Clinical Nursing Research最新文献

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.

We investigated the influence of social determinants of health (SDOH), healthcare services, and health behaviors on mental and physical health outcomes of cancer patients between the first winter and the following post-vaccine summer of the COVID-19 pandemic. A three-wave online survey of individuals diagnosed with incident cancer between January 2019 and January 2020 was conducted between November of 2020 and August of 2021 in northeast Ohio. Descriptive analysis and mixed-effect regression analyses were performed. A total of 322 newly diagnosed cancer patients, with 40 African Americans and 282 Whites (215 from metropolitan areas and 67 nonmetropolitan) responded to the survey questions. In Wave 3 ending in August 2021, the survey respondents reported significantly reduced depression (p = .019) on the Hamilton Depression Rating Scale and improved global health (p = .036) on PROMIS. With age, comorbidity, and other demographic and medical variables controlled in the analyses, the feeling of loneliness (p < .001) and crowded living space (p = .001, p = .015) were the two most prominent factors associated with depression, irritability, and poor global health at baseline, with the lowest p values and persistent effect. Self-efficacy of taking preventive measures was associated with reduced depression (p = .001) and improved global health (p = .029). Increasing access to medicine (p < .01) and satisfaction with telehealth appointments (p < .01) were significantly associated with better global health and reduced irritability. Respondents who had private health insurance reported better health than those that had Medicare coverage only (p < .05). This longitudinal, observational study demonstrated the impact of SDOH on health outcomes of cancer patients. Substandard living conditions resulting in loneliness and crowdedness, quality of medical care (e.g., quality telehealth and access to medicine), and personal behaviors (e.g., self-efficacy) were significantly associated with health outcomes in newly diagnosed cancer patients during the pandemic and should be given adequate consideration for the purpose of improving clinical care.

Social determinants of health (SDOH) are structural factors that yield health inequities. Within the context of cancer, these inequities include screening rates and survival rates, as well as higher symptom burden during and after treatment. While pain is one of the most frequently reported symptoms, the relationship between SDOHs and cancer pain is not well understood. The purpose of this study is to describe and synthesize the published research that has evaluated the relationships between SDOH and cancer pain. A systematic search of PubMed, CINAHL, and Embase was conducted to identify studies in which cancer pain and SDOH were described. In all, 20 studies met the inclusion criteria. In total, 14 studies reported a primary aim related to SDOH and cancer pain. Demographic variables including education or income were used most frequently. Six specific measurements were utilized to measure SDOH, such as the acculturation scale, the composite measure of zip codes for poverty level and blight prevalence, or the segregation index. Among the five domains of SDOH based on Healthy People 2030, social and community was the most studied, followed by economic stability, and education access and quality. The neighborhood and built environment domain was the least studied. Despite increasing attention to SDOH, the majority of published studies use single-dimension variables derived from demographic data to evaluate the relationships between SDOH and cancer pain. Future research is needed to explore the intersectionality of SDOH domains and their impact on cancer pain. Additionally, intervention studies should be conducted to address existing disparities and to reduce the incidence and impact of cancer pain.

Significant morbidity and mortality from COVID-19-related illnesses have been observed among people of color within the United States. While theories involving healthcare inequity and political division have emerged to explain this observation, the role of chronic stress and inflammation is also being explored. Toxic stress is experienced disproportionately by race, ethnicity, and socioeconomic status and increases frailty and vulnerability to diseases such as COVID-19. C-reactive protein (CRP) is a biomarker associated with the inflammatory response that is typically elevated due to exposure to acute or chronic traumatic stress, as well as COVID-19. This study explored the relationship between CRP and Hispanic/non-Hispanic ethnicity among adults hospitalized with COVID-19 via a secondary analysis of retrospective electronic health record (EHR) data collected from a community healthcare system in Southern California. A total of 1,744 cases representing hospitalized adults with COVID-19 were reviewed. Data were extracted from the EHR to reflect demographics, medical diagnoses, medications, CRP, and comorbidity burden. Frequencies, percentages, and measures of central tendency were assessed to understand the distribution of data. Associations were conducted using Pearson's r and the chi-square test of independence. Differences between groups were examined via independent samples t-tests. The sample was 52% Hispanic, 56% male, and the mean age was 62 years (SD = 16.1). The mean age of Hispanic cases was younger than non-Hispanic cases (p < .001, η = 0.289). Serum CRP was significantly higher in the Hispanic cases, with a high degree of association (p < .001, η = 0.472). In addition, higher CRP levels were significantly associated with the need for mechanical ventilation (p < .001, φ_c = 0.216). No significant relationships were found between CRP and age, body mass index (BMI), or comorbidity burden. Findings challenge the assumption that the disproportionate morbidity and mortality suffered by the Hispanic population due to COVID-19 was due to age, BMI, or comorbidities such as metabolic syndrome or heart disease. CRP in the Hispanic population should be further investigated to understand its relationship to chronic stress, frailty, and risk for COVID-19 in this population.