Systems medicine and integrated care to combat chronic noncommunicable diseases

A network medicine approach to study comorbidities in heart failure with preserved ejection fraction

BACKGROUND

Comorbidities are expected to impact the pathophysiology of heart failure (HF) with preserved ejection fraction (HFpEF). However, comorbidity profiles are usually reduced to a few comorbid disorders. Systems medicine approaches can model phenome-wide comorbidity profiles to improve our understanding of HFpEF and infer associated genetic profiles.

METHODS

We retrospectively explored 569 comorbidities in 29,047 HF patients, including 8062 HFpEF and 6585 HF with reduced ejection fraction (HFrEF) patients from a German university hospital. We assessed differences in comorbidity profiles between HF subtypes via multiple correspondence analysis. Then, we used machine learning classifiers to identify distinctive comorbidity profiles of HFpEF and HFrEF patients. Moreover, we built a comorbidity network (HFnet) to identify the main disease clusters that summarized the phenome-wide comorbidity. Lastly, we predicted novel gene candidates for HFpEF by linking the HFnet to a multilayer gene network, integrating multiple databases. To corroborate HFpEF candidate genes, we collected transcriptomic data in a murine HFpEF model. We compared predicted genes with the murine disease signature as well as with the literature.

RESULTS

We found a high degree of variance between the comorbidity profiles of HFpEF and HFrEF, while each was more similar to HFmrEF. The comorbidities present in HFpEF patients were more diverse than those in HFrEF and included neoplastic, osteologic and rheumatoid disorders. Disease communities in the HFnet captured important comorbidity concepts of HF patients which could be assigned to HF subtypes, age groups, and sex. Based on the HFpEF comorbidity profile, we predicted and recovered gene candidates, including genes involved in fibrosis (COL3A1, LOX, SMAD9, PTHL), hypertrophy (GATA5, MYH7), oxidative stress (NOS1, GSST1, XDH), and endoplasmic reticulum stress (ATF6). Finally, predicted genes were significantly overrepresented in the murine transcriptomic disease signature providing additional plausibility for their relevance.

CONCLUSIONS

We applied systems medicine concepts to analyze comorbidity profiles in a HF patient cohort. We were able to identify disease clusters that helped to characterize HF patients. We derived a distinct comorbidity profile for HFpEF, which was leveraged to suggest novel candidate genes via network propagation. The identification of distinctive comorbidity profiles and candidate genes from routine clinical data provides insights that may be leveraged to improve diagnosis and identify treatment targets for HFpEF patients.

Developing integrated care pathways for atopic dermatitis-Challenges and unmet needs

BACKGROUND

GA² LEN-ADCARE is a branch of the largest multidisciplinary network of research centres and clinical care in allergy and asthma, GA² LEN, focussing on the field of atopic dermatitis (AD). AD is a chronic inflammatory skin disease with high burden and many comorbidities requiring different levels of treatment. The need for aligned information from all involved healthcare providers led to the discussion of an integrated care pathway (ICP) plan for AD patient care involving all stakeholders and considering the complexity and variability of the disease, with a particular focus placed on the large number of patients with milder forms of AD.

METHODS

The GA² LEN ADCARE network and all stakeholders, abbreviated the AD-ICPs working group, were involved in the discussion and preparation of the AD-ICPs during a series of subgroup workshops and meetings in years 2020 and 2021.

RESULTS

Here we discuss the unmet needs in AD, the methodology for devising an AD-ICP and the ICP action plan.

CONCLUSION

The GA² LEN ADCARE network has outlined the unmet needs in AD and provided an action plan for devising AD-ICPs, considering the complexity and variability of the disease.

Systems Medicine Approach for Tinnitus with Comorbid Disorders

Despite the fact that chronic diseases usually occur together with a spectrum of possible comorbidities that may differ strongly between patients, they are classically still viewed as distinct disease entities and, consequently, are often treated with uniform therapies. Unfortunately, such an approach does not take into account that different combinations of symptoms and comorbidities may result from different pathological (e.g., environmental, genetic, dietary, etc.) factors, which require specific and individualised therapeutic strategies. In this opinion paper, we aim to put forward a more differentiated, systems medicine approach to disease and patient treatment. To elaborate on this concept, we focus on the interplay of tinnitus, depression, and chronic pain. In our view, these conditions can be characterised by a variety of phenotypes composed of variable sets of symptoms and biomarkers, rather than distinct disease entities. The knowledge of the interplay of such symptoms and biomarkers will provide the key to a deeper, mechanistic understanding of disease pathologies. This paves the way for prediction and prevention of disease pathways, including more personalised and effective treatment strategies.

Open Innovation as the Catalyst in the Personalized Medicine to Personalized Digital Medicine Transition

Personalized medicine (PM) bridges several disciplines for understanding and addressing prevalent, complex, or rare situations in human health (e.g., complex phenotyping, risk stratification, etc.); therefore, digital and technological solutions have been integrated in the field to boost innovation and new knowledge generation. The open innovation (OI) paradigm proposes a method by which to respectfully manage disruptive change in biomedical organizations, as experienced by many organizations during digital transformation and the COVID-19 pandemic. In this article, we focus on how this paradigm has catalyzed the transition from PM to personalized digital medicine in a large-volume research hospital. Methods, challenges, and results are discussed. This case study is an endeavor to confirm that OI strategies could help manage urgent needs from the healthcare environment, while achieving sustainability-oriented, accountable innovation.

Mitochondrial Dysfunction in Alzheimer's Disease: Opportunities for Drug Development

Alzheimer's disease (AD) is one of the major reasons for 60-80% cases of senile dementia occurring as a result of the accumulation of plaques and tangles in the hippocampal and cortical neurons of the brain leading to neurodegeneration and cell death. The other pathological features of AD comprise abnormal microvasculature, network abnormalities, interneuronal dysfunction, increased β-amyloid production and reduced clearance, increased inflammatory response, elevated production of reactive oxygen species, impaired brain metabolism, hyperphosphorylation of tau, and disruption of acetylcholine signaling. Among all these pathologies, Mitochondrial Dysfunction (MD), regardless of it being an inciting insult or a consequence of the alterations, is related to all the associated AD pathologies. Observed altered mitochondrial morphology, distribution and movement, increased oxidative stress, dysregulation of enzymes involved in mitochondrial functioning, impaired brain metabolism, and impaired mitochondrial biogenesis in AD subjects suggest the involvement of mitochondrial malfunction in the progression of AD. Here, various pre-clinical and clinical evidence establishing MD as a key mediator in the progression of neurodegeneration in AD are reviewed and discussed with an aim to foster future MD based drug development research for the management of AD.

