External Validation of the ISAN, A2DS2, and AIS-APS Scores for Predicting Stroke-Associated Pneumonia

Machine learning using multimodal and autonomic nervous system parameters predicts clinically apparent stroke-associated pneumonia in a development and testing study

BACKGROUND

Stroke-associated pneumonia (SAP) is a preventable determinant for poor outcome after stroke. Machine learning (ML) using large-scale clinical data warehouses may be able to predict SAP and identify patients for targeted interventions. The aim of this study was to develop a prediction model for identifying clinically apparent SAP using automated ML.

METHODS

The ML model used clinical and laboratory parameters along with heart rate (HR), heart rate variability (HRV), and blood pressure (BP) values obtained during the first 48 h after stroke unit admission. A logistic regression classifier was developed and internally validated with a nested-cross-validation (nCV) approach. For every shuffle, the model was first trained and validated with a fixed threshold for 0.9 sensitivity, then finally tested on the out-of-sample data and benchmarked against a widely validated clinical score (A2DS2).

RESULTS

We identified 2390 eligible patients admitted to two-stroke units at Charité between October 2020 and June 2023, of whom 1755 had all parameters available. SAP was diagnosed in 96/1755 (5.5%). Circadian profiles in HR, HRV, and BP metrics during the first 48 h after admission exhibited distinct differences between patients with SAP diagnosis vs. those without. CRP, mRS at admission, leukocyte count, high-frequency power in HRV, stroke severity at admission, sex, and diastolic BP were identified as the most informative ML features. We obtained an AUC of 0.91 (CI 0.88-0.95) for the ML model on the out-of-sample data in comparison to an AUC of 0.84 (CI 0.76-0.91) for the previously established A2DS2 score (p < 0.001). The ML model provided a sensitivity of 0.87 (CI 0.75-0.97) with a corresponding specificity of 0.82 (CI 0.78-0.85) which outperformed the A2DS2 score for multiple cutoffs.

CONCLUSIONS

Automated, data warehouse-based prediction of clinically apparent SAP in the stroke unit setting is feasible, benefits from the inclusion of vital signs, and could be useful for identifying high-risk patients or prophylactic pneumonia management in clinical routine.

Predictive value of the Oxford Acute Severity of Illness Score in acute stroke patients with stroke-associated pneumonia

Background

Stroke-associated pneumonia (SAP) is associated with a poor prognosis and a high mortality rate in stroke patients. However, the accuracy of early prediction of SAP is insufficient, and there is a lack of effective prognostic evaluation methods. Therefore, in this study, we investigated the predictive value of the Oxford Acute Severity of Illness Score (OASIS) in SAP to provide a potential reference index for the incidence and prognosis of SAP.

Methods

We recruited a total of 280 patients with acute ischemic stroke who had been diagnosed and treated in the Zhumadian Central Hospital between January 2021 and January 2023. These patients were divided into an SAP group (86 cases) and a non-SAP group (194 cases) according to SAP diagnostic criteria by expert consensus on the diagnosis and treatment of SAP. We collated general and clinical data from all patients, including the survival of SAP patients during the follow-up period. Multivariate logistic regression was used to analyze the risk factors for SAP. Kaplan-Meier and multivariate COX regression analyses were used to investigate the relationship between OASIS and the prognosis of SAP, and a receiver operating characteristic (ROC) curve was drawn to analyze the predictive value of OASIS for SAP.

Results

Our analyses identified body temperature, C-reactive protein, procalcitonin, OASIS, and a prolonged length of intensive care unit (ICU) stay as the main risk factors for SAP (all Ps < 0.05). Advanced age and an elevated OASIS were identified as the main risk factors for death in SAP patients (all Ps < 0.05). The risk of death in patients with OASIS of 31-42 points was significantly higher than that in patients with OASIS of 12-20 points (HR = 5.588, 95% CI = 1.531-20.401, P = 0.009). ROC curve analysis further showed that OASIS had a high predictive value for morbidity and the incidence of death in SAP patients.

Conclusion

OASIS can effectively predict the onset and death of SAP patients and provides a potential reference index for early diagnosis and the prediction of prognosis in patients with SAP. Our findings should be considered in clinical practice.

The Potential of Gut Microbiota in Prediction of Stroke-Associated Pneumonia

BACKGROUND

Stroke-associated pneumonia (SAP) is a common stroke complication, and the changes in the gut microbiota composition may play a role. Our study aimed to evaluate the predictive ability of gut microbiota for SAP.

