Clinical Nursing Research最新文献

Hepatitis C virus (HCV), the most common blood-borne infection, disproportionately affects people experiencing homelessness (PEH); however, HCV interventions tailored for PEH are scarce. This study utilized a community-based participatory approach to assess perceptions of HCV treatment experiences among HCV-positive PEH, and homeless service providers (HSP) to develop and tailor the "I am HCV Free" intervention which integrates primary, secondary, and tertiary care to attain and maintain HCV cure. Four focus groups were conducted with PEH (N = 30, M_age = 51.76, standard deviation 11.49, range 22-69) and HSPs (n = 10) in Central City East (Skid Row) in Los Angeles, California. An iterative, thematic approach was used to ensure the trustworthiness of the data. Barriers and facilitators emerged from the data which have the potential to impact initiating HCV treatment and completion across the HCV care continuum. Understanding and addressing barriers and strengthening facilitators to HCV treatment will aid in HCV treatment completion and cure for PEH.

There is growing awareness of the significant mental health impacts of the COVID-19 pandemic on many Americans. Less is known about the effects on individuals who were living with mental health conditions prior to the pandemic's onset. In addition, little research has explored how this group is coping positively with the challenges of COVID-19. Understanding the strengths these individuals bring to pandemic demands and disruptions can inform recovery for these individuals in the aftermath of this public health emergency. Using results from a cross-sectional, online survey administered during April and May 2020, we use qualitative methods to examine how individuals with symptoms of depression and anxiety were coping with COVID-19. Participants were recruited from two networks of statewide behavioral health community programs in New Jersey and New York. Data come from 48 participants who reported current symptoms of anxiety assessed by the Generalized Anxiety Disorder-2 Scale and/or depression assessed by the Patient Health Questionnaire-2. These respondents demonstrated resilience in navigating disruptions brought on by COVID-19 and reported a range of healthy coping strategies. We identified three themes characterizing successful coping strategies, including utilizing social support systems, practicing self-care, and adjusting one's mindset to deal with challenging experiences. When designing programs, policies, and clinical approaches to support people with mental health conditions, it is essential to focus on strengths. The coping strategies shared by the individuals in this study demonstrate and build on their resilience. More research is needed to discover the strengths people exhibit to deal with the challenges caused by the COVID-19 pandemic.

Patients with ischemic stroke have an increased propensity to fall, resulting in significant physical and psychological distress. This study examined the association between falls in the 3 months prior to intensive care unit (ICU) admission and mortality within 28 days among 2950 adult ICU patients diagnosed with ischemic stroke from 2008 to 2019, focusing on the potential mediating role of delirium. The primary outcomes were short-term mortality (28, 60, and 90 days) and the risk of delirium. Each patient was followed for at least 1 year. Delirium was primarily assessed using the Confusion Assessment Method for the ICU and by reviewing nursing notes. Group differences between patients with and without a history of falls were compared using the Wilcoxon rank-sum test or the chi-squared test. Cox proportional risk or logistic regression models were used to explore the association between fall history and outcomes, and causal mediation analysis was performed. Results showed that patients with a recent fall history had a significantly increased risk of 28-day (hazard ratio [HR]: 1.62, 95% confidence interval [CI]: 1.35-1.94), 60-day (HR: 1.67, 95% CI: 1.42-1.98), and 90-day mortality (HR: 1.66, 95% CI: 1.41-1.95), as well as an increased risk of delirium (odds ratio: 2.00, 95% CI: 1.66-2.42). Delirium significantly mediated the association between fall history and 28-day mortality (total effect: HR: 1.77, 95% CI: 1.45-2.16; natural indirect effect: HR: 1.12, 95% CI: 1.05-1.21; proportion mediated: 24.6%). These findings suggest that ischemic stroke patients with a recent fall have an increased risk of short-term mortality, partly mediated by delirium. Strategies aimed at preventing delirium may potentially improve prognosis in this patient population.

To investigate and define the concept of perioperative sleep disturbance (PSD) among surgical patients, with the goal of aiding clinical practice and research. Walker and Avant's eight-step approach of concept analysis was applied. A systematic search of English literature was conducted in the following databases: PubMed, Web of Science, and CINAHL, with a time restriction from 2010 to August 2023. Based on the 54 eligible studies, the attributes of PSD in surgical patients were identified as individualized symptom manifestation, difficulty initiating and/or maintaining sleep, and altered sleep patterns. The antecedents included poor psychological state, inaccurate perception, surgery and/or anesthesia-related physiological changes, and environmental interference. PSD in surgical patients was found to result in physical discomfort, psychological disorder, impaired neurocognitive function, and prolonged recovery. A clearly defined and distinguishable concept of PSD in surgical patients was achieved through concept analysis, which provides a conceptual basis for future development in both clinical practice and related research.