Impact of the COVID-19 Pandemic on Patients Affected by Non-Communicable Diseases in Europe and in the USA

An international online patient community, Carenity, conducted a patient study in two independent waves among adults affected by non-communicable diseases (NCDs) in Europe and in the United States of America (USA). The study aimed to assess the real time impact of the coronavirus disease 2019 (COVID-19) on the medical conditions of patients with NCDs, their access to health care, and their adaptation to daily life as well as to describe their sources of information on COVID-19 and their needs for specific information and support. During the pandemic, 50% of the patients reported a worsening of their medical condition, and 17% developed a new disease. Additionally, 26% of the respondents reported an impact of the pandemic on regular/long-term treatment intake. 54% of the patients felt very or completely socially isolated and reported a strong impact of the COVID-19 pandemic on their stress level and state of mind, with higher levels observed in the USA compared to Europe. 59% of the respondents wished to have received additional information regarding the risks associated to their medical condition during the pandemic. Television was the most used source of information, whereas physicians were the most trusted one. This study describes the substantial impact of the COVID-19 pandemic on NCD patients.

Barriers and facilitators in the implementation of bio-psychosocial care at the primary healthcare level in South Kivu, Democratic Republic of Congo

Background

In the Democratic Republic of Congo (DRC), healthcare services are still focused on disease control and mortality reduction in specific groups. The need to broaden the scope from biomedical criteria to bio-psychosocial (BPS) dimensions has been increasingly recognized.

Aim

The objective of this study was to identify the barriers and facilitators to providing healthcare at the health centre (HC) level to enable BPS care.

Settings

This qualitative study was conducted in six HCs (two urban and four rural) in South-Kivu (eastern DRC) which were selected based on their accessibility and their level of primary healthcare organization.

Methods

Seven focus group discussions (FGDs) involving 29 healthcare workers were organized. A data synthesis matrix was created based on the Rainbow Model framework. We identified themes related to plausible barriers and facilitators for BPS approach.

Results

Our study reports barriers common to a majority of HCs: misunderstanding of BPS care by healthcare workers, home visits mainly used for disease control, solidarity initiatives not locally promoted, new resources and financial incentives expected, accountability summed up in specific indicators reporting. Availability of care teams and accessibility to patient information were reported as facilitators to change.

Conclusion

This analysis highlighted major barriers that condition providers’ mindset and healthcare provision at the primary care level in South-Kivu. Accessibility to the information regarding BPS status of individuals within the community, leadership of HC authorities, dynamics of HC teams and local social support initiatives should be considered in order to develop an effective BPS approach in this region.

A Systematic Review of Asthma Phenotypes Derived by Data-Driven Methods

Classification of asthma phenotypes has a potentially relevant impact on the clinical management of the disease. Methods for statistical classification without a priori assumptions (data-driven approaches) may contribute to developing a better comprehension of trait heterogeneity in disease phenotyping. This study aimed to summarize and characterize asthma phenotypes derived by data-driven methods. We performed a systematic review using three scientific databases, following Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) criteria. We included studies reporting adult asthma phenotypes derived by data-driven methods using easily accessible variables in clinical practice. Two independent reviewers assessed studies. The methodological quality of included primary studies was assessed using the ROBINS-I tool. We retrieved 7446 results and included 68 studies of which 65% (n = 44) used data from specialized centers and 53% (n = 36) evaluated the consistency of phenotypes. The most frequent data-driven method was hierarchical cluster analysis (n = 19). Three major asthma-related domains of easily measurable clinical variables used for phenotyping were identified: personal (n = 49), functional (n = 48) and clinical (n = 47). The identified asthma phenotypes varied according to the sample’s characteristics, variables included in the model, and data availability. Overall, the most frequent phenotypes were related to atopy, gender, and severe disease. This review shows a large variability of asthma phenotypes derived from data-driven methods. Further research should include more population-based samples and assess longitudinal consistency of data-driven phenotypes.

Inflammation/bioenergetics-associated neurodegenerative pathologies and concomitant diseases: a role of mitochondria targeted catalase and xanthophylls

Various inflammatory stimuli are able to modify or even "re-program" the mitochondrial metabolism that results in generation of reactive oxygen species. In noncommunicable chronic diseases such as atherosclerosis and other cardiovascular pathologies, type 2 diabetes and metabolic syndrome, these modifications become systemic and are characterized by chronic inflammation and, in particular, "neuroinflammation" in the central nervous system. The processes associated with chronic inflammation are frequently grouped into "vicious circles" which are able to stimulate each other constantly amplifying the pathological events. These circles are evidently observed in Alzheimer's disease, atherosclerosis, type 2 diabetes, metabolic syndrome and, possibly, other associated pathologies. Furthermore, chronic inflammation in peripheral tissues is frequently concomitant to Alzheimer's disease. This is supposedly associated with some common genetic polymorphisms, for example, Apolipoprotein-E ε4 allele carriers with Alzheimer's disease can also develop atherosclerosis. Notably, in the transgenic mice expressing the recombinant mitochondria targeted catalase, that removes hydrogen peroxide from mitochondria, demonstrates the significant pathology amelioration and health improvements. In addition, the beneficial effects of some natural products from the xanthophyll family, astaxanthin and fucoxanthin, which are able to target the reactive oxygen species at cellular or mitochondrial membranes, have been demonstrated in both animal and human studies. We propose that the normalization of mitochondrial functions could play a key role in the treatment of neurodegenerative disorders and other noncommunicable diseases associated with chronic inflammation in ageing. Furthermore, some prospective drugs based on mitochondria targeted catalase or xanthophylls could be used as an effective treatment of these pathologies, especially at early stages of their development.

Shifting the Paradigm: The Dress-COV Telegram Bot as a Tool for Participatory Medicine

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic management is limited by great uncertainty, for both health systems and citizens. Facing this information gap requires a paradigm shift from traditional approaches to healthcare to the participatory model of improving health. This work describes the design and function of the Doing Risk sElf-assessment and Social health Support for COVID (Dress-COV) system. It aims to establish a lasting link between the user and the tool; thus, enabling modeling of the data to assess individual risk of infection, or developing complications, to improve the individual's self-empowerment. The system uses bot technology of the Telegram application. The risk assessment includes the collection of user responses and the modeling of data by machine learning models, with increasing appropriateness based on the number of users who join the system. The main results reflect: (a) the individual's compliance with the tool; (b) the security and versatility of the architecture; (c) support and promotion of self-management of behavior to accommodate surveillance system delays; (d) the potential to support territorial health providers, e.g., the daily efforts of general practitioners (during this pandemic, as well as in their routine practices). These results are unique to Dress-COV and distinguish our system from classical surveillance applications.

