Temporal Profile of Pneumonia After Stroke

The Trends of Neutrophil-to-Lymphocyte Ratio, Platelet-to-Lymphocyte Ratio, and Systemic Immunoinflammatory Index in Patients with Intracerebral Hemorrhage and Clinical Value in Predicting Pneumonia 30 Days After Surgery

BACKGROUND

Inflammatory response is closely associated with secondary brain injury and pneumonia in intracerebral hemorrhage (ICH). In this study, we aimed to investigate the value of neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), and systemic immunoinflammatory index (SII) in the development of pneumonia in ICH patients 30 days after surgery.

METHODS

We retrospectively collected clinical data on patients with ICH who underwent surgical treatment at our institution from January 2016 to December 2022, mainly including NLR, PLR, and SII at different time points. Receiver operating characteristic curves were used to compare the value of different inflammatory indicators in predicting the development of postoperative pneumonia 30 days after surgery in ICH patients, and multivariate logistic regression analyses were used to identify independent risk factors for pneumonia 30 days after surgery.

RESULTS

Among 112 patients with ICH undergoing surgical treatment, 31 (27.7%) developed pneumonia postoperatively. The results of the univariate analysis demonstrated that patients in the pneumonia group experienced significantly higher blood glucose, NLR at 72 hours postoperatively, PLR at 72 hours postoperatively, and SII at 72 hours postoperatively (SII3) than those in the nonpneumonia group, and significantly lower admission Glasgow Coma Scale scores than those in the nonpneumonia group (all P < 0.05). NLR, PLR, and SII showed increasing and then decreasing in the disease process of ICH and peaked at 48 hours postoperatively. Multivariable logistic regression analysis revealed that SII3 was an independent risk factor for postoperative pneumonia 30 days after surgery in ICH patients (odds ratio = 1.001, 95% confidence interval: 1.000-1.002, P = 0.008). The area under the curve of the developed nomogram model was 0.895 (95% confidence interval = 0.823-0.967), with a sensitivity and specificity of 0.903 and 0.815, respectively, providing good predictive power.

CONCLUSIONS

In the course of ICH, NLR, PLR, and SII increased and then decreased and peaked at 48 hours postoperatively. The SII3 was the best predictor of the occurrence of pneumonia postoperatively in ICH patients.

Extra-pulmonary control of respiratory defense

Pneumonia persists as a public health crisis, representing the leading cause of death due to infection. Whether respiratory tract infections progress to pneumonia and its sequelae such as acute respiratory distress syndrome and sepsis depends on numerous underlying conditions related to both the causative agent and host. Regarding the former, pneumonia burden remains staggeringly high, despite the effectiveness of pathogen-targeting strategies such as vaccines and antibiotics. This demands a greater understanding of host features that collaborate to promote immune resistance and tissue resilience in the infected lung. Such features inside the pulmonary compartment have drawn much attention, where major advances have been made related to resident and recruited immune activity. By comparison, extra-pulmonary processes guiding pneumonia susceptibility are relatively elusive, constituting the focus of this review. Here we will highlight examples of when, how, and why tissues outside of the lungs dispatch signals that modulate local immunity in the airspaces. Topics include the liver, gut, bone marrow, brain and more, all of which contribute in direct and indirect ways to pneumonia outcome. When tuned appropriately, it has become clear that these responses can serve protective roles, and this will be considered distinctly from what would otherwise be aberrant responses characteristic of pneumonia-induced organ injury and sepsis. Further advances in this area may reveal novel targetable areas for clinical intervention that are not confined to the intra-pulmonary space.

Prevalence, early predictors, and outcomes of sepsis in neurocritical illnesses: A prospective cohort study

BACKGROUND

Patients with neurocritically illness are an under-recognized population at high risk of sepsis. We aimed to investigate the prevalence, early predictors, and outcomes of sepsis in neuro-ICU.

METHODS

Daily and accumulative incidences of sepsis in neuro-ICU were explored. Demographics, medical history, baseline disease severity scores, and baseline biomarkers regarding inflammation, immunology, organ function, and nutritional status were collected and analyzed as potential predictors of sepsis. Logistic regression analyses were used to determine the independent predictors, and a nomogram was used to estimate the individual probability of sepsis in neuro-ICU.

