviews were conducted with CKD patients at the renal clinic, employing a semi-structured interview guide for thematic analysis of audio data. The findings revealed a gap in understanding among CKD patients, with participants acknowledging their vulnerability to renal failure, often linked to a medical history of diabetes and hypertension. Despite recognizing potential outcomes such as dialysis dependency or death, some patients retained hope for a cure, relying on faith. The initial kidney failure diagnosis induced shock and distress, leading many patients to prefer the comfort and familiarity of home-based care, including dialysis. Meanwhile, a minority favored hospital care to protect their children from psychological trauma. Most patients deemed legal preparations unnecessary, citing limited assets or a lack of concern for posthumous estate execution. These insights emphasize the necessity for targeted education and support in ACP to enhance patient outcomes in chronic kidney disease care and end-of-life planning.

Patient listening on social media for patient-focused drug development: a synthesis of considerations from patients, industry and regulators

Patients, life science industry and regulatory authorities are united in their goal to reduce the disease burden of patients by closing remaining unmet needs. Patients have, however, not always been systematically and consistently involved in the drug development process. Recognizing this gap, regulatory bodies worldwide have initiated patient-focused drug development (PFDD) initiatives to foster a more systematic involvement of patients in the drug development process and to ensure that outcomes measured in clinical trials are truly relevant to patients and represent significant improvements to their quality of life. As a source of real-world evidence (RWE), social media has been consistently shown to capture the first-hand, spontaneous and unfiltered disease and treatment experience of patients and is acknowledged as a valid method for generating patient experience data by the Food and Drug Administration (FDA). While social media listening (SML) methods are increasingly applied to many diseases and use cases, a significant piece of uncertainty remains on how evidence derived from social media can be used in the drug development process and how it can impact regulatory decision making, including legal and ethical aspects. In this policy paper, we review the perspectives of three key stakeholder groups on the role of SML in drug development, namely patients, life science companies and regulators. We also carry out a systematic review of current practices and use cases for SML and, in particular, highlight benefits and drawbacks for the use of SML as a way to identify unmet needs of patients. While we find that the stakeholders are strongly aligned regarding the potential of social media for PFDD, we identify key areas in which regulatory guidance is needed to reduce uncertainty regarding the impact of SML as a source of patient experience data that has impact on regulatory decision making.

Prevalence and influencing factors of fatigue among patients undergoing continuous ambulatory peritoneal dialysis: A cross-sectional study

Background

Fatigue is one of the most commonly reported symptoms among patients undergoing continuous ambulatory peritoneal dialysis (CAPD). However, only a few studies have addressed the prevalence of fatigue and its influencing factors within this population in Thailand.

Objective

This study aimed to explore the prevalence of fatigue and its influencing factors, including duration of CAPD initiation, body mass index, insomnia, social support, functional status, and depression among patients undergoing CAPD.

Methods

This cross-sectional study involved 136 participants from the outpatient department of a general hospital in Thailand. Data were collected from January to March 2023, utilizing validated self-reported questionnaires, which included the Center for Epidemiologic Study Depression Scale, Insomnia Severity Index, Multidimensional Scale of Perceived Social Support, Functional Status Scale, and Fatigue Severity Scale. Data were analyzed using descriptive statistics, Pearson's product-moment correlation, and Stepwise multiple regressions.

Results

The study achieved a 100% participation rate among the selected participants. The prevalence of fatigue among patients undergoing CAPD was 55.88%, including mild fatigue (20.59%), moderate fatigue (19.85%), and severe fatigue (15.44%). Bivariate analysis indicated that fatigue-related factors were insomnia, depression, body mass index, social support, and functional status. However, the multiple regression analysis revealed that only insomnia (β = 0.399, p <0.001), social support (β = -0.302, p <0.001), depression (β = 0.201, p = 0.003), and functional status (β = -0.149, p = 0.021) jointly influenced fatigue among patients undergoing CAPD, explaining 50.10 percent of the variance (R2 = 0.501, F(4, 131) = 32.871, p <0.001).

Conclusion

The findings indicated that more than half of the participants experienced fatigue. Insomnia, social support, depression, and functional status emerged as significant predictive factors of fatigue. Therefore, it is advisable for nurses and other healthcare providers to evaluate fatigue and its associated factors routinely. Nursing interventions to alleviate fatigue should prioritize improvements in sleep quality, reduction of depression, preservation of functional status, and promotion of family engagement.