Implementation of interprofessional team-based care: A cross-case analysis

Two out of five Canadians have at least one chronic disease and four out of five are at risk of developing a chronic disease. Successful disease management relies on interprofessional team-based approaches, yet lack of purposeful cultivation and patient engagement has led to systematic inefficiencies. Two primary care teams in Southwestern Ontario implementing interprofessional chronic care programs for patients with chronic obstructive pulmonary disease were compared. A mixed-methods cross-case analysis was conducted including interviews, focus groups, observations and document analysis. Cases (n = 2) were chosen based on intrinsic and unique value. Participants (n = 46) were sampled using a combination of purposive and multi-level sampling. Data was analyzed using an iterative process; inductive coding was used to gain a sense of context followed by a deductive cross-case analysis to compare and contrast themes across sites. Kompier's five-step framework was used to assess factors contributing to successful implementation and to provide insight into interactions between teams, providers and patients. Both cases satisfied all five factors (systemic and gradual approach, identification of risk factors, theory-driven, participatory approach and sustained committed support). However, one case was more successful at fully implementing their model, attributed to a flexible implementation, plans to mitigate risks, theory use, a supportive team and continued buy-in from leadership. By better understanding key facilitators and barriers, we can support the implementation of chronic disease management programs, foster sustainability of high-performing interprofessional teams, and engage patients in the development and maintenance of team-based chronic disease management.

Risk factors for depression in patients with chronic obstructive pulmonary disease

BACKGROUND

The prevalence of depression in patients with chronic obstructive pulmonary disease (COPD) ranges from 10% to 42%, but the diagnosis of depression in patients with COPD is often unrecognized and untreated. Unrecognized depression has major implications for compliance with medical treatment, prolonged lengths of stay, increased frequency of hospital admissions, and increased consultations with primary care physicians. Many studies have attempted to identify risk factors for progression, prognosis and response to therapy in patients with depression. However, few studies have examined the risk factors for depression in patients with COPD, and some results remain controversial.

AIM

To identify the potential risk factors to define patients with COPD who are at “high risk” of depression.

METHODS

The clinical data of 293 patients with COPD were reviewed from January 2017 to December 2018. The correlations between demographics, clinical characteristics and depression were analyzed. The risk factors for depression in patients with COPD were identified by multivariate logistic regression analysis. The cutoff value, sensitivity and specificity of the independent correlation factors were calculated with a receiver operating characteristic curve.

RESULTS

Of the 293 patients included, 65 (22.18%) individuals were identified to have depression. Significant differences were detected between patients with and without depression in terms of body mass index (BMI), forced expiratory volume in 1 s (FEV1), and COPD assessment test (CAT) score (all P < 0.05). Low BMI, low FEV1, and high CAT were independent risk factors for depression in patients with COPD and the cutoff values of BMI, FEV1, and CAT scores were 21.373 kg/m², 0.855 L and 12.5, respectively.

CONCLUSION

Low BMI, low FEV1, and high CAT score were identified as independent risk factors for depression in patients with COPD.

Aligning the Good Practice MASK With the Objectives of the European Innovation Partnership on Active and Healthy Ageing

The reference sites of the European Innovation Partnership on Active and Healthy Ageing (EIP on AHA) were renewed in 2019. The DG Santé good practice Mobile Airways Sentinel networK was reviewed to meet the objectives of the EIP on AHA. It included 1) Management of care process, 2) Blueprint of digital transformation, 3) EIP on AHA, innovation to market, 4) Community for monitoring and assessment framework, 5) Political, organizational, technological and financial readiness, 6) Contributing to European co-operation and transferability, 7) Delivering evidence of impact against the triple win approach, 8) Contribution to the European Digital Transformation of Health and Care and 9) scale of demonstration and deployment of innovation.

Identifying Frameworks for Validation and Monitoring of Consensual Behavioral Intervention Technologies: Narrative Review

BACKGROUND

Changing health behaviors, such as smoking, unhealthy eating, inactivity, and alcohol abuse, may have a greater impact on population health than any curative strategy. One of the suggested strategies is the use of behavioral intervention technologies (BITs). They open up new opportunities in the area of prevention and therapy and have begun to show benefits in the durable change of health behaviors in patients or those at risk. A consensual and international paradigm was adopted by health authorities for drugs 50 years ago. It guides their development from research units to their authorization and surveillance. BITs' generalization brings into question their upstream evaluation before being placed on the market and their downstream monitoring once on the market; this is especially the case in view of the marketing information provided by manufacturers and the scarcity and methodological limits of scientific studies on these tools.

OBJECTIVE

This study aims to identify and categorize the frameworks for the validation and monitoring of BITs proposed in the literature.

METHODS

We conducted a narrative literature review using MEDLINE, PsycINFO, and Web of Science. The review items included the following: name, publication year, name of the creator (ie, first author), country, funding organization, health focus, target group, and design (ie, linear, iterative, evolutive, and/or concurrent). The frameworks were then categorized based on (1) translational research thanks to a continuum of steps and (2) the three paradigms that may have inspired the frameworks: biomedical, engineering, and/or behavioral.

RESULTS

We identified 46 frameworks besides the classic US Food and Drug Administration (FDA) five-phase drug development model. A total of 57% (26/46) of frameworks were created in the 2010s and 61% (28/46) involved the final user in an early and systematic way. A total of 4% (2/46) of frameworks had a linear-only sequence of their phases, 37% (17/46) had a linear and iterative structure, 33% (15/46) added an evolutive structure, and 24% (11/46) were associated with a parallel process. Only 12 out of 46 (26%) frameworks covered the continuum of steps and 12 (26%) relied on the three paradigms.

CONCLUSIONS

To date, 46 frameworks of BIT validation and surveillance coexist, besides the classic FDA five-phase drug development model, without the predominance of one of them or convergence in a consensual model. Their number has increased exponentially in the last three decades. Three dangerous scenarios are possible: (1) anarchic continuous development of BITs that depend on companies amalgamating health benefits and usability (ie, user experience, data security, and ergonomics) and limiting implementation to several countries; (2) the movement toward the type of framework for drug evaluation centered on establishing its effectiveness before marketing authorization to guarantee its safety for users, which is heavy and costly; and (3) the implementation of a framework reliant on big data analysis based on a posteriori research and an autoregulation of a market, but that does not address the safety risk for the health user, as the market will not regulate safety or efficacy issues. This paper recommends convergence toward an international validation and surveillance framework based on the specificities of BITs, not equivalent to medical devices, to guarantee their effectiveness and safety for users.

Next-generation ARIA care pathways for rhinitis and asthma: a model for multimorbid chronic diseases

BACKGROUND

In all societies, the burden and cost of allergic and chronic respiratory diseases are increasing rapidly. Most economies are struggling to deliver modern health care effectively. There is a need to support the transformation of the health care system into integrated care with organizational health literacy.

