Wenwei Ren

Background: This study examined the association between fatigue and sleep disturbance among night‐shift nurses during the COVID‐19 pandemic and the mediating roles of depressive symptoms and social support. Methods: A cross‐sectional survey was conducted involving 1667 night‐shift nurses from multiple hospitals. The Pittsburgh Sleep Quality Index (PSQI), Fatigue Scale‐14, Hospital Anxiety and Depression Scale, and Social Support Rating Scale were used for assessment. Correlation analysis and mediation effect analysis were performed using SPSS 26.0 and PROCESS macro. Results: Poor sleep (PSQI ≥ 6) was reported by 70.7% of nurses. Increased workload (89.7%) and fear of infection (58.1%) were significantly associated with worse sleep (both p < 0.05). Fatigue correlated positively with depression ( r = 0.607, p < 0.001) and sleep disturbance ( r = 0.550, p < 0.001) and negatively with social support ( r = −0.242, p < 0.001). Mediation analysis identified three pathways: depressive symptoms alone (30.25% of the total effect), social support alone (0.80%), and the sequential depression ⟶ social support path (2.23%). Conclusions: Among night‐shift nurses, fatigue is directly and indirectly associated with poorer sleep quality via three mediating pathways: depressive symptoms alone, social support alone, and the sequential effect of depressive symptoms on social support. Interventions should address these pathways to improve sleep in night‐shift nurses under high‐load public health emergency contexts. Implications for Nursing Management: For night‐shift nurses facing excess workload during public health emergencies, managers could adopt a tiered strategy: (1) optimize shift schedules to reduce fatigue and (2) strengthen peer and family support systems. Regular monitoring of sleep and fatigue indicators is recommended to evaluate intervention effectiveness under such high‐stress contexts.

Background The HALP score (hemoglobin, albumin, lymphocyte, and platelet) is a novel indicator that measures systemic inflammation and nutritional status. The goal of this study was to look into the relationship between the HALP score and post-stroke cognitive impairment (PSCI) in people who had an acute ischemic stroke (AIS). Methods A total of 592 individuals with ischemic stroke were included in the research, and the PSCI (n = 382) and non-PSCI (n = 210) groups were determined using the Mini-Mental State Examination scale 2 weeks following the stroke. HALP score was computed by the formula: hemoglobin (g/L) × albumin (g/L) × lymphocytes (/L) / platelets (/L), and was split into three layers according to the tertiles. The connection between the HALP and cognitive results was investigated by binary logistic regression. Results The PSCI group’s HALP score was much lower than the non-PSCI group’s (p < 0.001). The HALP score was divided into three layers: T1 ≤ 34.0, T2 34.1–49.4, and T3 ≥ 49.5, respectively. In the binary regression analysis, taking the T3 layer as the reference, the T1 layer showed the highest risk of PSCI after adjusting for confounding factors (odds ratio (OR) = 1.965, 95% confidence interval (CI) = 1.237–3.122, p = 0.004), while there was no increased risk of PSCI in the T2 layer (OR = 1.538, 95%CI = 0.983–2.404, p = 0.059). Conclusion Low HALP score at admission was found to be correlated with early-onset PSCI and may help clinicians in the early identification of high-risk patients.

Glucose and platelet are two easily obtained clinical indicators; the present research aimed to demonstrate their association with hemorrhagic transformation (HT) in acute ischemic stroke (AIS) patients without thrombolytic or thrombectomy therapy. This was a single-center retrospective study. Patients who were diagnosed with HT after AIS were included in the HT group. Meanwhile, using the propensity score matching (PSM) approach, with a ratio of 1:2, matched patients without HT were included in the non-HT group. Serum G/P levels were measured on the first morning after admission (at least eight hours after the last meal). Characteristics were compared between the two groups. Multivariate logistic regression was used to determine the independent relationship between G/P and HT after AIS, with G/P being divided into quartiles. From January 2013 to March 2022, we consecutively included 643 AIS patients with HT (426/643 [66.25%] with HI and 217/643 [33.75%] with PH), and 1282 AIS patients without HT, at the First Affiliated Hospital of Wenzhou Medical University. The HT group had higher G/P levels than the non-HT group (0.04 ± 0.02 vs. 0.03 ± 0.02, p < 0.001). However, there was no difference in G/P levels between HI and PH subgroups (0.04 ± 0.02 vs. 0.04 ± 0.02, p > 0.05). Moreover, the G/P levels were divided into quartiles (Q1 ≤ 0.022; Q2 = 0.023–0.028; Q3 = 0.029–0.039; Q4 ≥ 0.040), with Q1 being settled as the reference layer. After controlling the confounders, multivariate regression analyses showed that the Q4 layer (Q4: G/P ≥ 0.040) was independently associated with elevated HT risk (odds ratio [OR] = 1.85, 95% CI = 1.31–2.63, p < 0.001). G/P levels on admission were independently associated with HT risk in AIS patients. In clinical practice, adequate attention should be paid to AIS patients with elevated G/P levels (G/P ≥ 0.040).

