Stroke-associated Pneumonia In Patients Research Articles

Association between lymphocyte-to-monocyte ratio and stroke-associated pneumonia: a retrospective cohort study

Background Stroke-associated pneumonia (SAP) is a common complication of acute ischemic stroke (AIS) and is associated with increased mortality and prolonged hospital stays. The lymphocyte-to-monocyte ratio (LMR) is a novel inflammatory marker that has been shown to be associated with various diseases. However, the relationship between the LMR and SAP in patients with AIS remains unclear. Methods A retrospective cohort study was conducted on 1,063 patients with AIS admitted to our hospital within 72 hours of symptom onset. Patients were divided into two groups: the SAP group (n = 99) and the non-SAP group (n = 964). The LMR was measured within 24 hours of admission, and the primary outcome was the incidence of SAP. We used univariate and multivariate logistic regression analyses to assess the relationship between the LMR and SAP. Additionally, curve-fitting techniques and subgroup analyses were conducted. Result The incidence of SAP was 9.31%. We found that the LMR was significantly lower in the SAP group than in the non-SAP group (2.46 ± 1.44 vs. 3.86 ± 1.48, P < 0.001). A nonlinear relationship was observed between the LMR and the incidence of SAP. Subgroup analysis revealed that an elevated LMR was associated with a reduced incidence of SAP in individuals with an LMR below 4. Multivariate logistic regression analysis demonstrated that LMR was an independent predictor of SAP (OR = 0.37, 95% CI [0.27–0.53]). Conclusion Our study suggests that the LMR is an independent predictor of SAP in patients with AIS, particularly when the LMR is less than 4. The LMR may serve as a promising biomarker for the early identification of patients with AIS at a high risk of SAP.

Systemic inflammatory response index as a predictor of stroke-associated pneumonia in patients with acute ischemic stroke treated by thrombectomy: a retrospective study

BackgroundThe predictive value of systemic inflammatory response index (SIRI) for stroke-associated pneumonia (SAP) risk in patients with acute ischemic stroke (AIS) treated by thrombectomy remains unclear. This study aimed to investigate the predictive value of SIRI for SAP in patients with AIS treated by thrombectomy.MethodsWe included AIS patients treated by thrombectomy between August 2018 and August 2022 at our institute. We used multivariate logistic regression to construct the prediction model and performed a receiver operating characteristic curve analysis to evaluate the ability of SIRI to predict SAP and constructed a calibration curve to evaluate the prediction accuracy of the model. We evaluated the clinical application value of the nomogram using decision curve analysis.ResultsWe included 84 eligible patients with AIS in the analysis, among which 56 (66.7%) had SAP. In the univariate analysis, there were significant differences in sex (p = 0.035), National Institute of Health Stroke Scale score at admission ≥ 20 (p = 0.019) and SIRI (p < 0.001). The results of multivariable logistic analysis showed that the risk of SAP increased with the SIRI value (OR = 1.169, 95% CI = 1.049–1.344, p = 0.014). Age ≥ 60 (OR = 4.076, 95% CI = 1.251–14.841, p = 0.024) was also statistically significant. A nomogram with SIRI showed good prediction accuracy for SAP in AIS patients treated by thrombectomy (C-index value = 0.774).ConclusionsSIRI is an independent predictor for SAP in patients with AIS treated by thrombectomy. A high SIRI value may allow for the early identification of patients with AIS treated by thrombectomy at high risk for SAP.

The clinical value of nutritional and inflammatory indicators in predicting pneumonia among patients with intracerebral hemorrhage

Immunosuppression and malnutrition play pivotal roles in the complications of intracerebral hemorrhage (ICH) and are intricately linked to the development of stroke-associated pneumonia (SAP). Inflammatory markers, including NLR (neutrophil-to-lymphocyte ratio), SII (systemic immune inflammation index), SIRI (systemic inflammatory response index), and SIS (systemic inflammation score), along with nutritional indexes such as CONUT (controlling nutritional status) and PNI (prognostic nutritional index), are crucial indicators influencing the inflammatory state following ICH. In this study, our objective was to compare the predictive efficacy of inflammatory and nutritional indices for SAP in ICH patients, aiming to determine and explore their clinical utility in early pneumonia detection. Patients with severe ICH requiring ICU admission were screened from the Medical Information Mart for Intensive Care IV (MIMIC-IV) database. The outcomes included the occurrence of SAP and in-hospital death. Receiver operating characteristic (ROC) analysis, multivariate logistic regression, smooth curve analysis, and stratified analysis were employed to investigate the relationship between the CONUT index and the clinical outcomes of patients with severe ICH. A total of 348 patients were enrolled in the study. The incidence of SAP was 21.3%, and the in-hospital mortality rate was 17.0%. Among these indicators, multiple regression analysis revealed that CONUT, PNI, and SIRI were independently associated with SAP. Further ROC curve analysis demonstrated that CONUT (AUC 0.6743, 95% CI 0.6079–0.7408) exhibited the most robust predictive ability for SAP in patients with ICH. Threshold analysis revealed that when CONUT < 6, an increase of 1 point in CONUT was associated with a 1.39 times higher risk of SAP. Similarly, our findings indicate that CONUT has the potential to predict the prognosis of patients with ICH. Among the inflammatory and nutritional markers, CONUT stands out as the most reliable predictor of SAP in patients with ICH. Additionally, it proves to be a valuable indicator for assessing the prognosis of patients with ICH.

