Influence of Age, Gender, Frailty, and Body Mass Index on Serum IL-17A Levels in Mature Type 2 Diabetic Patients.

BACKGROUND The cytokine IL-17A is emerging as a marker of chronic inflammation in cardio-metabolic conditions. This study aimed to identify relevant factors that in older primary care patients with type 2 diabetes (T2D) could influence serum IL-17A concentrations. The results have a potential to improve risk stratification and therapy options for these patients. MATERIAL AND METHODS The study was conducted during a period of 4 months, in 2020, in the south-eastern region of Croatia. Patients from primary health care, diagnosed with T2D (N=170, M: F 75: 95, ≥50 years old), were recruited at their visits. Those with malignant diseases, on chemotherapy or biological therapy, with amputated legs, or at hemodialysis, were excluded. The multinomial regression models were used to determine independent associations of the groups of variables, indicating sociodemographic and clinical characteristics of these patients, with increasing values (quartiles) of serum IL-17A. RESULTS The regression models indicated the frailty index and sex bias are the key modifying factors in associations of other variables with IL-17A serum values. CONCLUSIONS Sex bias and the existence of different frailty phenotypes could be the essential determining factors of the serum IL-17A levels in community-dwelling patients with T2D age 50 years and older. The results support the concept of T2D as a complex disorder.

Lung Ultrasound Reduces Chest X-rays in Postoperative Care after Thoracic Surgery: Is There a Role for Artificial Intelligence?-Systematic Review.

BACKGROUND: Chest X-ray (CXR) remains the standard imaging modality in postoperative care after non-cardiac thoracic surgery. Lung ultrasound (LUS) showed promising results in CXR reduction. The aim of this review was to identify areas where the evaluation of LUS videos by artificial intelligence could improve the implementation of LUS in thoracic surgery. METHODS: A literature review of the replacement of the CXR by LUS after thoracic surgery and the evaluation of LUS videos by artificial intelligence after thoracic surgery was conducted in Medline. RESULTS: Here, eight out of 10 reviewed studies evaluating LUS in CXR reduction showed that LUS can reduce CXR without a negative impact on patient outcome after thoracic surgery. No studies on the evaluation of LUS signs by artificial intelligence after thoracic surgery were found. CONCLUSION: LUS can reduce CXR after thoracic surgery. We presume that artificial intelligence could help increase the LUS accuracy, objectify the LUS findings, shorten the learning curve, and decrease the number of inconclusive results. To confirm this assumption, clinical trials are necessary. This research is funded by the Slovak Research and Development Agency, grant number APVV 20-0232.

Explainability of deep learning models in medical video analysis: a survey.

Deep learning methods have proven to be effective for multiple diagnostic tasks in medicine and have been performing significantly better in comparison to other traditional machine learning methods. However, the black-box nature of deep neural networks has restricted their use in real-world applications, especially in healthcare. Therefore, explainability of the machine learning models, which focuses on providing of the comprehensible explanations of model outputs, may affect the possibility of adoption of such models in clinical use. There are various studies reviewing approaches to explainability in multiple domains. This article provides a review of the current approaches and applications of explainable deep learning for a specific area of medical data analysis-medical video processing tasks. The article introduces the field of explainable AI and summarizes the most important requirements for explainability in medical applications. Subsequently, we provide an overview of existing methods, evaluation metrics and focus more on those that can be applied to analytical tasks involving the processing of video data in the medical domain. Finally we identify some of the open research issues in the analysed area.

A Critical Appraisal of the Diagnostic and Prognostic Utility of the Anti-Inflammatory Marker IL-37 in a Clinical Setting: A Case Study of Patients with Diabetes Type 2.

