Facing multimorbidity in the precision medicine era

Can social prescribing put the 'social' into the biopsychosocial management of people with long-term musculoskeletal disorders?

BACKGROUND

Chronic musculoskeletal disorders (MSD) are a significant burden on individuals' quality of life and society and are made more complex by the presence of multimorbidity. It is recommended that interventions targeting MSD be sustainable, equitable and incorporate the biopsychosocial model of care (BPS).

AIMS

A criticism of the BPS approach is that the social component of this model is not addressed adequately during the management of people with long-term MSD and that a gap exists between theory and implementation. The use of social prescribing (SP) as an intervention to bridge this gap is discussed.

RESULTS AND DISCUSSION

Social prescribing is a holistic non-medical person-centered approach to well-being that utilizes link workers (LW) to support individuals with long-term conditions (LTC) in the community. Social prescribing referrals are received from primary healthcare practitioners to LW and range from light touch signposting for employment or financial advice to more intensive support for LTC such as obesity, decreased physical activity and mental health needs.

CONCLUSION

There is evidence to suggest that SP interventions are effective in the management of LTC; however, due to the paucity of high-quality evidence, it is difficult to be conclusive. Large-scale randomised controlled trials are recommended to support the use of SP interventions in the management of LTC.

Incontinence-Associated Dermatitis in Older Intensive Care Patients: A Review and Case Report

Background

Incontinence-Associated Dermatitis is a serious skin injury causing suffering, secondary infection, and inducing almost six times more pressure sore than normal skin patients. This moisture and chemical skin irritation is a concern, especially for intensive care unit nurses. This study aimed to review the effective nursing strategy for preventing and caring for incontinence-associated dermatitis in older intensive care unit patients and pilot its feasibility.

Methods

The five databases, including PubMed, Google Scholar, CINAHL Complete, WanFang, and CNKI, were searched, and articles were screened and extracted. The strategies and details of prevention and care for incontinence-associated dermatitis were reviewed and summarized. Finally, selected strategies were applied to five intensive care unit patients with various health conditions and levels of Incontinence-Associated Dermatitis severity.

Results

The literature review found that there is a standardized nursing process for incontinence-associated dermatitis. The prevention and care strategies for incontinence-associated dermatitis include assessment, risk factor management, skin cleaning, skin protection, and health education and training. In actual clinical application, personalized nursing measures can positively impact patients. Five case studies from our pilot confirmed this finding.

Conclusion

Personalized nursing measures can positively impact patients in actual clinical applications. Our five case studies implementing the strategies from the review confirmed this finding. In clinical work, it is recommended to develop personalized nursing programs for specific risk factors of older intensive care unit patients.

Protocol Registration

TCTR20230808004.

Single-tablet-scale direct-compression: An on-demand manufacturing route for personalized tablets

Today's pharmaceutical industry is facing various challenges. Two of them are issues with supply chain security and the increasing demand for personalized medicine. Both can be addressed by increasing flexibility and a more decentralized approach to pharmaceutical manufacturing. In this study, we present a setup that provides flexibility in terms of supplied raw materials and the product, i.e., a direct-compression setup for personalized tablets operating at a single-tablet-scale. The performance of the implemented single-tablet-scale technology for dosing and mixing was investigated. In addition, an analysis of the critical quality attributes (CQAs) of immediate release ibuprofen and loratadine tablets was performed. The developed dosing device achieved acceptance rates of > 90 % for doses ≥ 20 mg for various pharmaceutical powders. Regarding the vibratory mixing process, a dependency of the performance on the applied frequencies and acceleration was observed, with 100 Hz and ∼ 90 G performing best, yet still exhibiting varying mixing efficacies depending on the granular system. The tablets produced met U.S. Pharmacopeia requirements regarding mechanical stability and dissolution characteristics. Given these results, we consider the developed setup a proof of concept of a tool to provide personalized tablets to patients while minimizing the dependency on complex supply chains.