Background: It has been proven that the ratio of albumin to globulin (A/G) is a typical biomarker for monitoring inflammation and nutritional status. But the potential role of A/G in stroke-associated pneumonia (SAP) after acute ischemic stroke (AIS) remained unknown.Patients and methods: Following inclusion criteria, 5,173 AIS patients were included and segmented into SAP (n = 897) and non-SAP (n = 4,276) groups. The differences in variables between groups were compared. The logistic regression model was used to determine the association between A/G and SAP, and a forest plot was drawn.Results: Compared with the non-SAP group, the SAP group had a lower A/G level (P < 0.001). Then, A/G was divided into quartiles. In comparison to Q3 (A/G = 1.25–1.39), logistic regression revealed that patients with a lower A/G (A/G ≤ 1.09) had a higher risk of SAP (OR = 1.96, 95% CI, 1.56–2.46, P < 0.001). On the contrary, those with a higher A/G (A/G ≥ 1.4) had a lower SAP risk (OR = 0.73, 95% CI, 0.54–0.97, P = 0.029).Conclusion: The study revealed that a low A/G level was associated with an increased SAP risk. Appropriate preventative measures for SAP should be taken in AIS patients with a low A/G level.

Objective: Stroke‐associated pneumonia (SAP) is the most common early consequence in patients suffering from an acute ischaemic stroke (AIS). The purpose of this study was to explore the possible relationship between low triiodothyronine (T3) syndrome and SAP in stroke patients.Methods: This study recruited 2460 consecutive AIS patients. SAP was defined according to the modified Centers for Disease Control and Prevention criteria for hospital‐acquired pneumonia. The thyroid hormones levels were measured within 24 h after admission. Low T3 syndrome was characterized as T3 below the lower limit of the reference interval accompanied by normal TSH levels.Results: Among the total patients, 336 (13.7%) patients were diagnosed with SAP. SAP in individuals with low T3 syndrome was substantially greater (p < .001) as compared to those without low T3 syndrome. After adjusting for possible confounders, low T3 syndrome (adjusted odds ratio [aOR] = 1.59; 95% confidence interval [CI], 1.20–2.09; p = .001) remained significant in our logistic model. Patients with low T3 syndrome had a higher risk of severe SAP (aOR = 2.17, 95% confidence interval [CI] 1.38–3.44; p = .001).Conclusion: Low T3 syndrome, independent of recognized risk factors, is a possible risk factor for in‐hospital SAP, which can help clinicians in the early detection and treatment of high‐risk patients.

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.

Background: Complications such as cognitive impairment are common in stroke victims. The goal of this study was to see if there was a link between blood iron levels and post-stroke cognitive impairment (PSCI) within 2 weeks after stroke. Methods: A total of 313 patients with ischemic stroke were recruited and separated into two groups: PSCI (n = 202) and non-PSCI (n = 111). The Mini-mental state examination scale was used to evaluate the cognitive status within 2 weeks after stroke (acute phase). The serum iron levels were divided into 4 layers: Q1 ≤ 11.7 μmol/L, Q2 11.8-15.1 μmol/, Q3 15.2-19.3 μmol/L, Q4 ≥ 19.4 μmol/L, respectively. The connection between serum iron and PSCI was then investigated further using binary logistic regression, which was adjusted for confounders. Results: The difference in serum iron levels between the PSCI and non-PSCI group was initially conducted by the Mann-Whitney test, and a significant difference was found (14.5 (11.0-17.8) vs. 16.9 (13.7-21.8), p < .001), with no confounders being adjusted. After adjusting for confounding factors, the binary regression analysis showed that the Q4 layer showed the lowest risk of PSCI, with the Q1 layer being the reference. (odds ratio (OR) = 0.297, 95% confidence interval (CI) = 0.136-0.649, p = 0.002). Conclusion: A decreased risk of early-onset PSCI was linked to high serum iron levels. Low serum iron levels were found to be a risk factor for acute cognitive impairment following stroke, which could help physicians identify and take intervention measures early to reduce the risk of cognitive impairment after stroke.

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) is one of the common complications of stroke patients. Higher lactic dehydrogenase (LDH) and lower albumin levels were associated with SAP, but the contribution of the LDH to albumin ratio (LAR) to the risk of SAP in acute ischemic stroke (AIS) patients remained unclear.Methods:A total of 3173 AIS patients were included in this study, divided into SAP (n= 417) and non-SAP groups (n= 2756). Characteristics were compared between these two groups. The receiver operating characteristic curves (ROC) were used to evaluate the discrimination ability of the LAR, LDH, and albumin levels in predicting SAP. Logistic regression analysis was furtherly adopted to estimate the association between LAR and SAP. We also used the restricted cubic spline (RCS) to clarify the relationship between LAR and the risk of SAP.Results:LAR in the SAP group was significantly higher than that of the non-SAP group (8.75 ± 4.58 vs. 6.10 ± 2.55,P< 0.001). According to the results of ROC, LAR had the highest prognostic accuracy compared to LDH and albumin (P< 0.05). Besides, the logistic regression model showed that higher LAR (LAR > 6.75) were more vulnerable to SAP (OR, 2.80; 95% CI, 2.18–3.59,P< 0.001), controlling the confounders. The RCS model showed that there was a non-linear relationship between LAR and the risk of SAP.Conclusion:High LAR was associated with an increased risk of SAP in patients with AIS. LAR may be a potential predictor for the incidence of SAP. Appropriate prevention measures were needed in patients with high LAR (LAR > 6.75).

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.