METHODS

Acute ischemic stroke patients were prospectively enrolled and divided into two groups based on the presence or absence of SAP. The composition of gut microbiota was characterized by the 16S RNA Miseq sequencing. The gut microbiota that differed significantly between groups were incorporated into the conventional risk scores, the Acute Ischemic Stroke-Associated Pneumonia Score (AIS-APS), and the Age, Atrial fibrillation, Dysphagia, Sex, Stroke Severity Score (A2DS2). The predictive performances were assessed in terms of the area under the curve (AUC), the Net Reclassification Improvement (NRI), and the Integrated Discrimination Improvement (IDI) indices.

RESULTS

A total of 135 patients were enrolled, of whom 43 had SAP (31%). The short-chain fatty acids (SCFAs)-producing bacteria, such as Bacteroides, Fusicatenibacter, and Butyricicoccus, were decreased in the SAP group. The integrated models showed better predictive ability for SAP (AUC = 0.813, NRI = 0.333, p = 0.052, IDI = 0.038, p = 0.018, for AIS-APS; AUC = 0.816, NRI = 0.575, p < 0.001, IDI = 0.043, p = 0.007, for A2DS2) in comparison to the differential genera (AUC = 0.699) and each predictive score (AUCAISAPS = 0.777; AUCA2DS2 = 0.777).

CONCLUSIONS

The lower abundance of SCFAs-producing gut microbiota after acute ischemic stroke was associated with SAP and may play a role in SAP prediction.

A brain CT-based approach for predicting and analyzing stroke-associated pneumonia from intracerebral hemorrhage

Introduction

Stroke-associated pneumonia (SAP) is a common complication of stroke that can increase the mortality rate of patients and the burden on their families. In contrast to prior clinical scoring models that rely on baseline data, we propose constructing models based on brain CT scans due to their accessibility and clinical universality.

Methods

Our study aims to explore the mechanism behind the distribution and lesion areas of intracerebral hemorrhage (ICH) in relation to pneumonia, we utilized an MRI atlas that could present brain structures and a registration method in our program to extract features that may represent this relationship. We developed three machine learning models to predict the occurrence of SAP using these features. Ten-fold cross-validation was applied to evaluate the performance of models. Additionally, we constructed a probability map through statistical analysis that could display which brain regions are more frequently impacted by hematoma in patients with SAP based on four types of pneumonia.

Results

Our study included a cohort of 244 patients, and we extracted 35 features that captured the invasion of ICH to different brain regions for model development. We evaluated the performance of three machine learning models, namely, logistic regression, support vector machine, and random forest, in predicting SAP, and the AUCs for these models ranged from 0.77 to 0.82. The probability map revealed that the distribution of ICH varied between the left and right brain hemispheres in patients with moderate and severe SAP, and we identified several brain structures, including the left-choroid-plexus, right-choroid-plexus, right-hippocampus, and left-hippocampus, that were more closely related to SAP based on feature selection. Additionally, we observed that some statistical indicators of ICH volume, such as mean and maximum values, were proportional to the severity of SAP.

Discussion

Our findings suggest that our method is effective in classifying the development of pneumonia based on brain CT scans. Furthermore, we identified distinct characteristics, such as volume and distribution, of ICH in four different types of SAP.

Optimal time point for neutrophil-to-lymphocyte ratio to predict stroke-associated pneumonia

PURPOSE

This study aimed at the population receiving thrombolytic therapy and to explore the optimal time point for neutrophil-to-lymphocyte ratio (NLR) in predicting stroke-associated pneumonia (SAP).

METHODS

We assessed patients undergoing intravenous thrombolysis (IVT) for acute ischemic stroke. Blood parameters were sampled before thrombolysis (within 30 min after admission) and within 24-36 h after thrombolysis, respectively. The primary outcome measure was the occurrence of SAP. Multivariate logistic regression analysis was performed to analyze the association between admission blood parameters and the event of SAP. We also used receiver operating characteristic (ROC) curve analysis to assess the discriminative ability of blood parameters measured at different times in predicting SAP.

RESULTS

Among the 388 patients, SAP occurred in 60 (15%) patients. Multivariate logistic regression analysis showed that NLR was significantly associated with SAP (NLR before IVT: aOR = 1.288; 95%CI = 1.123-1.476; p < 0.001; NLR after IVT: (aOR = 1.127, 95%CI = 1.017-1.249; p = 0.023). The ROC curve showed that the predictive ability of NLR after IVT was better than NLR before IVT, not only in predicting the occurrence of SAP but also in predicting short-term and long-term functional outcomes, hemorrhagic transformation, and 1-year mortality.

CONCLUSION

Increased NLR measured within 24-36 h after IVT has a significant predictive effect on the occurrence of SAP and can be used to predict short-term and long-term poor functional outcomes, hemorrhagic transformation, and 1-year mortality.

A predictive model using risk factor categories for hospital-acquired pneumonia in patients with aneurysmal subarachnoid hemorrhage

Objectives

To identify risk factors for hospital-acquired pneumonia (HAP) in patients with aneurysmal subarachnoid hemorrhage (aSAH) and establish a predictive model to aid evaluation.