Individualized interventions and precision health: Lessons learned from a systematic review and implications for analytics-driven geriatric research

Older adults are characterized by profound clinical heterogeneity. When designing and delivering interventions, there exist multiple approaches to account for heterogeneity. We present the results of a systematic review of data-driven, personalized interventions in older adults, which serves as a use case to distinguish the conceptual and methodologic differences between individualized intervention delivery and precision health-derived interventions. We define individualized interventions as those where all participants received the same parent intervention, modified on a case-by-case basis and using an evidence-based protocol, supplemented by clinical judgment as appropriate, while precision health-derived interventions are those that tailor care to individuals whereby the strategy for how to tailor care was determined through data-driven, precision health analytics. We discuss how their integration may offer new opportunities for analytics-based geriatric medicine that accommodates individual heterogeneity but allows for more flexible and resource-efficient population-level scaling.

Exploring human biology with N-of-1 clinical trials

Studies on humans that exploit contemporary data-intensive, high-throughput 'omic' assay technologies, such as genomics, transcriptomics, proteomics and metabolomics, have unequivocally revealed that humans differ greatly at the molecular level. These differences, which are compounded by each individual's distinct behavioral and environmental exposures, impact individual responses to health interventions such as diet and drugs. Questions about the best way to tailor health interventions to individuals based on their nuanced genomic, physiologic, behavioral, etc. profiles have motivated the current emphasis on 'precision' medicine. This review's purpose is to describe how the design and execution of N-of-1 (or personalized) multivariate clinical trials can advance the field. Such trials focus on individual responses to health interventions from a whole-person perspective, leverage emerging health monitoring technologies, and can be used to address the most relevant questions in the precision medicine era. This includes how to validate biomarkers that may indicate appropriate activity of an intervention as well as how to identify likely beneficial interventions for an individual. We also argue that multivariate N-of-1 and aggregated N-of-1 trials are ideal vehicles for advancing biomedical and translational science in the precision medicine era since the insights gained from them can not only shed light on how to treat or prevent diseases generally, but also provide insight into how to provide real-time care to the very individuals who are seeking attention for their health concerns in the first place.

Relationship between Cardiometabolic Factors and the Response of Blood Pressure to a One-Year Primary Care Lifestyle Intervention in Metabolic Syndrome Patients

Systemic hypertension has been recognized as a modifiable traditional cardiovascular risk factor and influenced by many factors such as eating habits, physical activity, diabetes, and obesity. The objective of this cross-sectional study was to identify factors that predict changes in blood pressure induced by a one-year lifestyle intervention in primary care settings involving a collaboration between family physicians, dietitians, and exercise specialists. Patients with metabolic syndrome diagnosis were recruited by family physicians participating in primary care lifestyle intervention among several family care clinics across Canada. Participants for whom all cardiometabolic data at the beginning (T0) and the end (T12) of the one-year intervention were available were included in the present analysis (n = 101). Patients visited the dietitian and the exercise specialist weekly for the first three months and monthly for the last nine months. Diet quality, exercise capacity, anthropometric indicators, and cardiometabolic variables were evaluated at T0 and at T12. The intervention induced a statistically significant decrease in waist circumference (WC), systolic (SBP) and diastolic (DBP) blood pressure, and plasma triglycerides, and an increase in cardiorespiratory fitness (estimated VO2max). Body weight (p < 0.001), body mass index (BMI) (p < 0.001), and fasting blood glucose (p = 0.006) reduction, and VO2max increase (p = 0.048) were all related to changes in SBP. WC was the only variable for which changes were significantly correlated with those in both SBP (p < 0.0001) and DBP (p = 0.0004). Variations in DBP were not associated with changes in other cardiometabolic variables to a statistically significant extent. Twelve participants were identified as adverse responders (AR) in both SBP and DBP and displayed less favorable changes in WC. The beneficial effects of the primary care lifestyle intervention on blood pressure were significantly associated with cardiometabolic variables, especially WC. These findings suggest that a structured lifestyle intervention in primary care can help improve cardiometabolic risk factors in patients with metabolic syndrome and that WC should be systematically measured to better stratify the patient’s hypertension risk.

