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

Comparison of the Predictive Value of Neutrophil Percentage-to-Albumin Ratio and Modified Glasgow Prognostic Score for the Risk of Stroke-Associated Pneumonia Among Stroke Patients

Objective

To assess the predicting value of neutrophil percentage-to-albumin ratio (NPAR) and modified Glasgow Prognostic Score (mGPS) for Stroke-Associated Pneumonia (SAP) occurrence among stroke patients.

Methods

We recruited stroke patients (aged 18 years) hospitalized at Tianjin First Central Hospital from January 2022 to February 2023 for this retrospective cohort study. NPAR was categorized into four groups by considering the quartiles: Q1 (<1.38), Q2 (≥1.38 and <1.62), Q3 (≥1.62 and <1.87), Q4 (≥1.87). SAP incident was the primary outcome in this study. Univariate and multivariate logistic regression models were employed to explore the association between NPAR, mGPS and SAP occurrence among individuals with stroke. Besides, we compared the predicting value of NPAR and mGPS for SAP occurrence by the receiver operating characteristic (ROC) curve.

Results

Our study encompassed 851 patients with stroke. One hundred and forty-seven patients (17.27%) developed SAP. After accounting for confounding factors, we observed significant positive association of high NPAR with SAP occurrence [(for the third quartile: odds ratio (OR)=2.35, 95% confidence interval (CI): 1.01-5.47; for the fourth quartile: OR=3.35, 95% CI: 1.44-7.77)]. Additionally, the results also indicated that mGPS 1 (OR=2.26, 95% CI: 1.25-4.08) and mGPS 2 (OR=7.37, 95% CI: 2.63-20.70) were related to the increased probability of SAP, respectively. ROC analysis demonstrated that both the NPAR [area under the curve (AUC)=0.729, 95% CI: 0.687-0.771] and mGPS (AUC=0.671, 95% CI: 0.627-0.716) exhibited good predictive power for SAP occurrence. Based on the DeLong test, the predictive value of NPAR for SAP may be significantly superior to that of mGPS (P<0.05).

Conclusion

Our findings suggest that both NPAR and mGPS serve as reliable biomarker for assessing SAP risk in stroke patients, with NPAR demonstrating superior predictive value for SAP compared to mGPS.

Association between nutritional status and pneumonia in patients with spontaneous intracerebral hemorrhage

Background

Stroke-associated pneumonia (SAP) is a common and serious complication in patients with spontaneous intracerebral hemorrhage (SICH), contributing to prolonged hospital stays and poor outcomes. Nutritional status has been linked to the development of SAP in patients with ischemic stroke, but its role in SICH patients remains understudied. This study aims to evaluate the predictive value of the Nutritional Risk Screening-2002 (NRS-2002) score for SAP in SICH patients and to compare it with other nutritional assessment tools.

Methods

This retrospective observational study included 404 consecutive SICH patients admitted to Dongyang People's Hospital from January 2023 to May 2024. Nutritional risk was assessed using the NRS-2002 score upon admission, and SAP was diagnosed within the first 7 days of hospitalization. Univariate and multivariate logistic regression analyses identified risk factors for SAP, and receiver operating characteristic (ROC) curves were used to compare the predictive accuracy of the NRS-2002, Controlling Nutritional Status (CONUT) score, and Prognostic Nutritional Index (PNI) for SAP.

Results

Among the 404 patients, 97 developed SAP. A higher NRS-2002 score was significantly associated with an increased risk of SAP (OR: 1.575, 95% CI: 1.134-2.186, p = 0.007). ROC analysis showed that the NRS-2002 score (AUC: 0.768, 95% CI: 0.716-0.820) outperformed the CONUT (AUC: 0.597, 95% CI: 0.530-0.663) and PNI (AUC: 0.588, 95% CI: 0.519-0.657) in predicting SAP (p < 0.05). Subgroup analysis revealed that the NRS-2002 score ≥ 3 was particularly predictive of SAP in patients with weight loss, severe stroke, and those without hypertension or with diabetes.

Conclusion

The NRS-2002 score is a valuable predictor of pneumonia in SICH patients, with higher scores correlating with a significantly increased risk of SAP. This highlights the importance of early nutritional assessment in identifying high-risk patients and potentially guiding clinical interventions to reduce SAP incidence.

