Clinical risk scores for predicting stroke-associated pneumonia: A systematic review

Early Complications After Mild to Moderate Ischemic Stroke and Impact on 3-Month Outcome: The Multicenter Prospective Stroke Unit Plus Cohort Study

BACKGROUND

Early stroke complications (ESCs) impair recovery and provide an important therapeutic target. Our multicenter prospective cohort study examined the prevalence and amount of poor outcomes attributable to ESCs.

METHODS AND RESULTS

In patients with stroke, 16 ESCs were monitored by clinical assessment ("basic definition") and by inclusion of laboratory values and clinical scales ("extended definition"). The association of complications with poor outcome (death or functional impairment: modified Rankin Scale score >3 or Barthel Index <60) at 3 months was analyzed using multiple logistic regression adjusted for stroke severity, age, and sex. Complications were stratified by their early treatment options, and average sequential population-attributable fractions were estimated for treatable early complications. Of 1202 patients, outcome data were available for 1105 (91.9%; 43.0% women; mean age 68.3 years; baseline median National Institutes of Health Stroke Scale score on admission 3 points). Poor outcome was recorded for 100 patients (9.0%). By basic definition, recurrent stroke, fever, pneumonia, depression, urinary tract infection, and delirium were significantly associated with poor outcomes. The occurrence of 1 or more of these ESCs was associated with up to 19.6% of poor outcomes. By extended definition, urinary tract infection, depression, and delirium were significantly associated with poor outcomes. The occurrence of 1 or more of these ESCs was associated with up to 15.9% of poor outcomes.

CONCLUSIONS

In our cohort of patients with mild to moderate stroke, we identified a set of treatable ESCs with clinically relevant impact on outcome that may be targeted in future interventional trials.

REGISTRATION

URL: https://www.bfarm.de; Unique identifier: DRKS00004712.

Endovascular thrombectomy for large vessel occlusion stroke patients with baseline disability: Long-term outcomes and end-of-life care

BACKGROUND

One third of all patients with acute ischemic strokes have a pre-existing disability. Patients with pre-existing disabilities have historically been excluded from landmark clinical trials of acute stroke interventions, leading to ongoing controversy about the risks and benefits of acute stroke interventions such as endovascular thrombectomy (EVT). To address this controversy, we compared long-term outcomes and end-of-life care in large vessel occlusion (LVO) patients with moderate-to-severe baseline disability treated with EVT versus medical management alone.

METHODS

Patients who presented with an LVO to our comprehensive stroke center between January 2017 and December 2020 were retrospectively identified from a prospectively maintained database. Moderate-to-severe baseline disability was defined as a pre-stroke modified Rankin Scale (mRS) of 3-5. Delta mRS was defined as the difference between the 90-day and baseline mRS. Logistic and ordinal regressions were performed to evaluate the relationships between EVT and outcomes. An analysis of rates and reasons for transitions to comfort care was also performed, where applicable.

RESULTS

A total of 175/1008 (17 %) LVO patients with moderate-to-severe baseline disability were identified. The median age was 82 (IQR 70-89), and 59 % were female. Thirty-two patients (18 %) with moderate-to-severe baseline disability were treated with EVT. EVT was independently associated with improved delta mRS (B=-1.048; 95 %CI=-1.777,-0.318; p = 0.005) accounting for age and NIHSS. However, EVT did not reduce the odds of transitioning to comfort care (aOR=0.794; 95 %CI=0.347,1.818; p = 0.585) accounting for age and NIHSS. Seventy-six (43 %) patients were transitioned to comfort care during their hospitalization. Of the 99 who were not transitioned to comfort care, 18 were treated with EVT, and EVT was independently associated with improved delta mRS (B=-2.794; 95 %CI=-4.002,-1.586; p < 0.0001). The median time from presentation to transition to comfort care was 2 days (IQR 1-7) in the non-EVT group, compared to 7 (IQR 4-11) in the EVT group (H(1)= 5.46, p = 0.019). The primary reasons for transitions to comfort care were poor perceived prognosis and medical complications.

CONCLUSIONS

Among patients with moderate-to-severe baseline disability, EVT is associated with less post-stroke accumulated disability without limiting transitions to comfort care. EVT is compatible with goal-concordant care and should not be routinely withheld because of baseline disability alone.

Bacterial stroke-associated pneumonia: microbiological analysis and mortality outcome

BACKGROUND

Stroke-associated pneumonia (SAP) considerably burden healthcare systems. This study aimed to identify predictors of developing SAP in acute ischemic stroke patients admitted to the Stroke Unit at Manial Specialized Hospital factors with microbiological causality and impact on 30-day mortality.

METHODS

This was a retrospective cohort study. All patients with acute ischemic stroke admitted to the Stroke Unit at Manial Specialized Hospital (from February 2021 to August 2023) were divided into the SAP and non-SAP groups. Detailed clinical characteristics and microbiological results were recorded.

RESULTS

Five hundred twenty-two patients diagnosed with acute ischemic stroke (mean age of 55 ± 10) were included. One hundred sixty-nine (32.4%) of stroke patients developed SAP; Klebsiella pneumoniae was the most commonly detected pathogen (40.2%), followed by Pseudomonas aeruginosa (20.7%). Bacteremia was identified in nine cases (5.3%). The number of deaths was 11, all of whom were diagnosed with SAP, whereas none from the non-SAP group died (P < 0.001). The binary logistic regression model identified three independent predictors of the occurrence of SAP: previous history of TIA/stroke (OR = 3.014, 95%CI = 1.281-7.092), mechanical ventilation (OR = 4.883, 95%CI = 1.544-15.436), and bulbar dysfunction (OR = 200.460, 95%CI = 80.831-497.143).

CONCLUSIONS

Stroke-associated pneumonia was reported in one-third of patients with acute ischemic stroke, adversely affecting mortality outcomes. Findings showed that the main predictors of SAP were bulbar dysfunction, the use of mechanical ventilation and previous history of TIA/stroke. More attention to these vulnerable patients is necessary to reduce mortality.

Association Between TCBI (Triglycerides, Total Cholesterol, and Body Weight Index) and Stroke-Associated Pneumonia in Acute Ischemic Stroke Patients

Purpose

Stroke-associated pneumonia (SAP) usually complicates stroke and is linked to adverse prognoses. Triglycerides, total cholesterol, and body weight index (TCBI) is a new and simple calculated nutrition index. This study seeks to investigate the association between TCBI and SAP incidence, along with its predictive value.

