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Pressure ulcers carry a significant risk in clinical practice. This paper proposes a practical and interpretable approach to estimate the risk levels of pressure ulcers using decision tree models. In order to address the common problem of imbalanced learning in nursing classification datasets, various oversampling configurations are analyzed to improve the data quality prior to modeling. The decision trees built are based on three easily identifiable and clinically relevant pressure ulcer risk indicators: mobility, activity, and skin moisture. Additionally, this research introduces a novel tabular visualization method to enhance the usability of the decision trees in clinical practice. Thus, the primary aim of this approach is to provide nursing professionals with valuable insights for assessing the potential risk levels of pressure ulcers, which could support their decision-making and allow, for example, the application of suitable preventive measures tailored to each patient’s requirements. The interpretability of the models proposed and their performance, evaluated through stratified cross-validation, make them a helpful tool for nursing care in estimating the pressure ulcer risk level.

Background: This study considers care management for older chronic patients during and after the COVID-19 pandemic. Aims: To identify groups of variables at previous time points as a basis for deriving efficient classification models during and after a pandemic situation and to quantify the effect of each variable within the model to predict levels of worsening risk in diastolic and systolic arterial hypertension (AHT). Material and Methods: In this prospective longitudinal study, data were collected at three time points: before, during, and after the COVID-19 pandemic period. Results: The study included 148 patients with an average age of 81.6 years. During the study period, mean systolic blood pressure among this population rose by 5 mmHg to 128.8 mmHg; the number of patients with systolic blood pressure > 140 mmHg rose by 45.3%; among those with diastolic blood pressure > 90, the number rose by 41.2%; mean triglycerides levels rose to 152.6 mg/dL; cholesterol levels rose to 147 mg/dL; and LDL cholesterol rose to 112.2 mg/dL. Meanwhile, mean levels of HDL cholesterol decreased to 46.5 mg/dL. Binary-response logistic regression models were constructed to identify the most relevant variables for predicting AHT risk during and after the pandemic. The heart rate (OR = 1.79; 95% CI: 1.22–2.72) and body mass index (OR = 1.75; 95% CI: 1.08–2.94) variables were significant at the population level (p < 0.05) for diastolic and systolic AHT in the pandemic period risk models. The body mass index variable was also significant for diastolic AHT in the post-pandemic period risk model (OR = 1.97; 95% CI: 1.32–2.94), whilst the triglycerides variable was significant in the systolic AHT post-pandemic period risk model (OR = 1.49; 95% CI: 1.01–1.86). Conclusions: Bad control of arterial hypertension in older patients with chronic disease is associated with elevated levels of LDL cholesterol, total cholesterol, systolic blood pressure, heart rate and triglycerides, and lower levels of HDL cholesterol.

Angiotensin II-converting enzyme inhibitors (ACEIs) and selective angiotensin II receptor antagonists (ARAIIs) are widely used antihypertensive agents. Their use has generated controversy due to their possible influence on the health status of chronic patients infected with COVID-19. The objective of this work is to analyze the influence of COVID-19 on chronic hypertensive patients treated with ACEI and ARAII inhibitors. A systematic review and meta-analysis in the databases Pubmed, Pro-Quest and Scopus were carried out. The systematic review was conducted in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. The search equation descriptors were obtained from the Medical Subject Headings (MeSH) thesaurus. The search equation was: “Older AND hypertension AND (COVID-19 OR coronavirus) AND primary care” and its equivalent in Spanish. Nineteen articles were obtained, with n = 10,806,159 subjects. Several studies describe the COVID-19 association with ACEI or ARAII treatment in hypertension patients as a protective factor, some as a risk factor, and others without a risk association. In the case of ACEI vs. ARAII, the risk described for the former has an odds ratio (OR) of 0.55, and for ARAII, an OR of 0.59. Some authors talk about mortality associated with COVID-19 and ACEI with a half ratio (HR) of 0.97, and also associated ARAIIs with an HR of 0.98. It is recommended to maintain the use of the renin–angiotensin–aldosterone axis in the context of the COVID-19 disease.

The analysis of spectral reflectance data is an important tool for obtaining relevant information about the mineral composition of objects and has been used for research in chemistry, geology, biology, archaeology, pharmacy, medicine, anthropology, and other disciplines. In archaeology, the use of spectroscopic data allows us to characterize and classify artifacts and ecofacts, to analyze patterns, and to study the exchange of materials, etc., as well as to explain some properties, such as color or post-depositional processes. The spectroscopic data are of the so-called “big data” type and must be analyzed using multivariate statistical techniques, usually principal component analysis and cluster analysis. Although there are different transformations of the raw data, in this paper, we propose preprocessing by means of an affine transformation. From a mathematical point of view, this process modifies the values of reflectance for each spectral signature scaling them into a [0, 1] interval using minimum and maximum values of reflectance, thus highlighting the features of spectral curves. This method optimizes the characteristics of amplitude and shape, reduces the influence of noise, and improves results by highlighting relevant features as peaks and valleys that may remain hidden using the raw data. This methodology has been applied to a case study of prehistoric chert (flint) artifacts retrieved in archaeological excavations in the Andévalo area located in the Archaeological Museum of Huelva (Huelva, Andalusia). The use of transformed data considerably improves the results obtained with raw data, highlighting the peaks, valleys, and the shape of spectral signatures.

