&lt;p&gt;Value of Combination of the A&lt;sup&gt;2&lt;/sup&gt;DS&lt;sup&gt;2&lt;/sup&gt; Score and IL-6 in Predicting Stroke-Associated Pneumonia&lt;/p&gt;

Yang, Jun; Yong-hong, Dai; Zhang, Zuowen; Chen, Yue

doi:10.2147/ndt.s268878

Cited by 12 publications

(9 citation statements)

References 39 publications

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“…First, it was the first study to explore the association between SAP and CSVD. Second, we also proposed models to predict the occurrence of SAP and the development of poor outcomes, continuing the findings of previous research (13,14,46), and improving the ability of clinical prediction. However, some limitations in our study should be noted.…”

Section: Discussionsupporting

confidence: 53%

“…Previous studies had identified older age, atrial fibrillation, congestive heart failure, stroke severity, stroke subtype, and dysphagia, as important risk factors (10)(11)(12)(13), and established several SAP scales for early prediction, such as the A 2 DS 2 scale, the ISAN scale and the AIS-APS scale (6,12,14). The prediction efficiency of the A 2 DS 2 scale was better than others (15).…”

Section: Introductionmentioning

confidence: 99%

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A2DS2 Score Combined With Clinical and Neuroimaging Factors Better Predicts Stroke-Associated Pneumonia in Hyperacute Cerebral Infarction

Xia²,

Tan³

et al. 2022

Front. Neurol.

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ObjectiveTo investigate the predictors of stroke-associated pneumonia (SAP) and poor functional outcome in patients with hyperacute cerebral infarction (HCI) by combining clinical factors, laboratory tests and neuroimaging features.MethodsWe included 205 patients with HCI from November 2018 to December 2019. The diagnostic criterion for SAP was occurrence within 7 days of the onset of stroke. Poor outcome was defined as a functional outcome based on a 3-months MRS score >3. The relationship of demographic, laboratory and neuroimaging variables with SAP and poor outcome was investigated using univariate and multivariate analyses.ResultsFifty seven (27.8%) patients were diagnosed with SAP and 40 (19.5%) developed poor outcomes. A2DS2 score (OR = 1.284; 95% CI: 1.048–1.574; P = 0.016), previous stroke (OR = 2.630; 95% CI: 1.122–6.163; P = 0.026), consciousness (OR = 2.945; 95% CI: 1.514–5.729; P < 0.001), brain atrophy (OR = 1.427; 95% CI: 1.040–1.959; P = 0.028), and core infarct volume (OR = 1.715; 95% CI: 1.163–2.528; P = 0.006) were independently associated with the occurrence of SAP. Therefore, we combined these variables into a new SAP prediction model with the C-statistic of 0.84 (95% CI: 0.78–0.90). Fasting plasma glucose (OR = 1.404; 95% CI: 1.202–1.640; P < 0.001), NIHSS score (OR = 1.088; 95% CI: 1.010–1.172; P = 0.026), previous stroke (OR = 4.333; 95% CI: 1.645–11.418; P = 0.003), SAP (OR = 3.420; 95% CI: 1.332–8.787; P = 0.011), basal ganglia-dilated perivascular spaces (BG-dPVS) (OR = 2.124; 95% CI: 1.313–3.436; P = 0.002), and core infarct volume (OR = 1.680; 95% CI: 1.166–2.420; P = 0.005) were independently associated with poor outcome. The C-statistic of the outcome model was 0.87 (95% CI: 0.81–0.94). Furthermore, the SAP model significantly improved discrimination and net benefit more than the A2DS2 scale, with a C-statistic of 0.76 (95% CI: 0.69–0.83).ConclusionsAfter the addition of neuroimaging features, the models exhibit good differentiation and calibration for the prediction of the occurrence of SAP and the development of poor outcomes in HCI patients. The SAP model could better predict the SAP, representing a helpful and valid tool to obtain a net benefit compared with the A2DS2 scale.

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Section: Discussionsupporting

confidence: 53%

Section: Introductionmentioning

confidence: 99%

A2DS2 Score Combined With Clinical and Neuroimaging Factors Better Predicts Stroke-Associated Pneumonia in Hyperacute Cerebral Infarction

Xia²,

Tan³

et al. 2022

Front. Neurol.