Patients and Methods

Nine hundred and sixty-two patients with acute ischemic stroke were divided into SAP group and Non-SAP group. The TCBI was divided into three layers: T1, TCBI < 948.33; T2, TCBI 948.33-1647.15; T3, TCBI > 1647.15. Binary Logistic regression analysis was used to determine the relationship between TCBI levels and the incidence of SAP. Furthermore, restricted cubic splines (RCS) analysis was utilized to evaluate the influence of TCBI on the risk of SAP.

Results

TCBI in the SAP group was markedly lower compared to that in the Non-SAP group (P < 0.001). The Logistic regression model revealed that, using T3 layer as the reference, T1 layer had the highest risk for SAP prevalence (OR = 2.962, 95% CI: 1.600-5.485, P = 0.001), with confounding factors being controlled. The RCS model found that TCBI had a linear relationship with SAP (P for nonlinear = 0.490, P for overall = 0.004). Moreover, incorporating TCBI into the A2DS2 (Age, atrial fibrillation, dysphagia, sex, and severity) model substantially enhanced the initial model's predictive accuracy.

Conclusion

Low TCBI was associated with a higher risk of SAP. In clinical practice, TCBI has shown predictive value for SAP, contributing to early intervention and treatment of SAP.

Machine learning-based survival analysis approaches for predicting the risk of pneumonia post-stroke discharge

BACKGROUND

Post-stroke pneumonia (PSP) is common among stroke patients. PSP occurring after hospital discharge continues to increase the risk of poor functional outcomes and death among stroke survivors. Currently, there is no prediction model specifically designed to predict the occurrence of PSP beyond the acute stage of stroke. This study aimed to explore the use of machine learning (ML) methods in predicting the risk of PSP after hospital discharge.

METHODS

This study analyzed data from 5,754 hospitalized stroke patients. The dataset was randomly divided into a training set and a holdout test set, with a ratio of 80:20. Several clinical and laboratory variables were utilized as predictors and different ML algorithms were employed to model time-to-event data. The ML model's predictive performance was compared to existing risk-scoring systems. A model-agnostic method based on Shapley additive explanations was utilized to interpret the ML model.

RESULTS

The study found that 5.7% of the study patients experienced pneumonia within one year after discharge. Based on repeated 5-fold cross-validation on the training set, the random survival forest (RSF) model had the highest C-index among the various ML algorithms and traditional Cox regression analysis. The final RSF model achieved a C-index of 0.787 (95% confidence interval: 0.737-0.840) on the holdout test set, outperforming five existing risk-scoring systems. The top three important predictors were the Glasgow Coma Scale score, age, and length of hospital stay.

CONCLUSIONS

The RSF model demonstrated superior discriminative ability compared to other ML algorithms and traditional Cox regression analysis, suggesting a non-linear relationship between predictors and outcomes. The developed ML model can be integrated into the hospital information system to provide personalized risk assessments.

Usability of serum AIM2 as a predictive biomarker of stroke-associated pneumonia and poor prognosis after acute supratentorial intracerebral hemorrhage: A prospective longitudinal cohort study

Background

Absent in melanoma 2 (AIM2) is implicated in inflammatory processes. We measured serum AIM2 with intent to unveil its predictive significance for stroke-associated pneumonia (SAP) and functional prognosis following acute intracerebral hemorrhage (ICH).

Methods

In this prospective cohort study, serum AIM2 concentrations of 163 ICH patients were gauged upon admission and 57 of them also consented for measurements at days 1, 3, 5, 7, 10 and 14. Coupled with 57 individuals without health conditions, dynamic change of serum AIM2 levels were uncovered. National Institutes of Health Stroke Scale (NIHSS) scores and hematoma volume were identified as the dual indicators of severity. Poststroke six-month modified Rankin Scale (mRS) scores ranging from 3 to 6 indicated an unfavorable outcome. SAP was observed during the first seven days after ICH. Sequential univariate and multivariate analyses were performed to discern predictors of SAP and adverse prognosis.

Results

The serum levels of AIM2 in patients exhibited a marked elevation upon admission, reaching peak levels on the third and fifth days, and remained notably elevated until day 14 compared to those of the control group. Serum AIM2 levels showed independent correlations with both NIHSS scores and the volume of hematoma. Additionally, AIM2 concentrations were independently associated with a poor prognosis and SAP at the six-month mark. Within the framework of restricted cubic spline analysis, serum AIM2 concentrations exhibited a linear correlation with the likelihood of developing SAP and experiencing a poor prognosis. In the context of receiver operating characteristic (ROC) curve analysis, serum AIM2 concentrations effectively differentiated risks of SAP and poor prognosis. By employing segmented analysis, serum AIM2 concentrations showed negligible interactions with several traditional variables, such as age, gender, smoking habits, alcohol consumption, and more. The integrated model incorporating serum AIM2, NIHSS scores, and the volume of hematoma was depicted by employing a nomogram and demonstrated strong predictive performance for poor prognosis or SAP across various evaluation metrics, including ROC curve analysis, calibration curve analysis, and decision curve analysis.

Conclusion

Serum AIM2 levels show a marked increase shortly after intracerebral hemorrhage (ICH), which may accurately reflect stroke severity, and effectively predict SAP and poor neurological outcomes, and therefore serum AIM2 stands out as an encouraging predictive indicator for ICH.

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.

Prevalence of Stroke-Associated Pneumonia and Its Predictors Among Hyperglycaemia Patients During Acute Ischemic Stroke

BACKGROUND

Hyperglycaemia (HG) during an acute ischemic stroke (AIS) is not only associated with unfavourable functional outcomes but also associated with stroke-associated pneumonia (SAP). This study aimed to determine the prevalence of SAP among Malaysian patients with AIS and the predictors of SAP among patients with HG during AIS.

METHODS

This is a retrospective cross-sectional study that included patients with AIS admitted to Hospital Sultanah Nur Zahirah, Malaysia from 2017 to 2020. SAP was defined as infection with pneumonia during the first seven days after IS. HG was defined as a blood glucose level > 7.8 mmol/L within 72 h after admission. Patients with SAP were divided into two groups according to HG status. Multivariate logistic regression analysis was performed using SPSS software, version 22 (IBM Corp., Armonk, NY) to identify SAP predictors among patients with HG. Kaplan-Meier log-rank test was used to compare the survival rate from unfavourable functional outcomes between hyperglycaemic patients with and without SAP.