MAIN BODY

As an example for chronic disease care, MASK (Mobile Airways Sentinel NetworK), a new project of the ARIA (Allergic Rhinitis and its Impact on Asthma) initiative, and POLLAR (Impact of Air POLLution on Asthma and Rhinitis, EIT Health), in collaboration with professional and patient organizations in the field of allergy and airway diseases, are proposing real-life ICPs centred around the patient with rhinitis, and using mHealth to monitor environmental exposure. Three aspects of care pathways are being developed: (i) Patient participation, health literacy and self-care through technology-assisted "patient activation", (ii) Implementation of care pathways by pharmacists and (iii) Next-generation guidelines assessing the recommendations of GRADE guidelines in rhinitis and asthma using real-world evidence (RWE) obtained through mobile technology. The EU and global political agendas are of great importance in supporting the digital transformation of health and care, and MASK has been recognized by DG Santé as a Good Practice in the field of digitally-enabled, integrated, person-centred care.

CONCLUSION

In 20 years, ARIA has considerably evolved from the first multimorbidity guideline in respiratory diseases to the digital transformation of health and care with a strong political involvement.

Sleep disturbance in patients with mild-moderate chronic obstructive pulmonary disease

INTRODUCTION

Although sleep problems have an important impact on daily life and health outcome measures in patients with chronic obstructive pulmonary disease (COPD), patterns of sleep disturbance in patients with mild-moderate COPD remain unknown.

OBJECTIVE

The aim of this study was to investigate patterns of sleep disturbance and factors associated with sleep impairment in patients with mild-moderate COPD.

METHODS

This prospective cross-sectional study enrolled 148 male patients with COPD. At enrolment, all patients completed a disease-specific sleep measure, the COPD and Asthma Sleep Impact Scale (CASIS) to assess sleep impairment. Health-related quality of life (HRQL) was measured using the St. George's Respiratory Questionnaire (SGRQ) and the 36-item Short-Form health survey (SF-36). Anxiety and depression status were assessed using the Hospital Anxiety and Depression Scale (HADS). Self-efficacy was measured by the COPD Self-Efficacy Scale (CSES).

RESULTS

The mean age of the subjects was 67 years [standard deviation (SD) = 8.11]. The proportion of patients who indicated that they had a bad night's sleep was 33.1%. Univariated analysis showed that CASIS score was significantly correlated with the modified Medical Research Council (mMRC) dyspnea scale, SGRQ, SF-36 Physical Component Summary, SF-36 Mental Component Summary, HADS-anxiety, HADS-depression and CSES scores (all P < 0.05). In a multivariate analysis, SGRQ and CSES were independently associated with CASIS score.

CONCLUSION

Poorer HRQL and lower self-efficacy are significantly associated with sleep disturbance and perhaps may be improved by better sleep quality. Future research is required to enhance sleep quality in patients with mild-moderate COPD.

Identification of CD4+ T cell biomarkers for predicting the response of patients with relapsing‑remitting multiple sclerosis to natalizumab treatment

Multiple sclerosis (MS) is a chronic inflammatory disease of the central nervous system of autoimmune etiopathogenesis, and is characterized by various neurological symptoms. Glatiramer acetate and interferon-β are administered as first-line treatments for this disease. In non-responsive patients, several second-line therapies are available, including natalizumab; however, a percentage of MS patients does not respond, or respond partially. Therefore, it is of the utmost importance to develop a diagnostic test for the prediction of drug response in patients suffering from complex diseases, such as MS, where several therapeutic options are already available. By a machine learning approach, the UnCorrelated Shrunken Centroid algorithm was applied to identify a subset of genes of CD4⁺ T cells that may predict the pharmacological response of relapsing-remitting MS patients to natalizumab, before the initiation of therapy. The results from the present study may provide a basis for the design of personalized therapeutic strategies for patients with MS.

The effectiveness of integrative healthcare for chronic disease: A systematic review

BACKGROUND

The past few decades have witnessed a surge in consumer, clinician and academic interest in the field of integrative healthcare (IHC). Yet, there is still uncertainty regarding the effectiveness of IHC for complex, long-term health conditions.

OBJECTIVE

To assess the effectiveness of IHC for the management of any chronic health condition.

METHODS

Seven databases and four clinical trial registries were searched from inception through to May 2018 for comparative/controlled clinical trials investigating the effectiveness of IHC for any chronic disease, and assessing any outcome. Risk of bias was assessed using the Cochrane Collaboration Risk of Bias tool.

RESULTS

The search yielded 6,926 results. Eight studies met the inclusion criteria. All studies had at least three design features that carried an uncertain/high risk of bias. Differences in physiological, psychological and functional outcomes, and quality of life between patients receiving IHC and patients receiving conventional/usual care were varied and inconsistent. Changes in patient satisfaction with care were inconclusive. No studies reported the effectiveness of IHC on workforce- or administration-related parameters. Evidence from one trial suggested IHC may be more cost-effective than conventional care.

CONCLUSIONS

The findings indicate some promising effects for the use of IHC to manage chronic disease. However, the uncertain/high risk of bias across multiple domains, diverse and inconsistent findings, and heterogeneity of outcome measures and study populations prevents firm conclusions from being reached. Along with conducting further well-designed, long-term studies in this field, there is a need to ensure interventions closely align with the definition/principles of IHC.

Disentangling the heterogeneity of allergic respiratory diseases by latent class analysis reveals novel phenotypes

BACKGROUND

Refined phenotyping of allergic diseases may unravel novel phenotypes. Conjunctivitis as an independent disorder has never been approached.

AIM

To identify distinct classes of allergic respiratory diseases using latent class analysis (LCA) and distinguish each class using classification and regression tree (CART) analysis.

METHODS

Seven hundred and twenty-eight adults from the Portuguese general population study ICAR had a structured medical interview combined with blood collection, skin prick tests, spirometry with bronchodilation, and exhaled nitric oxide. LCA was applied to 19 variables. The CART algorithm selected the most likely variables distinguishing LCA-classes.

RESULTS

A six-class model was obtained. Class 1 (25%): nonallergic participants without bronchial or ocular symptoms. Classes 2 (22%) and 3 (11%): nasal and ocular (low levels) symptoms without nasal impairment, monosensitized (Class 2) or polysensitized (Class 3). Class 4 (13%): polysensitized participants with high levels of nasal and ocular symptoms, and nasal impairment. Classes 5 (16%) and 6 (14%): high level of nasal, bronchial and ocular symptoms with nasal impairment (non-allergic or polysensitized, respectively). Participants in classes 5 and 6 had more bronchial exacerbations and unscheduled medical visits (P < 0.001). Ocular symptoms were significantly higher in classes with nasal impairment, compared to those without impairment (P < 0.001) or no nasal symptom (P < 0.001). CART highlighted ocular symptoms as the most relevant variable in distinguishing LCA-classes.

