Patterns of Locus of Control in People Suffering from Heart Failure: An Approach by Clustering Method.

Background and Objectives: The assumption of responsibility in dealing with chronic diseases is of relevance in a resource-oriented and not only deficit-oriented medicine, especially in dealing with chronic diseases, including patients with chronic heart failure. The aim of the present study is to examine, based on the model of "locus of control", whether there are different patterns that would be relevant for a more targeted education and support of self-management in dealing with heart failure. Materials and Methods: For this purpose, a sample (n = 758) from 11 Polish cardiology centers have been assessed using the standardized self-assessment scale Multidimensional Health Locus of Control (MHLC), consisting of three dimensions: (i) internal localization of health control; (ii) external control by powerful others; (iii) external control by chance. Results: Using these three criteria, nine different clusters were extracted (mean size: 84 ± 33 patients, min 31, max 129). Three clusters included over 100 patients, whereas only two included less than 50 people. Only one cluster gathered 42 patients who will be able to cooperate with professionals in the most fruitful way. There were two clusters, including patients with beliefs related to the risk of ignoring professional recommendations. Clusters where patients declared beliefs about others' control with low internal control should also be provided with specific help. Conclusions: The division into clusters revealed significant variability of belief structures about health locus of control within the analyzed group. The presented methodological approach may help adjust education and motivation to a selected constellation of beliefs as a compromise between group-oriented vs. individual approach.

The relationship between knowledge about the pandemic and willingness to get vaccinated against SARS-CoV-2 in medical students in Poland: A cross-sectional survey.

The COVID-19 pandemic underlines the importance of targeting the groups with the highest risk of vaccine hesitancy, understanding their fears, and alleviating them. As the pandemic situation is very dynamic due to the appearance of new SARS-CoV-2 variants, concerns might also change over time. This is the first study to evaluate the vaccination rate and state of knowledge among medical students in Poland, comparing English and Polish divisions. We collected the data in 2 months. A total of 1,521 surveys were collected as follows: 273 students from the English division and 1,248 students from the Polish division answered the survey. The questionnaire was aimed at investigating students' awareness, knowledge, and apprehensions toward the COVID-19 pandemic and vaccines. The results were obtained for the following statements: good knowledge about ways of transmission is not statistically significant in determining if a student is vaccinated. Moreover, a year of study is not statistically significant in determining if a student knows all ways of COVID-19 transmission. Interestingly, the correlation between the statement "Keeping up to date with the upcoming vaccines is important for my role as a future health care worker" and being vaccinated against SARS-CoV-2 showed that 45.5% of unvaccinated students did not update their information about the vaccines and rated 1 out of 5 for this statement (p < 0.001). Even though the pandemic will not last forever, the obtained knowledge about the role of individual interests can be applied in many different life situations as this feature is statistically significant.

An Artificial Intelligence Approach to Guiding the Management of Heart Failure Patients Using Predictive Models: A Systematic Review.

Heart failure (HF) is one of the leading causes of mortality and hospitalization worldwide. The accurate prediction of mortality and readmission risk provides crucial information for guiding decision making. Unfortunately, traditional predictive models reached modest accuracy in HF populations. We therefore aimed to present predictive models based on machine learning (ML) techniques in HF patients that were externally validated. We searched four databases and the reference lists of the included papers to identify studies in which HF patient data were used to create a predictive model. Literature screening was conducted in Academic Search Ultimate, ERIC, Health Source Nursing/Academic Edition and MEDLINE. The protocol of the current systematic review was registered in the PROSPERO database with the registration number CRD42022344855. We considered all types of outcomes: mortality, rehospitalization, response to treatment and medication adherence. The area under the receiver operating characteristic curve (AUC) was used as the comparator parameter. The literature search yielded 1649 studies, of which 9 were included in the final analysis. The AUCs for the machine learning models ranged from 0.6494 to 0.913 in independent datasets, whereas the AUCs for statistical predictive scores ranged from 0.622 to 0.806. Our study showed an increasing number of ML predictive models concerning HF populations, although external validation remains infrequent. However, our findings revealed that ML approaches can outperform conventional risk scores and may play important role in HF management.

Novel Phenotyping for Acute Heart Failure-Unsupervised Machine Learning-Based Approach.

Acute heart failure (AHF) is a life-threatening, heterogeneous disease requiring urgent diagnosis and treatment. The clinical severity and medical procedures differ according to a complex interplay between the deterioration cause, underlying cardiac substrate, and comorbidities. This study aimed to analyze the natural phenotypic heterogeneity of the AHF population and evaluate the possibilities offered by clustering (unsupervised machine-learning technique) in a medical data assessment. We evaluated data from 381 AHF patients. Sixty-three clinical and biochemical features were assessed at the admission of the patients and were included in the analysis after the preprocessing. The K-medoids algorithm was implemented to create the clusters, and optimization, based on the Davies-Bouldin index, was used. The clustering was performed while blinded to the outcome. The outcome associations were evaluated using the Kaplan-Meier curves and Cox proportional-hazards regressions. The algorithm distinguished six clusters that differed significantly in 58 variables concerning i.e., etiology, clinical status, comorbidities, laboratory parameters and lifestyle factors. The clusters differed in terms of the one-year mortality (p = 0.002). Using the clustering techniques, we extracted six phenotypes from AHF patients with distinct clinical characteristics and outcomes. Our results can be valuable for future trial constructions and customized treatment.

Identification of risk groups for mental disorders, headache and oral behaviors in adults during the COVID-19 pandemic.

The dramatically changing situation during COVID-19 pandemic, is anticipated to provoke psycho-emotional disturbances and somatization arising from the current epidemiological situation that will become a significant problem for global and regional healthcare systems. The aim of this study was to identify the predictors, risk factors and factors associated with mental disorders, headache and potentially stress-modulated parafunctional oral behaviors among the adult residents of North America and Europe as indirect health effects of the COVID-19 pandemic. This may help limit the long-term effects of this and future global pandemic crises. The data were collected from 1642 respondents using an online survey. The results demonstrated increased levels of anxiety, depression, headache and parafunctional oral behaviors during the COVID-19 pandemic in both North American and European residents. The results of this study facilitated the definition of the group most predicted to experience the aforementioned secondary effects of the pandemic. This group included females younger than 28.5 years old, especially those who were single, less well educated and living in Europe. In case of this and other global crises this will allow faster defining the most vulnerable groups and providing rapid and more targeted intervention.