RESULTS

153 patients were included in this study. Fifty-nine (38.6%) patients developed sepsis, and 21 (14%) patients developed septic shock. More than 86% of the septic cases occurred within the first week. Sequential organ failure assessment score ((relative risk) RR 1.334, P = .026), history of diabetes (RR 2.346, P = .049), and transferrin (RR 0.128, P = .042) on admission are independent predictors of sepsis. Septic patients had significantly higher mortality (P = .011), higher medical cost (P = .028), and a lower rate of functional independence (P = .010), compared to patients without sepsis.

CONCLUSIONS

Sepsis afflicted more than one-third of neurocritically-ill patients and occurred mostly in the first week of admission. History of diabetes, serum transferrin, and sequential organ failure assessment score on admission were early predictors. Sepsis led to significantly worse outcomes and higher medical costs.

Association of follow-up neutrophil-to-lymphocyte ratio and systemic inflammation response index with stroke-associated pneumonia and functional outcomes in cerebral hemorrhage patients: a case-controlled study

BACKGROUND

The neutrophil-to-lymphocyte ratio (NLR) and systemic inflammation response index (SIRI) at admission are independent diagnostic biomarkers in stroke-associated pneumonia (SAP). Our study aimed to investigate the association between NLR, SIRI, specifically follow-up NLR and SIRI, and SAP, as well as their relationship with functional outcomes.

PATIENTS AND METHODS

We retrospectively included 451 consecutive intracerebral hemorrhage patients from May 2017 to May 2019. We conducted univariate and multivariable analyses to identify the factors independently associated with SAP and poor functional outcomes.

RESULTS

Compared to 127 (28.16%) patients diagnosed with SAP, those without SAP had both lower baseline and follow-up NLR and SIRI values ( P <0.001). After adjustments, we found that baseline NLR [OR, 1.039 (95% CI, 1.003-1.077); P =0.036] and follow-up NLR [OR, 1.054 (95% CI, 1.011-1.098); P =0.012] were independently associated with SAP. The follow-up NLR was also associated with a higher mRS [OR, 1.124 (95% CI, 1.025-1.233); P =0.013] and lower ADL-MBI score [OR, 1.167 (95% CI, 1.057-1.289); P =0.002] at discharge. Multivariable analysis indicated that advanced age and nasogastric tube feeding were independently associated with SAP ( P <0.05). We constructed a dynamic nomogram to identify SAP risk. Further subgroup analysis revealed that baseline NLR [OR, 1.062 (95% CI, 1.007-1.120); P =0.026] is independently associated with SAP in the nasogastric feeding group, while follow-up NLR [OR, 1.080 (95% CI, 1.024-1.139); P =0.005] was associated with the occurrence of SAP in non-nasogastric feeding patients.

CONCLUSIONS

We found elevated baseline and follow-up NLR values were associated with SAP occurrence, and increasing follow-up NLR indicated poor functional outcomes. Inflammatory markers at different stages may offer individualized guidance for patients receiving various treatments.

Prevalence, predictors, and outcomes of acute respiratory distress syndrome in severe stroke

OBJECTIVES

Patients with severe stroke are at high risk of developing acute respiratory distress syndrome (ARDS), but this severe complication was often under-diagnosed and rarely explored in stroke patients. We aimed to investigate the prevalence, early predictors, and outcomes of ARDS in severe stroke.

METHODS

This prospective study included consecutive patients admitted to neurological intensive care unit (neuro-ICU) with severe stroke, including acute ischemic stroke, intracerebral hemorrhage, and subarachnoid hemorrhage. The incidence of ARDS was examined, and baseline characteristics and severity scores on admission were investigated as potential early predictors for ARDS. The in-hospital mortality, length of neuro-ICU stay, the total cost in neuro-ICU, and neurological functions at 90 days were explored.

RESULTS

Of 140 patients included, 35 (25.0%) developed ARDS. Over 90% of ARDS cases occurred within 1 week of admission. Procalcitonin (OR 1.310 95% CI 1.005-1.707, P = 0.046) and PaO2/FiO2 on admission (OR 0.986, 95% CI 0.979-0.993, P < 0.001) were independently associated with ARDS, and high brain natriuretic peptide (OR 0.994, 95% CI 0.989-0.998, P = 0.003) was a red flag biomarker warning that the respiratory symptoms may be caused by cardiac failure rather than ARDS. ARDS patients had longer stays and higher expenses in neuro-ICU. Among patients with ARDS, 25 (62.5%) were moderate or severe ARDS. All the patients with moderate to severe ARDS had an unfavorable outcome at 90 days.

