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: To explore whether poor sleep is associated with post‐stroke anxiety (PSA) in Chinese patients with acute ischemic stroke (AIS) and to verify whether poor sleep is a predictor of PSA.Methods: A total of 327 patients with AIS were enrolled and followed up for 1 month. Sleep quality within 1 month before stroke was evaluated using the Pittsburgh Sleep Quality Index (PSQI) at admission. The patients were divided into the poor sleep group (PSQI > 7, n = 76) and good sleep group (PSQI ≤ 7, n = 251). One month after stroke, patients with obvious anxiety symptoms and a Hamilton Anxiety Scale score >7 were diagnosed with PSA.Results: Eighty‐seven patients (26.6%) were diagnosed with PSA. Compared to the good sleep quality group, the incidence of PSA in patients with poor sleep quality was higher (42.1% vs. 21.9%, p = .001). Poor sleep quality is more common in patients with PSA (35.6% vs. 18.8%, p = .001). A logistic regression analysis indicated that poor sleep quality was significantly associated with PSA (OR: 2.265, 95% CI: 1.262–4.067, p = .003). After adjusting for conventional and identified risk factors, poor sleep quality was found to be independently associated with PSA (OR: 2.676, 95% CI: 1.451–4.936, p = .001).Conclusions: Poor sleep quality before stroke was associated with PSA and may be an independent risk factor of PSA 1 month after AIS onset.

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).

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.