Anxiety in Polish adult patients with inborn errors of immunity: a cross-sectional study

Background

Patients with inborn errors of immunity (IEI) experience recurrent infections, autoimmunity, and malignancies. Owing to repeated medical procedures, the need for constant treatment and surveillance, and the unpredictable course of the disease, patients with IEI are prone to develop mental health disorders, including anxiety. In this study, we aimed to assess the prevalence and level of anxiety symptoms in adult Polish patients with IEI and explore the determinants of anxiety in this group of patients.

Methods

Data from 105 Polish patients with IEI were collected via the hospital anxiety and depression scale (HADS), brief illness perception questionnaire (B-IPQ), illness cognition questionnaire (ICQ), Pittsburgh sleep quality index (PSQI), and a questionnaire on general health and demographic data. For statistical analyses of data, the normality of distribution of quantitative data was assessed, and internal consistency of tests was investigated using Cronbach's alpha coefficient; moreover, we performed the analysis of correlations and between-group differences, and path analysis to explore causal relationships. Significance was considered at p < 0.050.

Results

Thirty-eight (36.2%) patients had anxiety symptoms (HADS-A ≥ 8); 14 (13.3%) patients had severe anxiety (score ≥ 11), and 24 (22.9%) had moderate anxiety (score of 8-10). Patients with poor sleep quality, higher pain frequency, younger age, and no fixed income had higher anxiety scores than others. Emotional and cognitive representations of illness were positively correlated with anxiety levels. Intense anxiety was related to more negative illness perception, higher helplessness, lower illness acceptance, and lower perceived benefits.

Discussion

Anxiety is common in patients with IEI. However, results indicate that it is not related to a more severe course of IEI or several comorbidities, whereas, pain frequency and poor sleep quality were identified to be important clinical factors for anxiety. Because anxiety was related to negative illness perception, psychological therapy may apply to this group of patients.

Applying Machine Learning to Construct a Model of Risk of Depression in Patients Following Cardiac Surgery with the Use of the SF-12 Survey

BACKGROUND

Depression is a common problem in patients with cardiovascular diseases. Identifying a risk factor model of depression has been postulated. A model of the risk of depression would provide a better understanding of this disorder in this population. We sought to construct a model of the risk factors of depression in patients following cardiac surgery, with the use of machine learning.

METHODS AND MEASURES

Two hundred and seventeen patients (65.4% men; mean age 65.14 years) were asked to complete the short form health survey-12 (SF-12v.2), three months after hospital discharge. Those at risk of depression were identified based on the SF-12 mental component summary (MCS). Centroid class principal component analysis (CCPCA) and the classification and regression tree (CART) were used to design a model.

RESULTS

A risk of depression was identified in 29.03% of patients. The following variables explained 82.53% of the variance in depression risk: vitality, limitation of activities due to emotional problems (role-emotional, RE), New York Heart Association (NYHA) class, and heart failure. Additionally, CART revealed that decreased vitality increased the risk of depression to 45.44% and an RE score > 68.75 increased it to 63.11%. In the group with an RE score < 68.75, the NYHA class increased the risk to 41.85%, and heart failure further increased it to 44.75%.

CONCLUSION

Assessing fatigue and vitality can help health professionals with identifying patients at risk of depression. In addition, assessing functional status and dimensions of fatigue, as well as the impact of emotional state on daily functioning, can help determine effective intervention options.

The impact of the COVID-19 pandemic on the mental health of children with psychiatric diagnoses - multidimensional CCPCA Model

BACKGROUND

The study aimed to assess the severity of symptoms of anxiety and depression in children with previously diagnosed psychiatric disorders during the COVID-19 pandemic in Poland.

METHODS

Online questionnaires were used to investigate three groups of subjects: patients with a psychiatric diagnosis, primary school pupils, and children from children's homes. A total of 167 children with their parents or guardians participated in the study. In addition to basic statistics, a multidimensional Centroid Class Principal Component Analysis (CCPCA) model was used.

RESULTS

It was found that the strongest fear of the coronavirus was experienced by children from children's homes, while the most severe depressive symptoms and state anxiety were observed among patients diagnosed with psychiatric disorders. Parental care by assisting with school education and lack of close contact with other people (less than two metres) at parents/guardians' work had the most potent protective effect in reducing the fear of COVID-19.

CONCLUSIONS

There is a need for further research in children and adolescents to develop effective strategies for protecting their mental well-being when faced with social isolation or disease.

Prevalence and Predictors of Suicidal Ideation in Patients Following Cardiac Surgery

BACKGROUND

Patient-reported outcomes (PROs) which demand special attention and immediate help are referred to as PROs alert. Suicidal ideation (SI) is one of the PROs alerts which are insufficiently investigated. The aim was to assess the prevalence and risk factors for SI in patients following cardiac surgery.

METHODS

A total of 190 patients (mean age: 66.09, SD = 10.19; 57 women) were assessed at three months following cardiac surgery. SI was identified using the Patient Health Qustionnaire-9 (PHQ-9) question. The Hospital Anxiety and Depression Scale-Modified was used to assess anxiety, depression, and irritability. Additionally, self-perceived health improvement and level of hope were assessed using the Likert scale. Dyspnea and chest pain were assessed using a visual analogue scale.