RESULTS

Among 412 patients with AIS, 69 (16.74%) had SAP. The prevalence of SAP among patients with HG and normoglycemia during AIS was 20.98%, and 10.65%, respectively. Age above 60 years, leucocytosis, and National Institute of Health Stroke Scale (NIHSS) > 14 on admission were independent predictors of SAP with aOR of 2.08 (95% CI;1.01-4.30), 2.83 (95% CI; 1.41-5.67), and 3.67 (95% CI; 1.53-8.80), respectively. No significant difference in unfavourable functional outcomes survival was found among patients with and without SAP (p = 0.653).

CONCLUSION

This study demonstrated the prevalence of SAP was higher among patients with HG compared to normoglycemia during AIS. The patient being old, leucocytosis and severe stroke upon admission predict the occurrence of SAP among patients with HG during AIS.

Association between oral microbial dysbiosis and poor functional outcomes in stroke-associated pneumonia patients

BACKGROUND

Despite advances in our understanding of the critical role of the microbiota in stroke patients, the oral microbiome has rarely been reported to be associated with stroke-associated pneumonia (SAP). We sought to profile the oral microbial composition of SAP patients and to determine whether microbiome temporal instability and special taxa are associated with pneumonia progression and functional outcomes.

METHODS

This is a prospective, observational, single-center cohort study that examined patients with acute ischemic stroke (AIS) who were admitted within 24 h of experiencing a stroke event. The patients were divided into three groups based on the occurrence of pneumonia and the use of mechanical ventilation: nonpneumonia group, SAP group, and ventilator-associated pneumonia (VAP) group. We collected oral swabs at different time points post-admission and analyzed the microbiota using 16 S rRNA high-throughput sequencing. The microbiota was then compared among the three groups.

RESULTS

In total, 104 nonpneumonia, 50 SAP and 10 VAP patients were included in the analysis. We found that SAP and VAP patients exhibited significant dynamic differences in the diversity and composition of the oral microbiota and that the magnitude of this dysbiosis and instability increased during hospitalization. Then, by controlling the potential effect of all latent confounding variables, we assessed the changes associated with pneumonia after stroke and explored patients with a lower abundance of Streptococcus were more likely to suffer from SAP. The logistic regression analysis revealed that an increase in specific taxa in the phylum Actinobacteriota was linked to a higher risk of poor outcomes. A model for SAP patients based on oral microbiota could accurately predict 30-day clinical outcomes after stroke onset.

CONCLUSIONS

We concluded that specific oral microbiota signatures could be used to predict illness development and clinical outcomes in SAP patients. We proposed the potential of the oral microbiota as a non-invasive diagnostic biomarker in the clinical management of SAP patients.

CLINICAL TRIAL REGISTRATION

NCT04688138. Registered 29/12/2020, https://clinicaltrials.gov/ct2/show/NCT04688138 .

Predictors of stroke-associated pneumonia and the predictive value of neutrophil percentage-to-albumin ratio

BACKGROUND

Early recognition of stroke-associated pneumonia (SAP) is critical to reducing morbidity and mortality associated with SAP. This study investigated the predictors of SAP, and the predictive value of the neutrophil percentage-to-albumin ratio (NPAR) for SAP.

METHODS

This retrospective cohort study was conducted among stroke patients admitted to Jordan University Hospital from January 2015 to May 2021. Multivariable logistic regression was used to identify independent predictors for SAP. The predictive performance was assessed using C-statistics, described as the area under the receiver-operating characteristic curve (AUC, ROC) with a 95% confidence interval.

RESULTS

Four hundred and six patients were included in the analysis, and the prevalence of SAP was 19.7%. Multivariable logistic analysis showed that males (Adjusted Odds Ratio (AOR): 5.74; 95% Confidence Interval (95%CI): 2.04-1 6.1)], dysphagia (AOR: 5.29; 95% CI: 1.80-15.5), hemiparesis (AOR: 3.27; 95% CI: 1.13-9.47), lower GCS score (AOR: 0.73; 95% CI: 0.58-0.91), higher levels of neutrophil-lymphocyte ratio (NLR) (AOR: 1.15; 95% CI: 1.07-1.24), monocyte-lymphocyte ratio (MLR) (AOR: 1.49; 95% CI: 1.13-1.96), and neutrophil percentage to albumin ratio (NPAR) (AOR: 1.53; 95% CI: 1.33-1.76) were independent predictors of SAP. The NPAR demonstrated a significantly higher AUC than both the NLR (0.939 versus 0.865, Z = 3.169, p = 0.002) and MLR (0.939 versus 0.842, Z = 3.940, p < 0.001). The AUCs of the NLR and MLR were comparable (0.865 versus 0.842, Z = 1.274, p = 0.203).

CONCLUSION

Male gender, dysphagia and hemiparesis were the strongest predictors of SAP, and NPAR has an excellent performance in predicting SAP which was better than high NLR and MLR.

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

BACKGROUND

Malnutrition 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.

METHODS

Patients 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.

RESULTS

We 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.

CONCLUSION

Malnutrition 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.

Neutrophil-lymphocyte ratio, monocyte-lymphocyte ratio, and platelet-lymphocyte ratio in stroke-associated pneumonia: a systematic review and meta-analysis

BACKGROUND

Predicting stroke-associated pneumonia (SAP) is crucial for intensifying preventive measures and decreasing morbidity and mortality. This meta-analysis aims to evaluate the association between baseline neutrophil-lymphocyte ratio (NLR), monocyte-lymphocyte ratio (MLR), and platelet-lymphocyte ratio (PLR) with SAP and to determine the strength of the association.

METHODS

The Web of Science, SCOPUS, and PUBMED databases were searched to find eligible studies. The standardized mean difference (SMD) and 95% confidence interval (CI) were used to evaluate the differences in NLR, MLR, and PLR levels between SAP and non-SAP patients. The meta-analysis was conducted using the software "Review Manager" (RevMan, version 5.4.1, September 2020). The random-effect model was used for the pooling analysis if there was substantial heterogeneity. Otherwise, the fixed-effect model was adopted.

