Clinical Nursing Research最新文献

This study aimed to develop and validate a machine learning-based predictive model for assessing the risk of fear of childbirth in pregnant women during late pregnancy. A cross-sectional observational study was conducted from November 2022 to July 2023, involving 406 pregnant women. Six machine learning algorithms, including Lasso-assisted logistic regression (LR), random forest (RF), eXtreme Gradient Boosting (XGB), support vector machine (SVM), Bayesian network (BN), and k-nearest neighbors (KNN), were used to construct the models with 10-fold cross-validation. The results showed that the XGB model achieved the best performance, with an area under the receiver operating characteristic curve (AUC) of 0.874, accuracy of 0.795, sensitivity of 0.764, and specificity of 0.878. The LR model also performed well (AUC = 0.873). Key predictors of fear of childbirth included pain catastrophizing, expectation for painless childbirth, childbirth delivery preferences, medication use during pregnancy, and use of birth-related apps. The LR model was used to create a nomogram for clinical use. These machine learning models can help healthcare professionals identify and intervene early in cases of fear of childbirth.

The COVID-19 pandemic necessitated a triad of therapies for patients: oxygen, nutrition, and patient positioning. In the progressive care units, patients were placed in a prone position while receiving continuous enteral nutrition (EN) to optimize healing and oxygenation. The study aimed to identify the rate of aspiration pneumonia in non-ventilated COVID-19 patients placed in a prone position while receiving continuous EN. This was a single-group, descriptive retrospective study. The study was conducted at a two-time Magnet^® designated academic medical and health science center in the Southwestern United States. The sample included 97 electronic health records (EHRs) of patients diagnosed with COVID-19, receiving continuous EN, and placed in a prone position from March 15, 2020 to June 1, 2022. Data were extracted from EHRs using ICD-10 codes, including patient demographics, EN frequency, gastric tube placement, patient positioning, and incidence of aspiration pneumonia. Descriptive statistics and non-parametric tests were used. The Kruskal-Wallis rank sum test and Fisher's exact test were employed for comparisons. Statistical significance was set at p ≤ .05. Out of 97 patients, 8 (8.25%) developed aspiration pneumonia. The majority of patients (75%) had post-pyloric feeding tubes. All patients who developed aspiration pneumonia had post-pyloric tubes. Placing COVID-19 patients in a prone position while receiving continuous EN may be a safe practice. Diligent nursing assessment is crucial to minimize aspiration risk and optimize patient outcomes.

The increasing prevalence of diabetes mellitus (DM) and patients' lack of self-management awareness have led to a decline in health-related quality of life (HRQoL). Studies identifying potential risk factors for HRQoL in DM patients and presenting generalized models are relatively scarce. The study aimed to develop and evaluate a machine learning (ML)-based model to predict the HRQoL in adult diabetic patients and to examine the important factors affecting HRQoL. This study extracted factors from the Korea National Health and Nutrition Examination Survey database (2016-2020) based on situation-specific theory, and using data from 2,501 adult DM patients. We developed five ML-based HRQoL classifiers (logistic regression, naïve Bayes, random forest, support vector machine, and extreme gradient boosting (XGBoost) in DM patients. The developed ML model was evaluated using six evaluation metrics to determine the best model, and feature importance was computed based on Shapley additive explanations (SHAP) value. The XGBoost model showed the best performance, with an accuracy of 0.940, a recall of 0.943, a precision of 0.940, a specificity of 0.919, an F1-score of 0.942, and an area under the curve score of 0.984. Based on SHAP values, the top five significant predictors of HRQoL were self-rated health (1.898), employment (0.822), triglycerides (0.781), education level (0.618), and aspartate transaminase/alanine transaminase ratio (0.611). The findings confirmed that the ML-based prediction model achieved high accuracy (over 90%) in distinguishing stable and at-risk groups in terms of HRQoL among adult DM patients. The XGBoost model's superior performance supports its potential integration into routine diabetes care as a decision-support tool. Identifying high-risk individuals early can help healthcare providers implement targeted interventions to improve long-term health outcomes.