The effect of prognostic nutritional indices on stroke hospitalization outcomes

OBJECTIVES

Stroke is the second leading cause of death and the third leading cause of disability globally, so monitoring inflammation and nutritional levels is essential for the secondary prevention. The impact of the Prognostic Nutritional Index (PNI) on tumor and perioperative outcomes has been demonstrated as an optimal combination of immune and nutritional indicators. However, the role of PNI on hospitalized outcomes in stroke patients remains unknown. This study aimed to investigate the clinical predictive value of PNI on hospitalized outcomes in stroke patients.

MATERIALS AND METHODS

In this study, stroke cases in the Medical Information Mart for Intensive Care IV database were analyzed using two-sample comparisons, proportional hazards model, subgroup analyses, and ROC analyses, and a nomogram was constructed.

RESULTS

1795 stroke cases were included in this study, including 1537 in the survival group and 258 in the death group. The results showed that PNI was higher in the survival group than in the death group (43.98±0.21 vs. 36.09±0.49, P=0.001). The optimal regression equation obtained after screening variables using COX stepwise regression included age, GCS score, hypertension, PNI, leukocytes, and PT (C-index=0.730). The optimal regression equation showed that each increase in the PNI value was associated with a 6.6 % reduction in patient mortality, holding all other factors constant (HR 0.934, 95 %CI 0.914-0.954, P<0.008). Subgroup analyses showed that the Optimum regression equation was more effective in predicting hospitalized mortality in Hemorrhagic Stroke than in Ischemic Stroke (C-index: 0.803 vs. 0.703). ROC analysis revealed that the cut-off value of PNI for predicting hospital mortality in stroke patients was 37.45. The Kaplan-Meier curves clearly show that patients with PNI>37.45 have a higher survival rate than the low PNI group.

CONCLUSIONS

Higher PNI is associated with better hospitalization outcomes for stroke patients. PNI can be used as a supplement to existing indicators, which helps predict the survival of stroke inpatients and provides reference value for clinical treatment.

Prevalence and Related Factors of Hypokalemia in Patients with Acute Ischemic Stroke

Aim

This study aimed to investigate the prevalence and associated factors of hypokalemia in patients with acute ischemic stroke.

Methods

A cohort of 996 patients was assessed using a general data questionnaire, laboratory indicators, the NIH Stroke Scale (NIHSS), the Barthel Index (BI), the Frail scale, Nutritional Risk Screening (NRS-2002), and the Kubota drinking water test.

Results

Among the 996 patients, 205 (20.6%) were found to have hypokalemia. Logistic regression analysis identified several independent predictors of hypokalemia: age (OR 1.020, 95% CI 1.001-1.039, P=0.041), hypertension (OR 2.691, 95% CI 1.190-6.089, P=0.017), Frail score (OR 1.756, 95% CI 1.034-2.981, P=0.037), Kubota drinking water test grade 3 (OR 2.124, 95% CI 1.055-4.276, P=0.035), Kubota drinking water test grade 4 (OR 3.016, 95% CI 1.113-8.174, P=0.037), NIHSS score (OR 1.135, 95% CI 1.018-1.264, P=0.022), platelet count (OR 0.997, 95% CI 0.994-0.999, P=0.021), and urea nitrogen levels (OR 0.833, 95% CI 0.750-0.926, P=0.001).

Conclusion

The prevalence of hypokalemia is high in patients with acute ischemic stroke. Independent risk factors included age, hypertension, frailty, neurological function, swallowing function, platelet count and blood urea level.

Association between nutritional status, injury severity, and physiological responses in trauma patients

Purpose

To evaluate the predictive value of the Controlling Nutritional Status (CONUT) score and Injury Severity Score (ISS) in assessing physiological abnormalities and outcomes in trauma patients.

Methods

A retrospective analysis was conducted on 354 trauma patients. Physiological parameters were assessed, including cardiovascular function, inflammatory response, liver and kidney function, and nutritional status. The CONUT score and ISS were calculated for each patient. Binary logistic regression was used to identify independent predictors of trauma severity. Receiver operating characteristic (ROC) curve analysis evaluated the predictive accuracy of the CONUT and ISS scores for adverse outcomes.

Results

Severely injured patients exhibited more significant abnormalities in cardiovascular function, inflammatory response, liver and kidney function, and nutritional status compared to those with minor injuries. These patients had significantly higher CONUT scores. Logistic regression analysis identified white blood cell count, hemoglobin, and CONUT score as independent predictors of trauma severity. ROC analysis showed that both CONUT and ISS scores effectively predicted adverse outcomes, with ISS demonstrating better specificity.