Developing a common data model approach for DISCOVER CKD: A retrospective, global cohort of real-world patients with chronic kidney disease

Objectives

To describe a flexible common data model (CDM) approach that can be efficiently tailored to study-specific needs to facilitate pooled patient-level analysis and aggregated/meta-analysis of routinely collected retrospective patient data from disparate data sources; and to detail the application of this CDM approach to the DISCOVER CKD retrospective cohort, a longitudinal database of routinely collected (secondary) patient data of individuals with chronic kidney disease (CKD).

Methods

The flexible CDM approach incorporated three independent, exchangeable components that preceded data mapping and data model implementation: (1) standardized code lists (unifying medical events from different coding systems); (2) laboratory unit harmonization tables; and (3) base cohort definitions. Events between different coding vocabularies were not mapped code-to-code; for each data source, code lists of labels were curated at the entity/event level. A study team of epidemiologists, clinicians, informaticists, and data scientists were included within the validation of each component.

Results

Applying the CDM to the DISCOVER CKD retrospective cohort, secondary data from 1,857,593 patients with CKD were harmonized from five data sources, across three countries, into a discrete database for rapid real-world evidence generation.

Conclusions

This flexible CDM approach facilitates evidence generation from real-world data within the DISCOVER CKD retrospective cohort, providing novel insights into the epidemiology of CKD that may expedite improvements in diagnosis, prognosis, early intervention, and disease management. The adaptable architecture of this CDM approach ensures scalable, fast, and efficient application within other therapy areas to facilitate the combined analysis of different types of secondary data from multiple, heterogeneous sources.

Identifying individual enablers and barriers to the use of digital technology for the self-management of long-term conditions by older adults

Evidence suggests that much of the digital technology available and provided to older adults to enable self-management of long-term conditions is under-utilised. This research focuses on three conditions prevalent amongst older adults: diabetes, dementia and chronic kidney disease and explores the individual enablers and barriers to the use of digital self-management technology. The paper reports findings from a series of three systematic reviews of qualitative research (qualitative evidence syntheses). These reviews informed the design of a Delphi study. The first round of the Delphi involving 15 expert interviews is reported. The findings highlight common themes across the three conditions: how technology is used; barriers to use; assessing individual needs when selecting technology; support requirements; multi-functional self-management technologies; trust, privacy and data sharing; achieving accessible and aspirational design. Some emerging recommendations have been suggested to guide the design, and provision of technology to older adults. These will extended and refined through subsequent rounds of the Delphi method.

Methodological Issues in Analyzing Real-World Longitudinal Occupational Health Data: A Useful Guide to Approaching the Topic

Ever greater technological advances and democratization of digital tools such as computers and smartphones offer researchers new possibilities to collect large amounts of health data in order to conduct clinical research. Such data, called real-world data, appears to be a perfect complement to traditional randomized clinical trials and has become more important in health decisions. Due to its longitudinal nature, real-world data is subject to specific and well-known methodological issues, namely issues with the analysis of cluster-correlated data, missing data and longitudinal data itself. These concepts have been widely discussed in the literature and many methods and solutions have been proposed to cope with these issues. As examples, mixed and trajectory models have been developed to explore longitudinal data sets, imputation methods can resolve missing data issues, and multilevel models facilitate the treatment of cluster-correlated data. Nevertheless, the analysis of real-world longitudinal occupational health data remains difficult, especially when the methodological challenges overlap. The purpose of this article is to present various solutions developed in the literature to deal with cluster-correlated data, missing data and longitudinal data, sometimes overlapped, in an occupational health context. The novelty and usefulness of our approach is supported by a step-by-step search strategy and an example from the Wittyfit database, which is an epidemiological database of occupational health data. Therefore, we hope that this article will facilitate the work of researchers in the field and improve the accuracy of future studies.

The Development History and Research Tendency of Medical Informatics: Topic Evolution Analysis

BACKGROUND

Medical informatics has attracted the attention of researchers worldwide. It is necessary to understand the development of its research hot spots as well as directions for future research.

OBJECTIVE

The aim of this study is to explore the evolution of medical informatics research topics by analyzing research articles published between 1964 and 2020.

METHODS

A total of 56,466 publications were collected from 27 representative medical informatics journals indexed by the Web of Science Core Collection. We identified the research stages based on the literature growth curve, extracted research topics using the latent Dirichlet allocation model, and analyzed topic evolution patterns by calculating the cosine similarity between topics from the adjacent stages.