CONCLUSIONS

ARDS is common in patients with severe stroke, with most cases occurring in the first week of admission. Procalcitonin and PaO2/FiO2 on admission are early predictors of ARDS. ARDS worsens both short-term and long-term outcomes. The conflict in respiratory support strategies between ARDS and severe stroke needs to be further studied.

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.

Electroacupuncture ameliorates gastrointestinal dysfunction by modulating DMV cholinergic efferent signals to drive the vagus nerve in p-MCAO rats

Background

The use of proton pump inhibitors in the acute phase of cerebral infarction may lead to adverse long-term outcomes, this study aims to explore the potential of electroacupuncture (EA) in replacing omeprazole in exerting post-stroke gastrointestinal protection.

Methods

A permanent middle cerebral artery infarction model was established using the modified Longa thread occlusion technique. Gastrointestinal motility, gastrointestinal mucosal damage, cerebral infarct volume, and alterations in choline acetyltransferase (ChAT)-positive neurons within the dorsal motor nucleus of the vagus nerve (DMV) were assessed after 7 days of EA at Zusanli (ST36) or omeprazole intervention. To evaluate the role of the vagal nerve in mitigating post-stroke gastrointestinal dysfunction, we employed subdiaphragmatic vagotomy and the ChAT-specific inhibitor α-NETA. Additionally, we utilized methyllycaconitine (MLA), a selective inhibitor of the α7-type nicotinic acetylcholine receptor (α7nAChR), and PNU282987, an agonist, to identify the target of EA.

Results

EA restored ChAT neurons lost in the DMV, activated the vagus nerve and conferred cerebroprotection while ameliorating gastrointestinal mucosal injury and gastrointestinal motility disorders. In addition, following the administration of the α7nAChR antagonist, the attenuation of gastric mucosal injury and inflammatory factors induced by EA was hindered, although gastrointestinal motility still exhibited improvement.

Conclusion

EA at ST36 promotes the restoration of cholinergic signaling in the DMV of stroke-afflicted rats, and its excitation of the vagal nerve inhibits gastrointestinal inflammation after stroke via α7nAChR, while improvement in gastrointestinal motility could be mediated by other acetylcholine receptors.

Immunothrombosis in neurovascular disease

A State of the Art lecture titled "Immunothrombosis in Neurovascular Diseases" was presented at the International Society on Thrombosis and Haemostasis Congress in 2023. Despite significant clinical advancements in stroke therapy, stroke remains a prominent contributor to both mortality and disability worldwide. Brain injury resulting from an ischemic stroke is a dynamic process that unfolds over time. Initially, an infarct core forms due to the abrupt and substantial blockage of blood flow. In the subsequent hours to days, the surrounding tissue undergoes gradual deterioration, primarily driven by sustained hypoperfusion, programmed cell death, and inflammation. While anti-inflammatory strategies have proven highly effective in experimental models of stroke, their successful translation to clinical use has proven challenging. To overcome this translational hurdle, a better understanding of the distinct immune response driving ischemic stroke brain injury is needed. In this review article, we give an overview of current knowledge regarding the immune response in ischemic stroke and the contribution of immunothrombosis to this process. We discuss therapeutic approaches to overcome detrimental immunothrombosis in ischemic stroke and how these can be extrapolated to other neurovascular diseases, such as Alzheimer's disease and multiple sclerosis. Finally, we summarize relevant new data on this topic presented during the 2023 International Society on Thrombosis and Haemostasis Congress.

The application of cluster nursing to prevent bacterial pneumonia in stroke patients in neurology department

The research aims to explore the intervention effect of cluster nursing methods on bacterial pneumonia in stroke patients in neurology. A retrospective analysis is conducted on 120 patients diagnosed with stroke in the hospital from 2020 to 2022, who are diagnosed as stroke patients in the neurology department by doctors. 120 patients are randomly separated into a control group (CG) and a research group (RG) for the experiment, with 60 people in each group. The CG receive normal nursing; The RG adopt cluster nursing methods. Comparison have done in the general information, nursing satisfaction, physical improvement after intervention, and oral hygiene indicators of the 2 groups of patients, and the intervention effect of the 2 nursing methods on patients suffering from bacterial pneumonia is evaluated. When the time reached the 7th day, the nutritional risk screening scores of both groups were (9.53 ± 2.29) and (8.10 ± 2.12), respectively. The serum albumin levels were (36.46 ± 4.80) g/L and (34.16 ± 3.69) g/L, respectively. After simultaneous nursing intervention, there were P < .005 in respiratory rates in the 2nd and 4th months of intervention, as well as after the completion of nursing intervention. The respiratory rates of the RG patients improved toward a larger range of normal respiratory rates, with significant statistical significance (P < .005). As time passed, the number of people with shortness of breath in the CG increased sharply to 46, while those in the RG was only 27, indicating P < .05 between the 2 groups. At the beginning of the 3rd month, the satisfaction rates of the RG and the CG were (92.33 ± 1.17)% and (78.18 ± 1.07)%, respectively, and there was P < .0.05 between the 2 groups of data. The cluster nursing strategy has a significant relieving effect on bacterial pneumonia in stroke patients in the neurology department, which can effectively reduce the probability of pneumonia, improve their physical condition, and enhance their quality of life. It has certain clinical application value.