Methods

The cohorts of 253 aSAH patients were divided into the HAP group (n = 64) and the non-HAP group (n = 189). Univariate and multivariate logistic regression were performed to identify risk factors. A logistic model (Model-Logit) was established based on the independent risk factors. We used risk factor categories to develop a model (Model-Cat). Receiver operating characteristic curves were generated to determine the cutoff values. Areas under the curves (AUCs) were calculated to assess the accuracy of models and single factors. The Delong test was performed to compare the AUCs.

Results

The multivariate logistic analysis showed that the age [p = 0.012, odds ratio (OR) = 1.059, confidence interval (CI) = 1.013-1.107], blood glucose (BG; >7.22 mmol/L; p = 0.011, OR = 2.781, CI = 1.263-6.119), red blood distribution width standard deviation (RDW-SD; p = 0.024, OR = 1.118, CI = 1.015-1.231), and Glasgow coma scale (GCS; p < 0.001, OR = 0.710, CI = 0.633-0.798) were independent risk factors. The Model-Logit was as follows: Logit(P) = -5.467 + 0.057 * Age + 1.023 * BG (>7.22 mmol/L, yes = 1, no = 0) + 0.111 * RDW-SD-0.342 * GCS. The AUCs values of the Model-Logit, GCS, age, BG (>7.22 mmol/L), and RDW-SD were 0.865, 0.819, 0.634, 0.698, and 0.625, respectively. For clinical use, the Model-Cat was established. In the Model-Cat, the AUCs for GCS, age, BG, and RDW-SD were 0.850, 0.760, 0.700, 0.641, and 0.564, respectively. The AUCs of the Model-Logit were insignificantly higher than the Model-Cat (Delong test, p = 0.157). The total points from -3 to 4 and 5 to 14 were classified as low- and high-risk levels, respectively.

Conclusions

Age, BG (> 7.22 mmol/L), GCS, and RDW-SD were independent risk factors for HAP in aSAH patients. The Model-Cat was convenient for practical evaluation. The aSAH patients with total points from 5 to 14 had a high risk for HAP, suggesting the need for more attention during treatment.

Application of machine learning and natural language processing for predicting stroke-associated pneumonia

Background

Identifying patients at high risk of stroke-associated pneumonia (SAP) may permit targeting potential interventions to reduce its incidence. We aimed to explore the functionality of machine learning (ML) and natural language processing techniques on structured data and unstructured clinical text to predict SAP by comparing it to conventional risk scores.

Methods

Linked data between a hospital stroke registry and a deidentified research-based database including electronic health records and administrative claims data was used. Natural language processing was applied to extract textual features from clinical notes. The random forest algorithm was used to build ML models. The predictive performance of ML models was compared with the A²DS², ISAN, PNA, and ACDD⁴ scores using the area under the receiver operating characteristic curve (AUC).

Results

Among 5,913 acute stroke patients hospitalized between Oct 2010 and Sep 2021, 450 (7.6%) developed SAP within the first 7 days after stroke onset. The ML model based on both textual features and structured variables had the highest AUC [0.840, 95% confidence interval (CI) 0.806-0.875], significantly higher than those of the ML model based on structured variables alone (0.828, 95% CI 0.793-0.863, P = 0.040), ACDD⁴ (0.807, 95% CI 0.766-0.849, P = 0.041), A²DS² (0.803, 95% CI 0.762-0.845, P = 0.013), ISAN (0.795, 95% CI 0.752-0.837, P = 0.009), and PNA (0.778, 95% CI 0.735-0.822, P < 0.001). All models demonstrated adequate calibration except for the A²DS² score.

Conclusions

The ML model based on both textural features and structured variables performed better than conventional risk scores in predicting SAP. The workflow used to generate ML prediction models can be disseminated for local adaptation by individual healthcare organizations.

Bibliometric analysis of global research trends on post-stroke pneumonia: Current development status and research Frontiers

Background

As one of the most common complications of stroke, post-stroke pneumonia significantly increases the incidence of adverse outcomes, mortality, and healthcare costs for patients with stroke. As the field of post-stroke pneumonia has gained interest in the recent years, there has been an increasing number of publications on post-stroke pneumonia research worldwide. Therefore, a more comprehensive understanding of the field is needed now. This paper is intended to analyze the research status and detect the research frontiers in this field.

Methods

VOS viewer, CiteSpace, and the online scientometric platform (https://bibliometric.com/) were the main visualization tools used in this paper. They were used to perform citation analysis of countries/institutions, co-citation analysis of authors/journals/references, co-authorship analysis of authors, co-occurrence analysis of keywords, and citation bursts analysis of references.