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“…Therefore, the combination of serum albumin and body weight in GNRI may minimize the effects of confounding factors such as hydration status, which could better predict the adverse prognosis related to nutrition. Multiple biomarkers were associated with inflammation and showed the ability to predict outcomes or SAP events in stroke, such as CRP and IL‐6 (Lu et al., 2015 ; Yang et al., 2020 ). A study indicated that IL‐6 was related to SAP via activating the production of acute‐phase proteins and CRP in AIS (Yang et al., 2020 ).…”

Section: Discussionmentioning

confidence: 99%

“…Multiple biomarkers were associated with inflammation and showed the ability to predict outcomes or SAP events in stroke, such as CRP and IL‐6 (Lu et al., 2015 ; Yang et al., 2020 ). A study indicated that IL‐6 was related to SAP via activating the production of acute‐phase proteins and CRP in AIS (Yang et al., 2020 ). Besides, acute stroke may induce depression of the immune system with an elevated susceptibility to infections (Haeusler et al., 2008 ), which may be partly promoted by increased IL‐6, for the reason that IL‐6 plays a role in determining cellular immunity (Yang et al., 2020 ).…”

Section: Discussionmentioning

confidence: 99%

Geriatric Nutritional Risk Index is related to the risk of stroke‐associated pneumonia

Dai

Duan

et al. 2022

Brain and Behavior

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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.

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“…Stroke-associated pneumonia (SAP) often occurs after a stroke; approximately 7%–38% [ 3 – 8 ] of stroke patients are diagnosed with SAP during hospitalization. Once a patient develops SAP, it leads to a poor prognosis and a prolonged hospital duration [ 9 – 12 ].Early diagnosis and preventive treatment are necessary for patients at high risk of SAP. Therefore, an effective biomarker is needed for risk assessment and early detection of SAP.…”

Section: Introductionmentioning

confidence: 99%

Association of Platelet-to-Lymphocyte Ratio with Stroke-Associated Pneumonia in Acute Ischemic Stroke

2022

Journal of Healthcare Engineering

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A common consequence of acute ischemic stroke (AIS), stroke-associated pneumonia (SAP), might result in a poor prognosis after stroke. Based on the critical position of inflammation in SAP, this study aimed to explore the correlation between platelet-to-lymphocyte ratio (PLR) and the occurrence of SAP. We included 295 patients with acute ischemic stroke, 40 with SAP, and 255 without SAP. The area under the receiver operating characteristic curve was used to determine the diagnostic value of SAP risk factors using binary logistic regression analysis. The comparison between the two groups showed that age, the baseline National Institutes of Health Stroke Scale (NIHSS) score, and the proportion of dysphagia, atrial fibrillation, and total anterior circulation infarct were higher, and the proportion of lacunar circulation infarct was lower in the SAP group (P < 0.001). In terms of laboratory data, the SAP group had considerably greater neutrophil counts and PLR, while the non-SAP group (P < 0.001) had significantly lower lymphocyte counts and triglycerides. Binary logistic regression analysis revealed that older age (aOR = 1.062, 95% CI: 1.023–1.102, P = 0.002), atrial fibrillation (aOR = 3.585, 95% CI: 1.605–8.007, P = 0.019), and PLR (aOR = 1.003, 95% CI: 1.001–1.006, P = 0.020) were independent risk factors associated with SAP after adjusting for potential confounders. The sensitivity and specificity of PLR with a cutoff value of 152.22 (AUC: 0.663, 95% CI: 0.606–0.717, P = 0.0006) were 57.5% and 70.6%, respectively. This study showed that high PLR is an associated factor for SAP in AIS patients. Increased systemic inflammation is linked to SAP in ischemic stroke. Inflammatory biomarkers that are easily accessible may aid in the diagnosis of high-risk SAP patients.

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<p>Value of Combination of the A<sup>2</sup>DS<sup>2</sup> Score and IL-6 in Predicting Stroke-Associated Pneumonia</p>

Cited by 12 publications

References 39 publications

A2DS2 Score Combined With Clinical and Neuroimaging Factors Better Predicts Stroke-Associated Pneumonia in Hyperacute Cerebral Infarction

A2DS2 Score Combined With Clinical and Neuroimaging Factors Better Predicts Stroke-Associated Pneumonia in Hyperacute Cerebral Infarction

Geriatric Nutritional Risk Index is related to the risk of stroke‐associated pneumonia

Association of Platelet-to-Lymphocyte Ratio with Stroke-Associated Pneumonia in Acute Ischemic Stroke

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