This study aims to use patient feature and catheterization technology feature variables to train the corresponding machine learning (ML) models to predict peripherally inserted central catheters-deep vein thrombosis (PICCs-DVT) and analyze the importance of the two types of features to PICCs-DVT from the aspect of "input-output" correlation. To comprehensively and systematically summarize the variables used to describe patient features and catheterization technical features, this study combined 18 literature involving the two types of features in predicting PICCs-DVT. A total of 21 variables used to describe the two types of features were summarized, and feature values were extracted from the data of 1,065 PICCs patients from January 1, 2021 to August 31, 2022, to construct a data sample set. Then, 70% of the sample set is used for model training and hyperparameter optimization, and 30% of the sample set is used for PICCs-DVT prediction and feature importance analysis of three common ML classification models (i.e. support vector classifier [SVC], random forest [RF], and artificial neural network [ANN]). In terms of prediction performance, this study selected four metrics to evaluate the prediction performance of the model: precision (P), recall (R), accuracy (ACC), and area under the curve (AUC). In terms of feature importance analysis, this study chooses a single feature analysis method based on the "input-output" sensitivity principle-Permutation Importance. For the mean model performance, the three ML models on the test set are P = 0.92, R = 0.95, ACC = 0.88, and AUC = 0.81. Specifically, the RF model is P = 0.95, R = 0.96, ACC = 0.92, AUC = 0.86; the ANN model is P = 0.92, R = 0.95, ACC = 0.88, AUC = 0.81; the SVC model is P = 0.88, R = 0.94, ACC = 0.85, AUC = 0.77. For feature importance analysis, Catheter-to-vein rate (RF: 91.55%, ANN: 82.25%, SVC: 87.71%), Zubrod-ECOG-WHO score (RF: 66.35%, ANN: 82.25%, SVC: 44.35%), and insertion attempt (RF: 44.35%, ANN: 37.65%, SVC: 65.80%) all occupy the top three in the ML models prediction task of PICCs-DVT, showing relatively consistent ranking results. The ML models show good performance in predicting PICCs-DVT and reveal a relatively consistent ranking of feature importance from the data. The important features revealed might help clinical medical staff to better understand and analyze the formation mechanism of PICCs-DVT from a data-driven perspective.

This study aimed to explore whether differences exist in anesthesia care providers' use of intraoperative medication between African American and non-Hispanic White patients in adult surgical patients who underwent noncardiothoracic nonobstetric surgeries with general anesthesia. A retrospective observational cohort study used electronic health records between January 1, 2018 and August 31, 2019 at a large academic health system in the southeastern United States. To evaluate the isolated impact of race on intraoperative medication use, inverse probability of treatment weighting using the propensity scores was used to balance the covariates between African American and non-Hispanic White patients. Regression analyses were then performed to evaluate the impact of race on the total dose of opioid analgesia administered, and the use of midazolam, sugammadex, antihypotensive drugs, and antihypertensive drugs. Of the 31,790 patients included in the sample, 58.9% were non-Hispanic Whites and 13.6% were African American patients. After adjusting for significant covariates, African American patients were more likely to receive midazolam premedication (p < .0001; adjusted odds ratio [aOR] = 1.17, 99.9% CI [1.06, 1.30]), and antihypertensive drugs (p = .0002; aOR = 1.15, 99.9% CI [1.02, 1.30]), and less likely to receive antihypotensive drugs (p < .0001; aOR = 0.85, 99.9% CI [0.76, 0.95]) than non-Hispanic White patients. However, we did not find significant differences in the total dose of opioid analgesia administered, or sugammadex. This study identified differences in intraoperative anesthesia care delivery between African American and non-Hispanic White patients; however, future research is needed to understand mechanisms that contribute to these differences and whether these differences are associated with patient outcomes.