Conclusion

The CONUT and ISS scores are effective tools for predicting physiological abnormalities and adverse outcomes in trauma patients. Incorporating these scores into clinical practice may enhance prognostic assessments and improve management strategies for trauma patients.

A Machine Learning Model Based on CT Imaging Metrics and Clinical Features to Predict the Risk of Hospital-Acquired Pneumonia After Traumatic Brain Injury

Objective

To develop a validated machine learning (ML) algorithm for predicting the risk of hospital-acquired pneumonia (HAP) in patients with traumatic brain injury (TBI).

Materials and Methods

We employed the Least Absolute Shrinkage and Selection Operator (LASSO) to identify critical features related to pneumonia. Five ML models-Logistic Regression (LR), Extreme Gradient Boosting (XGB), Random Forest (RF), Naive Bayes Classifier (NB), and Support Vector Machine (SVC)-were developed and assessed using the training and validation datasets. The optimal model was selected based on its performance metrics and used to create a dynamic web-based nomogram.

Results

In a cohort of 858 TBI patients, the HAP incidence was 41.02%. LR was determined to be the optimal model with superior performance metrics including AUC, accuracy, and F1-score. Key predictive factors included Age, Glasgow Coma Score, Rotterdam Score, D-dimer, and the Systemic Immune Response to Inflammation Index (SIRI). The nomogram developed based on these predictors demonstrated high predictive accuracy, with AUCs of 0.818 and 0.819 for the training and validation datasets, respectively. Decision curve analysis (DCA) and calibration curves validated the model's clinical utility and accuracy.

Conclusion

We successfully developed and validated a high-performance ML algorithm to assess the risk of HAP in TBI patients. The dynamic nomogram provides a practical tool for real-time risk assessment, potentially improving clinical outcomes by aiding in early intervention and personalized patient management.

Association between fibrinogen levels and stroke-associated pneumonia in acute ischemic stroke patients

PURPOSE

Prior research had indicated a relationship between fibrinogen and stroke-associated pneumonia (SAP), yet the nature of this relationship had not been thoroughly investigated. Therefore, this study was designed to elucidate the prognostic value of fibrinogen levels in forecasting the occurrence of SAP among patients with acute ischemic stroke (AIS).

PATIENTS AND METHODS

In this retrospective cross-sectional analysis, we included 1092 patients who had experienced AIS and were admitted to our facility within 72 h of the onset of their symptoms. Based on the SAP diagnostic criteria, patients were classified into two groups: SAP and non-SAP. The correlation between serum fibrinogen concentration and SAP was examined using univariate analysis. Curve fitting and multivariable logistic regression model were utilized for statistical evaluation.

RESULTS

Out of the ischemic stroke patients included in the study, SAP was identified in 112 (10.26%) patients. A direct correlation was observed between fibrinogen levels and the incidence of SAP. An increase in fibrinogen levels corresponded with a heightened incidence of SAP. Multivariable logistic regression revealed a significant positive association between fibrinogen levels and SAP incidence (OR = 1.53, 95% confidence interval [CI]: 1.18, 1.99)).

CONCLUSION

A linear relationship between serum fibrinogen levels and the incidence of SAP in ischemic stroke patients was shown. The serum fibrinogen levels were positively and linearly correlated to SAP risk.

Factors and predictive model for malnutrition in poststroke disabled patients: A multicenter cross-sectional study

BACKGROUND

Although malnutrition has been shown to influence the clinical outcome of poststroke disabled patients, the associated factors and the prediction model have yet to be uncovered.

OBJECTIVES

This study aims to assess the current prevalence and factors associated with malnutrition in poststroke disabled patients and establish a prediction model.

METHODS

A multicenter cross-sectional survey among Chinese poststroke disabled patients (≥18 y old) was conducted in 2021. Information on patients' basic data, medical history, Barthel Index, dysphagia, and nutritional status was collected. A multivariable logistic regression model was used to identify the factors that influence malnutrition. Nomogram was developed and internal validation was conducted using 5-fold cross-validation. External validation was performed using the data from a preliminary survey. Receiver operating characteristic (ROC) analysis, calibration curves, and decision curve analysis (DCA) were used to analyze the predictive value of the nomogram.