Multiple Long-Term Conditions (MLTC) and the Environment: A Scoping Review

Background: Multiple Long Term conditions (MLTC) are a major health care challenge associated with high service utilisation and expenditure. Once established, the trajectory to an increased number and severity of conditions, hospital admission, increased social care need and mortality is multifactorial. The role of wider environmental determinants in the MLTC sequelae is unclear. Aim: the aim of this review was to summarise and collate existing evidence on environmental determinants on established MLTC. Methods: comprehensive search of Medline, Embase, Cochrane, CINAHL and Bielefeld Academic Search Engine (BASE), from inception to 4th June 2022 in addition to grey literature. Two authors independently screened and extracted papers. Disagreements were resolved with a third author. Results: searches yielded 9079 articles, 12 of which met the review's inclusion criteria. Evidence of correlations between some environmental determinants and increased or decreased risks of MLTC were found, including the quality of internal housing/living environments, exposure to airborne environmental hazards and a beneficial association with socially cohesive, accessible and greener neighbourhood environments. Conclusions: The majority of the 12 included papers focused on the built and social environments. The review uncovered very limited evidence, indicating a need for further research to understand the role of environmental determinants in MLTC.

Data Integration Challenges for Machine Learning in Precision Medicine

A main goal of Precision Medicine is that of incorporating and integrating the vast corpora on different databases about the molecular and environmental origins of disease, into analytic frameworks, allowing the development of individualized, context-dependent diagnostics, and therapeutic approaches. In this regard, artificial intelligence and machine learning approaches can be used to build analytical models of complex disease aimed at prediction of personalized health conditions and outcomes. Such models must handle the wide heterogeneity of individuals in both their genetic predisposition and their social and environmental determinants. Computational approaches to medicine need to be able to efficiently manage, visualize and integrate, large datasets combining structure, and unstructured formats. This needs to be done while constrained by different levels of confidentiality, ideally doing so within a unified analytical architecture. Efficient data integration and management is key to the successful application of computational intelligence approaches to medicine. A number of challenges arise in the design of successful designs to medical data analytics under currently demanding conditions of performance in personalized medicine, while also subject to time, computational power, and bioethical constraints. Here, we will review some of these constraints and discuss possible avenues to overcome current challenges.

Artificial Intelligence Solutions to Increase Medication Adherence in Patients With Non-communicable Diseases

Artificial intelligence (AI) tools are increasingly being used within healthcare for various purposes, including helping patients to adhere to drug regimens. The aim of this narrative review was to describe: (1) studies on AI tools that can be used to measure and increase medication adherence in patients with non-communicable diseases (NCDs); (2) the benefits of using AI for these purposes; (3) challenges of the use of AI in healthcare; and (4) priorities for future research. We discuss the current AI technologies, including mobile phone applications, reminder systems, tools for patient empowerment, instruments that can be used in integrated care, and machine learning. The use of AI may be key to understanding the complex interplay of factors that underly medication non-adherence in NCD patients. AI-assisted interventions aiming to improve communication between patients and physicians, monitor drug consumption, empower patients, and ultimately, increase adherence levels may lead to better clinical outcomes and increase the quality of life of NCD patients. However, the use of AI in healthcare is challenged by numerous factors; the characteristics of users can impact the effectiveness of an AI tool, which may lead to further inequalities in healthcare, and there may be concerns that it could depersonalize medicine. The success and widespread use of AI technologies will depend on data storage capacity, processing power, and other infrastructure capacities within healthcare systems. Research is needed to evaluate the effectiveness of AI solutions in different patient groups and establish the barriers to widespread adoption, especially in light of the COVID-19 pandemic, which has led to a rapid increase in the use and development of digital health technologies.