To clarify and refine the specific elements of post-transplant recovery in lung transplant recipients, we explored the four dimensions of recovery: physiological, psychological, social, and habitual. This study is a scoping review. Two authors conducted a comprehensive electronic literature search to identify studies published from the establishment of the database to August 2022. Deductive coding was utilized to identify and categorize elements using a predefined list of the four components (physiological, psychological, social, and habitual recovery) based on the framework of post-transplant recovery proposed by Lundmark et al. Inductive coding was applied for concepts requiring further classification. The review followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews guideline. Systematic searching identified 8,616 potential records, of which 51 studies met the inclusion criteria. Ten subdimensions and their corresponding elements were identified and categorized into four dimensions of recovery following lung transplantation. The subdimensions included physiological recovery (including symptom experience, complications, physical function, and energy reserve), psychological recovery (encompassing affective distress, psychological adaptation, and transition from illness to health), social recovery (involving family adaptation and social adaptation), and habit recovery (focusing on health behavior).

Background: Symptoms of anxiety and depression are common in women with gestational diabetes mellitus (GDM). Mobile health (mHealth)-based lifestyle interventions have been shown to be effective in improving glycemic control of these women.

Purpose/objective: The aim of the study was to evaluate the influence of mHealth-based lifestyle interventions on symptoms of anxiety and depression in women with GDM.

Design: A systematic review and meta-analysis or randomized controlled trials.

Setting: Clinical or community-based settings.

Sample: Nine studies involving 1,168 pregnant women with GDM were included.

Intervention: mHealth-based lifestyle interventions.

Measures: Symptoms of anxiety and depression quantitatively analyzed in clinical scales.

Analysis: A systematic literature search was performed in electronic databases, including PubMed, Cochrane library, Embase, Web of Science, Wanfang, and China National Knowledge Infrastructure to obtain relevant randomized controlled studies. A random-effects model was used to pool the results by incorporating the impact of the potential heterogeneity.

Results: Findings revealed that when compared to usual care, women who received mHealth-based lifestyle interventions had significant improvements in symptoms of anxiety (standardized mean difference [SMD]: -0.55, 95% CI [-0.77, -0.33], p < .001; I² = 67%) and depression (SMD: -0.51, [-0.72, -0.29], p < .001; I² = 65%). Sensitivity analyses by excluding one study at a time showed consistent results. Subgroup analyses showed similar results in mHealth achieved by phone, websites, and applications, in mHealth targeting diet and exercise with and without psychological support, in mHealth lead by nurse with and without other clinical specialists, and in studies with different evaluating tools for anxiety and depression.

Conclusions: mHealth-based lifestyle interventions could significantly improve the symptoms of anxiety and depression in women with GDM.

Dyspnea secondary to lung impairment can persist following the acute phase of COVID-19. Thoracic expansion measurements have been used as a diagnostic tool to evaluate chest wall mobility, respiratory function, and the effects of respiratory muscle strength training. Changes in chest wall mobility may occur because of altered chest biomechanics in individuals with respiratory diseases and an elevated body mass index (BMI). The purpose of this secondary analysis was to evaluate whether BMI influences thoracic expansion or forced expiratory volume over 1 second (FEV1) in individuals with persistent dyspnea following COVID-19. This study assessed the relationship between BMI and thoracic expansion, pulmonary symptoms, and exercise capacity following a home-based pulmonary rehabilitation intervention. A secondary data analysis was conducted with a sample of 19 adults with persistent dyspnea following COVID-19 infection who participated in a 12-week, home-based pulmonary rehabilitation study. Participants received expiratory muscle strength training devices and were instructed to perform pulmonary rehabilitation exercises three times per week over the study period. Pulmonary function, pulmonary symptoms, exercise capacity, and BMI measurements were collected. For analysis, study participants were divided into obese (BMI > 30 kg/m²) or nonobese (BMI < 30 kg/m²) categories. Correlations using the change scores from baseline to 12 weeks between thoracic expansion, FEV1, pulmonary symptoms, and exercise capacity were assessed. In addition, the minimal detectable change (MDC) in thoracic expansion was explored. Thoracic expansion was significantly improved after 12 weeks of training (p = .012) in the nonobese group. There was a significant correlation between the change in walking distance and pulmonary symptoms (r = -.738, p < .001) and in thoracic expansion (r = .544, p = .020), and walking distance, when controlling for BMI, but no change in FEV1. Average MDC was 1.28 for inspiration and 0.91 for expiration. Measurements of thoracic expansion were significantly lower in post-COVID individuals with an increased BMI. Individuals with persistent dyspnea and a higher BMI may require additional measures to increase chest mobility or to detect pulmonary changes following COVID-19.