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.

U-shaped association between low-density lipoprotein cholesterol levels and risk of futile reperfusion mediated by stroke-associated pneumonia in acute ischemic stroke after endovascular thrombectomy.

OBJECTIVE: Futile reperfusion (FR) is becoming a major challenge in the treatment of patients with acute ischaemic stroke (AIS) undergoing endovascular thrombectomy. This study aims to determine the dose-response relationship between low-density lipoprotein cholesterol (LDL-C) levels and the risk of FR in patients with AIS undergoing endovascular thrombectomy and to investigate potential mediators. METHODS: A total of 614 patients with AIS undergoing endovascular thrombectomy were enrolled and divided into five groups according to quintiles of LDL-C levels: Q1(≤2.27 mmol/l), Q2 (2.27-2.5 mmol/l), Q3 (2.5-2.59 mmol/l), Q4 (2.59-2.97 mmol/l) and Q5 (≥2.97 mmol/l). Associations between LDL-C levels and the risk of FR and stroke-associated pneumonia (SAP) were estimated using multivariate logistic regression models. Restricted cubic spline curves were used to describe the dose-response relationship between LDL-C levels and the risk of FR and SAP. Mediation effect analysis was performed in R software with 100 bootstrap samples. RESULTS: After adjustment for confounders, both low and high LDL-C levels were significantly associated with a higher risk of FR compared with the reference group (Q3). We observed a U-shaped association between LDL-C levels and the risk of FR (P for nonlinear =0.012). Mediation analysis showed that the association between LDL-C levels and the risk of FR was 29.7 % (95 % CI: 2.96 %-75.0 %, P=0.02) mediated by SAP. CONCLUSIONS: We found a U-shaped association between LDL-C levels and the risk of FR that was mediated by SAP. Clinicians should note that in AIS patients undergoing endovascular thrombectomy, lower LDL-C levels are not always better.

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

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

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.

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

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

Stroke-associated pneumonia according to mCDC criteria: impact on prognosis and antibiotic therapy.

Objective: The modified Centers for Disease Control and Prevention (mCDC) criteria have been proposed for diagnosing and managing stroke-associated pneumonia (SAP). The objective was to investigate the impact of SAP on stroke outcome depending on whether or not it conforms to mCDC criteria. Our secondary objective was to identify the responsible factors for antibiotic initiation in stroke patients. Methods: We conducted a prospective, multicenter, observational study of ischemic stroke patients with moderate to severe stroke (NIHSS≥4) admitted within 24 h. For 7 days, mCDC criteria were assessed daily, and infections and antibiotics were recorded. Pneumonias were divided into those fulfilling mCDC criteria (mCDC-SAP) or not (other pneumonias, OPn). The effect of each type of pneumonia on 3-month outcome was evaluated in separated logistic regression models. Factors associated with antibiotic initiation were explored using a random forest analysis. Results: Of the 342 patients studied, infections were diagnosed in 72 (21.6%), including 39 (11.7%) cases of pneumonia. Of them, 25 (7.5%) fulfilled mCDC criteria. Antibiotics were used in 92% of mCDC-SAP and 64.3% of OPn. In logistic regression analysis, mCDC-SAP, but not OPn, was an independent predictor of poor outcome [OR, 4.939 (1.022-23.868)]. The random forest analysis revealed that fever had the highest importance for antibiotic initiation. Interpretation: The mCDC criteria might be useful for detecting clinically relevant SAP, which is associated with poor outcomes. Isolated signs of infection were more important for antibiotic initiation than compliance with pre-defined criteria. Therefore, adherence to mCDC criteria might result in antibiotic saving without compromising clinical outcome.

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.

Predictive value of cytokines combined with human neutrophil lipocalinin acute ischemic stroke-associated pneumonia.