Predictive Value of Serum Inflammatory Factors and FT3 for Stroke-Associated Pneumonia in Patients With Acute Ischemic Stroke

Objective: The ability of serum inflammatory factors and free triiodothyronine (FT3) in predicting the occurrence of stroke-associated pneumonia (SAP) in patients with acute ischemic stroke (AIS) was assessed in this study. Methods: A retrospective analysis was conducted on 285 consecutive patients with AIS initially diagnosed and admitted to our hospital from January to December 2022. Patients were categorized into SAP and non-SAP groups based on the presence of SAP. Both groups were compared in terms of baseline characteristics, including National Institute of Health Stroke Scale (NIHSS) score, SAP risk assessment (A2DS2), TOAST classification. Independent risk factors for SAP were identified using multivariate logistic regression analysis, and the predictive value of inflammatory markers was evaluated through ROC curves. Results: Among 285 patients with AIS, 40 (14.03%) were found to have developed SAP. Higher NIHSS and A2DS2 scores, elevated serum IL-1β, IL-8, and IL-33 levels, increased age, atrial fibrillation, swallowing difficulties, and a higher proportion of patients with low FT3 levels were observed in the SAP group compared with the non-SAP group (all P&lt;0.05). Significant risk factors for SAP in patients with AIS were identified through multivariate logistic regression analysis, including age, swallowing difficulties, NIHSS, A2DS2 , IL-1β , IL-8 , IL-33, and FT3 (P&lt;0.05). The highest predictive values were observed for A2DS2, FT3, and IL-8 with AUC values of 0.854, 0.844, and 0.823, respectively. Conclusion: SAP can be highly predicted by A2DS2, FT3, and IL-8, enabling the early identification of patients with high-risk SAP and facilitating timely intervention and treatment.

Serial ASPECTS to predict stroke-associated pneumonia after thrombolysis in patients with acute ischemic stroke.

Stroke-associated pneumonia (SAP) is a serious complication in stroke patients, significantly increasing mortality. The Alberta Stroke Program Early CT Score (ASPECTS) is a recognized predictor of acute ischemic stroke outcomes. We aimed to investigate the performance of serial ASPECTS assessments (baseline ASPECTS, 24-h ASPECTS, and change in ASPECTS) for predicting SAP in patients with thrombolyzed acute anterior circulation ischemic stroke (AACIS). A retrospective observational cohort study of adult patients with thrombolyzed AACIS was conducted. Baseline and 24-h ASPECTS using non-contrast computed tomography (NCCT), complications of stroke, including SAP and swallowing dysfunction using the Modified Water Swallowing test, were collected. Baseline and 24-h ASPECTS were evaluated by a certified neurologist and neuroradiologist. The predictive performance was determined based on the receiver operating characteristic curve (ROC). Multivariable logistic regression analyses were employed to assess the impact of serial ASPECTS assessment on predicting SAP. Of the 345 patients with thrombolyzed AACIS in our study, 18.4% (64/345) experienced SAP. The patients' median age was 62 years [interquartile range (IQR): 52-73], with 53.4% being male. The median NIHSS score was 11 points (IQR: 8-17). The ROC analysis revealed areas under the curve for predicting SAP with baseline ASPECTS, 24-h ASPECTS, and change in ASPECTS were 0.75 (95% CI, 0.69-0.82), 0.84 (95% CI, 0.79-0.89), and 0.82 (95% CI, 0.76-0.87), respectively. Of the three measures, 24-h ASPECTS was a better predictor of SAP (odds ratio: 5.33, 95%CI: 2.08-13.67, p < 0.001) and had a higher sensitivity (0.84 [95%CI, 0.74-0.92]) and specificity (0.79 [95%CI, 0.74-0.84]) than both baseline ASPECTS and change in ASPECTS. 24-h NCCT-ASPECTS outperformed both baseline ASPECTS and change in ASPECTS for predicting SAP. Notably, 24-h ASPECTS, with a cut-off value of ≤6, exhibited good predictive performance and emerged as the better predictor for SAP.

Development and validation of a predictive model for stroke associated pneumonia in patients after thrombectomy for acute ischemic stroke.