BACKGROUND: The role of the cytokine interleukin-37 (IL-37) has been recognized in reversing inflammation-mediated metabolic costs. The aim was to evaluate the clinical utility of this cytokine as a diagnostic and prognostic marker in patients with type 2 diabetes (T2D). METHODS: We included 170 older (median: 66 years) individuals with T2D (females: 95) and classified as primary care attenders to assess the association of factors that describe patients with plasma IL-37 levels (expressed as quartiles) using multinomial regression models. We determined the diagnostic ability of IL-37 cut-offs to identify diabetes-related complications or patient subgroups by using Receiver Operating Characteristic analysis (c-statistics). RESULTS: Frailty status was shown to have a suppressive effect on IL-37 circulating levels and a major modifying effect on associations of metabolic and inflammatory factors with IL-37, including the effects of treatments. Situations in which IL-37 reached a clinically significant discriminating ability included the model of IL-37 and C-Reactive Protein in differentiating among diabetic patients with low-normal/high BMI ((<25/≥25 kg/m2), and the model of IL-37 and Thyroid Stimulating Hormone in discriminating between women with/without metabolic syndrome. CONCLUSIONS: The study has revealed limitations in using classical approaches in determining the diagnostic and prognostic utility of the cytokine IL-37 in patients with T2D and lain a foundation for new methodology approaches.

Agent-based models of human response to natural hazards: systematic review of tsunami evacuation.

This systematic review provides a comprehensive overview of tsunami evacuation models. The review covers scientific studies from the last decade (2012-2021) and is explicitly focused on models using an agent-based approach. The PRISMA methodology was used to analyze 171 selected papers, resulting in over 53 studies included in the detailed full-text analysis. This review is divided into two main parts: (1) a descriptive analysis of the presented models (focused on the modeling tools, validation, and software platform used, etc.), and (2) model analysis (e.g., model purpose, types of agents, input and output data, and modeled area). Special attention was given to the features of these models specifically associated with an agent-based approach. The results lead to the conclusion that the research domain of agent-based tsunami evacuation models is quite narrow and specialized, with a high degree of variability in the model attributes and properties. At the same time, the application of agent-specific methodologies, protocols, organizational paradigms, or standards is sparse. Supplementary Information: The online version contains supplementary material available at 10.1007/s11069-022-05643-x.

The possible role of machine learning in detection of increased cardiovascular risk patients - KSC MR Study (design).

Introduction: Currently, just a few major parameters are used for cardiovascular (CV) risk quantification to identify many of the high-risk subjects; however, they leave a lot of them with an underestimated level of CV risk which does not reflect the reality. Material and methods: The submitted study design of the Kosice Selective Coronarography Multiple Risk (KSC MR) Study will use computer analysis of coronary angiography results of admitted patients along with broad patients' characteristics based on questionnaires, physical findings, laboratory and many other examinations. Results: Obtained data will undergo machine learning protocols with the aim of developing algorithms which will include all available parameters and accurately calculate the probability of coronary artery disease. Conclusions: The KSC MR study results, if positive, could establisha base for development of proper software for revealing high-risk patients, as well as patients with suggested positive coronary angiography findings, based on the principles of personalised medicine.

Clustering Inflammatory Markers with Sociodemographic and Clinical Characteristics of Patients with Diabetes Type 2 Can Support Family Physicians' Clinical Reasoning by Reducing Patients' Complexity.

Diabetes mellitus type 2 (DM2) is a complex disease associated with chronic inflammation, end-organ damage, and multiple comorbidities. Initiatives are emerging for a more personalized approach in managing DM2 patients. We hypothesized that by clustering inflammatory markers with variables indicating the sociodemographic and clinical contexts of patients with DM2, we could gain insights into the hidden phenotypes and the underlying pathophysiological backgrounds thereof. We applied the k-means algorithm and a total of 30 variables in a group of 174 primary care (PC) patients with DM2 aged 50 years and above and of both genders. We included some emerging markers of inflammation, specifically, neutrophil-to-lymphocyte ratio (NLR) and the cytokines IL-17A and IL-37. Multiple regression models were used to assess associations of inflammatory markers with other variables. Overall, we observed that the cytokines were more variable than the marker NLR. The set of inflammatory markers was needed to indicate the capacity of patients in the clusters for inflammatory cell recruitment from the circulation to the tissues, and subsequently for the progression of end-organ damage and vascular complications. The hypothalamus-pituitary-thyroid hormonal axis, in addition to the cytokine IL-37, may have a suppressive, inflammation-regulatory role. These results can help PC physicians with their clinical reasoning by reducing the complexity of diabetic patients.