OBJECTIVE: To explore the predictive value of interleukin-6 (IL-6) combined with human neutrophil lipocalin (HNL) of stroke-associated pneumonia (SAP) in patients who were diagnosed with acute ischemic stroke (AIS). METHODS: 108patients were divided into two groups: pneumonia group (52 cases) and non-pneumonia group (56 cases), according to whether the patients developed SAP within 7 days of admission. General information was compared between the two groups, like age, gender, history of hypertension, diabetes mellitus, cardiovascular disease, dysphagia, smoking and alcoholhistory. Clinical data were recorded and compared, including lipid profile, interleukin-6 (IL-6), homocysteine (Hcy), National Institutes of Health Stroke Scale (NIHSS) score, and HNL. Multivariate Logistic regression analysis was used to screen the risk factors of AIS-AP, and the predictive value of IL-6 and HNL alone and in combination was evaluated by receiver operating characteristic curve (ROC curve). RESULTS: Logistic regression analysis showed that dysphagia (OR,0.018; 95% CI, 0.001 ~ 0.427; P = 0.013), increased NIHSS scores(OR,0.012; 95% CI, 0.000 ~ 0.434; P = 0.016), and high levels of IL-6 (OR,0.014; 95% CI, 0.000 ~ 0.695; P = 0.032)and HNL (OR,0.006; 95% CI, 0.000 ~ 0.280; P = 0.009) were independent risk factors for SAP with significant difference (all P < 0.05). According to the ROC curve analysis of IL-6, the area under the curve (AUC) was 0.881 (95% CI: 0.820 ~ 0.942), and the optimal cutoff value was 6.89 pg/mL with the sensitivity of 73.1% and specificity of 85.7%. As for the ROC curve analysis of HNL, the AUC was 0.896 (95% CI: 0.839 ~ 0.954), and the best cutoff value was 99.66ng/mL with the sensitivity of 76.9% and specificity of 89.3%. The AUC of the combination of IL-6 and HNL increased to 0.952 (95% CI: 0.914 ~ 0.989), and the sensitivity and specificity increased to 80.8% and 92.9%, respectively. CONCLUSION: In this research, the levels of IL-6 ≥ 6.89 pg/mL and HNL ≥ 99.66ng/mL were considered as risk factors for AIS patients complicated with SAP. The combined detection had higher predictive value for patients with SAP, which may help to identify who were in highrisk.

Feasibility of a clinical-radiomics combined model to predict the occurrence of stroke-associated pneumonia.

PURPOSE: To explore the predictive value of radiomics in predicting stroke-associated pneumonia (SAP) in acute ischemic stroke (AIS) patients and construct a prediction model based on clinical features and DWI-MRI radiomics features. METHODS: Univariate and multivariate logistic regression analyses were used to identify the independent clinical predictors for SAP. Pearson correlation analysis and the least absolute shrinkage and selection operator with ten-fold cross-validation were used to calculate the radiomics score for each feature and identify the predictive radiomics features for SAP. Multivariate logistic regression was used to combine the predictive radiomics features with the independent clinical predictors. The prediction performance of the SAP models was evaluated using receiver operating characteristics (ROC), calibration curves, decision curve analysis, and subgroup analyses. RESULTS: Triglycerides, the neutrophil-to-lymphocyte ratio, dysphagia, the National Institutes of Health Stroke Scale (NIHSS) score, and internal carotid artery stenosis were identified as clinically independent risk factors for SAP. The radiomics scores in patients with SAP were generally higher than in patients without SAP (P < 0. 05). There was a linear positive correlation between radiomics scores and NIHSS scores, as well as between radiomics scores and infarct volume. Infarct volume showed moderate performance in predicting the occurrence of SAP, with an AUC of 0.635. When compared with the other models, the combined prediction model achieved the best area under the ROC (AUC) in both training (AUC = 0.859, 95% CI 0.759-0.936) and validation (AUC = 0.830, 95% CI 0.758-0.896) cohorts (P < 0.05). The calibration curves and decision curve analysis further confirmed the clinical value of the nomogram. Subgroup analysis showed that this nomogram had potential generalization ability. CONCLUSION: The addition of the radiomics features to the clinical model improved the prediction of SAP in AIS patients, which verified its feasibility.