Results

The number of publications in this field has increased rapidly since 2010 and is expected to continue to increase in the next few years. The countries contributing most to post-stroke pneumonia research were the USA, China, and Germany. The most productive institution was Harvard University, followed by Humboldt University of Berlin, Charité Universitätsmedizin Berlin, and Free University of Berlin from Germany. Meanwhile, the German authors Meisel A, Meisel C, and Dirnagl U, who have contributed significantly to this field, were all associated with these three German institutions. The high-quality and high output journal was STROKE. In the coming years, the hot topic keywords “risk & risk-factors,” “outcome & impact,” “management & guidelines,” and “predictors” will gain more attention in this field. Finally, hot keywords were grouped into four clusters in this paper: cluster 1 (risk-factors studies of post-stroke pneumonia), cluster 2 (clinically relevant studies of post-stroke pneumonia), cluster 3 (mechanism studies of post-stroke pneumonia), and cluster 4 (care studies of post-stroke pneumonia).

Conclusion

This study shows the knowledge structure and evolution of the field of post-stroke pneumonia research and predicts research trends through visualization analysis. The future trend of post-stroke pneumonia research will gradually shift from clinical and mechanistic studies to treatment and prevention studies.

Predicting Poststroke Pneumonia in Patients With Anterior Large Vessel Occlusion: A Prospective, Population-Based Stroke Registry Analysis

Objective

To assess predictive factors for poststroke pneumonia (PSP) in patients with acute ischemic stroke (AIS) due to large vessel occlusion (LVO) of the anterior circulation, with special regard to the impact of intravenous thrombolysis (IVT) and endovascular treatment (EVT) on the risk of PSP. As a secondary goal, the validity of the A²DS², PNEUMONIA, and ISAN scores in LVO will be determined.

Methods

Analysis was based on consecutive data for the years 2017 to 2019 from the prospective inpatient stroke registry covering the entire federal state of Hesse, Germany, using the Kruskal-Wallis test and binary logistic regression.

Results

Data from 4,281 patients with LVO were included in the analysis (54.8% female, median age = 78 years, range = 18–102), of whom 66.4% (n = 2,843) received recanalization therapy (RCT). In total, 19.4% (n = 832) of all LVO patients developed PSP. Development of PSP was associated with an increase in overall in-hospital mortality of 32.1% compared with LVO patients without PSP (16.4%; p < 0.001). Incidence of PSP was increased in 2132 patients with either EVT (n = 928; 25.9% PSP incidence) or combined EVT plus IVT (n = 1,204; 24.1%), compared with 2,149 patients with IVT alone (n = 711; 15.2%) or conservative treatment only (n = 1,438; 13.5%; p < 0.001). Multivariate analysis identified EVT (OR 1.5) and combined EVT plus IVT (OR 1.5) as significant independent risk factors for PSP. Furthermore, male sex (OR 1.9), age ≥ 65 years (OR 1.7), dysphagia (OR 3.2) as well as impaired consciousness at arrival (OR 1.7) and the comorbidities diabetes (OR 1.4) and atrial fibrillation (OR 1.3) were significantly associated risk factors (each p < 0.001). Minor stroke (NIHSS ≤ 4) was associated with a significant lower risk of PSP (OR 0.5). Performance of risk stratification scores varied between A²DS² (96.1% sensitivity, 20.7% specificity), PNEUMONIA (78.2% sensitivity and 45.1% specificity) and ISAN score (98.0% sensitivity, 20.0% specificity).

Conclusion

Nearly one in five stroke patients with LVO develops PSP during acute care. This risk of PSP is further increased if an EVT is performed. Other predictive factors are consistent with those previously described for all AIS patients. Available risk stratification scores proved to be sensitive tools in LVO patients but lack specificity.

Stroke-induced immunosuppression: implications for the prevention and prediction of post-stroke infections

Stroke produces a powerful inflammatory cascade in the brain, but also a suppression of the peripheral immune system, which is also called stroke-induced immunosuppression (SIIS). The main processes that lead to SIIS are a shift from a lymphocyte phenotype T-helper (Th) 1 to a Th2 phenotype, a decrease of the lymphocyte counts and NK cells in the blood and spleen, and an impairment of the defense mechanisms of neutrophils and monocytes. The direct clinical consequence of SIIS in stroke patients is an increased susceptibility to stroke-associated infections, which is enhanced by clinical factors like dysphagia. Among these infections, stroke-associated pneumonia (SAP) is the one that accounts for the highest impact on stroke outcome, so research is focused on its early diagnosis and prevention. Biomarkers indicating modifications in SIIS pathways could have an important role in the early prediction of SAP, but currently, there are no individual biomarkers or panels of biomarkers that are accurate enough to be translated to clinical practice. Similarly, there is still no efficient therapy to prevent the onset of SAP, and clinical trials testing prophylactic antibiotic treatment and β-blockers have failed. However, local immunomodulation could open up a new research opportunity to find a preventive therapy for SAP. Recent studies have focused on the pulmonary immune changes that could be caused by stroke similarly to other acquired brain injuries. Some of the traits observed in animal models of stroke include lung edema and inflammation, as well as inflammation of the bronchoalveolar lavage fluid.