Physical Rehabilitation and Post-Stroke Pneumonia: A Retrospective Observational Study Using the Japanese Diagnosis Procedure Combination Database

In this study, the relationship between the duration of physical rehabilitation and occurrence of pneumonia after ischemic stroke was examined. We included 426,508 patients aged ≥75 years with acute ischemic stroke. A multilevel logistic regression analysis nested at the hospital level was conducted to examine the association between the duration of physical rehabilitation and occurrence of pneumonia. The duration of physical rehabilitation refers to the hours of physical rehabilitation performed daily until the 7th day of hospitalization. In the multivariable analysis, the intensity of rehabilitation for durations of 20-39 min/day (adjusted odds ratio [aOR]: 0.78, 95% Confidence Interval [CI]: 0.75-0.81, p < 0.001), 40-59 min/day (aOR: 0.68, 95% CI: 0.66-0.71, p < 0.001), 60-79 min/day (aOR:0.56, 95% CI: 0.53-0.58, p < 0.001), and ≥80 min/day (aOR: 0.46, 95% CI: 0.44-0.48, p < 0.001) were significantly associated with a reduced incidence of pneumonia. In addition, the trend identified for duration of rehabilitation was significant (p < 0.001). The results of this study suggest the usefulness of high-duration physical rehabilitation for preventing pneumonia in older patients with ischemic stroke.

Prevalence, associated factors and predictors of post stroke pneumonia in a Nigerian population: A retrospective study

OBJECTIVES

Stroke can trigger an immune response that can raise the risk of infection, alter tracheal epithelium, reduce pulmonary clearance and impair secretions drainage capacity. Infection, altered tracheal epithelium, reduced pulmonary clearance, impaired secretions drainage capacity and aspiration can cause pneumonia after stroke. The aim of this study is to find out the prevalence of post stroke pneumonia in a Nigerian population and factors that are associated with it.

MATERIALS AND METHOD

Study data was extracted from the case files of patients with stroke who were managed between 1st January, 2011 and 1st February, 2021 in the study setting.

RESULTS

The result showed that, there was a record of only 591 patients with stroke (mean age, 62.78 ± 14.86 years) who were managed in the two hospitals during the period of the study. Out of this number, only 102 (17.3 %) had pneumonia. Presence of the pneumonia was only significantly (p < 0.05) associated with sex, type of stroke, lower limb muscle power, and outcome (died or alive). However, only those with ischaemic stroke are less likely to have pneumonia (Odds ratio=  0.467; CI: 0.275 to 0.791, p=  0.005), and patients who survived the stroke and are alive are less likely to develop pneumonia (Odds ratio=  0.150; CI: 0.092 to 0.245, p < 0.001).

CONCLUSIONS

Pneumonia occurs to a large extent after stroke. Therefore, it is important measures are taken to prevent it or complications arising from it especially in those with a hemorrhagic stroke.

The relationship between neurogenic dysphagia, stroke-associated pneumonia and functional outcome in a cohort of ischemic stroke patients treated with mechanical thrombectomy

INTRODUCTION

Mechanical thrombectomy (MT) is an established treatment approach in acute ischemic stroke patients with large vessel occlusion (LVO). Recent studies suggest that the prevalence of dysphagia and pneumonia risk is increased in this patient population. The aim of this study was to systematically evaluate the prevalence, predictors, and influence of neurogenic dysphagia for 3-month outcome in a large population of patients receiving MT and to elucidate the relationship between dysphagia, stroke-associated pneumonia (SAP) and medium-term functional outcome.