The Role of Artificial Intelligence in Managing Multimorbidity and Cancer

Traditional healthcare paradigms rely on the disease-centered approach aiming at reducing human nature by discovering specific drivers and biomarkers that cause the advent and progression of diseases. This reductive approach is not always suitable to understand and manage complex conditions, such as multimorbidity and cancer. Multimorbidity requires considering heterogeneous data to tailor preventing and targeting interventions. Personalized Medicine represents an innovative approach to address the care needs of multimorbid patients considering relevant patient characteristics, such as lifestyle and individual preferences, in opposition to the more traditional “one-size-fits-all” strategy focused on interventions designed at the population level. Integration of omic (e.g., genomics) and non-strictly medical (e.g., lifestyle, the exposome) data is necessary to understand patients’ complexity. Artificial Intelligence can help integrate and manage heterogeneous data through advanced machine learning and bioinformatics algorithms to define the best treatment for each patient with multimorbidity and cancer. The experience of an Italian research hospital, leader in the field of oncology, may help to understand the multifaceted issue of managing multimorbidity and cancer in the framework of Personalized Medicine.

3D Printing in Development of Nanomedicines

Three-dimensional (3D) printing is gaining numerous advances in manufacturing approaches both at macro- and nanoscales. Three-dimensional printing is being explored for various biomedical applications and fabrication of nanomedicines using additive manufacturing techniques, and shows promising potential in fulfilling the need for patient-centric personalized treatment. Initial reports attributed this to availability of novel natural biomaterials and precisely engineered polymeric materials, which could be fabricated into exclusive 3D printed nanomaterials for various biomedical applications as nanomedicines. Nanomedicine is defined as the application of nanotechnology in designing nanomaterials for different medicinal applications, including diagnosis, treatment, monitoring, prevention, and control of diseases. Nanomedicine is also showing great impact in the design and development of precision medicine. In contrast to the "one-size-fits-all" criterion of the conventional medicine system, personalized or precision medicines consider the differences in various traits, including pharmacokinetics and genetics of different patients, which have shown improved results over conventional treatment. In the last few years, much literature has been published on the application of 3D printing for the fabrication of nanomedicine. This article deals with progress made in the development and design of tailor-made nanomedicine using 3D printing technology.

Digital Health Tools for Managing Noncommunicable Diseases During and After the COVID-19 Pandemic: Perspectives of Patients and Caregivers

Background

A reduction in the number of face-to-face medical examinations conducted for patients with noncommunicable diseases (NCDs) during the first wave of the COVID-19 pandemic has led to health care professionals quickly adopting different strategies to communicate with and monitor their patients. Such strategies include the increased use of digital health tools. However, patient preferences, privacy concerns, a lack of regulations, overregulation, and insufficient evidence on the efficacy of digital health tools may have hampered the potential positive benefits of using such tools to manage NCDs.

Objective

This viewpoint aims to discuss the views of an advisory board of patient and caregiver association members. Specifically, we aim to present this advisory board’s view on the role of digital health tools in managing patients with NCDs during and after the COVID-19 pandemic, and to identify future directions based on patients’ perspectives.

Methods

As an initiative under the NCD Partnership (PARTners in Ncds Engage foR building Strategies to improve Healthy ageing In Patients) model of Upjohn, a web-based advisory board of patient and caregiver advocates was held on July 28, 2020, to bring together key stakeholders from public and private sectors.

Results

The following key themes emerged: (1) technology developers should understand that the goals of patients may differ from those of health care professionals and other stakeholders; (2) patients, health care professionals, caregivers, and other end users need to be involved in the development of digital health tools at the earliest phase possible, to guarantee usability, efficacy, and adoption; (3) digital health tools must be better tailored to people with complex conditions, such as multimorbidity, older age, and cognitive or sensory impairment; and (4) some patients do not want or are unable to use digital health care tools, so adequate alternatives should always be available.

Conclusions

There was consensus that public-private partnership models, such as the Upjohn NCD Partnership, can be effective models that foster innovation by integrating multiple perspectives (eg, patients’ perspectives) into the design, development, and implementation of digital and nondigital health tools, with the main overall objective of improving the life of patients with NCDs.