Clusters of Physical Frailty and Cognitive Impairment and Their Associated Comorbidities in Older Primary Care Patients.

(1) Objectives: We aimed to identify clusters of physical frailty and cognitive impairment in a population of older primary care patients and correlate these clusters with their associated comorbidities. (2) Methods: We used a latent class analysis (LCA) as the clustering technique to separate different stages of mild cognitive impairment (MCI) and physical frailty into clusters; the differences were assessed by using a multinomial logistic regression model. (3) Results: Four clusters (latent classes) were identified: (1) highly functional (the mean and SD of the "frailty" test 0.58 ± 0.72 and the Mini-Mental State Examination (MMSE) test 27.42 ± 1.5), (2) cognitive impairment (0.97 ± 0.78 and 21.94 ± 1.95), (3) cognitive frailty (3.48 ± 1.12 and 19.14 ± 2.30), and (4) physical frailty (3.61 ± 0.77 and 24.89 ± 1.81). (4) Discussion: The comorbidity patterns distinguishing the clusters depend on the degree of development of cardiometabolic disorders in combination with advancing age. The physical frailty phenotype is likely to exist separately from the cognitive frailty phenotype and includes common musculoskeletal diseases.

AI and Big Data in Healthcare: Towards a More Comprehensive Research Framework for Multimorbidity.

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.

Cluster Analysis of the Associations among Physical Frailty, Cognitive Impairment and Mental Disorders.

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 AND 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.

Prescribing Analgesics to Older People: A Challenge for GPs.

BACKGROUND: Due to population aging, there is an increase in the prevalence of chronic diseases, and in particular musculoskeletal diseases. These trends are associated with an increased demand for prescription analgesics and an increased risk of polypharmacy and adverse medication reactions, which constitutes a challenge, especially for general practitioners (GPs), as the providers who are most responsible for the prescription policy. OBJECTIVES: To identify patterns of analgesics prescription for older people in the study area and explore associations between a long-term analgesic prescription and comorbidity patterns, as well as the prescription of psychotropic and other common medications in a continuous use. METHODS: A retrospective study was conducted in 2015 in eastern Croatia. Patients were GP attenders ≥40 years old (N = 675), who were recruited during their appointments (consecutive patients). They were divided into two groups: those who have been continuously prescribed analgesics (N = 432) and those who have not (N = 243). Data from electronic health records were used to provide information about diagnoses of musculoskeletal and other chronic diseases, as well as prescription rates for analgesics and other medications. Exploratory methods and logistic regression models were used to analyse the data. RESULTS: Analgesics have been continuously prescribed to 64% of the patients, mostly to those in the older age groups (50-79 years) and females, and they were indicated mainly for dorsalgia symptoms and arthrosis. Non-opioid analgesics were most common, with an increasing tendency to prescribe opioid analgesics to older patient groups aged 60-79 years. The study results indicate that there is a high rate of simultaneous prescription of analgesics and psychotropic medications, despite the intention of GPs to avoid prescribing psychotropic medications to patients who use any option with opioid analgesics. In general, receiving prescription analgesics does not exceed the prescription for chronic diseases over the rates that can be found in patients who do not receive prescription analgesics. CONCLUSION: Based on the analysis of comorbidities and parallel prescribing, the results of this study can improve GPs' prescription and treatment strategies for musculoskeletal diseases and chronic pain conditions.

The Effect of Hypertension Duration and the Age of Onset on CV Risk Factors Expression in Perimenopausal Women.

