Objective: Stroke-associated pneumonia (SAP) is a frequent complication in stroke patients. This present study aimed to investigate the association between stress hyperglycemia and SAP.Methods: Patients were screened between February 2013 and August 2020 from the First Affiliated Hospital of Wenzhou Medical University. We divided the blood glucose of the patients at admission by the glycated hemoglobin to calculate the stress hyperglycemia ratio (SHR). Binary logistic regression analysis was used to identify the association between SAP and SHR, with the confounders being controlled. Further, subgroup analyses were separately performed for stroke patients with and without diabetes.Results: A total of 2,039 patients were finally recruited, of which 533 (26.14%) were diagnosed with SAP. SHR were divided into four quartiles in the logistic regression analysis, the highest SHR quartile (SHR ≥ 1.15) indicated a higher risk of SAP (OR = 1.57; 95% CI = 1.13–2.19, p = 0.01) in total patients. In patients without diabetes, the third quantile (SHR = 0.96–1.14) and the highest quantile (SHR ≥ 1.15) were both related to a higher risk of SAP (both p < 0.05). However, we did not find such an association in diabetic patients.Conclusion: SHR was significantly associated with the risk of SAP in patients without diabetes. Adequate attention should be paid to the patients with high SHR levels at admission, especially those without diabetes.

Background: Stroke‐associated pneumonia (SAP) occurs frequently after a stroke. Geriatric Nutritional Risk Index (GNRI) is a valuable indicator of elderly individuals’ nutritional status. This research was designed to obtain insight into the link between GNRI and SAP.Methods: Patients with acute ischemic stroke (AIS) were categorized into the SAP and non‐SAP groups. GNRI scores were divided into four layers: Q1, GNRI < 82; Q2, 82≤ GNRI < 92; Q3, 92≤ GNRI ≤98; Q4, GNRI > 98. To identify the independent risk and protective factors of developing SAP, logistic regression analyses were conducted. Additionally, we utilized the restricted cubic spline (RCS) analysis to test the effect of GNRI on the SAP risk.Results: The SAP group showed lower GNRI scores than the non‐SAP group (96.88 ± 9.36 vs. 100.88 ± 8.25, p < 0.001). According to the logistic regression model, the Q1 and Q2 layers showed a higher risk of SAP than the Q3 layer, while the Q4 layer showed a lower SAP risk (all p < 0.05). Besides, the RCS model found that the risk of SAP dropped dramatically as GNRI scores increased, which got stable when the GNRI score was more significant than 100.Conclusion: Lower GNRI scores were linked to a higher prevalence of SAP. In clinical practice, GNRI showed predictive value for SAP, which could be helpful in early SAP intervention and therapy.

Objective: Pneumonia is a common complication in patients with stroke. There was a close relationship between serum iron and inflammatory response. This study aimed to explore the relationship between serum iron levels and stroke-associated pneumonia (SAP). Methods: Patients with acute stroke were recruited from the First Affiliated Hospital of Wenzhou Medical University and divided into SAP group and Non-SAP group. The demographic and clinical data of the patients were collected via the medical records, and the blood samples were collected within 24 hours after admission. The predictive value of serum iron to SAP was evaluated by receiver operating characteristic curve (ROC) and binary Logistic regression models. A restricted cubic spline (RCS) was used to furtherly clarify the relationship between serum iron and the risk of SAP. Results: A total of 906 participants were enrolled, including Non-SAP group (n = 755) and SAP group (n = 151). Serum iron levels in the SAP group were significantly lower than those in the Non-SAP group (9.77± 5.61 vs 14.01± 6.80, P < 0.001). Logistic regression showed that patients with high serum iron levels (≥ 7.8μmol/L) showed a lower risk of SAP (OR=0.43, 95% CI, 0.27– 0.69, P < 0.001). Besides, the RCS model showed that there was an L-shaped relationship between the serum iron and risk of SAP (P for non-linearity: 0.014). Conclusion: Low serum iron level was a risk factor for SAP, and there was an L-shaped relationship between them. Stroke patients with low serum iron levels should be alert to the risk of SAP.