CONCLUSION

Novel severe phenotypes of participants with co-occurrence of ocular, nasal and bronchial symptoms, and exacerbation-prone were identified. The tree algorithm showed the importance of the ocular symptoms in the expression of allergic diseases phenotypes.

Comparison of hypothesis- and data-driven asthma phenotypes in NHANES 2007-2012: the importance of comprehensive data availability

Background

Half of the adults with current asthma among the US National Health and Nutrition Examination Survey (NHANES) participants could be classified in more than one hypothesis-driven phenotype. A data-driven approach applied to the same subjects may allow a more useful classification compared to the hypothesis-driven one.

Aim

To compare previously defined hypothesis-driven with newly derived data-driven asthma phenotypes, identified by latent class analysis (LCA), in adults with current asthma from NHANES 2007–2012.

Methods

Adults (≥ 18 years) with current asthma from the NHANES were included (n = 1059). LCA included variables commonly used to subdivide asthma. LCA models were derived independently according to age groups: < 40 and ≥ 40 years old.

Results

Two data-driven phenotypes were identified among adults with current asthma, for both age groups. The proportions of the hypothesis-driven phenotypes were similar among the two data-driven phenotypes (p > 0.05). Class A < 40 years (n = 285; 75%) and Class A ≥ 40 years (n = 462; 73%), respectively, were characterized by a predominance of highly symptomatic asthma subjects with poor lung function, compared to Class B < 40 years (n = 94; 25%) and Class B ≥ 40 years (n = 170; 27%). Inflammatory biomarkers, smoking status, presence of obesity and hay fever did not markedly differ between the phenotypes.

Conclusion

Both data- and hypothesis-driven approaches using clinical and physiological variables commonly used to characterize asthma are suboptimal to identify asthma phenotypes among adults from the general population. Further studies based on more comprehensive disease features are required to identify asthma phenotypes in population-based studies.

Electronic supplementary material

The online version of this article (10.1186/s13601-019-0258-7) contains supplementary material, which is available to authorized users.

Accessibility of Primary, Specialist, and Allied Health Services for Aboriginal People Living in Rural and Remote Communities: Protocol for a Mixed-Methods Study

Background

Primary, specialist, and allied health services can assist in providing equitable access in rural and remote areas, where higher proportions of Aboriginal and Torres Strait Islander people (Aboriginal Australians) reside, to overcome the high rates of chronic diseases experienced by this population group. Little is currently known about the location and frequency of services and the extent to which providers believe delivery is occurring in a sustained and coordinated manner.

Objective

The objective of this study will be to determine the availability, accessibility, and level of coordination of a range of community-based health care services to Aboriginal people and identify potential barriers in accessing health care services from the perspectives of the health service providers.

Methods

This mixed-methods study will take place in 3 deidentified communities in New South Wales selected for their high population of Aboriginal people and geographical representation of location type (coastal, rural, and border). The study is designed and will be conducted in collaboration with the communities, Aboriginal Community Controlled Health Services (ACCHSs), and other local health services. Data collection will involve face-to-face and telephone interviews with participants who are health and community professionals and stakeholders. Participants will be recruited through snowball sampling and will answer structured, quantitative questions about the availability and accessibility of primary health care, specialist medical and allied health services and qualitative questions about accessing services. Quantitative data analysis will determine the frequency and accessibility of specific services across each community. Thematic and content analysis will identify issues relating to availability, accessibility, and coordination arising from the qualitative data. We will then combine the quantitative and qualitative data using a health ecosystems approach.

Results

We identified 28 stakeholder participants across the ACCHSs for recruitment through snowball sampling (coastal, n=4; rural, n=12; and border, n=12) for data collection. The project was funded in 2017, and enrolment was completed in 2017. Data analysis is currently under way, and the first results are expected to be submitted for publication in 2019.

Conclusions

The study will give an indication of the scope and level of coordination of primary, specialist, and allied health services in rural communities with high Aboriginal populations from the perspectives of service providers from those communities. Identification of factors affecting the availability, accessibility, and coordination of services can assist ways of developing and implementing culturally sensitive service delivery. These findings could inform recommendations for the provision of health services for Aboriginal people in rural and remote settings. The study will also contribute to the broader literature of rural and remote health service provision.

International Registered Report Identifier (IRRID)

DERR1-10.2196/11471

Vilnius Declaration on chronic respiratory diseases: multisectoral care pathways embedding guided self-management, mHealth and air pollution in chronic respiratory diseases

Background

Over 1 billion people suffer from chronic respiratory diseases such as asthma, COPD, rhinitis and rhinosinusitis. They cause an enormous burden and are considered as major non-communicable diseases. Many patients are still uncontrolled and the cost of inaction is unacceptable. A meeting was held in Vilnius, Lithuania (March 23, 2018) under the patronage of the Ministry of Health and several scientific societies to propose multisectoral care pathways embedding guided self-management, mHealth and air pollution in selected chronic respiratory diseases (rhinitis, chronic rhinosinusitis, asthma and COPD). The meeting resulted in the Vilnius Declaration that was developed by the participants of the EU Summit on chronic respiratory diseases under the leadership of Euforea.

Conclusion

The Vilnius Declaration represents an important step for the fight against air pollution in chronic respiratory diseases globally and has a clear strategic relevance with regard to the EU Health Strategy as it will bring added value to the existing public health knowledge.

The Utilization of Autoantibodies in Approaches to Precision Health

Precision health (PH) applied to autoimmune disease will need paradigm shifts in the use and application of autoantibodies and other biomarkers. For example, autoantibodies combined with other multi-analyte “omic” profiles will form the basis of disease prediction allowing for earlier intervention linked to disease prevention strategies, as well as earlier, effective and personalized interventions for established disease. As medical intervention moves to disease prediction and a model of “intent to PREVENT,” diagnostics will include an early symptom/risk-based, as opposed to a disease-based approach. Newer diagnostic platforms that utilize emerging megatrends such as deep learning and artificial intelligence and close the gaps in autoantibody diagnostics will benefit from paradigm shifts thereby facilitating the PH agenda.

Medical Systems Biology

The aim of this volume is to encourage the use of systems-level methodologies to contribute to the improvement of human-health . We intend to motivate biomedical researchers to complement their current theoretical and empirical practice with up-to-date systems biology conceptual approaches. Our perspective is based on the deep understanding of the key biomolecular regulatory mechanisms that underlie health, as well as the emergence and progression of human-disease . We strongly believe that the contemporary systems biology perspective opens the door to the effective development of novel methodologies to the improvement of prevention . This requires a deeper and integrative understanding of the involved underlying systems-level mechanisms. In order to explain our proposal in a simple way, in this chapter we privilege the conceptual exposition of our chosen framework over formal considerations. The formal exposition of our proposal will be expanded and discussed later in the next chapters.

