Multimorbidity refers to the coexistence of two or more chronic diseases in one person. Therefore, patients with multimorbidity have multiple and special care needs. However, in practice it is difficult to meet these needs because the organizational processes of current healthcare systems tend to be tailored to a single disease. To improve clinical decision making and patient care in multimorbidity, a radical change in the problem-solving approach to medical research and treatment is needed. In addition to the traditional reductionist approach, we propose interactive research supported by artificial intelligence (AI) and advanced big data analytics. Such research approach, when applied to data routinely collected in healthcare settings, provides an integrated platform for research tasks related to multimorbidity. This may include, for example, prediction, correlation, and classification problems based on multiple interaction factors. However, to realize the idea of this paradigm shift in multimorbidity research, the optimization, standardization, and most importantly, the integration of electronic health data into a common national and international research infrastructure is needed. Ultimately, there is a need for the integration and implementation of efficient AI approaches, particularly deep learning, into clinical routine directly within the workflows of the medical professionals.

BackgroundThis study aimed to identify the clustering of comorbidities, cognitive, and mental factors associated with increased risk of pre-frailty and frailty in patients ≥60 years in a primary healthcare setting in eastern Croatia.Material/MethodsThere were 159 patients included in the cluster analysis who were ≥60 years and who underwent four-month follow-up. The first cluster contained 50 patients, the second cluster contained 74 patients, and the third cluster contained 35 patients. Clinical parameters were identified from electronic health records and patient questionnaires. Laboratory tests, anthropometric measurements, the number of chronic diseases, the number of prescribed medications were recorded. Frailty was determined using the five criteria of Fried’s phenotype model. Levels of anxiety and depression were recorded using the Geriatric Anxiety Scale (GAS) and the Geriatric Depression Scale (GDS), and the Mini-Mental State Examination (MMSE) score assessed cognitive impairment. Logistic regression models were used to identify predictors of frailty and pre-frailty.ResultsThree overlapping clusters of phenotypes predicted frailty, and included obesity (n=50), multimorbidity with mental impairment (n=74), and decline in renal function with cognitive impairment (n=35). The predictors of outcome included increasing age, number of chronic diseases, inflammation, anemia, anxiety, and cognitive impairment, and reduced muscle mass.ConclusionsIn patients ≥60 years in a primary healthcare setting, multimorbidity predictors of pre-frailty and frailty included a decline in cognitive function and renal function.

Cardiovascular disease (CVD) is the leading cause of death in women, although traditionally, it has been considered as a male dominated disease. Chronic inflammation plays a crucial role in the development of insulin resistance, diabetes type 2 and CVD. Since studies on women were scarce, in order to improve diagnosis and treatment of CVD, there is a need to improve understanding of the role of inflammation in the development of CVD in women. The neutrophil-to-lymphocyte ratio (NLR) is an inexpensive and widely available marker of inflammation, and has been studied in cardio-metabolic disorders. There is a paucity of data on sex specific differences in the lifetime course of NLR. Men and women differ to each other in sex hormones and characteristics of immune reaction and the expression of CVD. These factors can determine NLR values and their variations along the life course. In particular, menopause in women is a period associated with profound physiological and hormonal changes, and is coincidental with aging. An emergence of CV risk factors with aging, and age-related changes in the immune system, are factors that are associated with an increase in prevalence of CVD in both sexes. The aim of this review is to comprehend the available evidence on this issue, and to discuss sex specific differences in the lifetime course of NLR in the light of immune and inflammation mechanisms.

Medical doctors and researchers in bio-medicine are increasingly confronted with complex patient data, posing new and difficult analysis challenges. These data are often comprising high-dimensional descriptions of patient conditions and measurements on the success of certain therapies. An important analysis question in such data is to compare and correlate patient conditions and therapy results along with combinations of dimensions. As the number of dimensions is often very large, one needs to map them to a smaller number of relevant dimensions to be more amenable for expert analysis. This is because irrelevant, redundant, and conflicting dimensions can negatively affect effectiveness and efficiency of the analytic process (the so-called curse of dimensionality). However, the possible mappings from high- to low-dimensional spaces are ambiguous. For example, the similarity between patients may change by considering different combinations of relevant dimensions (subspaces). We demonstrate the potential of subspace analysis for the interpretation of high-dimensional medical data. Specifically, we present SubVIS, an interactive tool to visually explore subspace clusters from different perspectives, introduce a novel analysis workflow, and discuss future directions for high-dimensional (medical) data analysis and its visual exploration. We apply the presented workflow to a real-world dataset from the medical domain and show its usefulness with a domain expert evaluation.

Background Physical frailty, cognitive impairment, and symptoms of anxiety and depression frequently co-occur in later life, but, to date, each has been assessed separately. The present study assessed their patterns in primary care patients aged ≥60 years. Material/Methods This cross-sectional study evaluated 263 primary care patients aged ≥60 years in eastern Croatia in 2018. Physical frailty, cognitive impairment, anxiety and depression, were assessed using the Fried phenotypic model, the Mini-Mental State Examination (MMSE), the Geriatric Anxiety Scale (GAS), and the Geriatric Depression Scale (GDS), respectively. Patterns were identified by latent class analysis (LCA), Subjects were assorted by age, level of education, and domains of psychological and cognitive tests to determine clusters. Results Subjects were assorted into four clusters: one cluster of relatively healthy individuals (61.22%), and three pathological clusters, consisting of subjects with mild cognitive impairment (23.95%), cognitive frailty (7.98%), and physical frailty (6.85%). A multivariate, multinomial logistic regression model found that the main determinants of the pathological clusters were increasing age and lower mnestic functions. Lower performance on mnestic tasks was found to significantly determine inclusion in the three pathological clusters. The non-mnestic function, attention, was specifically associated with cognitive impairment, whereas psychological symptoms of anxiety and dysphoria were associated with physical frailty. Conclusions Clustering of physical and cognitive performances, based on combinations of their grades of severity, may be superior to modelling of their respective entities, including the continuity and non-linearity of age-related accumulation of pathologic conditions.