MATERIALS AND METHODS

Data of a prospective collected registry of patients with LVO and MT between 2016 and 2019 were analyzed retrospectively. Binary logistic regression was carried out to determine predictors for dysphagia and 3-month outcome as measured by the modified Rankin Scale, respectively. A mediation analysis was performed to investigate the mediating influence of intercurrent SAP.

RESULTS

A total of 567 patients were included in the study. Mean age was 73.4 years, 47.8% of the patients were female, and median NIHSS was 15.0. The prevalence of dysphagia was 75.1% and 23.3% of all patients developed SAP. In the regression analysis, dysphagia was one of the main independent predictors for poor functional outcome at 3 months. The mediator analysis revealed that the effect of dysphagia on the functional outcome at 3 months was not mediated by the occurrence of SAP.

DISCUSSION

The prevalence of dysphagia is high and exerts both negative short- and medium-term effects on patients with large vessel occlusion who undergo MT.

Serum β-synuclein, neurofilament light chain and glial fibrillary acidic protein as prognostic biomarkers in moderate-to-severe acute ischemic stroke

We aimed to assess the prognostic value of serum β-synuclein (β-syn), neurofilament light chain (NfL) and glial fibrillary acidic protein (GFAP) in patients with moderate-to-severe acute ischemic stroke. We measured β-syn, GFAP and NfL in serum samples collected one day after admission in 30 adult patients with moderate-to-severe ischemic stroke due to middle cerebral artery (MCA) occlusion. We tested the associations between biomarker levels and clinical and radiological scores (National Institute of Health Stroke Scale scores, NIHSS, and Alberta Stroke Program Early CT Score, ASPECTS), as well as measures of functional outcome (modified Rankin Scale, mRS). Serum biomarkers were significantly associated with ASPECTS values (β-syn p = 0.0011, GFAP p = 0.0002) but not with NIHSS scores at admission. Patients who received mechanical thrombectomy and intravenous thrombolysis showed lower β-syn (p = 0.029) und NfL concentrations (p = 0.0024) compared to patients who received only mechanical thrombectomy. According to median biomarker levels, patients with high β-syn, NfL or GFAP levels showed, after therapy, lower clinical improvement (i.e., lower 24-h NIHSS change), higher NIHSS scores during hospitalization and higher mRS scores at 3-month follow-up. Elevated serum concentrations of β-syn (p = 0.016), NfL (p = 0.020) or GFAP (p = 0.010) were significantly associated with 3-month mRS of 3-6 vs. 0-2 even after accounting for age, sex and renal function. In patients with moderate-to-severe acute ischemic stroke, serum β-syn, NfL and GFAP levels associated with clinical and radiological scores at different timepoints and were able to predict short- and middle-term clinical outcomes.

From Admission to Discharge: Predicting National Institutes of Health Stroke Scale Progression in Stroke Patients Using Biomarkers and Explainable Machine Learning

As a result of social progress and improved living conditions, which have contributed to a prolonged life expectancy, the prevalence of strokes has increased and has become a significant phenomenon. Despite the available stroke treatment options, patients frequently suffer from significant disability after a stroke. Initial stroke severity is a significant predictor of functional dependence and mortality following an acute stroke. The current study aims to collect and analyze data from the hyperacute and acute phases of stroke, as well as from the medical history of the patients, in order to develop an explainable machine learning model for predicting stroke-related neurological deficits at discharge, as measured by the National Institutes of Health Stroke Scale (NIHSS). More specifically, we approached the data as a binary task problem: improvement of NIHSS progression vs. worsening of NIHSS progression at discharge, using baseline data within the first 72 h. For feature selection, a genetic algorithm was applied. Using various classifiers, we found that the best scores were achieved from the Random Forest (RF) classifier at the 15 most informative biomarkers and parameters for the binary task of the prediction of NIHSS score progression. RF achieved 91.13% accuracy, 91.13% recall, 90.89% precision, 91.00% f1-score, 8.87% FNrate and 4.59% FPrate. Those biomarkers are: age, gender, NIHSS upon admission, intubation, history of hypertension and smoking, the initial diagnosis of hypertension, diabetes, dyslipidemia and atrial fibrillation, high-density lipoprotein (HDL) levels, stroke localization, systolic blood pressure levels, as well as erythrocyte sedimentation rate (ESR) levels upon admission and the onset of respiratory infection. The SHapley Additive exPlanations (SHAP) model interpreted the impact of the selected features on the model output. Our findings suggest that the aforementioned variables may play a significant role in determining stroke patients' NIHSS progression from the time of admission until their discharge.