Prediction of stroke-associated pneumonia by the A2DS2, AIS-APS, and ISAN scores: a systematic review and meta-analysis

Background: Different scoring systems (A2DS2, AISAPS, ISAN) have been designed to predict the risk of in-hospital stroke-associated pneumonia (SAP). Studies have assessed the accuracy of these scores for predicting SAP. We performed this meta-analysis to consolidate the evidence on the predictive accuracies for SAP of the A2DS2, AISAPS, and ISAN scores.Materials and methods: We conducted a systematic search for all studies reporting the SAP predictive accuracy of A2DS2, AISAPS, or ISAN scores in the databases of PubMed Central, SCOPUS, MEDLINE, Embase, and Cochrane from inception until December 2020. We used the STATA software for the meta-analysis.Results: We included 19 studies with 35 849 patients. The pooled score sensitivities were 78% (95% CI, 71%-83%) for A2DS2, 79% (95% CI, 77%-81%) for AISAPS, and 79% (95% CI, 77%-81%) for ISAN. The pooled score specificities were 73% (95% CI, 65%-80%) for A2DS2, 74% (95% CI, 69%-79%) for AISAPS, and 74% (95% CI, 69%-79%) for ISAN. We found significant heterogeneity for all the scoring systems based on the chi-square test results and an I² statistic > 75%. We performed meta-regression to explore the source of heterogeneity and found that patient selection (p< 0.05) and reference standards (p< 0.05) in the sensitivity model, index test standards (p< 0.05), flow and timing of tests (p< 0.01) in the specificity model, and mean age (p < 0.001) in the joint model were the source of heterogeneity.Conclusions: To summarize, we found that A2S2, AISAPS and ISAN have moderate predictive accuracy for SAP with A2S2 having a stable cutoff value.

Variation of stroke-associated pneumonia in stroke units across England and Wales: A registry-based cohort study

Background

Pneumonia is common in stroke patients and is associated with worse clinical outcomes. Prevalence of stroke-associated pneumonia varies between studies, and reasons for this variation remain unclear. We aimed to describe the variation of observed stroke-associated pneumonia in England and Wales and explore the influence of patient baseline characteristics on this variation.

Methods

Patient data were obtained from the Sentinel Stroke National Audit Programme for all confirmed strokes between 1 April 2013 and 31 December 2018. Stroke-associated pneumonia was defined by new antibiotic initiation for pneumonia within the first seven days of admission. The probability of stroke-associated pneumonia occurrence within stroke units was estimated and compared using a multilevel mixed model with and without adjustment for patient-level characteristics at admission.

Results

Of the 413,133 patients included, median National Institutes of Health Stroke Scale was 4 (IQR: 2–10) and 42.3% were aged over 80 years. Stroke-associated pneumonia was identified in 8.5% of patients. The median within stroke unit stroke-associated pneumonia prevalence was 8.5% (IQR: 6.1–11.5%) with a maximum of 21.4%. The mean and variance of the predicted stroke-associated pneumonia probability across stroke units decreased from 0.08 (0.68) to 0.05 (0.63) when adjusting for patient admission characteristics. This difference in the variance suggests that clinical characteristics account for 5% of the observed variation in stroke-associated pneumonia between units.

Conclusions

Patient-level clinical characteristics contributed minimally to the observed variation of stroke-associated pneumonia between stroke units. Additional explanations for the observed variation in stroke-associated pneumonia need to be explored which could reduce variation in antibiotic use for stroke patients.

Use of the A2DS2 scale to predict morbidity in stroke-associated pneumonia: a systematic review and meta-analysis

BACKGROUND

This review aims to evaluate the performance and clinical applicability of the A2DS2 scale via systematic review and meta-analysis.

METHODS

The Medline, Embase, Cochrane Library, CBM, CNKI, and Wanfang databases were searched. The risk of bias was assessed using the Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2). Funnel plots and Egger's test were used to evaluate publication bias. The bivariate random-effect model was used for calculating the sensitivity, specificity, positive likelihood ratio, negative likelihood ratio, diagnostic odds ratio, and area under the curve (AUC). A Fagan nomogram was applied to evaluate the clinical applicability of the A2DS2 scale.