This study aims to identify the risk factors associated with stroke-associated pneumonia (SAP) in patients who have undergone thrombectomy for acute ischemic stroke and to develop a nomogram chart model for predicting the occurrence of pneumonia. Consecutive patients who underwent thrombectomy for acute ischemic stroke were enrolled from three hospitals at Taizhou Enze Medical Center. They were randomly divided into a training group and a validation group in a 7:3 ratio. The training group data was used to screen for effective predictive factors using LASSO regression. Multiple logistic regression was then conducted to determine the predictive factors and construct a nomogram chart. The model was evaluated using the validation group, analyzing its discrimination, calibration, and clinical decision curve. Finally, the newly constructed model was compared with the AIS-APS, A2DS2, ISAN, and PANTHERIS scores for acute ischemic stroke-associated pneumonia. Out of 913 patients who underwent thrombectomy, 762 were included for analysis, consisting of 473 males and 289 females. The incidence rate of SAP was 45.8%. The new predictive model was constructed based on three main influencing factors: NIHSS ≥16, postoperative LMR, and difficulty swallowing. The model demonstrated good discrimination and calibration. When applying the nomogram chart to threshold probabilities between 7 and 90%, net returns were increased. Furthermore, the AUC was higher compared to other scoring systems. The constructed nomogram chart in this study outperformed the AIS-APS, A2DS2 score, ISAN score, and PANTHERIS score in predicting the risk of stroke-associated pneumonia in patients with acute ischemic stroke after thrombectomy. It can be utilized for clinical risk prediction of stroke-associated pneumonia in patients after thrombectomy for acute ischemic stroke.

Role of Prophylactic Antibiotics in Preventing Stroke-Associated Pneumonia in Patients with Ischemic Stroke

Objective: To compare the frequency of stroke-associated pneumonia in patients given prophylactic antibiotics versus those not given prophylactic antibiotics.Study Design: Comparative Cross-sectional study.Place and Duration of Study: The study was carried out at the Department of Internal Medicine, Combined st st Military Hospital (CMH), Peshawar, Pakistan from 1 August 2022 to 31 January 2023.Methods: We included 250 ischemic stroke patients and divided them into groups A, “Prophylactic group,” and B, “no prophylactic antibiotics group.” Patients were assessed at 7 days after admission for presence of “stroke associated pneumonia (SAP)” through clinical examination and chest X-rays. Data were analyzed using a statistical package for social sciences 22. For qualitative variables, frequency and percentages were used, whereas for quantitative data, the median (IQR) was used as the data were not normally distributed and contained outliers; using the median and IQR can be more appropriate for describing the central tendency and spread of the data compared to the mean and standard deviation.Results: In our study, the median age of the patients was 68(46-83) years. 61 (24.40%) of the patients were male while 189(75.60%) were female. The median BMI was 31 (19-44) kg/m2. Diabetes was present in 123 (49.20%) patients and 96(38.40%) had hypertension. We found that in the the “prophylactic antibiotics” group (A), 9 (7.20%) patients developed SAP, while in the “no prophylactic antibiotics” group (B) frequency of SAP was 34 (27.20%) (p&lt; 0.001).Conclusion: Prophylactic antibiotics used in “ischemic stroke” patients provide better outcomes by reducing the incidence of “stroke-associated pneumonia”. Therefore, it should be given to all patients admitted to the hospital with ischemic CVA.&#x0D; How to cite this: Aslam MJ, Amir M, Sayed TM, Hashmat A, Hussain M, Fatima M.Role of Prophylactic Antibiotics in Preventing Stroke Associated Pneumonia in Patients with Ischemic Stroke. Life and Science. 2024; 5(1): 82-88. doi: http://doi.org/10.37185/LnS.1.1.465

A Simple Nomogram for Predicting Stroke-Associated Pneumonia in Patients with Acute Ischemic Stroke

The purpose of this study was to develop a prediction model for stroke-associated pneumonia (SAP) based on risk factors for SAP and to suggest nursing interventions to prevent SAP. In addition, a nomogram was developed to enhance its utility in nursing practice. The retrospective cohort study included 551 patients hospitalized for acute ischemic stroke at a university hospital in South Korea. Data were collected through a structured questionnaire and a review of the electronic medical record (EMR). In the development of a predictive model for SAP, multivariate logistic regression analysis showed that independent risk factors for SAP were age ≥ 65 years, National Institute of Health Stroke Scale (NIHSS) score ≥ 7, nasogastric tube feeding, and C-reactive protein (CRP) ≥ 5.0 mg/dL. The logit model was used to construct the SAP prediction nomogram, and the area under the curve (AUC) of the nomogram was 0.94. Furthermore, the slope of the calibration plot was close to the 45-degree line, indicating that the developed nomogram may be useful for predicting SAP. It is necessary to monitor the age, NIHSS score, nasogastric tube feeding status, and CRP level of stroke patients and identify high-risk groups using the developed nomogram to provide active nursing interventions to prevent SAP.