Mental disorders in chronic obstructive pulmonary diseases

PURPOSE

Anxiety and depression are underdiagnosed in chronic obstructive pulmonary disease (COPD) patients. Few studies have tried to identify their association with hospitalization (severe exacerbation). The objective of this study was to determine whether the anxiety/depression was associated with severe exacerbation.

DESIGN AND METHODS

A prospective cohort study, based on a sample of 512 patients diagnosed with COPD originating from primary care in a rural area in Lleida (Spain) and monitored between November 1, 2012 and October 31, 2014. For each patient, variables of interest were recorded; they were administered the HADS (Hospital Anxiety and Depression Scale) test to determine the possible presence of anxiety/depression, and its association with severe exacerbation was analyzed using a logistic regression model.

FINDINGS

Initially, the prevalence of anxiety/depression was 15.6%. The incidence of global hospitalization in the first year was 8.2% and 11.3% in the second year. In patients with anxiety/depression, it increased to 17.5% in the first year and 18.8% in the second year. In the multivariate regression model, the diagnosis of anxiety/depression almost doubled the risk of hospitalization (OR = 1.94) (p < .041).

PRACTICE IMPLICATIONS

Anxiety and depression are associated with an increased risk of hospitalization. Intervention studies are needed to evaluate the effects of anxiety/depression in the hospitalization.

Depression is a major determinant of both disease-specific and generic health-related quality of life in people with severe COPD

The quality of life of patients with chronic obstructive pulmonary disease (COPD) decreases significantly as the disease progresses; those with severe COPD are affected most. This article investigates predictors of the disease-specific and generic health-related quality of life (HRQL) in patients with severe COPD. This multicentre prospective cross-sectional study enrolled 80 patients with severe COPD. At enrolment, all patients completed a disease-specific instrument, the St George’s Respiratory Questionnaire (SGRQ), and a generic instrument, the Short Form 36 Health Survey Questionnaire (SF-36). The data were analyzed by Pearson’s correlation and multiple linear regression. The mean age of the patients was 66 ± 8 years; 93% were males. The SGRQ and SF-36 scores were not influenced by age or sex. Depression, dyspnea, the number of exacerbations, and exercise capacity significantly predicted the total SGRQ score (p < 0.05). Depression was the strongest determinant of the total SGRQ score. The SF-36 physical component summary scores were related to depression, dyspnea, and the number of exacerbations (p < 0.05). In comparison, the SF-36 mental component summary scores were related to depression and anxiety (p < 0.05). Depression is a significant determinant of both the disease-specific and generic HRQL in patients with severe COPD. Screening and early intervention for depression in patients with severe COPD could improve the HRQL.

Child maltreatment and adult multimorbidity: results from the Canadian Community Health Survey

OBJECTIVES

This study investigated associations between three types of child maltreatment (exposure to intimate partner violence, sexual, and physical abuse) and multimorbidity (chronic physical conditions, pain conditions, and mental disorders) in adults.

METHODS

Multinomial logistic regression was used to analyze weighted data from the 2012 Canadian Community Health Survey (CCHS - MH 2012), a representative population sample (N = 23,846) of respondents ages 18+.

RESULTS

All three subtypes of child maltreatment independently predicted increased odds of experiencing multimorbidity as an adult, while adjusting for covariates (adjusted odds ratios ranged from 1.34 (95% CI = 1.00, 1.80) to 4.87 (95% CI = 2.75, 8.63)). A dose-response relationship between the number of child maltreatment subtypes and risk for multimorbidity was also observed (adjusted odds ratios ranged from 1.38 (95% CI = 1.11, 1.73) to 10.96 (95% CI = 6.12, 19.64)).

CONCLUSION

The current results highlight the importance of considering a range of childhood adversities and suggest that public health approaches that aim to decrease the prevalence and severity of child maltreatment have the potential to ameliorate adult multimorbidities. Future research is encouraged to investigate these issues using longitudinal population-level data.

Having concomitant asthma phenotypes is common and independently relates to poor lung function in NHANES 2007-2012

Background

Evidence for distinct asthma phenotypes and their overlap is becoming increasingly relevant to identify personalized and targeted therapeutic strategies. In this study, we aimed to describe the overlap of five commonly reported asthma phenotypes in US adults with current asthma and assess its association with asthma outcomes.

Methods

Data from the National Health and Nutrition Examination Surveys (NHANES) 2007-2012 were used (n = 30,442). Adults with current asthma were selected. Asthma phenotypes were: B-Eos-high [if blood eosinophils (B-Eos) ≥ 300/mm³]; FeNO-high (FeNO ≥ 35 ppb); B-Eos&FeNO-low (B-Eos < 150/mm³ and FeNO < 20 ppb); asthma with obesity (AwObesity) (BMI ≥ 30 kg/m²); and asthma with concurrent COPD. Data were weighted for the US population and analyses were stratified by age (< 40 and ≥ 40 years old).

Results

Of the 18,619 adults included, 1059 (5.6% [95% CI 5.1-5.9]) had current asthma. A substantial overlap was observed both in subjects aged < 40 years (44%) and ≥ 40 years (54%). The more prevalent specific overlaps in both age groups were AwObesity associated with either B-Eos-high (15 and 12%, respectively) or B-Eos&FeNO-low asthma (13 and 11%, respectively). About 14% of the current asthma patients were "non-classified". Regardless of phenotype classification, having concomitant phenotypes was significantly associated with (adjusted OR, 95% CI) ≥ 2 controller medications (2.03, 1.16-3.57), and FEV₁ < LLN (3.21, 1.74-5.94), adjusted for confounding variables.

Conclusions

A prevalent overlap of commonly reported asthma phenotypes was observed among asthma patients from the general population, with implications for objective asthma outcomes. A broader approach may be required to better characterize asthma patients and prevent poor asthma outcomes.