RESULTS

A total of 29 full-text articles met the inclusion criteria, including 19,056 patients. Bivariate mixed-effects regression models yielded a mean sensitivity of 0.78 (95 % CI: 0.73-0.83), a specificity of 0.79 (95 % CI: 0.73-0.84), a positive likelihood ratio of 3.7 (95 % CI: 2.9-4.6), and a negative likelihood ratio of 0.27 (95 % CI: 0.23-0.33). The area under the receiver operating characteristic curve was 0.85 (95 % CI: 0.82-0.88). If given a pre-test probability of 50 %, the Fagan nomogram showed that when A2DS2 was positive, the post-test probability improved to 79 %. In contrast, when A2DS2 was negative, it decreased to 22 %. The results of the subgroup analysis showed no effect on the diagnostic accuracy of the A2DS2 scale in predicting stroke-associated pneumonia, except for the optimal cut-off value.

CONCLUSIONS

The A2DS2 scale demonstrates high clinical applicability and could be a valid scale for the early prediction of stroke-associated pneumonia in stroke patients.

Ischaemic stroke-induced distal organ damage: pathophysiology and new therapeutic strategies

Acute ischaemic stroke is associated with a high risk of non-neurological complications, which include respiratory failure, cardiovascular dysfunction, kidney and liver injury, and altered immune and endocrine function. The aim of this manuscript is to provide an overview of the main forms of stroke-induced distal organ damage, providing new pathophysiological insights and recommendations for clinical management.Non-neurological complications of stroke can affect outcomes, with potential for serious short-term and long-term consequences. Many of these complications can be prevented; when prevention is not feasible, early detection and proper management can still be effective in mitigating their adverse impact. The general care of stroke survivors entails not only treatment in the acute setting but also prevention of secondary complications that might hinder functional recovery. Acute ischaemic stroke triggers a cascade of events-including local and systemic activation of the immune system-which results in a number of systemic consequences and, ultimately, may cause organ failure. Understanding the pathophysiology and clinical relevance of non-neurological complications is a crucial component in the proper treatment of patients with acute stroke.Little evidence-based data is available to guide management of these complications. There is a clear need for improved surveillance and specific interventions for the prevention, early diagnosis, and proper management of non-neurological complications during the acute phase of ischaemic stroke, which should reduce morbidity and mortality.

Detection of airway protective level of the cough reflex in acute stroke patients

Background

The most common respiratory complications in cerebrovascular strokes were respiratory infection, pulmonary edema, acute lung injury (ALI) acute respiratory distress syndrome (ARDS) and pulmonary embolism (PE). The inhalation cough challenge facilitates the quantification of cough.

Objectives

To detect the level of cough reflex that is enough to protect against respiratory infection in stroke patients, and to identify predictors of post-stroke respiratory infection.

Patients and methods

One hundred and one of cerebrovascular stroke patients were assessed in the first week of symptoms by National Institutes of Health Stroke Scale (NIHSS) Arabic version, Mann Assessment of Swallowing Ability (MASA), cough challenge test, cough flow meter, and A2DS2 score. Then, follow up after 1 week.

Results

Post-stroke respiratory infection was higher in older patients and those who were not working. Respiratory infection was significantly associated with high A2DS2 and NIHSS score (p value < 0.001). A highly significant increase in the levels of the inflammatory markers was detected in patients with a respiratory infection. Eighty percent of stroke patients with no cough developed a respiratory infection. The Mann total scores and the peak cough flow were lower in patients who had a respiratory infection.

Conclusion

Preserved cough reflex is essential in preventing aspirations and consequent respiratory infections.

High Monocyte-To-Lymphocyte Ratio Is Associated With Stroke-Associated Pneumonia

Purpose: Stroke-associated pneumonia (SAP), a common complication in acute ischemic stroke (AIS) patients, is associated with poor prognosis after AIS. Inflammation plays an important role in the development of SAP. In this study, we aimed to explore the association between the monocyte-to-lymphocyte ratio (MLR) and SAP in AIS patients.

Methods: We continuously enrolled 972 AIS patients. SAP was diagnosed by two trained neurologists and confirmed by radiography, meeting the modified Centers for Disease Control and Prevention criteria. MLR values were measured for all participants, and all patients were evenly classified into three tertiles according to the MLR levels. We used the values that Youden's index max points corresponded to represent the optimal cutoffs, which represented the balance in sensitivity and specificity.

Results: 104 (10.7%) patients were diagnosed with SAP. SAP patients showed a significant increased (P < 0.001) MLR when compared with non-SAP. The optimal cutoff points of MLR were (T1) <0.2513, (T2) 0.2513–0.3843, and (T3) > 0.3843. The incidence of SAP was significantly higher in the third MLR tertile than the first and second MLR tertiles (21.7 vs. 4 vs. 6.5%, respectively, P < 0.001). After adjusting for confounding and risk factors, multivariate regression analysis showed that the third MLR tertile was an independent variable predicting the occurrence of SAP (odds ratio = 3.503, 95%CI = 1.066–11.515, P = 0.039).