Predictive value of the systemic immune inflammation (SII) index for stroke-associated pneumonia.

To investigate the predictive value of the systemic immune inflammation (SII) index on the occurrence of stroke-associated pneumonia (SAP) in patients with acute stroke. Data of patients with or without a previous history of pulmonary who visited the First Affiliated Hospital of Kunming Medical University within 24h of the onset of stroke were collected between January 2017 and December 2019. Patient's demographic data, stroke type, past medical history, National Institutes of Health Stroke Scale score, Glasgow Coma score, and laboratory tests were collected. Logistic regression models and receiver-operating characteristic (ROC) curves were used to investigate the predictive value of SII for the development of SAP in patients with stroke. We included 395 patients with acute stroke, with a mean age of 63.89±13.42 years, of whom 340 (86.1%) had ischemic stroke, and 55 (13.9%) had hemorrhagic stroke. Out of 395, 113 (28.6%) had SAP and 282 (71.4%) did not, and the SII level in the SAP group was higher than that of the non-SAP group (p<.05). Logistic regression analysis of patients with stroke showed that higher SII was a risk factor for SAP in patients with stroke (per 100 units, HR=1.081, 95% CI: 1.035-1.130, p<.001), and tertile grouping of SII showed that the risk of SAP was 5.059 times higher in the SIIQ3 group than in the SIIQ1 group (95% CI: 2.061-12.418, p<.001). ROC curve analysis indicated that the SII index had predictive value for the occurrence of SAP in patients with stroke, with an area under the curve of 0.752 (95% CI: 0.698-0.806). When the cutoff value was 861.01, the SII predicted SAP in patients with stroke with a sensitivity of 61.9% and a specificity of 76.2%. Higher SII is an independent risk factor for the development of SAP in patients with stroke and has some predictive value for the development of SAP.

Development and validation of a nomogram model for prediction of stroke-associated pneumonia associated with intracerebral hemorrhage

BackgroundWe aimed to establish risk factors for stroke-associated pneumonia (SAP) following intracerebral hemorrhage (ICH) and develop an efficient and convenient model to predict SAP in patients with ICH.MethodsOur study involved 1333 patients consecutively diagnosed with ICH and admitted to the Neurology Department of the First Affiliated Hospital of Wenzhou Medical University. The 1333 patients were randomly divided (3:1) into the derivation cohort (n = 1000) and validation Cohort (n = 333). Variables were screened from demographics, lifestyle-related factors, comorbidities, clinical symptoms, neuroimaging features, and laboratory tests. In the derivation cohort, we developed a prediction model with multivariable logistic regression analysis. In the validation cohort, we assessed the model performance and compared it to previously reported models. The area under the receiver operating characteristic curve (AUROC), GiViTI calibration belt, net reclassification index (NRI), integrated discrimination index (IDI) and decision curve analysis (DCA) were used to assess the prediction ability and the clinical decision-making ability.ResultsThe incidence of SAP was 19.9% and 19.8% in the derivation (n = 1000) and validation (n = 333) cohorts, respectively. We developed a nomogram prediction model including age (Odds Ratio [OR] 1.037, 95% confidence interval [CI] 1.020–1.054), male sex (OR 1.824, 95% CI 1.206–2.757), multilobar involvement (OR 1.851, 95% CI 1.160–2.954), extension into ventricles (OR 2.164, 95% CI 1.456–3.215), dysphagia (OR 3.626, 95% CI 2.297–5.725), disturbance of consciousness (OR 2.113, 95% CI 1.327–3.362) and total muscle strength of the worse side (OR 0.93, 95% CI 0.876–0.987). Compared with previous models, our model was well calibrated and showed significantly higher AUROC, better reclassification ability (improved NRI and IDI) and a positive net benefit for predicted probability thresholds between 10% and 73% in DCA.ConclusionsWe developed a simple, valid, and clinically useful model to predict SAP following ICH, with better predictive performance than previous models. It might be a promising tool to assess the individual risk of developing SAP for patients with ICH and optimize decision-making.

Comparison of the Predictive Value of Inflammatory Biomarkers for the Risk of Stroke-Associated Pneumonia in Patients with Acute Ischemic Stroke.