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

BACKGROUND: Stroke-associated pneumonia (SAP) is a preventable determinant for poor outcome after stroke. Machine learning (ML) using large-scale clinical data warehouses may be able to predict SAP and identify patients for targeted interventions. The aim of this study was to develop a prediction model for identifying clinically apparent SAP using automated ML. METHODS: The ML model used clinical and laboratory parameters along with heart rate (HR), heart rate variability (HRV), and blood pressure (BP) values obtained during the first 48 h after stroke unit admission. A logistic regression classifier was developed and internally validated with a nested-cross-validation (nCV) approach. For every shuffle, the model was first trained and validated with a fixed threshold for 0.9 sensitivity, then finally tested on the out-of-sample data and benchmarked against a widely validated clinical score (A2DS2). RESULTS: We identified 2390 eligible patients admitted to two-stroke units at Charité between October 2020 and June 2023, of whom 1755 had all parameters available. SAP was diagnosed in 96/1755 (5.5%). Circadian profiles in HR, HRV, and BP metrics during the first 48 h after admission exhibited distinct differences between patients with SAP diagnosis vs. those without. CRP, mRS at admission, leukocyte count, high-frequency power in HRV, stroke severity at admission, sex, and diastolic BP were identified as the most informative ML features. We obtained an AUC of 0.91 (CI 0.88-0.95) for the ML model on the out-of-sample data in comparison to an AUC of 0.84 (CI 0.76-0.91) for the previously established A2DS2 score (p < 0.001). The ML model provided a sensitivity of 0.87 (CI 0.75-0.97) with a corresponding specificity of 0.82 (CI 0.78-0.85) which outperformed the A2DS2 score for multiple cutoffs. CONCLUSIONS: Automated, data warehouse-based prediction of clinically apparent SAP in the stroke unit setting is feasible, benefits from the inclusion of vital signs, and could be useful for identifying high-risk patients or prophylactic pneumonia management in clinical routine.

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

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

GNRI, PLR and Stroke-Associated Pneumonia: From Association to Development of a Web-Based Dynamic Nomogram.

Objective: Discussing the relationship between geriatric nutritional risk index (GNRI) and platelet-to-lymphocyte ratio (PLR) on stroke-associated pneumonia (SAP) in acute ischemic stroke (AIS) patients, developing and validating a web-based dynamic nomogram. Methods: A total of 996 AIS patients admitted to the Department of General Medicine and Neurology at Xuzhou Medical University Affiliated Hospital were collected. They were divided into Non-SAP group and SAP group based on the occurrence of SAP. The data was randomly divided into training set and validation set in a ratio of 7:3. LASSO regression and multivariable logistic regression analysis were used to screen for independent risk factors and develop a dynamic nomogram. Area under the receiver operating characteristic curve (AUC-ROC), calibration curve, and decision curve analysis (DCA) curve were used to validate the model's discriminative ability, calibration, and clinical value, respectively. Results: Among AIS patients, a total of 221 cases (22.19%) developed SAP. Age, NIHSS score, comorbid atrial fibrillation, dysphagia, PLR, and GNRI were identified as independent factors influencing the occurrence of SAP in AIS patients. A web-based dynamic nomogram was developed based on these six variables. The training set showed an AUC-ROC of 0.864 (95% CI: 0.828-0.892), while the validation set showed an AUC-ROC of 0.825 (95% CI: 0.772-0.882), indicating good predictive ability and discrimination of the model. The calibration curve demonstrated good calibration of the model, and the DCA curve showed its clinical value. This model can be accessed and utilized by anyone on the website (https://moonlittledoctor.shinyapps.io/ANADPG/). Conclusion: PLR and GNRI are independent factors influencing the occurrence of SAP in AIS patients, and a dynamic nomogram was constructed to predict the risk of SAP in AIS patients. It can guide clinical decision-making and improve patient prognosis.

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 .

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

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

A Dentist-Led Oral Care System Can Prevent Stroke-Associated Pneumonia: The Effects of Early Intervention by Dental Team.

Background: Our facility's dental team consists of a full-time dentists and dental hygienists who work exclusively in the wards to implement best practices in oral healthcare. We executed the dental care system (DCS) that includes lectures and practical training for nurses conducted by dentists and dental hygienists, the introduction to oral assessment, standardization of oral care procedures, a process for nurses to request the dental team, and early bedside oral screening conducted by the dental team. This study investigated the DCS's effects on the incidence of stroke-associated pneumonia (SAP). Methods: This single-center retrospective cohort study included 2,771 acute stroke patients who were newly hospitalized between April 1, 2012, and March 31, 2020. The 8-year period was divided into four phases at two-year intervals as follows: Pre (N=632), Post-1 (N=642), Post-2 (N=716), and Post-3 (N=781). Pre was prior to DCS practice. Post-1 was an early introduction to DCS. Post-2 simplified dental team requests from nurses, and Post-3 added bedside oral screening within 72 hours of admission by the dental team. Statistical analysis was performed using the Cochran-Armitage trend test, followed by multivariate logistic regression. Results: A decrease in SAP rates was observed across the four groups (P<0.0001). Logistic regression analysis revealed a significant difference for respiratory disease (odds ratio 7.74, 95% confidence interval 5.49-10.90), hypertension (2.28, 1.39-3.73), cardiac failure (1.72, 1.04-2.85), and diabetes (1.59, 1.11-2.26), 3-digit code on the Japan coma scale (3.57, 2.53-5.05 [reference ≤2-digit code]), age ≥90 years (2.34, 1.15-4.77 [reference 18-59 years]), male (1.86, 1.31-2.67), and the Post-1 (0.49, 0.31-0.76 [reference Pre]), Post-2 (0.38, 0.25-0.61 [reference Pre]), and Post-3 (0.24, 0.15-0.40 [reference Pre]) periods. Conclusion: The suppression of SAP is effectively achieved through early intervention and education of nurses by dental professionals.