Conclusions: Our study showed that higher MLR was significantly associated with SAP in AIS patients. MLR is beneficial for clinicians to recognize patients with a high risk of SAP at an early stage and is an effective way to improve clinical care of SAP patients. Higher MLR could be a helpful and valid biomarker for predicting SAP in clinical practice.

Value of Combination of the A2DS2 Score and IL-6 in Predicting Stroke-Associated Pneumonia

PURPOSE

To evaluate the value of the combination of the age, atrial fibrillation, dysphagia, male sex, and National Institutes of Health Stroke Scale (A²DS²) score and serum interleukin 6 (IL-6) concentration in predicting stroke-associated pneumonia (SAP).

PATIENTS AND METHODS

A total of 398 patients with acute ischemic stroke (AIS) from the medical ward was included in this retrospective study. They were divided into the SAP group and non-SAP group according to the diagnostic criteria of SAP. Multivariate analysis was performed to analyze the association between the A²DS² score, serum IL-6 concentration, and SAP using a backward stepwise logistic regression model. The receiver operating characteristic (ROC) curve was used to evaluate the value of the A²DS² score, serum IL-6 concentration and combination of A²DS² score and IL-6 in predicting SAP.

RESULTS

SAP was diagnosed in 70 patients (17.6%). Multivariate analysis showed that the A²DS² score (odds ratio [OR]: 2.25, 95% confidence interval [CI]: 1.17-4.99, P=0.017) and serum IL-6 concentration (OR: 1.76, 95% CI: 1.44-1.95, P<0.001) was independently associated with SAP after adjusting for age, smoking, hypertension, hyperlipidemia, and atrial fibrillation. When the A²DS² score, serum IL-6 concentration and combination of A²DS² score and IL-6 were employed to predict SAP, the AUC was 0.824 (SE: 0.026, 95% CI: 0.773-0.875), 0.715 (SE: 0.034, 95% CI: 0.641-0.788) and 0.917 (SE: 0.015, 95% CI: 0.887-0.946), respectively. The AUC of combinative prediction was significantly higher than independent prediction (0.917 vs. 0.824, Z=3.098, P<0.001; 0.917 vs. 0.715, Z=5.436, P<0.001).

CONCLUSION

The addition of serum IL-6 to the A²DS² score could significantly enhance the AUC of predicting SAP in AIS patients from the medical ward.

Reduced Vitamin D Levels are Associated with Stroke-Associated Pneumonia in Patients with Acute Ischemic Stroke

Background and aim

Stroke-associated pneumonia (SAP) is a common complication in patients with acute ischemic stroke (AIS). This study explored the potential relationship between serum vitamin D levels and SAP.

Methods

This study recruited 863 consecutive AIS patients. In-hospital SAP was defined as a complication that occurred after stroke, during hospitalization, that was confirmed radiographically. Serum vitamin D levels were measured within 24 hrs of admission and the patients were divided into vitamin D sufficient (>50 nmol/L), insufficient (25–50 nmol/L), and deficient (<25 nmol/L) groups.

Results

In this study, 102 (11.8%) patients were diagnosed with SAP. Compared to the patients without SAP, patients with SAP had significantly lower vitamin D levels (P = 0.023). The incidence of SAP was significantly higher in patients with vitamin D deficiency than in those with vitamin D insufficiency or sufficiency (21.2% vs 16.2% & 9.5%, P = 0.006). After adjusting for confounders, vitamin D deficiency and insufficiency were independently associated with SAP (OR = 3.034, 95% CI = 1.207–7.625, P = 0.018; OR = 1.921, 95% CI = 1.204–3.066, P = 0.006, respectively). In multiple-adjusted spline regression, vitamin D levels showed a linear association with the risk of SAP (P < 0.001 for linearity).

Conclusion

Reduced vitamin D is a potential risk factor of in-hospital SAP, which can help clinicians identify high-risk SAP patients.

Mechanical ventilation in patients with acute ischaemic stroke: from pathophysiology to clinical practice

Most patients with ischaemic stroke are managed on the ward or in specialty stroke units, but a significant number requires higher-acuity care and, consequently, admission to the intensive care unit. Mechanical ventilation is frequently performed in these patients due to swallowing dysfunction and airway or respiratory system compromise. Experimental studies have focused on stroke-induced immunosuppression and brain-lung crosstalk, leading to increased pulmonary damage and inflammation, as well as reduced alveolar macrophage phagocytic capability, which may increase the risk of infection. Pulmonary complications, such as respiratory failure, pneumonia, pleural effusions, acute respiratory distress syndrome, lung oedema, and pulmonary embolism from venous thromboembolism, are common and found to be among the major causes of death in this group of patients. Furthermore, over the past two decades, tracheostomy use has increased among stroke patients, who can have unique indications for this procedure—depending on the location and type of stroke—when compared to the general population. However, the optimal mechanical ventilator strategy remains unclear in this population. Although a high tidal volume (V_T) strategy has been used for many years, the latest evidence suggests that a protective ventilatory strategy (V_T = 6–8 mL/kg predicted body weight, positive end-expiratory pressure and rescue recruitment manoeuvres) may also have a role in brain-damaged patients, including those with stroke. The aim of this narrative review is to explore the pathophysiology of brain-lung interactions after acute ischaemic stroke and the management of mechanical ventilation in these patients.