Difficult-to-control hypertension: identification of clinical predictors and use of ICT-based integrated care to facilitate blood pressure control

Difficult-to-control (DTC) hypertension represents a burden in real life that can be partially solved through identification of the characteristics of clinical patterns and tailoring antihypertensive strategies, including ICT-enabled integrated care (ICT-IC). In the quest for clinical predictors of DTC hypertension, we screened 482 hypertensive patients who were consecutively referred to the departmental hypertension clinic. Following a data quality check, patients were divided into controlled (C, 49.37%) and uncontrolled (UC, 50.63%) groups based on their systolic blood pressure (BP) at follow-up. We then performed statistical analysis on the demographic, clinical, laboratory, and ultrasound data and observed that older age, female sex, higher BP levels, and a family history of hypertension were predictors of DTC hypertension. We then developed a pilot service of ICT-IC, including weekly home visits by nurses and patient education on self-monitoring of BP, heart rate, body weight, and oxygen saturation using 3G-connected devices. Self-monitored data were transmitted to the hospital servers on the electronic chart of the patient for remote assessment by the hospital hypertension specialists. A total of 20 UC patients (M/F = 10/10; age: 72.04 ± 2.17 years) were enrolled to verify the efficacy of BP control without changes in medical treatment. After 1 month of the ICT-IC program, BP was reduced both at the office assessment (systolic BP (SBP): 162.40 ± 2.23 mm Hg, beginning of the program vs. 138.20 ± 4.26 mm Hg at 1 month, p < 0.01) and at home (SBP: 149.83 ± 3.44, beginning of the program vs. 134.16 ± 1.67 mm Hg at 1 month, p < 0.01). We concluded that DTC hypertension can be predicted based on the clinical characteristics at the first visit. For these patients, ICT-IC is a feasible therapeutic strategy to achieve BP control.

Food allergy phenotypes: The key to personalized therapy

Food allergies (FAs) are of increasing public health concern and are characterized by a large spectrum of diseases. Their diversity is well known for immunologic pathways (IgE, non-IgE-mediated FAs) and natural history. Many other factors and patient characteristics are involved including type of food, exposure route, allergic comorbidities, gender, racial and ethnic backgrounds, cofactors and health conditions. Food allergen components and sensitization profiles are also involved in FA phenotypes. A new approach to chronic disorders based on the identification of phenotypes through extensive knowledge of all the complex components is also applicable to FAs and could lead towards integrative care management. Diagnostic biomarkers for FAs are emerging which also contribute to better care modalities. The aim of this article was to highlight current knowledge regarding the phenotypic diversity of FA. This review will focus on IgE-mediated FAs and how identifying phenotypes may help to better understand the pathophysiological complexity, improve diagnosis and lead to personalized treatment strategies.

Models and Data Sources Used in Systems Medicine

Background: Systems medicine is a new approach for the development and selection of treatment strategies for patients with complex diseases. It is often referred to as the application of systems biology methods for decision making in patient care. For systems medicine computer applications, many different data sources have to be integrated and included into models. This is a challenging task for Medical Informatics since the approach exceeds traditional systems like Electronic Health Records. To prioritize research activities for systems medicine applications, it is necessary to get an overview over modelling methods and data sources already used in this field.

Objectives: We performed a systematic literature review with the objective to capture current use of 1) modelling methods and 2) data sources in systems medicine related research projects.

Methods: We queried the MEDLINE and ScienceDirect databases for papers associated with the search term systems medicine and related terms. Papers were screened and assessed in full text in a two-step process according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement guidelines.

Results: The queries returned 698 articles of which 34 papers were finally included into the study. A multitude of modelling approaches such as machine learning and network analysis was identified and classified. Since these approaches are also used in other domains, no methods specific for systems medicine could be identified. Omics data are the most widely used data types followed by clinical data. Most studies only include a rather limited number of data sources.

Conclusions: Currently, many different modelling approaches are used in systems medicine. Thus, highly flexible modular solutions are necessary for systems medicine clinical applications. However, the number of data sources included into the models is limited and most projects currently focus on prognosis. To leverage the potential of systems medicine further, it will be necessary to focus on treatment strategies for patients and consider a broader range of data.

Cystic fibrosis in the era of precision medicine

The treatment of people with cystic fibrosis (CF) has been transformed by the availability of drugs that target the basic chloride defect in the disease. The use of drugs that target specific molecular defects embodies the goals of precision medicine, which incorporate preventive and therapeutic strategies and takes into account differences among individuals. However, the entirety of CF care, from diagnosis to understanding the clinical phenotype and developing a therapeutic strategy, depends on taking into account individual characteristics to achieve optimal outcomes. Future therapies are likely to be even more individualized ushering in a new era of precision medicine.

Applying systems biology to biomedical research and health care: a précising definition of systems medicine

BACKGROUND

Systems medicine has become a key word in biomedical research. Although it is often referred to as P4-(predictive, preventive, personalized and participatory)-medicine, it still lacks a clear definition and is open to interpretation. This conceptual lack of clarity complicates the scientific and public discourse on chances, risks and limits of Systems Medicine and may lead to unfounded hopes. Against this background, our goal was to develop a sufficiently precise and widely acceptable definition of Systems Medicine.

METHODS

In a first step, PubMed was searched using the keyword "systems medicine". A data extraction tabloid was developed putting forward a means/ends-division. Full-texts of articles containing Systems Medicine in title or abstract were screened for definitions. Definitions were extracted; their semantic elements were assigned as either means or ends. To reduce complexity of the resulting list, summary categories were developed inductively. In a second step, we applied six criteria for adequate definitions (necessity, non-circularity, non-redundancy, consistency, non-vagueness, and coherence) to these categories to derive a so-called précising definition of Systems Medicine.

RESULTS

We identified 185 articles containing the term Systems Medicine in title or abstract. 67 contained at least one definition of Systems Medicine. In 98 definitions, we found 114 means and 132 ends. From these we derived the précising definition: Systems Medicine is an approach seeking to improve medical research (i.e. the understanding of complex processes occurring in diseases, pathologies and health states as well as innovative approaches to drug discovery) and health care (i.e. prevention, prediction, diagnosis and treatment) through stratification by means of Systems Biology (i.e. data integration, modeling, experimentation and bioinformatics). Our study also revealed the visionary character of Systems Medicine.

CONCLUSIONS

Our insights, on the one hand, allow for a realistic identification of actual ethical as well as legal issues arising in the context of Systems Medicine and, in consequence, for a realistic debate of questions concerning its matter and (future) handling. On the other hand, they help avoiding unfounded hopes and unrealistic expectations. This especially holds for goals like improving patient participation which are intensely debated in the context of Systems Medicine, however not implied in the concept.

Effectiveness of an Integrated Approach to HIV and Hypertension Care in Rural South Africa: Controlled Interrupted Time-Series Analysis

Supplemental Digital Content is Available in the Text.

Background:

South Africa faces a dual burden of HIV/AIDS and noncommunicable diseases. In 2011, a pilot integrated chronic disease management (ICDM) model was introduced by the National Health Department into selected primary health care (PHC) facilities. The objective of this study was to assess the effectiveness of the ICDM model in controlling patients' CD4 counts (>350 cells/mm³) and blood pressure [BP (<140/90 mm Hg)] in PHC facilities in the Bushbuckridge municipality, South Africa.