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.

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

Background: Stroke-associated pneumonia (SAP) is associated with a poor prognosis and a high mortality rate in stroke patients. However, the accuracy of early prediction of SAP is insufficient, and there is a lack of effective prognostic evaluation methods. Therefore, in this study, we investigated the predictive value of the Oxford Acute Severity of Illness Score (OASIS) in SAP to provide a potential reference index for the incidence and prognosis of SAP. Methods: We recruited a total of 280 patients with acute ischemic stroke who had been diagnosed and treated in the Zhumadian Central Hospital between January 2021 and January 2023. These patients were divided into an SAP group (86 cases) and a non-SAP group (194 cases) according to SAP diagnostic criteria by expert consensus on the diagnosis and treatment of SAP. We collated general and clinical data from all patients, including the survival of SAP patients during the follow-up period. Multivariate logistic regression was used to analyze the risk factors for SAP. Kaplan-Meier and multivariate COX regression analyses were used to investigate the relationship between OASIS and the prognosis of SAP, and a receiver operating characteristic (ROC) curve was drawn to analyze the predictive value of OASIS for SAP. Results: Our analyses identified body temperature, C-reactive protein, procalcitonin, OASIS, and a prolonged length of intensive care unit (ICU) stay as the main risk factors for SAP (all Ps < 0.05). Advanced age and an elevated OASIS were identified as the main risk factors for death in SAP patients (all Ps < 0.05). The risk of death in patients with OASIS of 31-42 points was significantly higher than that in patients with OASIS of 12-20 points (HR = 5.588, 95% CI = 1.531-20.401, P = 0.009). ROC curve analysis further showed that OASIS had a high predictive value for morbidity and the incidence of death in SAP patients. Conclusion: OASIS can effectively predict the onset and death of SAP patients and provides a potential reference index for early diagnosis and the prediction of prognosis in patients with SAP. Our findings should be considered in clinical practice.

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

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

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

BACKGROUND: Stroke-associated pneumonia (SAP) is a common stroke complication, and the changes in the gut microbiota composition may play a role. Our study aimed to evaluate the predictive ability of gut microbiota for SAP. METHODS: Acute ischemic stroke patients were prospectively enrolled and divided into two groups based on the presence or absence of SAP. The composition of gut microbiota was characterized by the 16S RNA Miseq sequencing. The gut microbiota that differed significantly between groups were incorporated into the conventional risk scores, the Acute Ischemic Stroke-Associated Pneumonia Score (AIS-APS), and the Age, Atrial fibrillation, Dysphagia, Sex, Stroke Severity Score (A2DS2). The predictive performances were assessed in terms of the area under the curve (AUC), the Net Reclassification Improvement (NRI), and the Integrated Discrimination Improvement (IDI) indices. RESULTS: A total of 135 patients were enrolled, of whom 43 had SAP (31%). The short-chain fatty acids (SCFAs)-producing bacteria, such as Bacteroides, Fusicatenibacter, and Butyricicoccus, were decreased in the SAP group. The integrated models showed better predictive ability for SAP (AUC = 0.813, NRI = 0.333, p = 0.052, IDI = 0.038, p = 0.018, for AIS-APS; AUC = 0.816, NRI = 0.575, p < 0.001, IDI = 0.043, p = 0.007, for A2DS2) in comparison to the differential genera (AUC = 0.699) and each predictive score (AUCAISAPS = 0.777; AUCA2DS2 = 0.777). CONCLUSIONS: The lower abundance of SCFAs-producing gut microbiota after acute ischemic stroke was associated with SAP and may play a role in SAP prediction.

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.