RESULTS

Twelve studies comprising 6302 stroke patients were included. The pooled analyses revealed that patients with SAP had significantly higher levels of NLR, MLR, and PLR than the non-SAP group. The SMD, 95% CI, p-value, and I² for them were respectively reported as (0.88, 0.70-1.07, .00001, 77%); (0.94, 0.43-1.46, .0003, 93%); and (0.61, 0.47-0.75, .001, 0%). Subgroup analysis of NLR studies showed no significant differences in the effect size index between the severity of the stroke, the sample size, and the period between the stroke onset and the blood sampling.

CONCLUSION

This systematic review and meta-analysis suggest that an elevated NLR, MLR, and PLR were associated with SAP, indicating that they could be promising blood-based biomarkers for predicting SAP. Large-scale prospective studies from various ethnicities are recommended to validate this association before they can be applied in clinical practice.

Effects of nurse-led hierarchical management care on acute stroke patients: A pilot study to promote stroke-associated pneumonia management

Background

Stroke-related pneumonia (SAP) is a common complication in acute ischemic stroke (AIS) patients, and it has adverse effects on the clinical outcomes and increases the burden on patients' families and society. Early identification and individualized care are necessary to reduce the incidence of SAP.

Objective

The present study aimed to explore the effect of nurse-led hierarchical management care based on the acute ischemic stroke-associated pneumonia score (AIS-APS) scale in AIS patients.

Methods

A quasi-intervention pilot study design was adopted for the present study. A total of 120 AIS patients were enrolled and assigned to the intervention group and the control group, with 60 subjects in each group in a tertiary hospital in Guangzhou, China. The control group received routine care, whereas the intervention group was given nurse-led hierarchical management care based on the AIS-APS scale. The intervention duration was more than 7 days, and the incidence of SAP, neurological function, swallowing function, and activities of daily living (ADLs) at discharge were observed. The outcomes were assessed at baseline and at outpatient time.

Results

A total of 120 participants were enrolled in our study. A significant decrease was found in the incidence of SAP in the intervention group (18.3%) compared with that in the control group (41.7%). Positive outcomes were shown in neurology function, swallowing function, and ADL in the intervention group.

Conclusion

Nurse-led hierarchical management care based on AIS-APS can reduce the incidence of SAP, promote AIS patients' neurological function, and maintain patients' ADL. The results of our study indicated that nurse-led hierarchical management care is feasible for AIS patients and provides individualized interventions for patients with different levels of SAP risk. Nurse-led hierarchical management care could be incorporated into routine nursing practice. Further study is needed and expected to solve more clinical problems.

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.

A Prognostic Model of Non-Small Cell Lung Cancer With a Radiomics Nomogram in an Eastern Chinese Population

Background

The aim of this study was to build and validate a radiomics nomogram by integrating the radiomics features extracted from the CT images and known clinical variables (TNM staging, etc.) to individually predict the overall survival (OS) of patients with non-small cell lung cancer (NSCLC).

Methods

A total of 1,480 patients with clinical data and pretreatment CT images during January 2013 and May 2018 were enrolled in this study. We randomly assigned the patients into training (N = 1036) and validation cohorts (N = 444). We extracted 1,288 quantitative features from the CT images of each patient. The Least Absolute Shrinkage and Selection Operator (LASSO) Cox regression model was applied in feature selection and radiomics signature building. The radiomics nomogram used for the prognosis prediction was built by combining the radiomics signature and clinical variables that were derived from clinical data. Calibration ability and discrimination ability were analyzed in both training and validation cohorts.

Results

Eleven radiomics features were selected by LASSO Cox regression derived from CT images, and the radiomics signature was built in the training cohort. The radiomics signature was significantly associated with NSCLC patients' OS (HR = 3.913, p < 0.01). The radiomics nomogram combining the radiomics signature with six clinical variables (age, sex, chronic obstructive pulmonary disease, T stage, N stage, and M stage) had a better prognostic performance than the clinical nomogram both in the training cohort (C-index, 0.861, 95% CI: 0.843-0.879 vs. C-index, 0.851, 95% CI: 0.832-0.870; p < 0.001) and in the validation cohort (C-index, 0.868, 95% CI: 0.841-0.896 vs. C-index, 0.854, 95% CI: 0.824-0.884; p = 0.002). The calibration curves demonstrated optimal alignment between the prediction and actual observation.

Conclusion

The established radiomics nomogram could act as a noninvasive prediction tool for individualized survival prognosis estimation in patients with NSCLC. The radiomics signature derived from CT images may help clinicians in decision-making and hold promise to be adopted in the patient care setting as well as the clinical trial setting.

Are beta blockers effective in preventing stroke associated infections? - a systematic review and meta-analysis

BACKGROUND

Excessive sympathoexcitation could lead to stroke associated infection. Inhibiting sympathetic excitation may reduce the infection risk after stroke. Thus, the present study aimed to determine the protective effect of beta blockers on stroke associated infection through systematic review and meta-analysis.

METHODS

A systematic search of multiple databases were performed up to February 2022. The included studies required beta blockers therapy in stroke patients and assessed the incidence of stroke-associated infections. Outcomes of interest included infections, pneumonia, urinary tract infection and sepsis. Random-effects model was used for analysis. Heterogeneity was evaluated using I2 statistics and publication bias was evaluated by the funnel plot.

RESULT

A total of 83 potentially relevant publications was identified in the initial search. Six studies met the inclusion criteria for meta-analysis. The risk of bias in the included articles satisfies the quality requirement of meta-analysis. No significant associations between beta blockers therapy and the prevention of stroke associated infection, stroke associated pneumonia and septicemia were found, However, subgroup analyses revealed an association between beta blockers treatment and the increased risk of post-stroke urinary tract infection or stroke associated pneumonia in some stroke patients (OR = 1.69 [1.33, 2.14], P < 0.0001; OR = 1.85 [1.51, 2.26], P < 0.0001).

CONCLUSION

Due to the lack of robust evidence, this meta-analysis may not support the preventive effect of beta blockers on stroke associated infection. But beta blockers treatment may be associated with development of post-stroke urinary tract infection and stroke associated pneumonia in some stroke patients.

ICH-LR2S2: a new risk score for predicting stroke-associated pneumonia from spontaneous intracerebral hemorrhage

Purpose

We develop a new risk score to predict patients with stroke-associated pneumonia (SAP) who have an acute intracranial hemorrhage (ICH).