Surgical area nurses provide comprehensive care to patients throughout the surgical process. Increases in life expectancy lead to the appearance and development of diseases, translating into an increase in the number of necessary interventions. Increases in the workload can be another risk factor for the development of burnout in professionals in this area. Knowledge of psychological and personality-related variables provides relevant information of level changes in the dimensions of burnout syndrome. Three logistic regression models, based on a cross-sectional study with 214 nurses working in the surgical area in the Andalusian Health Service, Spain, were built for each dimension. These models included different variables related to depression and personality, with some being significant at the population level and consequently true risk or protection factors. Friendliness, responsibility and extraversion are protection factors for the personal accomplishment dimension, whilst neuroticism is a risk factor for this dimension. Friendliness is also a protection factor for depersonalization, whilst depression is a risk factor. Finally, neuroticism, responsibility and depression are risk factors for the emotional exhaustion dimension of burnout. These findings provide relevant information that makes anticipation of this syndrome in this group easier.

The prevalence of mental health disorders has increased during the COVID-19 pandemic. Patients with chronic diseases, such as diabetes, are a particularly vulnerable risk group. This study aims to assess the levels and prevalence of anxiety, distress, and stress in patients with diabetes during the COVID-19 pandemic. A systematic review was conducted in CINAHL, Cochrane, LILACS, Medline, SciELO, and Scopus in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement. Thirty-seven articles with a total of 13,932 diabetic patients were included. Five meta-analyses were performed. The prevalence of anxiety was 23% (95% CI = 19–28) in T1DM and 20% (95% CI = 6–40) in T2DM patients. For diabetes distress it was 41% (95% CI = 24–60) for T1DM and 36% in T2DM patients (95% CI = 2–84). For stress, the prevalence was 79% (95% CI = 49–98) in T1DM patients. People with diabetes have significant psychiatric comorbidity as well as psychological factors that negatively affect disease management, increasing their vulnerability in an emergency situation. To establish comprehensive care in diabetic patients addressing mental health is essential, as well as including specific policy interventions to reduce the potential psychological harm of the COVID-19 pandemic.

Data that have not been modeled cannot be correctly predicted. Under this assumption, this research studies how k-fold cross-validation can introduce dataset shift in regression problems. This fact implies data distributions in the training and test sets to be different and, therefore, a deterioration of the model performance estimation. Even though the stratification of the output variable is widely used in the field of classification to reduce the impacts of dataset shift induced by cross-validation, its use in regression is not widespread in the literature. This paper analyzes the consequences for dataset shift of including different regressand stratification schemes in cross-validation with regression data. The results obtained show that these allow for creating more similar training and test sets, reducing the presence of dataset shift related to cross-validation. The bias and deviation of the performance estimation results obtained by regression algorithms are improved using the highest amounts of strata, as are the number of cross-validation repetitions necessary to obtain these better results.

Real-world classification data usually contain noise, which can affect the accuracy of the models and their complexity. In this context, an interesting approach to reduce the effects of noise is building ensembles of classifiers, which traditionally have been credited with the ability to tackle difficult problems. Among the alternatives to build ensembles with noisy data, bagging has shown some potential in the specialized literature. However, existing works in this field are limited and only focus on the study of noise based on a random mislabeling, which is unlikely to occur in real-world applications. Recent research shows that other types of noise, such as that occurring at class boundaries, are more common and challenging for classification algorithms. This paper delves into the analysis of the usage of bagging techniques in these complex problems, in which noise affects the decision boundaries among classes. In order to investigate whether bagging is able to reduce the impact of borderline noise, an experimental study is carried out considering a large number of datasets with different noise levels, and several noise models and classification algorithms. The results obtained reflect that bagging obtains a better accuracy and robustness than the individual models with this complex type of noise. The highest improvements in average accuracy are around 2–4% and are generally found at medium-high noise levels (from 15–20% onwards). The partial consideration of noisy samples when creating the subsamples from the original training set in bagging can make it so that only some parts of the decision boundaries among classes are impaired when building each model, reducing the impact of noise in the global system.

Background and Objectives: Acute respiratory distress syndrome is a life-threatening lung condition that prevents enough oxygen from getting to the lungs and blood. The causes can be varied, although since the COVID-19 pandemic began there have been many cases related to this virus. The management and evolution of ARDS in emergency situations in the last 5 years was analyzed. Materials and Methods: A systematic review was carried out in the PubMed and Scopus databases. Using the descriptors Medical Subject Headings (MeSH), the search equation was: “Emergency health service AND acute respiratory distress syndrome”. The search was conducted in December 2021. Quantitative primary studies on the care of patients with ARDS in an emergency setting published in the last 5 years were included. Results: In the initial management, adherence to standard treatment with continuous positive airway pressure (CPAP) is recommended. The use of extracorporeal membrane reduces the intensity of mechanical ventilation or as rescue therapy in acute respiratory distress syndrome (ARDS). The prone position in both intubated and non-intubated patients with severe ARDS is associated with a better survival of these patients, therefore, it is very useful in these moments of pandemic crisis. Lack of resources forces triage decisions about which patients are most likely to survive to start mechanical ventilation and this reflects the realities of intensive care and emergency care in a resource-limited setting. Conclusions: adequate prehospital management of ARDS and in emergency situations can improve the prognosis of patients. The therapeutic options in atypical ARDS due to COVID-19 do not seem to vary substantially from conventional ARDS.