To investigate the predictive value of various inflammatory biomarkers in patients with acute ischemic stroke (AIS) and evaluate the relationship between stroke-associated pneumonia (SAP) and the best predictive index. We calculated the neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), monocyte-to-lymphocyte ratio (MLR), prognostic nutritional index (PNI), systemic inflammation response index (SIRI), systemic immune inflammation index (SII), Glasgow prognostic score (GPS), modified Glasgow prognostic score (mGPS), and prognostic index (PI). Variables were selectively included in the logistic regression analysis to explore the associations of NLR, PLR, MLR, PNI, SIRI, SII, GPS, mGPS, and PI with SAP. We assessed the predictive performance of biomarkers by analyzing receiver operating characteristic (ROC) curves. We further used restricted cubic splines (RCS) to investigate the association. Next, we conducted subgroup analyses to investigate whether specific populations were more susceptible to NLR. NLR, PLR, MLR, SIRI, SII, GPS, mGPS, and PI increased significantly in SAP patients, and PNI was significantly decreased. After adjustment for potential confounders, the association of inflammatory biomarkers with SAP persisted. NLR showed the most favorable discriminative performance and was an independent risk factor predicting SAP. The RCS showed an increasing nonlinear trend of SAP risk with increasing NLR. The AUC of the combined indicator of NLR and C-reactive protein (CRP) was significantly higher than those of NLR and CRP alone (DeLong test, P<0.001). Subgroup analyses suggested good generalizability of the predictive effect. NLR, PLR, MLR, PNI, SIRI, SII, GPS, mGPS, and PI can predict the occurrence of SAP. Among the indices, the NLR was the best predictor of SAP occurrence. It can therefore be used for the early identification of SAP.

Association between malnutrition and stroke-associated pneumonia in patients with ischemic stroke

BackgroundMalnutrition is associated with a high risk of mortality in adults with ischemic stroke (IS). This study aimed to investigate the relationship between malnutrition and the risk of stroke-associated pneumonia (SAP) as only a few studies examined the relationship between malnutrition and the risk of SAP in IS.MethodsPatients were included from emergency departments of five tertiary hospitals in the REtrospective Multicenter study for Ischemic Stroke Evaluation (REMISE) study from January 2020 to December 2020. Malnutrition was defined according to the Controlling Nutritional Status (CONUT), Geriatric Nutritional Risk Index (GNRI), and Prognostic Nutritional Index (PNI) systems. Multivariable logistic regression analysis was used to explore the association between malnutrition and risk of SAP.ResultsWe enrolled 915 patients with IS, 193 (14.75%), 495 (54.1%), and 148 (16.2%) of whom were malnourished according to the PNI, CONUT, and GNRI scores, respectively. SAP occurred in 294 (32.1%) patients. After adjusting for confounding influencing factors in the logistic regression analysis, malnutrition (moderate and severe risk vs. absent malnutrition) was independently associated with an increased risk of SAP based on the PNI (odds ratio [OR], 5.038; 95% confidence interval [CI] 2.435–10.421, P < 0.001), CONUT (OR, 6.941; 95% CI 3.034–15.878, P < 0.001), and GNRI (OR, 2.007; 95% CI 1.186–3.119, P = 0.005) scores. Furthermore, adding malnutrition assessment indices to the A2DS2 score significantly improved the ability to predict SAP by analysis of receiver operating characteristic curves and net reclassification improvement.ConclusionMalnutrition was notably prevalent in patients with IS and independently associated with an increased risk of SAP. Further studies are required to identify the effect of interventions on malnutrition to reduce the risk of SAP.

The clinical value of neutrophil-to-lymphocyte ratio (NLR), systemic immune-inflammation index (SII), platelet-to-lymphocyte ratio (PLR) and systemic inflammation response index (SIRI) for predicting the occurrence and severity of pneumonia in patients with intracerebral hemorrhage.

Inflammatory mechanisms play important roles in intracerebral hemorrhage (ICH) and have been linked to the development of stroke-associated pneumonia (SAP). The neutrophil-to-lymphocyte ratio (NLR), systemic immune-inflammation index (SII), platelet-to-lymphocyte ratio (PLR) and systemic inflammation response index (SIRI) are inflammatory indexes that influence systemic inflammatory responses after stroke. In this study, we aimed to compare the predictive value of the NLR, SII, SIRI and PLR for SAP in patients with ICH to determine their application potential in the early identification of the severity of pneumonia. Patients with ICH in four hospitals were prospectively enrolled. SAP was defined according to the modified Centers for Disease Control and Prevention criteria. Data on the NLR, SII, SIRI and PLR were collected at admission, and the correlation between these factors and the clinical pulmonary infection score (CPIS) was assessed through Spearman's analysis. A total of 320 patients were enrolled in this study, among whom 126 (39.4%) developed SAP. The results of the receiver operating characteristic (ROC) analysis revealed that the NLR had the best predictive value for SAP (AUC: 0.748, 95% CI: 0.695-0.801), and this outcome remained significant after adjusting for other confounders in multivariable analysis (RR=1.090, 95% CI: 1.029-1.155). Among the four indexes, Spearman's analysis showed that the NLR was the most highly correlated with the CPIS (r=0.537, 95% CI: 0.395-0.654). The NLR could effectively predict ICU admission (AUC: 0.732, 95% CI: 0.671-0.786), and this finding remained significant in the multivariable analysis (RR=1.049, 95% CI: 1.009-1.089, P=0.036). Nomograms were created to predict the probability of SAP occurrence and ICU admission. Furthermore, the NLR could predict a good outcome at discharge (AUC: 0.761, 95% CI: 0.707-0.8147). Among the four indexes, the NLR was the best predictor for SAP occurrence and a poor outcome at discharge in ICH patients. It can therefore be used for the early identification of severe SAP and to predict ICU admission.