Method

We applied logistic regression to develop a new risk score called ICH-LR2S2. It was derived from examining a dataset of 70,540 ICH patients between 2015 and 2018 from the Chinese Stroke Center Alliance (CSCA). During the training of ICH-LR2S2, patients were randomly divided into two groups – 80% for the training set and 20% for model validation. A prospective test set was developed using 12,523 patients recruited in 2019. To further verify its effectiveness, we tested ICH-LR2S2 on an external dataset of 24,860 patients from the China National Stroke Registration Management System II (CNSR II). The performance of ICH-LR2S2 was measured by the area under the receiver operating characteristic curve (AUROC).

Results

The incidence of SAP in the dataset was 25.52%. A 24-point ICH-LR2S2 was developed from independent predictors, including age, modified Rankin Scale, fasting blood glucose, National Institutes of Health Stroke Scale admission score, Glasgow Coma Scale score, C-reactive protein, dysphagia, Chronic Obstructive Pulmonary Disease, and current smoking. The results showed that ICH-LR2S2 achieved an AUC = 0.749 [95% CI 0.739–0.759], which outperforms the best baseline ICH-APS (AUC = 0.704) [95% CI 0.694–0.714]. Compared with the previous ICH risk scores, ICH-LR2S2 incorporates fasting blood glucose and C-reactive protein, improving its discriminative ability. Machine learning methods such as XGboost (AUC = 0.772) [95% CI 0.762–0.782] can further improve our prediction performance. It also performed well when further validated by the external independent cohort of patients (n = 24,860), ICH-LR2S2 AUC = 0.784 [95% CI 0.774–0.794].

Conclusion

ICH-LR2S2 accurately distinguishes SAP patients based on easily available clinical features. It can help identify high-risk patients in the early stages of diseases.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12967-022-03389-5.

The Timing of Stroke Care Processes and Development of Stroke Associated Pneumonia: A National Registry Cohort Study

Introduction

Timely stroke care can result in significant improvements in stroke recovery. However, little is known about how stroke care processes relate to complications such as the development of stroke associated pneumonia (SAP). Here we investigated associations between stroke care processes, their timing and development of SAP.

Methods

We obtained patient-level data from the Sentinel Stroke National Audit Programme for all confirmed strokes between 1st April 2013 and 31st December 2018. SAP was identified if new antibiotic initiation for pneumonia occurred within the first 7 days of admission. Time to key stroke care processes in the pre-hospital, hyperacute and acute phase were investigated. A mixed effects logistic regression model estimated the association between SAP [Odds ratios (OR) with 95% CI] and each process of care after controlling for pre-determined confounders such as age, stroke severity and comorbidities.

Results

SAP was identified in 8.5% of 413,133 patients in 169 stroke units. A long time to arrival at a stroke unit after symptom onset or time last seen well [OR (95% CI) = 1.29 (1.23-1.35)], from admission to assessment by a stroke specialist [1.10 (1.06-1.14)] and from admission to assessment by a physiotherapist [1.16 (1.12-1.21)] were all independently associated with SAP. Short door to needle times were associated with lower odds of SAP [0.90 (0.83-0.97)].

Conclusion

Times from stroke onset and admission to certain key stroke care processes were associated with SAP. Understanding how timing of these care processes relate to SAP may enable development of preventive interventions to reduce antibiotic use and improve clinical outcomes.

The use of anticholinergic medication is associated with an increased risk of stroke-associated pneumonia

BACKGROUND

Pneumonia is a frequent medical complication after stroke. A few studies showed that the use of anticholinergic medication is associated with a higher risk of community acquired pneumonia in the elderly. We aimed to determine if there is any association between anticholinergic medication used before stroke and stroke-associated pneumonia (SAP).

METHODS

We analysed prospectively collected data of 675 patients with acute stroke (mean age 71.4 ± 13.3; 53.1% female). We used the Anticholinergic Drug Scale to assess anticholinergic exposure during a month preceding stroke onset.

RESULTS

We diagnosed SAP in 14.7% of patients. The use of anticholinergic medication was associated with an elevated risk of SAP (OR 2.56, 95% CI 1.59-4.11, P < 0.01) in univariate analysis. This association remained significant in multivariable analysis adjusted for age, stroke severity, atrial fibrillation, previous myocardial infarction and respiratory tract diseases (OR 2.06, 95% CI 1.01-4.22, P = 0.04).

CONCLUSIONS

The use of anticholinergic medication before stroke is associated with an increased risk of SAP.

Immunodepression, Infections, and Functional Outcome in Ischemic Stroke

Stroke remains one of the main causes of mortality and morbidity worldwide. Immediately after stroke, a neuroinflammatory process starts in the brain, triggering a systemic immunodepression mainly through excessive activation of the autonomous nervous system. Manifestations of immunodepression include lymphopenia but also dysfunctional innate and adaptive immune cells. The resulting impaired antibacterial defenses render patients with stroke susceptible to infections. In addition, other risk factors like stroke severity, dysphagia, impaired consciousness, mechanical ventilation, catheterization, and older age predispose stroke patients for infections. Most common infections are pneumonia and urinary tract infection, both occur in ≈10% of the patients. Especially pneumonia increases unfavorable outcome and mortality in patients with stroke; systemic effects like hypotension, fever, delay in rehabilitation are thought to play a crucial role. Experimental and clinical data suggest that systemic infections enhance autoreactive immune responses against brain antigens and thus negatively affect outcome but convincing evidence is lacking. Prevention of poststroke infections by preventive antibiotic therapy did not improve functional outcome after stroke. Immunomodulatory approaches counteracting immunodepression to prevent stroke-associated pneumonia need to account for neuroinflammation in the ischemic brain and avoid further tissue damage. Experimental studies discovered interesting targets, but these have not yet been investigated in patients with stroke. A better understanding of the pathobiology may help to develop optimized approaches of preventive antibiotic therapy or immunomodulation to effectively prevent stroke-associated pneumonia while improving long-term outcome after stroke. In this review, we aim to characterize epidemiology, risk factors, cause, diagnosis, clinical presentation, and potential treatment of poststroke immunosuppression and associated infections.

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

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.

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.

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

Objective

To 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.

Methods

We 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.

Results

Fifty 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).

Conclusions

After 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.