Coronary heart disease is the leading cause of death and disability worldwide. Traditionally, cardiac rehabilitation programmes are offered after cardiac events to aid recovery, improve quality of life, and reduce adverse events. The objective of this review was to assess the health-related quality of life, after a supervised cardiac rehabilitation programme, of patients who suffered a myocardial infarction. A systematic review was carried out in the CINAHL, Cochrane, LILACS, Medline, Scopus, and SciELO databases, according to the Preferred Reporting Items for Systematic Reviews and Meta-analysis (PRISMA) guidelines. Randomised controlled trials were selected. Meta-analyses were performed for the Short Form Health Survey SF-36, Myocardial Infarction Dimensional Assessment Scale (MIDAS), MacNew Heart Disease-Health-Related Quality of Life (HRQL) questionnaire, and European Quality of Life-Visual Analogue Scale (EuroQol-VAS) with the software Cochrane RevMan Web. Ten articles were found covering a total of 3577 patients. In the meta-analysis, the effect size of the cardiac rehabilitation programme was statistically significant in the intervention group for physical activity, emotional reaction, and dependency dimensions of the MIDAS questionnaire. For the control group, the score improved for SF-36 physical functioning, and body pain dimensions. The mean difference between the control and intervention group was not significant for the remaining dimensions, and neither for the MacNew Heart Disease-HRQL and EuroQol-VAS questionnaires. Supervised cardiac rehabilitation programmes were effective in improving health-related quality of life, however, there was a potential variability in the interventions; therefore, the results should be interpreted with caution. This study supports the importance of providing care and evaluating interventions via the supervision of trained health professionals, and further randomised clinical trials are needed to analyse the positive changes in mental and physical health outcomes.

ObjectiveTo determine levels of burnout among surgical area nurses in Andalusia (Spain), to identify the phase of burnout in each participant and to consider its relationship with sociodemographic, occupational variables and personality factors considered.Data sourceData were collected by means of questionary. All nurses working in the surgical area on the date of data collection participated in the study. Sociodemographic and related to work variables were addressed in the questionnaire. Symptoms of anxiety and depression were measured using the Educational-Clinical Questionnaire: Anxiety and Depression (CECAD). Psychological personality variables were assessed using the NEO Five-Factor Inventory (NEO-FFI), adapted for a Spanish population. Burnout was measured using the Maslach Burnout Inventory (MBI).Study designMulticentre, cross-sectional, quantitative study carried out from August to October 2021.Data analysisDescriptive analysis, Student’s t-test for independent samples, Pearson’s correlation and multiple linear regression were performed with SPSS 25.0.Data extraction methodsThe study sample consisted of 214 surgical area nurses at 23 hospitals in Andalusia (Spain). Sociodemographic, occupational and personality variables were studied using the Maslach Burnout Inventory, the NEO Five-Factor Inventory (NEO-FFI) and the Educational-Clinical Questionnaire: Anxiety and Depression. The STROBE statement guidelines were applied.Principal findings29.4% of the nurses in the sample presented high levels of emotional exhaustion, 25.7% suffered from depersonalization and 28% had low levels of personal accomplishment. These three dimensions were significantly correlated with the NEO-FFI subscales (neuroticism, agreeableness, openness, conscientiousness and extraversion), and with all the anxiety and depression items considered. Agreeableness was a statistically significant predictor (p

Background: B cells are central to the pathogenesis of systemic lupus erythematosus (SLE). We aimed to analyze the efficacy and safety of new B cell-targeted drug therapies for SLE. Methods: A systematic review of randomized controlled trials (RCTs) and reference lists of relevant articles published from inception to 2022 were selected from PubMed, Scopus and Web of Science databases. Random effects meta-analyses were performed to estimate an overall effect size for the risk of adverse events (AEs) and serious adverse events (SAEs) with belimumab and tabalumab treatment. Heterogeneity was assessed using the I2 statistic and meta-regression. Funnel asymmetry was evaluated using Egger’s test. Results: This study included 13 RCTs, of which three showed high risk of bias. Egger’s test showed no asymmetry. The risk of SAEs and AEs was lower in the treatment group with belimumab treatment. The risk of AEs for tabalumab treatment was lower in the treatment group and lower for SAEs. Conclusion: Belimumab and tabalumab therapies are effective and safe in the treatment of SLE, although tabalumab does not show sufficient statistical power. Advances in understanding the underlying mechanisms of SLE will be directed towards correlating clinical manifestations with specific pathogenic pathways and the development of precision medicine.