RESULTS

SI was observed in 14.7% of participants. Patients experiencing SI had significantly higher levels of depression, anxiety, irritability, dyspnea and chest pain. They perceived the surgery to be less effective and had lower levels of hope. No significant relationships were found regarding age, sex, employment status, myocardial infarction, heart failure, operation mode, type of procedure, extracorporal circulation, hospital stay and postsurgical complications. Logistic regression revealed female sex (B = 2.363), higher anxiety level (B = 0.451) and older age (B = 0.062) to be risk factors for SI. The total variance explained by the model was 46%.

CONCLUSIONS

Assessing suicidality and negative emotions with special emphasis on anxiety simultaneously with somatic complaints is vital to address PROs alerts and improve care for patients following cardiac surgery. In-depth evaluation and psychological care are recommended in case of positive screening.

Novel feature extraction method for signal analysis based on independent component analysis and wavelet transform

Feature extraction is an important part of data processing that provides a basis for more complicated tasks such as classification or clustering. Recently many approaches for signal feature extraction were created. However, plenty of proposed methods are based on convolutional neural networks. This class of models requires a high amount of computational power to train and deploy and large dataset. Our work introduces a novel feature extraction method that uses wavelet transform to provide additional information in the Independent Component Analysis mixing matrix. The goal of our work is to combine good performance with a low inference cost. We used the task of Electrocardiography (ECG) heartbeat classification to evaluate the usefulness of the proposed approach. Experiments were carried out with an MIT-BIH database with four target classes (Normal, Vestibular ectopic beats, Ventricular ectopic beats, and Fusion strikes). Several base wavelet functions with different classifiers were used in experiments. Best was selected with 5-fold cross-validation and Wilcoxon test with significance level 0.05. With the proposed method for feature extraction and multi-layer perceptron classifier, we obtained 95.81% BAC-score. Compared to other literature methods, our approach was better than most feature extraction methods except for convolutional neural networks. Further analysis indicates that our method performance is close to convolutional neural networks for classes with a limited number of learning examples. We also analyze the number of required operations at test time and argue that our method enables easy deployment in environments with limited computing power.

Antibiotic consumption in long-term care facilities in Poland and other European countries in 2017

Introduction

The aim of this research study was to compare the situation concerning the use of microbiology testing, the epidemiology of healthcare-associated infection (HAI) and antimicrobial consumption (AMC) in Polish long-term care facilities (LTCFs) with other European countries, using the most recent findings available in the European databases. Furthermore, this study aimed to highlight several basic factors that contribute to the observable differences in AMC between countries participating in the HALT-3 study, especially the relationship with demographic indicators, as well as the health care resources utilization rates.

Patients and methods

The most recent HAIs in Long-Term care facilities Point Prevalence Survey (HALT PPS) was carried out in EU/EEA countries in 2016–2017, and in Poland it was carried out in April–June 2017 in 24 LTCFs. AMC data was collected with use of methodology of the Anatomical Therapeutic Chemical (ATC) classification system of the WHO.

Results

In total total in HALT-3 study on the day of the PPS, 5035 out of the 102,301 eligible residents received at least one antimicrobial agent, with prevalence of 4.9%, and in Poland 3.2%. The most common HAIs in the countries included into the study was urinary tract infection with relative frequency of 32%, in Poland it was skin infection, 30.4%. The respiratory tract infections, excluding pneumonia (PNU) were observed in 29.5% of residents in total, in Poland 17.4%, the prevalence rate of PNU were 1.4% and 5.4%, respectively. The lack of microbiological results of HAIs testing concerned the vast majority of all HAIs, 75.8% in total and 81.5% in Poland. The most frequently used antibacterial for systemic use were beta-lactams and the most frequently prescribed antimicrobial agent was ‘amoxicillin and enzyme inhibitor’. AMC was closely correlated with the age of the general population (65 years of age and more) and the availability of doctors in general population.

Conclusions

A significant problem observed in LTCFs was the empirical use of antibiotics and the scarcity of microbiological testing. In the studied Polish LTCFs, where the age of residents was low, also the AMC was found to be lower.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13756-021-01019-1.

Application of Feature Extraction Methods for Chemical Risk Classification in the Pharmaceutical Industry

The features that are used in the classification process are acquired from sensor data on the production site (associated with toxic, physicochemical properties) and also a dataset associated with cybersecurity that may affect the above-mentioned risk. These are large datasets, so it is important to reduce them. The author’s motivation was to develop a method of assessing the dimensionality of features based on correlation measures and the discriminant power of features allowing for a more accurate reduction of their dimensions compared to the classical Kaiser criterion and assessment of scree plot. The method proved to be promising. The results obtained in the experiments demonstrate that the quality of classification after extraction is better than using classical criteria for estimating the number of components and features. Experiments were carried out for various extraction methods, demonstrating that the rotation of factors according to centroids of a class in this classification task gives the best risk assessment of chemical threats. The classification quality increased by about 7% compared to a model where feature extraction was not used and resulted in an improvement of 4% compared to the classical PCA method with the Kaiser criterion, with an evaluation of the scree plot. Furthermore, it has been shown that there is a certain subspace of cybersecurity features, which complemented with the features of the concentration of volatile substances, affects the risk assessment of chemical hazards. The identified cybersecurity factors are the number of packets lost, incorrect Logins, incorrect sensor responses, increased email spam, and excessive traffic in the computer network. To visualize the speed of classification in real-time, simulations were carried out for various systems used in Industry 4.0.