The Effect of Prognostic Nutritional Index on Infection in Acute Ischemic Stroke Patients

Nonlinear dose-response relationship between prognostic nutritional index and short-term outcome in acute ischemic stroke: a prospective cohort study

Objective

The evidence surrounding the connection between the Prognostic Nutritional Index (PNI) and the prognosis of patients with Acute Ischemic Stroke (AIS) remains insufficient. Therefore, this study is designed to examine how PNI relates to short-term outcomes in individuals affected by AIS.

Methods

This study is a single-center, prospective cohort investigation. The study sample comprised 1,697 patients with AIS who received treatment at Shenzhen Second People's Hospital between January 2022 and June 2024. To evaluate the association between the PNI and the risk of at 90-day unfavorable outcomes, as well as 90-day mortality, a binary logistic regression model was employed. Furthermore, a logistic regression model incorporating cubic spline functions was utilized to explore the potential non-linear relationship between PNI and 90-day unfavorable outcomes. Additionally, a series of sensitivity analyses and subgroup analyses were performed to enhance the robustness of the findings.

Results

Following the adjustment for covariates, the binary logistic regression analysis demonstrated a notable inverse connection between PNI and the occurrence of unfavorable outcomes at 90 days among patients diagnosed with AIS (OR = 0.951, 95% CI: 0.925-0.979). A similarly significant negative relationship was found between PNI and 90-day mortality (OR = 0.868, 95% CI: 0.806-0.934). Additionally, the study revealed a non-linear association between PNI and 90-day, identifying an inflection point at PNI = 49.3. To the left of this inflection point, the OR for the risk of 90-day unfavorable outcomes in AIS patients was 0.910 (95% CI: 0.880-0.942). Conversely, to the right of the inflection point, the OR was 1.149 (95% CI: 0.998-1.249), although this finding was not statistically significant. The findings were further supported by sensitivity analyses, which reinforced the reliability of these results.

Conclusion

This study reveals a significant negative association between the PNI and 90-day unfavorable outcomes as well as 90-day mortality in patients with AIS. A non-linear relationship between PNI and 90-day unfavorable outcomes was observed. Specifically, a significant inverse association between them was evident when PNI values were below 49.3. These findings offer valuable insights for refining rehabilitation strategies and improving the clinical management of AIS patients.

Prognostic nutritional index (PNI) as an influencing factor for in-hospital mortality in patients with stroke-associated pneumonia: a retrospective study

Background

Stroke-associated pneumonia (SAP) significantly increases patients' risk of death after stroke. The identification of patients at high risk for SAP remains difficult. Nutritional assessment is valuable for risk identification in stroke patients. The aim of this study was to evaluate the relationship between prognostic nutritional index (PNI) levels and in-hospital mortality in SAP patients.

Methods

A total of 336 SAP patients who visited the Third People's Hospital of Chengdu from January 2019 to December 2023 were included in this study, and PNI were calculated based on the results of admission examinations. Linear regression was used to analyze the influencing factors of baseline PNI in SAP patients. Logistic regression as well as restricted cubic splines (RCS) were used to analyze the relationship between baseline PNI levels and hospital mortality events in SAP patients. Receiver operating characteristic (ROC) curves were plotted to assess the predictive value of PNI for in-hospital mortality by area under the curve (AUC).

Results

Thirty out of 336 SAP patients presented with in-hospital mortality and these patients had significantly lower PNI levels. In our study, PNI levels were influenced by age, body mass index, and total cholesterol. Increased PNI levels are an independent protective factor for the risk of in-hospital mortality in SAP patients (OR: 0.232, 95% CI [0.096-0.561], P = 0.001). There was a nonlinear correlation between PNI and in-hospital mortality events (P for nonlinear <0.001). In terms of predictive effect, PNI levels were more efficacious in predicting in-hospital mortality in SAP patients with higher sensitivity and/or specificity compared to individual indicators (AUC = 0.750, 95% CI [0.641-0.860], P < 0.001).

Conclusion

PNI levels in SAP patients were associated with the short-term prognosis of patients, and SAP patients with elevated PNI levels had a reduced risk of in-hospital mortality.

Predictive nomogram for risk of pulmonary infection in lung cancer patients undergoing radiochemotherapy: development and performance evaluation

OBJECTIVE

To develop an accurate predictive model for identifying patients at high risk of pulmonary infection during radiochemotherapy.

METHODS

We retrospectively analyzed data from 544 lung cancer patients treated at Hubei Cancer Hospital between May 2019 and October 2022. The patients were divided into training and validation groups (7:3 ratio). An external validation cohort of 100 patients treated from November 2022 to January 2024 was also included. Feature selection and model development were performed using machine learning algorithms, including Lasso regression, Random Forest, XGBoost, and Support Vector Machine (SVM). Model performance was evaluated using Receiver Operating Characteristic (ROC) curves, calibration curves, and decision curve analysis.