BACKGROUND: The impact of hypertension duration and the time of onset on the expression of metabolic syndrome (MS) and other CV risk factors, in perimenopausal women, have not been studied so far. Methods. A total of 202 women, old 47-59 years, and diagnosed with hypertension, were recruited from primary care practices in eastern Croatia. The categories of hypertension duration were defined as <5, 5-10, and >10 years. Data were analyzed by standard statistical procedures. RESULTS: The proportion of women with MS increases in parallel with hypertension duration (p = 0.025). Among the examined CV risk factors, significant increase in parallel with hypertension duration was found for body mass index (p = 0.007) and triglycerides (p = 0.07). The highest proportion of women with diabetes duration of less than 5 years, indicating recent diabetes onset, was found in the category of hypertension duration of less than 5 years, corresponding with the onset of hypertension in the time around menopause (p = 0.003). The strongest linear correlations with BMI and waist circumference were found for total serum cholesterol (r = 0.355 and 0.499, respectively). CONCLUSION: Hypertension onset at the time around menopause appears together with abdominal obesity and may be a driving force for CV risk factor accumulation in postmenopausal women.

Clustering of Mental and Physical Comorbidity and the Risk of Frailty in Patients Aged 60 Years or More in Primary Care.

BACKGROUND This 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 AND METHODS There 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. RESULTS Three 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. CONCLUSIONS In patients ≥60 years in a primary healthcare setting, multimorbidity predictors of pre-frailty and frailty included a decline in cognitive function and renal function.

Metabolic syndrome in hypertensive women in the age of menopause: a case study on data from general practice electronic health records.

BACKGROUND: There is potential for medical research on the basis of routine data used from general practice electronic health records (GP eHRs), even in areas where there is no common GP research platform. We present a case study on menopausal women with hypertension and metabolic syndrome (MS). The aims were to explore the appropriateness of the standard definition of MS to apply to this specific, narrowly defined population group and to improve recognition of women at high CV risk. METHODS: We investigated the possible uses offered by available data from GP eHRs, completed with patients interview, in goal of the study, using a combination of methods. For the sample of 202 hypertensive women, 47-59 years old, a data set was performed, consisted of a total number of 62 parameters, 50 parameters used from GP eHRs. It was analysed by using a mixture of methods: analysis of differences, cutoff values, graphical presentations, logistic regression and decision trees. RESULTS: The age range found to best match the emergency of MS was 51-55 years. Deviations from the definition of MS were identified: a larger cut-off value of the waist circumference measure (89 vs 80 cm) and parameters BMI and total serum cholesterol perform better as components of MS than the standard parameters waist circumference and HDL-cholesterol. The threshold value of BMI at which it is expected that most of hypertensive menopausal women have MS, was found to be 25.5. The other best means for recognision of women with MS include triglycerides above the threshold of 1.7 mmol/L and information on statins use. Prevention of CVD should focus on women with a new onset diabetes and comorbidities of a long-term hypertension with anxiety/depression. CONCLUSIONS: The added value of this study goes beyond the current paradigm on MS. Results indicate characteristics of MS in a narrowly defined, specific population group. A comprehensive view has been enabled by using heterogenoeus data and a smart combination of various methods for data analysis. The paper shows the feasibility of this research approach in routine practice, to make use of data which would otherwise not be used for research.

Medical Data Analytics Is Not a Simple Task.

Data analytics represents a new chance for medical diagnosis and treatment to make it more effective and successful. This expectation is not so easy to achieve as it may look like at a first glance. The medical experts, doctors or general practitioners have their own vocabulary, they use specific terms and type of speaking. On the other side, data analysts have to understand the task and to select the right algorithms. The applicability of the results depends on the effectiveness of the interactions between those two worlds. This paper presents our experiences with various medical data samples in form of SWOT analysis. We identified the most important input attributes for the target diagnosis or extracted decision rules and analysed their interestingness with cooperating doctors, for most promising new cut-off values or an investigation of possible important relations hidden in data sample. In general, this type of knowledge can be used for clinical decision support, but it has to be evaluated on different samples, conditions and ideally in long-term studies. Sometimes, the interaction needed much more time than we expected at the beginning but our experiences are mostly positive.