Risk Factors for Post-Stroke Pneumonia in a Patient Population with Subacute Stroke: A Retrospective Cohort Study

The risk of pneumonia and death is higher in acute stroke patients with signs of pulmonary infection on chest computed tomography (CT) at admission. However, few reports have examined the incidence of pneumonia and its predictors in subacute stroke patients. The aim of this study was to examine factors related to post-stroke pneumonia in subacute stroke patients. A total of 340 subacute stroke patients were included. Univariable logistic regression analysis was performed using variables that may contribute to pneumonia, with the development of pneumonia as the dependent variable. Multivariable logistic regression analysis using the three independent variables with the lowest p-values on the univariable logistic regression analysis was also performed to calculate adjusted odds ratios. Twenty-two patients developed pneumonia during hospitalization. The univariable logistic regression analysis showed that the top three items were serum albumin (Alb), functional Oral Intake Scale (FOIS) score, and signs of pulmonary infection on chest CT at admission. Multivariable logistic regression analysis adjusted for these three items showed that the presence of signs of pulmonary infection on chest CT at admission was the independent variable (OR: 4.45; 95% CI: 1.54-12.9). When signs of pulmonary infection are seen on admission chest CT, careful follow-up is necessary because pneumonia is significantly more likely to occur during hospitalization.

C-reactive protein levels are associated with early cardiac complications or death in patients with acute ischemic stroke: a propensity-matched analysis of a global federated health from the TriNetX network

The role of inflammation in predicting early cardiac complications among stroke patients is unclear. Electronic medical records from TriNetX, a global federated health research network, were used for this retrospective analysis. Patients with ischemic stroke and C-Reactive Protein (CRP) levels measured within 24 h post-stroke were categorized into three groups: (i) < 1 mg/L, (ii)1-3 mg/L and (iii) > 3 mg/L. The primary outcome was a composite outcome of cardiac complications (heart failure (HF), ischemic heart disease, atrial fibrillation (AF), ventricular arrhythmias and Takotsubo cardiomyopathy) or death at 30 days from the index event. Cox-regression analyses were used to produce hazard ratios (HRs) and 95% confidence intervals (CI) following 1:1 propensity score matching (PSM). Of the 104,741 patients enrolled, 51% were female and the mean age was 66 ± 16 years. After PSM, a new cardiac complication or death within 30 days occurred in 5624 (33.1%) patients with CRP > 3 mg/L, in 4243 (25.6%) patients with CRP 1-3 mg/L and in 3891 (23.5%) patients with CRP < 1 mg/L. Patients with CRP levels of 1-3 mg/L and > 3 mg/L had higher risk of the composite outcome (HR 1.10, 95%CI 1.05-1.52; HR 1.51, 95%CI 1.45-1.58), death (HR 1.43, 95%CI 1.24-1.64; HR 3.50, 95%CI 3.01-3.96), HF (HR 1.08, 95%CI 1.01-1.16; HR 1.51, 95%CI 1.41-1.61), AF (HR 1.10, 95% CI:1.02-1.18; HR 1.42, 95%CI 1.33-1.52) and ventricular arrhythmias (HR 1.25, 95%CI 1.02-1.52; HR 1.67, 95% CI 1.38-2.01) compared to those with CRP < 1 mg/L. Ischemic heart disease were more common among patients with CRP levels > 3 mg/L compared to those with CRP < 1 mg/L (HR:1.33, 95% CI:1.26-1.40), while no association with Takotsubo cardiomyopathy was found in all the analyses. CRP levels within the first 24 h of an ischemic stroke predict 30-day cardiac complications or death.

Neutrophil percentage to albumin ratio is associated with stroke-associated pneumonia and poor outcome in patients with spontaneous intracerebral hemorrhage

Background

The purpose of this study was to investigate the diagnostic performance of the neutrophil percentage-to-albumin ratio (NPAR) for predicting stroke-associated pneumonia (SAP) and functional outcome in patients with intracerebral hemorrhage (ICH).

Methods

We analyzed our prospective database of consecutive ICH patients who were admitted to the First Affiliated Hospital of Chongqing Medical University from January 2016 to September 2021. We included subjects with a baseline computed tomography available and a complete NPAR count performed within 6h of onset. The patients' demographic and radiological characteristics were analyzed. Good outcome was defined as a modifed Rankin Scale score of 0-3 at 90 days. Poor outcome was defined as a modifed Rankin Scale score of 4-6 at 90 days. Multivariable logistic regression models were used to investigate the association between NPAR, SAP, and functional outcome. Receiver operating characteristic (ROC) curve analysis was conducted to identify the optimal cutoff of NPAR to discriminate between good and poor outcomes in ICH patients.