The prevalence of poor sleep quality tends to increase progressively as renal function deteriorates. Patients with declining kidney function frequently encounter both physical and psychological discomfort. However, research investigating the factors influencing sleep quality in individuals with early-stage chronic kidney disease (CKD) has been limited. This study aims to identify specific factors associated with early-stage CKD that may contribute to poor sleep quality and to elucidate the relationship between these factors and sleep quality. A hundred and six patients completed the assessment, including the renal function test, Pittsburgh Sleep Quality Index, Beck Depression Inventory-Second Edition, and fatigue severity scale. Logistic regression analyses and partial least squares structural equation modeling were used to analyze the data. Poor sleep quality was reported in 59.43% of patients with early-stage CKD. Significant associations were found between poor sleep quality and age (p < .01; 95% confidence interval: [1.03, 1.17]), severity of depression (p = .01, [1.05, 1.48]), and severity of fatigue (p = .02, [1.09, 2.71]). By contrast, estimated glomerular filtration rate (eGFR) (p = 0.53, [0.96, 1.02]) did not demonstrate a significant association with poor sleep quality. Further analysis revealed that fatigue and depression are key contributors to poor sleep quality and may be influenced by declining renal function, even though the eGFR is not directly related to sleep outcomes. Addressing psychological factors, particularly fatigue and depression, is crucial for the improvement of sleep quality in patients with early-stage CKD. Future strategies should be focused on comprehensive care approaches that specifically target fatigue and depression to enhance sleep quality in this patient population.

The identification of traumatic birth is becoming a major global health concern. Evaluating the existing research can help shape future directions for traumatic birth studies. This study aimed to provide a comprehensive and up-to-date summary of research articles on traumatic birth. We performed a bibliometric analysis using the Science Citation Index Expanded of the Web of Science Core Collection database, covering the period from January 1, 1985, to June 30, 2023. A total of 1,568 original articles were found, indicating a significant increase in traumatic birth research. The United States was the most prolific country, followed by Australia and Canada. The University of Sydney, the University of Toronto, and the University of Pittsburgh were the top 3 institutions in terms of published documents. The terms "infants," "perceptions," and "birth injuries" had the highest burst strengths. MeSH Bibliographic Item Co-Occurrence Matrix Builder analysis identified six major research topics, with birth injuries and their prevention and control, as well as brachial plexus/shoulder injuries and surgery, being the most concentrated areas. While traumatic birth is not yet universally recognized and its scope remains under discussion, it is increasingly becoming a significant issue. Understanding the priorities and trends of research can guide future academic endeavors, highlighting areas that require further investigation and development.

Stroke is a major global health concern, often resulting in depression, anxiety, and disability. Effective management strategies, particularly nurse-led interventions, are essential for enhancing health-related quality of life (HRQoL) in stroke patients. This study evaluates the impact of these interventions on post-stroke HRQoL outcomes. To evaluate nurse-led interventions on HRQoL in stroke patients, this systematic review followed PRISMA guidelines. Searches were conducted across multiple databases, including Cochrane Central, Scopus, PubMed, Web of Science, and PsycINFO, to identify relevant randomized controlled trials (RCTs) while applying strict inclusion and exclusion criteria. Data extraction and risk of bias (ROB) assessments using the ROB-2 tool were performed independently by two investigators, with statistical analyses executed using Stata 17 software. From the initial 4,834 studies, nine studies were included for final analysis. Nine studies with 1,135 stroke patients (572 intervention, 563 control) assessed nurse-led interventions on HRQoL. Results showed a significant positive effect of nurse-led interventions (standardized mean difference (SMD): 5.26 [2.09, 8.42], I2: 99.72). Subgroup analysis revealed that Asian countries had a higher effect (SMD: 6.75 [3.45, 10.04]) compared to American and European countries (SMD: 0.08 [-0.87, 1.04]), and interventions over 10 weeks showed greater impact (SMD: 8.55 [5.56, 11.55]) compared to shorter ones (SMD: 3.59 [-0.35, 7.53]). Differences between assessment tools were also significant (p < .001). This meta-analysis shows that nurse-led interventions significantly improve stroke patients' HRQoL, with variations based on geography, intervention duration, and assessment tools. Further research is needed to optimize these interventions in clinical practice.