RESULTS

Four hundred fifty-seven cases were enrolled, with the prevalence of malnutrition as 71.77%. Age (aOR = 1.039, 95% CI: 1.006-1.078), pulmonary infection (aOR = 4.301, 95% CI: 2.268-14.464), dysphagia (aOR = 24.605, 95% CI: 4.966-191.058), total intake volume (aOR = 0.997, 95% CI: 0.995-0.999), Barthel Index (aOR = 0.965, 95% CI: 0.951-0.980), and nasogastric tube (aOR = 16.529, 95% CI: 7.418-52.518) as nutrition support mode (compared to oral intake) were identified as the associated factors of malnutrition in stroke-disabled patients (P < 0.05). ROC analysis showed that the area under the curve (AUC) for nomogram was 0.854 (95% CI: 0.816-0.892). Fivefold cross-validation showed the mean AUC as 0.829 (95% CI: 0.784-0.873). There were no significant differences between predicted and actual probabilities. The DCA revealed that the model exhibited a net benefit when the risk threshold was between 0 and 0.4.

CONCLUSIONS

Age, pulmonary infection, dysphagia, nutrition support mode, total intake volume, and Barthel Index were factors associated with malnutrition in stroke-related disabled patients. The nomogram based on the result exhibited good accuracy, consistency and values.

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.

Geriatric nutritional risk index predicts postoperative prognosis in older patients with hip fracture: A meta-analysis

BACKGROUND

Hip fracture is common in elderly individuals and is accompanied by a relatively high mortality rate. However, it is currently difficult to accurately predict postoperative prognosis for older patients with hip fractures. The aim of this meta-analysis was to further determine the prognostic value of the geriatric nutritional risk index (GNRI) for patients who underwent hip fracture surgery.

METHODS

The Medline, EMBASE, Web of Science, and CNKI databases were searched up to September 19, 2023, for available studies. The primary and secondary outcomes were the mortality and complication rates, respectively. Hazard ratios (HRs) and relative risks with corresponding 95% confidence intervals (CIs) were separately combined to assess the associations between the GNRI and mortality and complication rates. All the statistical analyses were performed with STATA 15.0 and SPSS 22.0 software.

RESULTS

A total of 9 studies with 3959 patients were included. The pooled results demonstrated that a lower GNRI was significantly related to an increased risk of postoperative mortality (HR = 0.82, 95% CI = 0.72-0.92, P = .001). In addition, the GNRI predicted the risk of overall postoperative complications (52% vs 35.5%, P = .04) and pneumonia (33.3% vs 13.6%, P = .010).

CONCLUSION

The GNRI might serve as a novel prognostic indicator for older patients with hip fractures, and a lower GNRI indicates an increased risk of postoperative mortality and complication rates.

Prevalence and influencing factors of malnutrition in stroke patients with bulbar paralysis: a cross-sectional study in China

Background

Although malnutrition has been shown to influence the clinical outcomes of Stroke Patients with Bulbar Paralysis (SPBP), the prevalence and influencing factors have yet to be uncovered.

Objective

This study aims to assess the current prevalence and factors associated with malnutrition in SPBP.

Methods

A multicenter cross-sectional investigation was conducted among SPBP in China from 2019 to 2021. Information was collected on basic information, health condition, diagnosis, treatment, neurological function, activities of daily living, swallowing function, and nutritional status. A multivariable logistic regression model was used to identify the factors that influenced nutritional status. ROC analysis was used to assess the predictive value of each independent influencing factor and the logit model.

Results

In total, 774 SPBP were enrolled, and the prevalence of malnutrition was 60.59%. Pulmonary infection [aOR:2.849, 95%CI: (1.426, 5.691)], hemoglobin [aOR: 0.932, 95%CI: (0.875, 0.982)], serum albumin [aOR: 0.904, 95%CI: (0.871, 0.938)], total protein [aOR: 0.891, 95%CI: (0.819, 0.969)], prealbumin [aOR: 0.962, 95%CI: (0.932, 0.993)], and National Institute of Health Stroke Scale (NIHSS) scores [aOR: 1.228, 95%CI: (1.054, 1.431)] were independent factors associated with malnutrition in SPBP. ROC analysis revealed that the logit model had the best predictive value [area under the curve: 0.874, 95% CI: (0.812, 0.936); specificity: 83.4%; sensitivity: 79.3%; p < 0.05]. Subgroup analysis showed that the nutritional status in dysphagic SPBP was additionally influenced by swallowing function and nutrition support mode.

Conclusion

The prevalence of malnutrition in SPBP was 60.59%. Pulmonary infection, hemoglobin level, and NIHSS score were the independent factors associated with malnutrition. Swallowing function and nutrition support mode were the factors associated with malnutrition in dysphagic SPBP.