Pneumonia in Acute Ischemic Stroke Patients with Proximal Occlusions within the Anterior Circulation after Endovascular Therapy or Systemic Thrombolysis

While endovascular treatment (ET) improves clinical outcomes in patients with proximal vessel occlusions compared to thrombolysis (IVT), the impact of ET on the frequency of stroke-associated pneumonia (SAP) is uncertain. We compared the rates of SAP in patients with large vessel occlusions in the anterior circulation after IVT or ET. We also determined risk factors for SAP, as well as the impact of SAP on early clinical outcomes. A total of 544 patients were treated with IVT, and 1061 patients received ET (with or without IVT). The rates of SAP did not differ significantly between ET (217/1061; 20%) and IVT (100/544; 18%) (p = 0.3). Overall, the occurrence of SAP was significantly associated with mortality and a poor clinical outcome. In the multivariable regression analysis, age, sex, the presence of dysphagia, early signs of ischemia on imaging and a history of stroke and mechanical ventilation were all significantly associated with the occurrence of SAP. In patients with large vessel occlusions, the introduction of ET did not result in lower rates of SAP compared with IVT. There is an ongoing need to reduce the rates of SAP in this patient population, for which the risk factors found here could become useful.

Breath and plasma metabolomics to assess inflammation in acute stroke

Inflammation is strongly implicated in both injury and repair processes occurring after stroke. In this exploratory study we assessed the feasibility of repeated sampling of exhaled volatile organic compounds and performed an untargeted metabolomic analysis of plasma collected at multiple time periods after stroke. Metabolic profiles were compared with the time course of the inflammatory markers C-reactive protein (CRP) and interleukin-6 (IL-6). Serial breath sampling was well-tolerated by all patients and the measurement appears feasible in this group. We found that exhaled decanal tracks CRP and IL-6 levels post-stroke and correlates with several metabolic pathways associated with a post-stroke inflammatory response. This suggests that measurement of breath and blood metabolites could facilitate development of novel therapeutic and diagnostic strategies. Results are discussed in relation to the utility of breath analysis in stroke care, such as in monitoring recovery and complications including stroke associated infection.

Stroke-associated pneumonia: A bibliometric analysis of worldwide trends from 2003 to 2020

Stroke-associated pneumonia (SAP) is a spectrum of pulmonary infections in patients within 7 days of stroke. Which is one of the most common complications after stroke and is significantly associated with a poor prognosis of stroke. To the best of our knowledge, a bibliometric method was not previously used to analyze the topic of SAP; we aim to describe the situation and evolution of SAP from 2003 to 2020, and to discuss the research hotspots and frontiers.A total of 151 articles were retrieved from the Scopus database. Bibliometric analysis was used to explore the dynamic trends of articles and the top subject areas, journals, institutes, citations, and co-keywords. VOS viewer software (version 1.6.15) was used to graphically map the hot topics of SAP based on the co-keywords.A total of 151 articles were identified. Articles have increased over the recent years and faster in the last 2 years (55 articles, 36.4%), the majority of subject areas are medicine (124 articles, 82.1%) and neuroscience (38 articles, 25.2%). The "Journal Of Stroke And Cerebrovascular Diseases" with 15 articles has been scored as the first rank followed by "Plos One." Regarding the geographical distribution of articles, China is the most productive country with 50 articles (33.1%), others are more prominent in Europe, and most institutes are universities. Citations have increased over time, the main country of the top five highly cited published articles are Germany and before 2008. The co-keywords are mainly divided into four aspects: risk factors, predictive scores, preventions, and outcomes.This study could provide practical sources for researchers to find the top subject areas, journals, institutes, citations, and co-keywords. Moreover, the study could pave the way for researchers to be engaged in studies potentially lead to more articles in this field.

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.

Predictors of mortality and disability in stroke-associated pneumonia

Whilst stroke-associated pneumonia (SAP) is common and associated with poor outcomes, less is known about the determinants of these adverse clinical outcomes in SAP. To identify the factors that influence mortality and morbidity in SAP. Data for patients with SAP (n = 854) were extracted from a regional Hospital Stroke Register in Norfolk, UK (2003–2015). SAP was defined as pneumonia occurring within 7 days of admission by the treating clinicians. Mutlivariable regression models were constructed to assess factors influencing survival and the level of disability at discharge using modified Rankin Scale [mRS]. Mean (SD) age was 83.0 (8.7) years and ischaemic stroke occurred in 727 (85.0%). Mortality was 19.0% at 30 days and 44.0% at 6 months. Stroke severity assessment using National Institutes of Health Stroke Scale was not recorded in the data set although Oxfordshire Community Stroke Project was Classification. In the multivariable analyses, 30-day mortality was independently associated with age (OR 1.04, 95% CI 1.01–1.07, p = 0.01), haemorrhagic stroke (2.27, 1.07–4.78, p = 0.03) and pre-stroke disability (mRS 4–5 v 0–1: 6.45, 3.12–13.35, p < 0.001). 6-month mortality was independently associated with age (< 0.001), pre-stroke disability (p < 0.001) and certain comorbidities, including the following: dementia (6.53, 4.73–9.03, p < 0.001), lung cancer (2.07, 1.14–3.77, p = 0.017) and previous transient ischemic attack (1.94, 1.12–3.36, p = 0.019). Disability defined by mRS at discharge was independently associated with age (1.10, 1.05–1.16, p < 0.001) and plasma C-reactive protein (1.02, 1.01–1.03, p = 0.012). We have identified non-modifiable determinants of poor prognosis in patients with SAP. Further studies are required to identify modifiable factors which may guide areas for intervention to improve the prognosis in SAP in these 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.

Value of Combining of the NLR and the Fibrinogen Level for Predicting Stroke-Associated Pneumonia

PURPOSE

To evaluate the value of the NLR (neutrophil-to-lymphocyte ratio) and the fibrinogen level in predicting stroke-associated pneumonia (SAP) in acute ischemic stroke (AIS) patients.

PATIENTS AND METHODS

In total, we enrolled 734 medical-ward patients with AIS in this retrospective study. Patients were divided into SAP (n=52) and non-SAP (n=682) groups according to the diagnostic criteria of SAP. Binary logistic regression analysis was used to analyze the relationship between the NLR, serum fibrinogen concentration and SAP. Receiver operating characteristic (ROC) curves were generated to identify the optimal cutoff points and assess the diagnostic value of the NLR, serum fibrinogen and the combination of NLR and fibrinogen in predicting SAP.