Results

A total of 918 patients with ICH confirmed by non-contrast computed tomography were included. Of those, 316 (34.4%) had SAP, and 258 (28.1%) had poor outcomes. Multivariate regression analysis showed that higher NPAR on admission was an independent predictor of SAP (adjusted odds ratio: 2.45; 95% confidence interval, 1.56-3.84; P<0.001) and was associated with increased risk of poor outcome (adjusted odd ratio:1.72; 95% confidence interval, 1.03-2.90; P=0.040) in patients with ICH. In ROC analysis, an NPAR of 2 was identified as the optimal cutoff value to discriminate between good and poor functional outcomes.

Conclusion

Higher NPAR is independently associated with SAP and poor functional outcome in patients with ICH. Our findings suggest that early prediction of SAP is feasible by using a simple biomarker NPAR.

Age- and Sex-Specific Trends in Medical Complications After Acute Ischemic Stroke in the United States

BACKGROUND AND OBJECTIVES

To test the hypothesis that the age and sex-specific prevalence of infectious (pneumonia, sepsis, and urinary tract infection [UTI]) and noninfectious (deep venous thrombosis [DVT], pulmonary embolism [PE], acute renal failure [ARF], acute myocardial infarction [AMI], and gastrointestinal bleeding [GIB]) complications increased after acute ischemic stroke (AIS) hospitalization in the United States from 2007 to 2019.

METHODS

We conducted a serial cross-sectional study using the 2007-2019 National Inpatient Sample. Primary AIS admissions in adults (aged 18 years or older) with and without complications were identified using International Classification of Diseases codes. We quantified the age/sex-specific prevalence of complications and used negative binomial regression models to evaluate trends over time.

RESULTS

Of 5,751,601 weighted admissions, 51.4% were women. 25.1% had at least 1 complication. UTI (11.8%), ARF (10.1%), pneumonia (3.2%), and AMI (2.5%) were the most common complications, while sepsis (1.7%), GIB (1.1%), DVT (1.2%), and PE (0.5%) were the least prevalent. Marked disparity in complication risk existed by age/sex (UTI: men 18-39 years 2.1%; women 80 years or older 22.5%). Prevalence of UTI (12.9%-9.7%) and pneumonia (3.8%-2.7%) declined, but that of ARF increased by ≈3-fold (4.8%-14%) over the period 2007-2019 (all p < 0.001). AMI (1.9%-3.1%), DVT (1.0%-1.4%), and PE (0.3%-0.8%) prevalence also increased (p < 0.001), but that of sepsis and GIB remained unchanged over time. After multivariable adjustment, risk of all complications increased with increasing NIH Stroke Scale (pneumonia: prevalence rate ratio [PRR] 1.03, 95% CI 1.03-1.04, for each unit increase), but IV thrombolysis was associated with a reduced risk of all complications (pneumonia: PRR 0.80, 85% CI 0.73-0.88; AMI: PRR 0.85, 95% CI 0.78-0.92; and DVT PRR 0.87, 95% CI 0.78-0.98). Mechanical thrombectomy was associated with a reduced risk of UTI, sepsis, and ARF, but DVT and PE were more prevalent in MT hospitalizations compared with those without. All complications except UTI were associated with an increased risk of in-hospital mortality (sepsis: PRR 1.97, 95% CI 1.78-2.19).

DISCUSSION

Infectious complications declined, but noninfectious complications increased after AIS admissions in the United States in the last decade. Utilization of IV thrombolysis is associated with a reduced risk of all complications.

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

Background

Inflammatory mechanisms play important roles in intracerebral hemorrhage (ICH) and have been linked to the development of stroke-associated pneumonia (SAP). The neutrophil-to-lymphocyte ratio (NLR), systemic immune-inflammation index (SII), platelet-to-lymphocyte ratio (PLR) and systemic inflammation response index (SIRI) are inflammatory indexes that influence systemic inflammatory responses after stroke. In this study, we aimed to compare the predictive value of the NLR, SII, SIRI and PLR for SAP in patients with ICH to determine their application potential in the early identification of the severity of pneumonia.

Methods

Patients with ICH in four hospitals were prospectively enrolled. SAP was defined according to the modified Centers for Disease Control and Prevention criteria. Data on the NLR, SII, SIRI and PLR were collected at admission, and the correlation between these factors and the clinical pulmonary infection score (CPIS) was assessed through Spearman's analysis.