Methods:

A controlled interrupted time-series study was conducted using the data from patients' clinical records collected multiple times before and after the ICDM model was initiated in PHC facilities in Bushbuckridge. Patients ≥18 years were recruited by proportionate sampling from the pilot (n = 435) and comparing (n = 443) PHC facilities from 2011 to 2013. Health outcomes for patients were retrieved from facility records for 30 months. We performed controlled segmented regression to model the monthly averages of individuals' propensity scores using autoregressive moving average model at 5% significance level.

Results:

The pilot facilities had 6% greater likelihood of controlling patients' CD4 counts than the comparison facilities (coefficient = 0.057; 95% confidence interval: 0.056 to 0.058; P < 0.001). Compared with the comparison facilities, the pilot facilities had 1.0% greater likelihood of controlling patients' BP (coefficient = 0.010; 95% confidence interval: 0.003 to 0.016; P = 0.002).

Conclusions:

Application of the model had a small effect in controlling patients' CD4 counts and BP, but showed no overall clinical benefit for the patients; hence, the need to more extensively leverage the HIV program for hypertension treatment.

Systems Metabolomics for Prediction of Metabolic Syndrome

The evolution of human health is a continuum of transitions, involving multifaceted processes at multiple levels, and there is an urgent need for integrative biomarkers that can characterize and predict progression toward disease development. The objective of this work was to perform a systems metabolomics approach to predict metabolic syndrome (MetS) development. A case-control design was used within the French occupational GAZEL cohort (n = 112 males: discovery study; n = 94: replication/validation study). Our integrative strategy was to combine untargeted metabolomics with clinical, sociodemographic, and food habit parameters to describe early phenotypes and build multidimensional predictive models. Different models were built from the discriminant variables, and prediction performances were optimized either when reducing the number of metabolites used or when keeping the associated signature. We illustrated that a selected reduced metabolic profile was able to reveal subtle phenotypic differences 5 years before MetS occurrence. Moreover, resulting metabolomic markers, when combined with clinical characteristics, allowed improving the disease development prediction. The validation study showed that this predictive performance was specific to the MetS component. This work also demonstrates the interest of such an approach to discover subphenotypes that will need further characterization to be able to shift to molecular reclassification and targeting of MetS.

Recent progress in genetics, epigenetics and metagenomics unveils the pathophysiology of human obesity

In high-, middle- and low-income countries, the rising prevalence of obesity is the underlying cause of numerous health complications and increased mortality. Being a complex and heritable disorder, obesity results from the interplay between genetic susceptibility, epigenetics, metagenomics and the environment. Attempts at understanding the genetic basis of obesity have identified numerous genes associated with syndromic monogenic, non-syndromic monogenic, oligogenic and polygenic obesity. The genetics of leanness are also considered relevant as it mirrors some of obesity's aetiologies. In this report, we summarize ten genetically elucidated obesity syndromes, some of which are involved in ciliary functioning. We comprehensively review 11 monogenic obesity genes identified to date and their role in energy maintenance as part of the leptin-melanocortin pathway. With the emergence of genome-wide association studies over the last decade, 227 genetic variants involved in different biological pathways (central nervous system, food sensing and digestion, adipocyte differentiation, insulin signalling, lipid metabolism, muscle and liver biology, gut microbiota) have been associated with polygenic obesity. Advances in obligatory and facilitated epigenetic variation, and gene-environment interaction studies have partly accounted for the missing heritability of obesity and provided additional insight into its aetiology. The role of gut microbiota in obesity pathophysiology, as well as the 12 genes associated with lipodystrophies is discussed. Furthermore, in an attempt to improve future studies and merge the gap between research and clinical practice, we provide suggestions on how high-throughput '-omic' data can be integrated in order to get closer to the new age of personalized medicine.

Towards a systems approach for chronic diseases, based on health state modeling

Rising pressure from chronic diseases means that we need to learn how to deal with challenges at a different level, including the use of systems approaches that better connect across fragments, such as disciplines, stakeholders, institutions, and technologies. By learning from progress in leading areas of health innovation (including oncology and AIDS), as well as complementary indications (Alzheimer's disease), I try to extract the most enabling innovation paradigms, and discuss their extension to additional areas of application within a systems approach. To facilitate such work, a Precision, P4 or Systems Medicine platform is proposed, which is centered on the representation of health states that enable the definition of time in the vision to provide the right intervention for the right patient at the right time and dose. Modeling of such health states should allow iterative optimization, as longitudinal human data accumulate. This platform is designed to facilitate the discovery of links between opportunities related to a) the modernization of diagnosis, including the increased use of omics profiling, b) patient-centric approaches enabled by technology convergence, including digital health and connected devices, c) increasing understanding of the pathobiological, clinical and health economic aspects of disease progression stages, d) design of new interventions, including therapies as well as preventive measures, including sequential intervention approaches. Probabilistic Markov models of health states, e.g. those used for health economic analysis, are discussed as a simple starting point for the platform. A path towards extension into other indications, data types and uses is discussed, with a focus on regenerative medicine and relevant pathobiology.

Clinical factors and comorbidities affecting the cost of hospital-treated COPD

PURPOSE

We aimed to assess the effects of comorbidities on COPD costs and to investigate the relationship between comorbidities and clinical variables.

PATIENTS AND METHODS

All patients hospitalized with a diagnosis of COPD exacerbation between January 1, 2014, and December 31, 2014, at all state hospitals of Aydın province, a city located in the western part of Turkey, were included in this study. The costs examined in the study pertained to medications, laboratory tests, hospital stays, and other treatment-related factors, such as consumption of materials, doctor visits, and consultation fees.

RESULTS

A total of 3,095 patients with 5,237 exacerbations (mean age, 71.9±10.5 years; 2,434 males and 661 females) were evaluated. For 880 of the patients (28.9%), or 3,852 of the exacerbations (73.1%), at least one comorbid disease was recorded. The mean cost of each exacerbation was $808.5±1,586, including $325.1±879.9 (40.7%) for hospital stays, $223.1±1,300.9 (27.6%) for medications, $46.3±49.6 (0.9%) for laboratory expenditures, and $214±1,068 (26.5%) for other treatment-related factors, such as consumption of materials, doctor visits, and consultation fees. The cost of each exacerbation was $1,014.9 in patients with at least one comorbidity, whereas it was $233.6 in patients without comorbidity (P<0.001). Age >65 years, female gender, hospitalization in an intensive care unit, invasive or noninvasive mechanical ventilation, and a long duration of hospitalization were all found to be significant factors in increasing total costs during the exacerbations requiring hospitalization (P<0.05 for all).

CONCLUSION

Comorbidities have an important role in the total costs of acute exacerbations of COPD. Strategies for the prevention, diagnosis, and effective management of comorbidities would decrease the overall financial burden associated with acute exacerbations of COPD.