RESULTS

SAP occurred in 52 (7.08%) patients among the enrolled AIS patients. Binary logistic regression analysis showed that the NLR (odds ratio [OR]: 2.802, 95% confidence interval [CI]: 1.302-6.032, P=0.008) and serum fibrinogen concentration (OR: 7.850, 95% CI: 3.636-16.949, P=0.000) were independently associated with a higher risk of SAP incidence after adjusting for age, sex, ASPECT score, atrial fibrillation, nasogastric tube feeding, LDL-C and TC, temperature at admission and mechanical ventilation. The optimal cutoff points of the NLR and serum fibrinogen to distinguish SAP among AIS patients were 3.603 (AUC, 0.690; NPV, 95.78; PPV, 19.01) and 4.595 (AUC, 0.727; NPV, 95.60; PPV, 24.49), respectively. When the combination of NLR and fibrinogen was used to predict SAP, the optimal cutoff points were >2.436 for NLR and >3.24 for fibrinogen (AUC, 0.758; NPV, 98.50; PPV, 11.80).

CONCLUSION

The NLR and serum fibrinogen might have greater negative diagnostic value in predicting SAP among AIS patients. Combining the NLR and serum fibrinogen showed an increased AUC for predicting SAP among AIS patients.

External Validation of Five Scores to Predict Stroke-Associated Pneumonia and the Role of Selected Blood Biomarkers

BACKGROUND AND PURPOSE

Several clinical scoring systems as well as biomarkers have been proposed to predict stroke-associated pneumonia (SAP). We aimed to externally and competitively validate SAP scores and hypothesized that 5 selected biomarkers would improve performance of these scores.

METHODS

We pooled the clinical data of 2 acute stroke studies with identical data assessment: STRAWINSKI and PREDICT. Biomarkers (ultrasensitive procalcitonin; mid-regional proadrenomedullin; mid-regional proatrionatriuretic peptide; ultrasensitive copeptin; C-terminal proendothelin) were measured from hospital admission serum samples. A literature search was performed to identify SAP prediction scores. We then calculated multivariate regression models with the individual scores and the biomarkers. Areas under receiver operating characteristic curves were used to compare discrimination of these scores and models.

RESULTS

The combined cohort consisted of 683 cases, of which 573 had available backup samples to perform the biomarker analysis. Literature search identified 9 SAP prediction scores. Our data set enabled us to calculate 5 of these scores. The scores had area under receiver operating characteristic curve of 0.543 to 0.651 for physician determined SAP, 0.574 to 0.685 for probable and 0.689 to 0.811 for definite SAP according to Pneumonia in Stroke Consensus group criteria. Multivariate models of the scores with biomarkers improved virtually all predictions, but mostly in the range of an area under receiver operating characteristic curve delta of 0.05.

CONCLUSIONS

All SAP prediction scores identified patients who would develop SAP with fair to strong capabilities, with better discrimination when stricter criteria for SAP diagnosis were applied. The selected biomarkers provided only limited added predictive value, currently not warranting addition of these markers to prediction models. Registration: URL: https://www.clinicaltrials.gov. Unique identifier: NCT01264549 and NCT01079728.

Infection as a Stroke Risk Factor and Determinant of Outcome After Stroke

Understanding the relationship between infection and stroke has taken on new urgency in the era of the coronavirus disease 2019 (COVID-19) pandemic. This association is not a new concept, as several infections have long been recognized to contribute to stroke risk. The association of infection and stroke is also bidirectional. Although infection can lead to stroke, stroke also induces immune suppression which increases risk of infection. Apart from their short-term effects, emerging evidence suggests that poststroke immune changes may also adversely affect long-term cognitive outcomes in patients with stroke, increasing the risk of poststroke neurodegeneration and dementia. Infections at the time of stroke may also increase immune dysregulation after the stroke, further exacerbating the risk of cognitive decline. This review will cover the role of acute infections, including respiratory infections such as COVID-19, as a trigger for stroke; the role of infectious burden, or the cumulative number of infections throughout life, as a contributor to long-term risk of atherosclerotic disease and stroke; immune dysregulation after stroke and its effect on the risk of stroke-associated infection; and the impact of infection at the time of a stroke on the immune reaction to brain injury and subsequent long-term cognitive and functional outcomes. Finally, we will present a model to conceptualize the many relationships among chronic and acute infections and their short- and long-term neurological consequences. This model will suggest several directions for future research.

A Comparison of the National Institutes of Health Stroke Scale and the Gugging Swallowing Screen in Predicting Stroke-Associated Pneumonia

Background

There have been many scales to predict pneumonia in stroke patients, but they are so complex, making it difficult to apply in practice. Therefore, we conducted this study to assess the role of the National Institutes of Health Stroke Scale (NIHSS) and the Gugging Swallowing Screen (GUSS) in predicting stroke-associated pneumonia (SAP). These scales are routinely used in stroke patients. Therefore, their application in predicting SAP risk will be of high value in clinical practice. There has been no previous study evaluating the effectiveness of SAP risk prediction for each of these scales.

Aim

This study aimed to compare the value of NIHSS and GUSS in SAP prediction and their convenience in clinical practice.

Methods

It was a cohort study. The receiver operating characteristics (ROC) curves were constructed to assess the sensitivity (Se) and specificity (Sp) of the scales. Area under the curves (AUC) were calculated, and we compared them.

Results

NIHSS had a medium value of predictor of SAP with AUC 0.764 (95% CI 0.735–0.792), 65.4% Se, 76.5% Sp. GUSS had good value in predicting SAP with AUC 0.858 (95% CI 0.833–0.880), 80.5% Se, 80.1% Sp. Pairwise comparison of ROCs curves demonstrated that the difference between two AUCs was significant (p < 0.01). Performing GUSS required 24.5 ± 6.7 minutes, 2.5 times longer than NIHSS (9.9 ± 2.0 minutes).

Conclusion

GUSS had a better predictive value of SAP than NIHSS. But NIHSS was more convenient in clinical practice because of its simple instrument and quick performance.

Inflammatory and stress markers predicting pneumonia, outcome, and etiology in patients with stroke: Biomarkers for predicting pneumonia, functional outcome, and death after stroke

Objective

Prognosis of stroke is negatively affected by complications, in particular stroke-associated pneumonia (SAP). We hypothesized that inflammatory and stress biomarkers predict SAP during hospitalization and outcome 3 months after stroke.