Stroke-associated pneumonia risk estimation in patients admitted to a comprehensive stroke unit at different phases after stroke

stroke is one of the leading causes of death and acquired disability among adults worldwide. Various complications frequently occur in patients with stroke and can significantly increase the risk for poor outcomes. Stroke-associated pneumonia, with the rate of up to 15%, is considered the most common serious medical complication in stroke patients. The aim of the study was to determine the independent predictors of the stroke-associated pneumonia in patients admitted to a comprehensive stroke unit at different time from stroke onset, as well as to develop a prognostic model for stroke-associated pneumonia risk estimation. Medical complications, including stroke-associated pneumonia, were documented in patients with clinical diagnosis of intraparenchymal cerebral stroke at admission and during their stay on the comprehensive stroke unit in 2010 to 2018. The workup at admission included assessment using the National Institutes of Health Stroke Scale and the modified Rankin scale. The diagnosis of stroke-associated pneumonia was made by specialists, in accordance with Centers for Disease Control recommendations. Qualitative variables are displayed as numbers and percentages, quantitative variables are displayed using the median (interquartile range). The method of logistic regression models was used to quantitatively assess the impact of characteristics on the PSP risk. The analysis was carried out using the package MedCalc v. 19.1. The study enrolled 539 patients, 309 (57.3%) men and 230 (42.7%) women. Median age of the patients was 66.0 years (57.9-75.1). Among the participants 101 (18.7%) were diagnosed with intracerebral hemorrhage, and 438 (81.3%) were diagnosed with ischemic stroke. Median initial National Institutes of Health Stroke Scale total score was 11 (6–18), median baseline modified Rankin scale score was 4 (3–5). Majority (64.1%) individuals were admitted within the first 30 days from onset, while 194 (35.9%) patients presented later. History of medical complications before the admission was present in 192 (35.6%) of the study participants, while during our in-hospital stay they occurred in 103 (19.1%) of the patients. The rate of new medical complications at the unit was significantly lower (p&lt;0.001) than prior to the admission. The diagnosis of stroke-associated pneumonia before the admission was established in 20 (3.7%), and during our in-patient treatment was made in 7 (1.3%) subjects (p&lt;0.001). Independent predictors of the stroke-associated pneumonia development were older age (odds ratio 1.11; 95% confidence interval 1.02-1.21, on average, for each additional year, p=0.02) and history of stroke-associated pneumonia at admission (odds ratio 12.2; 95 % confidence interval 2.0-72.4, p=0.006). Excellent sensitivity (85.7%), good specificity (75.0%) and area under the operating characteristic curve of 0.86 (95% confidence interval 0.83 – 0.89) for the developed prognostic model based on these of two variables, indicate their strong association with the risk of stroke-associated pneumonia during a comprehensive stroke unit stay. Therefore, stroke patients frequently develop medical complications at various stroke phases. The risk of stroke-associated pneumonia is higher in older patients and, especially, in those with stroke-associated pneumonia history. The lower rate of medical complications, in particular stroke-associated pneumonia, observed during in-hospital stay compared to the period prior to the admission may indicate the benefits of comprehensive stroke unit.

Inflammatory response biomarkers nomogram for predicting pneumonia in patients with spontaneous intracerebral hemorrhage.

Inflammatory response biomarkers are promising prognostic factors to improve the prognosis of stroke-associated pneumonia (SAP) after ischemic stroke. This study aimed to investigate the prognostic significance of inflammatory response biomarkers on admission in SAP after spontaneous intracerebral hemorrhage (SICH) and establish a corresponding nomogram. The data of 378 patients with SICH receiving conservative treatment from January 2019 to December 2021 at Taizhou People's Hospital were selected. All eligible patients were randomized into the training (70%, 265) and validation cohorts (30%, 113). In the training cohort, multivariate logistic regression analysis was used to establish an optimal nomogram, including inflammatory response biomarkers and clinical risk factors. The area under the receiver operating characteristic (ROC) curve (AUC), calibration curve, and decision curve analysis (DCA) were used to evaluate the nomogram's discrimination, calibration, and performance, respectively. Moreover, this model was further validated in a validation cohort. A logistic regression analysis showed that intraventricular hemorrhage (IVH), hypertension, dysphagia, Glasgow Coma Scale (GCS), National Institute of Health Stroke Scale (NIHSS), systemic inflammation response index (SIRI), and platelet/lymphocyte ratio (PLR) were correlated with SAP after SICH (P < 0.05). The nomogram was composed of all these statistically significant factors. The inflammatory marker-based nomogram showed strong prognostic power compared with the conventional factors, with an AUC of 0.886 (95% CI: 0.841-0.921) and 0.848 (95% CI: 0.799-0.899). The calibration curves demonstrated good homogeneity between the predicted risks and the observed outcomes. In addition, the model has a significant net benefit for SAP, according to DCA. Also, internal validation demonstrated the reliability of the prediction nomogram. The length of hospital stay was shorter in the non-SAP group than in the SAP group. At the 3-month follow-up, clinical outcomes were worse in the SAP group (P < 0.001). SIRI and PLR at admission can be utilized as prognostic inflammatory biomarkers in patients with SICH in the upper brain treated with SAP. A nomogram covering SIRI and PLR can more accurately predict SAP in patients' supratentorial SICH. SAP can influence the length of hospital stay and the clinical outcome.