RESULTS

The following three research stages were identified: early birth, early development, and rapid development. Medical informatics has entered the fast development stage, with literature growing exponentially. Research topics in medical informatics can be classified into the following two categories: data-centered studies and people-centered studies. Medical data analysis has been a research hot spot across all 3 stages, and the integration of emerging technologies into data analysis might be a future hot spot. Researchers have focused more on user needs in the last 2 stages. Another potential hot spot might be how to meet user needs and improve the usability of health tools.

CONCLUSIONS

Our study provides a comprehensive understanding of research hot spots in medical informatics, as well as evolution patterns among them, which was helpful for researchers to grasp research trends and design their studies.

Patient-centred approaches for the management of unpleasant symptoms in kidney disease

Patients with chronic kidney disease (CKD) frequently experience unpleasant symptoms. These can be gastrointestinal (constipation, nausea, vomiting and diarrhoea), psychological (anxiety and sadness), neurological (lightheadedness, headache and numbness), cardiopulmonary (shortness of breath and oedema), dermatological (pruritus and dry skin), painful (muscle cramps, chest pain and abdominal pain) or involve sexual dysfunction, sleep disorders and fatigue. These symptoms often occur in clusters, with one of them as the lead symptom and others as secondary symptoms. Uraemic toxins (also called uremic toxins) are often considered to be the main cause of CKD-associated symptom burden, but treatment of uraemia by dialysis often fails to resolve them and can engender additional symptoms. Indeed, symptoms can be exacerbated by comorbid conditions, pharmacotherapies, lifestyle and dietary regimens, kidney replacement therapy and ageing. Patients with kidney disease, including those who depend on dialysis or transplantation, should feel actively supported in their symptom management through the identification and targeting of unpleasant symptoms via a tailored palliative care approach. Such an approach may help minimize the burden and consequences of kidney disease, and lead to improved patient outcomes including health-related quality of life and better life participation.

[Analgesic Drug Therapy in Kidney Patients]

Analgesic drug therapy in kidney patients needs special expertise. Patients with kidney disease frequently have pain and they have chronic pain more than others. Some analgesic drugs have a nephrotoxic potential and co-analgesics such as anticonvulsive, antidepressive and antipsychotic drugs need a dose adjustment to kindey function. When treating kidney patients with morphine, the accumulation kinetics of the M6 glucuronide must be observed. Otherwise, the risk increases for respiratory depression.

Methods and rationale of the DISCOVER CKD global observational study

Background

Real-world data for patients with chronic kidney disease (CKD), specifically pertaining to clinical management, metabolic control, treatment patterns, quality of life (QoL) and dietary patterns, are limited. Understanding these gaps using real-world, routine care data will improve our understanding of the challenges and consequences faced by patients with CKD, and will facilitate the long-term goal of improving their management and prognosis.

Methods

DISCOVER CKD follows an enriched hybrid study design, with both retrospective and prospective patient cohorts, integrating primary and secondary data from patients with CKD from China, Italy, Japan, Sweden, the UK and the USA. Data will be prospectively captured over a 3-year period from >1000 patients with CKD who will be followed up for at least 1 year via electronic case report form entry during routine clinical visits and also via a mobile/tablet-based application, enabling the capture of patient-reported outcomes (PROs). In-depth interviews will be conducted in a subset of ∼100 patients. Separately, secondary data will be retrospectively captured from >2 000 000 patients with CKD, extracted from existing datasets and registries.

Results

The DISCOVER CKD program captures and will report on patient demographics, biomarker and laboratory measurements, medical histories, clinical outcomes, healthcare resource utilization, medications, dietary patterns, physical activity and PROs (including QoL and qualitative interviews).

Conclusions

The DISCOVER CKD program will provide contemporary real-world insight to inform clinical practice and improve our understanding of the epidemiology and clinical and economic burden of CKD, as well as determinants of clinical outcomes and PROs from a range of geographical regions in a real-world CKD setting.

Communicative and Discursive Perspectives on the Medication Experience

Taking the ‘medication experience’ in the broad sense of what individuals hear and say about their medication, as well as how they experience it, this paper explores diverse research on medication information available to patients and their modes and capacities for interaction, including personal circles, doctors and pharmacists, labeling and promotion, websites, and the patient’s own inner conversations and self-expression. The goal is to illustrate, for nonspecialists in communication, how the actors, messages, mediums, genres, and contextual factors within a standard ethnographic and social semiotic model of discourse and communication are operating, not always effectively or beneficially, to mediate or construct a patient’s medication experience. We also suggest how disparate insights can be integrated through such a model and might generate new research questions.