Methods

We pooled the clinical data of 2 acute stroke studies with identical assessment: the STRoke Adverse outcome is associated WIth NoSoKomial Infections (STRAWINSKI) and PREDICT studies. Measurement of biomarkers (ultrasensitive procalcitonin [PCTus]; midregional pro-adrenomedullin; midregional pro-atrial natriuretic peptide [MRproANP]; ultrasensitive copeptin [CPus]; C-terminal pro-endothelin) was performed from serum samples drawn on the first 4 days of hospital admission.

Results

The combined cohort consists of 573 cases with available backup samples to perform the analysis. SAP was associated with increased admission and maximum levels of all biomarkers. Furthermore, all biomarkers were associated with death and correlated with functional outcome 3 months after stroke. The multivariate logistic regression model retained ultrasensitive CPus and PCTus beyond clinical risk factors for predicting SAP, improving the receiver operating characteristic area under the curve (AUC) from 0.837 to 0.876. In contrast, the biomarkers did not improve the prediction of death and functional outcome in the multivariate model. Cardioembolic strokes were significantly associated with higher values of all biomarkers, whereas discrimination was best for MRproANP (AUC = 0.811 for maximum value).

Conclusions

The tested biomarkers are associated with SAP and poor functional outcome. However, these biomarkers only slightly improve prediction of SAP and do not improve long-term outcome prediction over clinical parameters. MRproANP showed the best discrimination for identifying cardioembolic stroke, warranting further studies to confirm our finding.

Clinical trial registration

clinicaltrials.gov NCT01264549 and NCT01079728.

Pneumonia risk factors in stroke patients

ABSTRACT Purpose: to assess the demographic and clinical characteristics associated with the development of pneumonia in post-stroke patients hospitalized in a tertiary hospital, located in the Vale do São Francisco, that covers the states of Pernambuco and Bahia, Brazil. Methods: a unicentric, observational, analytical, cross-sectional study, based on the medical records of patients diagnosed with stroke and included in the Stroke Registry (RAVESS study). The statistical analysis was made with the chi-square test, Fisher’s exact test, and the analysis of variance, with the Bonferroni’s post-test, and P≤0.05. Results: data from 69 patients presented with acute stroke were collected, aged 63.2±16.8 years; 37 (53.6%) were females; the prevalence of pneumonia during hospital stay was estimated at 31.9% (95% confidence interval: 21.2-44.2%). In the univariate analysis of predictors for post-stroke pneumonia, the following were identified: older age (72.6±17.9 vs. 58.8±14.5; P = 0.001), lower response signal to the Glasgow Coma Scale at admission (11.3±1.8 vs. 13.3±2.1; P = 0.001), and higher frequency of dysarthria at admission (61.9% vs. 27.9%; P = 0.009). Conclusion: pneumonia was a prevalent complication in post-stroke patients at a Brazilian tertiary hospital. It was related to the patient’s older age and the severity of the cerebral event.

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.

Risk Factors for Stroke Associated Pneumonia

BACKGROUND:

Stroke patients are at high risk for stroke-associated pneumonia (SAP). If patients suffer from pneumonia their prognosis will worsen.

AIM:

To identify factors that increases the risk of SAP in stroke patients.

METHODS:

A group of 508 patients hospitalized within 5 days after the onset of stroke were enrolled prospectively.

RESULTS:

The incidence of SAP was 13.4%. Some major risk factors for SAP are: mechanical ventilation (MV) had odds ratio (OR) 16.4 (p <0.01); the National Institutes of Health Stroke Scale (NIHSS) > 15 OR 9.1 (p <0.01); the Gugging Swallowing Screen (GUSS) 0-14 OR 11.7 (p <0.01).

CONCLUSION:

SAP is a frequent complication. We identified some risk factors of SAP, especially stroke severity (NIHSS > 15), swallowing disorder (GUSS < 15) and mechanical ventilation.

A 2 DS 2 Score to Predict the Risk of Stroke-Associated Pneumonia in Acute Stroke: An Indian Perspective

Background  Stroke-associated pneumonia (SAP) is an important cause of poststroke morbidity and mortality. Several clinical risk scores predict the risk of SAP. In this study, we used the A ² DS ² score (age, atrial fibrillation, dysphagia, sex, and stroke severity) to assess the risk of SAP in patients admitted with acute stroke. Methods  A high (5-10) and a low (0-4) A ² DS ² score was assigned to patients with acute stroke admitted to the neurology ward. Univariate binary logistic regression analysis was performed to find the strength of association of SAP and A ² DS ² score. Results  There were 250 patients with acute stroke of which 46 developed SAP. Forty-four patients developed SAP in high score as against 2 in low-score group (odds ratio [OR] = 0.03, 95% confidence interval [CI] = 0.01-0.15, p = 0.0001). A ² DS ² score >5 had sensitivity of 82.6% and specificity of 65.1% to predict SAP. The mean A ² DS ² score in patients with pneumonia was 7.02 ± 1.40 compared to 4.75 ± 1.92 in patients without pneumonia ( p = 0.0001). Conclusions  A ² DS ² score has a high sensitivity of 82% in predicting the risk of SAP and is a useful tool to monitor patients after acute stroke. A ² DS ² score can help in timely detection and prevention of SAP and reduction in caregiver's burden.

Consent, Refusal, and Waivers in Patient-Centered Dysphagia Care: Using Law, Ethics, and Evidence to Guide Clinical Practice

PURPOSE

When patients refuse medical or rehabilitation procedures, waivers of liability have been used to bar future lawsuits. The purpose of this tutorial is to review the myriad issues surrounding consent, refusal, and waivers. The larger goal is to invigorate clinical practice by providing clinicians with knowledge of ethics and law. This tutorial is for educational purposes only and does not constitute legal advice.

METHOD

The authors use a hypothetical case of a "noncompliant" individual under the care of an interdisciplinary neurorehabilitation team to illuminate the ethical and legal features of the patient-practitioner relationship; the elements of clinical decision-making capacity; the duty of disclosure and the right of informed consent or informed refusal; and the relationship among noncompliance, defensive practices, and iatrogenic harm. We explore the legal question of whether waivers of liability in the medical context are enforceable or unenforceable as a matter of public policy.

CONCLUSIONS

Speech-language pathologists, among other health care providers, have fiduciary and other ethical and legal obligations to patients. Because waivers try to shift liability for substandard care from health care providers to patients, courts usually find waivers of liability in the medical context unenforceable as a matter of public policy.