Comparison of six risk scores for stroke-associated pneumonia in patients with acute ischemic stroke: A systematic review and Bayesian network meta-analysis

BackgroundStroke-associated pneumonia (SAP) is one of the major causes of death after suffering a stroke. Several scoring systems have been developed for the early prediction of SAP. However, it is unclear which scoring system is more suitable as a risk prediction tool. We performed this Bayesian network meta-analysis to compare the prediction accuracy of these scoring systems.MethodsSeven databases were searched from their inception up to April 8, 2022. The risk of bias assessment of included study was evaluated by the QUADAS-C tool. Then, a Bayesian network meta-analysis (NMA) was performed by R 4.1.3 and STATA 17.0 software. The surface under the cumulative ranking curve (SUCRA) probability values were applied to rank the examined scoring systems.ResultsA total of 20 cohort studies involving 42,236 participants were included in this analysis. The results of the NMA showed that AIS-APS had excellent performance in prediction accuracy for SAP than Chumbler (MD = 0.030, 95%CI: 0.004, 0.054), A2DS2 (MD = 0.041, 95% CI: 0.023, 0.059), ISAN (MD = 0.045, 95% CI: 0.022, 0.069), Kwon (MD = 0.077, 95% CI: 0.055, 0.099) and PANTHERIS (MD = 0.082, 95% CI: 0.049, 0.114). Based on SUCRA values, AIS-APS (SUCRA: 99.8%) ranked the highest.ConclusionIn conclusion, the study found that the AIS-APS is a validated clinical tool for predicting SAP after the onset of acute ischemic stroke.Systematic review registrationhttps://www.crd.york.ac.uk/PROSPERO/display_record.php?RecordID=292375, identifier: CRD42021292375.

Reduced plasma levels of RGM-A predict stroke-associated pneumonia in patients with acute ischemic stroke: A prospective clinical study.

BackgroundStroke-induced immunodepression syndrome is considered the major etiology of stroke-associated pneumonia (SAP). Repulsive guidance molecule A (RGM-A) is an immunomodulatory protein that is closely related to inflammation and immune responses. To explore the relationship between RGM-A and SAP and facilitate the early identification of patients at high risk of developing SAP, we investigated the predictive value of RGM-A in SAP.MethodsWe enrolled 178 patients with acute ischemic stroke (AIS) and finally analyzed 150 patients, among whom 69 had SAP and 81 had non-SAP. During the same period, 40 patients with community-acquired pneumonia and 40 healthy participants were included as controls. SAP was defined according to the modified US Centers for Disease Control and Prevention criteria. Blood samples were collected at 24 h, 48 h, 3 days, 4 to 7 days, and 8 to 14 days after stroke onset. An enzyme-linked immunosorbent assay was used to detect the plasma levels of RGM-A and interleukin-6.ResultsThe plasma RGM-A levels were significantly decreased in both patients with community-acquired pneumonia and those with AIS, and the decline was most pronounced in patients with SAP (P < 0.001). RGM-A started to decline within 24 h after stroke in the SAP group, and the lowest levels were detected on day 3 and days 4 to 7 (P < 0.001). The RGM-A levels in the SAP group were lower than those in the non-SAP group at all blood collection time points (P < 0.05). In the logistic regression analyses, RGM-A was a protective factor for SAP after adjusting for confounders (adjusted odds ratio = 0.22, 95% confidence interval = 0.091–0.538, P = 0.001). Receiver operating characteristic curve analysis showed that the area under the curve for RGM-A was 0.766 (0.091–0.538; P = 0.001), the cutoff value was 4.881 ng/mL, and the sensitivity and specificity were 80.00 and 76.36%, respectively.ConclusionsWe demonstrated that reduced plasma levels of RGM-A might help in the early identification of high-risk patients with SAP and predict the occurrence of SAP in patients with AIS. RGM-A might provide new clues to a potential alternative therapy for SAP.