Health problems associated with post-acute COVID-19, also known as "Long COVID," range from mild to severe. The best defense against this potentially serious condition is to prevent COVID-19 infection and reinfection. The same preventive measures for COVID-19 may be used to help prevent the spread of Long COVID. This study used the Health Belief Model (HBM) to examine whether and how public understanding and awareness of Long COVID and its prevention shape the adoption of COVID-19 preventive behaviors. N = 605 English-speaking U.S.-based adults were recruited via Qualtrics. Predictors of intention to carry out COVID-19 preventive behaviors were investigated. Outcomes included behaviors relevant to preventing both acute and Long COVID. Across all models, except the one examining intent to get a vaccine booster, Black respondents were more likely than White respondents to express intent to carry out COVID-19 preventive behaviors. In addition, HBM constructs added significantly to the regression models. Susceptibility to Long COVID was significant for all behavioral outcomes (all ps < .05), self-efficacy for wearing a mask (p < .001), and self-efficacy for testing for COVID-19 after exposure and before a social event (ps < .001). In addition, perceived benefits for Long COVID prevention predicted intent of mask-wearing (p < .001), testing before a social event (p = .002), and getting a vaccine booster (p = .001). Perceived severity of Long COVID did not significantly predict adherence to preventive behaviors. U.S. adults are more likely to express intent to carry out COVID-19 preventive behaviors, such as masking and receiving booster vaccines, when they report feeling greater susceptibility to Long COVID as well as greater self-efficacy for engaging in these preventive behaviors. Public health messaging about Long COVID with incorporation of HBM constructs may be an effective means of increasing continued recommended COVID-19 preventive behaviors, which also hold co-benefits for prevention of infections, such as influenza and measles, as well as emerging viruses such as avian flu.

Limited studies have explored the effects of individual and environmental factors on sleep disturbance in individuals with inflammatory bowel disease (IBD), which is vital for informing future sleep interventions. Thus, the purpose of this study was to explore the possible mediated effects of potential precipitating and/or perpetuating factors on the relationship between sleep disturbance and symptoms in adults with IBD. This is a correlational study of adults with IBD recruited from ResearchMatch. Survey questions asked about demographics, clinical characteristics, sleep hygiene, sleep beliefs and attitudes, sleep environment, sleep control, and symptoms (sleep disturbance, abdominal pain, anxiety, depression, fatigue). Data analysis included descriptive statistics, Pearson correlations, and mediation analysis. We included 250 adults with IBD (M = 37.9 years old, 64.8% female, 72.4% white). Fifty-four percent of the sample self-reported having a diagnosed sleep disorder. Sleep hygiene and sleep beliefs and attitudes significantly mediated the relationship between sleep disturbance and symptoms (i.e., fatigue, depression, anxiety, and abdominal pain). Whereas the sleep environment and sleep control did not significantly mediate the relationship between sleep disturbance and symptoms. Individuals with IBD experience sleep disturbances, alongside symptoms of anxiety, depression, abdominal pain, and fatigue. Mediation analysis identified sleep hygiene and sleep beliefs, and attitudes as partial mediators. Considering cognitive-behavioral therapy for insomnia is recommended for restructuring these mediating factors. Participants also faced moderate environmental disturbances, suggesting a need for further investigation in this population.

Implantable Cardioverter Defibrillators (ICDs) are essential for managing life-threatening arrhythmias but can impact patients' quality of life (QoL), mood, and sexual health. Although QoL may improve shortly after implantation, factors such as age, psychological state, and ICD shocks can influence long-term outcomes. Anxiety, depression, and fears around physical and sexual activity are common among ICD patients, yet the depth of these effects remains underexplored. This cross-sectional, correlational study examined associations between QoL, mood, and sexual health in 30 adult ICD patients (ages 27-83) within 3 years postimplantation, recruited from a Southeastern U.S. academic medical center. Participants completed the SF-36, Profile of Mood States, and Multidimensional Sexuality Questionnaire. Spearman's correlations indicated that lower QoL was significantly associated with higher mood disturbance (rho = -0.645, p < .001) and lower sexual health (rho = 0.535, p = .005), though no significant link was found between mood disturbance and sexual health (rho = -0.279, p = .168). Multiple linear regression confirmed that QoL was influenced by both mood and sexual health. Post-hoc power analysis using EpiData verified that the sample size (n = 30) provided 90% power with a 5% error rate. These findings underscore the importance of addressing emotional and sexual well-being in ICD patient care. Targeted interventions could improve outcomes, but further research with larger samples is needed to deepen understanding of these relationships.