Risk of stroke-associated pneumonia during hospitalization: predictive ability of combined A2DS2 score and hyperglycemia

BACKGROUND

Stroke-associated pneumonia (SAP) is a common complication of cerebrovascular disease. The A²DS² score has been used to predict the risk of SAP. However, hyperglycemia is not included in this scale. The purpose of the present study was to explore whether the A²DS² scoring system and hyperglycemia could predict the risk of SAP more effectively than the conventional A²DS² scale.

METHODS

This retrospective study enrolled 2552 patients with acute ischemic stroke. The A²DS² scores, fasting blood glucose level and blood glucose level on admission were collected. Regression analysis was used to identify the independent risk factors of SAP. ROC curve analysis was used to evaluate the specificity and sensitivity of the combined A²DS² score and fasting hyperglycemia for predicting SAP.

RESULTS

Fasting hyperglycemia was an independent risk factor for SAP (OR = 2.95; 95% confidence interval: 2.11-4.12; P < 0.001). The area under curve of the combined A²DS² score and fasting hyperglycemia was significantly higher than that of the A²DS² score alone (0.814 vs. 0.793; P = 0.020).

CONCLUSION

Fasting hyperglycemia is an independent risk factor for predicting SAP. Compared with the A2DS2 score, the modified A2DS2 score (combined A2DS2 score and fasting hyperglycemia) is more effective in predicting the risk of SAP.

Biomarkers predictive value for early diagnosis of Stroke-Associated Pneumonia

To confirm the diagnostic accuracy of candidate biomarkers in stroke-associated pneumonia (SAP), we prospectively enrolled ischemic stroke patients with NIHSS ≥ 10 on admission from March-2016 to August-2017. Blood samples were collected at baseline, 24 and 48 h after stroke onset. Biomarkers (MR-proADM, suPAR, SAA) were determined by immunoassays. Regarding biomarkers, MR-proADM at 24 h (P = 0.04) and both suPAR and SAA at 48 h (P = 0.036 and P = 0.057) were associated with pneumonia. The combination of SAA > 25.15 mg/dL and suPAR> 3.14 ng/mL at 48 h had 80% sensitivity and 95.8% specificity when both biomarkers were above the cut-off. The evaluated biomarkers represent promising tools to be evaluated in future large, prospective studies on SAP. An accurate SAP diagnosis by thorax CT might help to reduce variability in such studies.

Pathogens distribution and drug resistance in patients with acute cerebral infarction complicated with diabetes and nosocomial pulmonary infection

Background

This study aims to investigate the pathogen distribution and drug resistance in patients with acute cerebral infarction complicated with diabetes mellitus and nosocomial pulmonary infection.

Methods

From August 2015 to December 2017, 172 pathogenic bacterial strains from patients with acute cerebral infarction complicated with diabetes mellitus and nosocomial pulmonary infection in our hospital were identified, and the drug sensitivity was analyzed.

Results

Among these 172 strains of pathogenic bacteria, gram negative bacteria was the main cause of pulmonary infection in hospitalized patients with acute cerebral infarction, accounting for 75.6% of all pathogens. Furthermore, 80% of diabetic patients with cerebral infarction had lung infection induced by gram negative bacteria, which was significantly higher than that in non-diabetic patients (72.2%). Moreover, the drug resistance rate in the diabetic group (68.3%) was significantly higher than that in the non-diabetic group (54.3%). Gram positive bacteria accounted for 19.1% of all pathogenic bacteria. The infection rate of gram-positive bacteria in diabetic patients with cerebral infarction was 14.7%, which was lower than that in the non-diabetic group (22.6%). The drug-resistance rate was higher in the diabetic group (45.5%) than in the non-diabetic group (28.2%). Furthermore, the fungal infection rate in patients with lung infection in these two groups was 5.3 and 5.2%, respectively, and fungi presented with high sensitivity to commonly used antifungal agents.

Conclusion

In patients with acute cerebral infarction complicated with diabetes mellitus and nosocomial pulmonary infection, the majority of pathogens are multidrug-resistant gram negative bacilli. Pathogen culture should be conducted as soon as possible before using antibiotics, and antimicrobial agents should be reasonably used according to drug sensitivity test results.