During the COVID-19 epidemic in China, clinical nurses are at an elevated risk of suffering fatigue. This research sought to investigate the correlation between dispositional mindfulness and fatigue among nurses, as well as the potential mediation role of sleep quality in this relationship. This online cross-sectional survey was performed from August to September 2022 to collect data from 2143 Chinese nurses after the re-emergence of COVID-19. The significance of the mediation effect was determined through a bootstrap approach with SPSS PROCESS macro. Higher levels of dispositional mindfulness were significantly negatively related to fatigue (r = -0.518, P < 0.001) and sleep disturbance (r = -0.344, P < 0.001). Besides, insufficient sleep was associated with fatigue (r = 0.547, P < 0.001). Analyses of mediation revealed that sleep quality mediated the correlation of dispositional mindfulness to fatigue (β = -0.137, 95% Confidence Interval = [-0.156, -0.120]). In the post-COVID-19 pandemic era, Chinese nurses’ dispositional awareness was related to the reduction of fatigue, which was mediated by sleep quality. Intervention strategies and measures should be adapted to improve dispositional mindfulness and sleep quality to reduce fatigue in nurses during the pandemic.

Background The COVID-19 pandemic has led to increased depressive symptoms and diminished sleep quality among nurses. This study explores the relationships among social support, sleep quality, and depressive symptoms among Chinese nurses during the COVID-19 outbreak, with a focus on the mediating role of sleep quality. Methods A cross-sectional study involving 2140 nurses was conducted from August to September 2022. The participants completed the Social Support Rating Scale, the Hospital Anxiety and Depression Scale, and the Pittsburgh Sleep Quality Index. The data were analyzed via serial multiple mediation analysis with the PROCESS macro in SPSS. Results Depressive symptoms affected an estimated 40.4% of the participants, with 69.2% reporting poor sleep quality. The present study revealed a significant negative correlation between social support and sleep quality and depressive symptoms (r = -0.229, p < 0.001; r = -0.322, p < 0.001, respectively). Furthermore, a substantial positive correlation was observed between sleep quality and depressive symptoms (r = 0.514, p < 0.001). Additionally, social support had a significant indirect effect on depressive symptoms through sleep quality (standardized effect = -0.0535, 95% CI = [-0.0648, -0.0424]). Sleep quality was found to mediate the relationship between social support and depressive symptoms in all subgroups, with variations based on years of experience, education levels, and hospital type. Conclusion Depressive symptoms were inversely associated with social support, which was moderated by sleep quality. Interventions targeting social support provision and sleep quality promotion are suggested for managing depressive symptoms among nurses during the COVID-19 pandemic.

Nurses who work during the global pandemic are known to experience physical and psychological exhaustion, as well as severe anxiety and depression symptoms. This study aimed to explore the relationships between sleep duration, sleep quality, and depression symptoms among nurses during the outbreak of the Omicron variant. A cross-sectional study was conducted between August 2022 and September 2022. Participants (N = 2140) were evaluated for depression symptoms via the Hospital Anxiety and Depression Scale (HADS), and sleep was evaluated via the Pittsburgh Sleep Quality Index (PSQI), and "short sleep duration" was defined as ≤ 5 h per day. Demographic information was also collected. Binary and multivariate logistic regression was performed to assess the relationships between sleep duration, sleep quality, and depression symptoms. In total, 2140 nurses were included in this study; 1481 (69.2%) had poor sleep quality, while 866 (40.4%) had depression symptom scores > 7 according to the HADS criteria. Both duration and quality of sleep were significantly correlated with depression symptoms among nurses (P < 0.001). In multivariable analyses adjusted for potential confounders, short sleep duration (≤ 5 h) was associated with an odds ratio (OR) of 2.26 (95% confidence interval [CI] 1.25–4.07), whereas poorer sleep quality was associated with an OR of 1.97 (95% CI 1.32–2.94) for experiencing depression symptoms. Following the COVID-19 pandemic, there was a strong association between the sleep quality, sleep duration and depression symptoms among nurses. We recommend the development of targeted interventions to increase sleep duration, enhance sleep quality and alleviate depression symptoms.