Reducing the incidence of stroke-associated pneumonia: an evidence-based practice

BackgroundPulmonary infection is a frequent complication among stroke patients and adversely affects clinical outcomes, increases the length of hospitalization stay and costs, and aggravates the financial burden of the national medical health system. Early identification and management of high-risk patients are necessary and imperative to reduce the incidence of stroke-associated pneumonia (SAP).AimThe evidence-based practice project evaluated the effectiveness of a standard care bundle intervention in preventing the occurrence of SAP.MethodsThe project was conducted in a neurology department of a teaching hospital. Given the variation in assessment and management standards, evidence-based practice (EBP) methodology was used to establish a process for quality improvement. A thorough literature search was conducted to identify evidence-based interventions to manage and prevent SAP. Thorough critiques of the literature and synthesis of the evidence were completed. A systematic management flow and care bundle interventions were established. The care bundle included interventions, such as the utilization of tools for SAP risk screening; dysphagia screening and rehabilitation; feeding modification, oral care, airway management, position management, and the nursing techniques of traditional Chinese medicine.ResultsA significant improvement was observed in preventing SAP in patients in the postimplementation group compared with those in the preimplementation group (14.0% vs. 37.2%, p = 0.025). In addition, significantly lower duration of hospitalization, lower rate of aspiration, and improvements in albumin and oral hygiene were found after the implementation of the care bundle.ConclusionsEvidence-based care bundles successfully empower nurses to reduce the incidence of SAP. The management flow of SAP prevention could be promoted to other units of the neurology department in the future. The results of the project reflect positively on the capacity to implement EBP in an acute care setting for stroke. The EBP methodology can be utilized to solve other clinical problems.

Predictive Ability of Systemic Inflammation Response Index for the Risk of Pneumonia in Patients with Acute Ischemic Stroke

Introduction: Several studies have examined the crucial role of inflammatory indexes such as the ratio of monocyte and lymphocyte (MLR), systemic-immune-inflammation-index, and the ratio of neutrophil and lymphocyte (NLR) in stroke-associated pneumonia (SAP). However, the function of the systemic inflammation response index (SIRI) in SAP is not known. This study investigated whether SIRI at admission could predict the incidence of SAP in patients with acute ischemic stroke (AIS). Patients and Methods: 2,802 AIS patients collected from 2013 to 2021 were divided into the SAP and non-SAP groups. The predictive performance of SIRI in SAP was evaluated by the receiver operating characteristic curve. Multivariate regression analysis and the restricted cubic spline (RCS) were performed to explore the relationship between SIRI and SAP risk. Results: The SIRI at admission in SAP patients was significantly higher than that in non-SAP patients (median [IQR]: 3.75 [2.05, 6.99] vs. 1.51 [0.94, 2.62], p < 0.001). SIRI had a predictive ability for predicting the incidence of SAP with area under the curve of 0.757, better than NLR and MLR (both p < 0.05). SIRI ≥2.74 was an independent risk factor for the incidence of SAP (odds ratio: 5.82, 95% confidence interval: 4.54, 7.49, p < 0.001). The RCS model showed an increasing trend of the SAP risk with the increase of SIRI. Conclusion: SIRI showed a good predictive value for SAP. In clinical practice, AIS patients with high SIRI levels (SIRI ≥2.74) should be aware of the risk of SAP.

Novel Predictors of Stroke-Associated Pneumonia: A Single Center Analysis.

Stroke-associated pneumonia (SAP) is a common cause of disability or death. Although the researches on SAP have been relatively mature, the method that can predict SAP with great accuracy has not yet been determined. It is necessary to discover new predictors to construct a more accurate predictive model for SAP. We continuously collected 2,366 patients with acute ischemic stroke, and then divided them into the SAP group and non-SAP group. Data were recorded at admission. Through univariate analyses and multivariate regression analyses of the data, the new predictive factors and the predictive model of SAP were determined. The receiver operating characteristic (ROC) curve and the corresponding area under the curve (AUC) were used to measure their predictive accuracy. Of the 2,366 patients, 459 were diagnosed with SAP. International normalized ratio (INR) (odds ratio = 37.981; 95% confidence interval, 7.487–192.665; P < 0.001), age and dysphagia were independent risk factors of SAP. However, walking ability within 48 h of admission (WA) (odds ratio = 0.395; 95% confidence interval, 0.287–0.543; P < 0.001) was a protective factor of SAP. Different predictors and the predictive model all could predict SAP (P < 0.001). The predictive power of the model (AUC: 0.851) which included age, homocysteine, INR, history of chronic obstructive pulmonary disease (COPD), dysphagia, and WA was greater than that of age (AUC: 0.738) and INR (AUC: 0.685). Finally, we found that a higher INR and no WA could predict SAP in patients with acute ischemic stroke. In addition, we designed a simple and practical predictive model for SAP, which showed relatively good accuracy. These findings might help identify high-risk patients with SAP and provide a reference for the timely use of preventive antibiotics.