Results

A total of 320 patients were enrolled in this study, among whom 126 (39.4%) developed SAP. The results of the receiver operating characteristic (ROC) analysis revealed that the NLR had the best predictive value for SAP (AUC: 0.748, 95% CI: 0.695-0.801), and this outcome remained significant after adjusting for other confounders in multivariable analysis (RR=1.090, 95% CI: 1.029-1.155). Among the four indexes, Spearman's analysis showed that the NLR was the most highly correlated with the CPIS (r=0.537, 95% CI: 0.395-0.654). The NLR could effectively predict ICU admission (AUC: 0.732, 95% CI: 0.671-0.786), and this finding remained significant in the multivariable analysis (RR=1.049, 95% CI: 1.009-1.089, P=0.036). Nomograms were created to predict the probability of SAP occurrence and ICU admission. Furthermore, the NLR could predict a good outcome at discharge (AUC: 0.761, 95% CI: 0.707-0.8147).

Conclusions

Among the four indexes, the NLR was the best predictor for SAP occurrence and a poor outcome at discharge in ICH patients. It can therefore be used for the early identification of severe SAP and to predict ICU admission.

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

INTRODUCTION

Several studies have examined the crucial role of inflammatory indexes such as the ratio of monocyte and lymphocyte (MLR), systemic-immune-inflammation-index, and the ratio of neutrophil and lymphocyte (NLR) in stroke-associated pneumonia (SAP). However, the function of the systemic inflammation response index (SIRI) in SAP is not known. This study investigated whether SIRI at admission could predict the incidence of SAP in patients with acute ischemic stroke (AIS).

PATIENTS AND METHODS

2,802 AIS patients collected from 2013 to 2021 were divided into the SAP and non-SAP groups. The predictive performance of SIRI in SAP was evaluated by the receiver operating characteristic curve. Multivariate regression analysis and the restricted cubic spline (RCS) were performed to explore the relationship between SIRI and SAP risk.

RESULTS

The SIRI at admission in SAP patients was significantly higher than that in non-SAP patients (median [IQR]: 3.75 [2.05, 6.99] vs. 1.51 [0.94, 2.62], p < 0.001). SIRI had a predictive ability for predicting the incidence of SAP with area under the curve of 0.757, better than NLR and MLR (both p < 0.05). SIRI ≥2.74 was an independent risk factor for the incidence of SAP (odds ratio: 5.82, 95% confidence interval: 4.54, 7.49, p < 0.001). The RCS model showed an increasing trend of the SAP risk with the increase of SIRI.

CONCLUSION

SIRI showed a good predictive value for SAP. In clinical practice, AIS patients with high SIRI levels (SIRI ≥2.74) should be aware of the risk of SAP.

Evaluation of Blood Biomarkers and Parameters for the Prediction of Stroke Survivors' Functional Outcome upon Discharge Utilizing Explainable Machine Learning

Despite therapeutic advancements, stroke remains a leading cause of death and long-term disability. The quality of current stroke prognostic models varies considerably, whereas prediction models of post-stroke disability and mortality are restricted by the sample size, the range of clinical and risk factors and the clinical applicability in general. Accurate prognostication can ease post-stroke discharge planning and help healthcare practitioners individualize aggressive treatment or palliative care, based on projected life expectancy and clinical course. In this study, we aimed to develop an explainable machine learning methodology to predict functional outcomes of stroke patients at discharge, using the Modified Rankin Scale (mRS) as a binary classification problem. We identified 35 parameters from the admission, the first 72 h, as well as the medical history of stroke patients, and used them to train the model. We divided the patients into two classes in two approaches: "Independent" vs. "Non-Independent" and "Non-Disability" vs. "Disability". Using various classifiers, we found that the best models in both approaches had an upward trend, with respect to the selected biomarkers, and achieved a maximum accuracy of 88.57% and 89.29%, respectively. The common features in both approaches included: age, hemispheric stroke localization, stroke localization based on blood supply, development of respiratory infection, National Institutes of Health Stroke Scale (NIHSS) upon admission and systolic blood pressure levels upon admission. Intubation and C-reactive protein (CRP) levels upon admission are additional features for the first approach and Erythrocyte Sedimentation Rate (ESR) levels upon admission for the second. Our results suggest that the said factors may be important predictors of functional outcomes in stroke patients.

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.

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.