RESULTS

Key predictive factors for pulmonary infection risk were identified, including diabetes, chronic obstructive pulmonary disease, chemotherapy intensity, chemotherapy cycles, antibiotic use, age, Karnofsky Performance Status score, systemic inflammation index, prognostic nutritional index, and C-reactive protein. A nomogram-based prediction model was constructed, achieving ROC curve Area Under the Curve values of 0.889 in the training set, 0.897 in the validation set, and 0.875 in the external validation set, demonstrating strong classification ability and stability.

CONCLUSION

We developed a robust nomogram-based model incorporating eight key factors to predict the risk of pulmonary infection in lung cancer patients undergoing radiochemotherapy. This model can assist clinicians in early identification of high-risk patients, enabling timely interventions to improve patient outcomes and quality of life.

Prognostic nutritional index as a predictor of mortality in acute ischemic stroke

OBJECTIVE

Nutritional and immune status have been recognized as important factors influencing the prognosis of acute ischemic stroke. This study aims to evaluate the prognostic value of the Prognostic Nutritional Index (PNI) in predicting 30-day mortality in patients with acute ischemic stroke.

METHODS

A retrospective cohort study was conducted in a tertiary care hospital emergency department. Patients diagnosed with acute ischemic stroke between January 1, 2019, and January 1, 2024, were included. PNI was calculated using serum albumin levels and total lymphocyte count. Data on demographics, clinical parameters, laboratory results, and 30-day mortality were collected from electronic medical records and patient follow-up via phone calls. Appropriate statistical tests were applied based on the data distribution, and p-values < 0.05 were considered statistically significant.

RESULTS

A total of 169 patients were included in the study, with a mean age of 74.2 ± 10.5 years, and 55 % were male. Of these patients, 25 (14.8 %) did not survive within the 30-day follow-up period. PNI was significantly lower in deceased patients compared to survivors (p < 0.001). The area under the receiver operating characteristic (AUROC) curve was 0.831, indicating good predictive accuracy for 30-day mortality. The optimal PNI cut-off of 37.21 demonstrated a sensitivity of 72.0 % and a specificity of 81.2 %.

CONCLUSION

The PNI is a reliable predictor of 30-day mortality in acute ischemic stroke patients. Lower PNI values were associated with higher mortality, highlighting the importance of nutritional and immune status in stroke outcomes. PNI could be used in clinical practice to help identify high-risk patients and inform more appropriate treatment decisions.

Association between prognostic nutritional index and stroke: A nationally representative cross-sectional study from NHANES

BACKGROUND

The correlation between the prognostic nutritional index (PNI) and the risk of stroke has become a hot spot within the medical research community. The available evidence from a large sample regarding the correlation between PNI and stroke is inadequate. There is also a need for more research analysis from national surveys.

OBJECTIVE

The principal goal of this research is to improve our understanding of the relationship between PNI and the risk of stroke. This study also intends to investigate the potential synergistic influence of PNI on stroke in combination with other interacting variables.

METHODS

A cross-sectional study was conducted with 35,549 participants selected from the National Health and Nutrition Examination Survey (NHANES) 2005-2018 cycle. Information was gathered from all participants regarding the following: lymphocyte count, albumin levels, stroke occurrence, age, sex, race/ethnicity, education level, poverty income ratio (PIR), marital status, body mass index (BMI), smoking habits, drinking status, physical activity measured in total metabolic equivalents (PA_total_MET), diabetes status, glycohemoglobin levels, total cholesterol, direct high-density lipoprotein cholesterol(direct HDL-cholesterol), hypertension, and coronary heart disease. Curve fitting, subgroup analysis, and multifactor weighted logistic regression analysis were used to examine the relationship between PNI and stroke.

RESULTS

The overall prevalence of stroke was found to be 3.9%, PNI quartile 4(Q4) had an increased prevalence of stroke than quartile 1, 2, and 3(Q1,2,3). Our research suggested a negative association between PNI and the risk of stroke, as indicated by the odds ratio (OR=0.98) with a 95% confidence interval (CI=0.97∼0.99) and a p-value <0.05 (P=0.005), even after adjusting for all confounders. Subsequent subgroup analysis indicated a significant difference in the impact of PNI on stroke between individuals with different body mass index(BMI) levels (p for interaction = 0.02).

CONCLUSION

Our findings underscore that lower PNI in US adults is associated with an increased stroke risk, shedding light on a potential interrelationship between nutrition, inflammatory parameters, and stroke.

"The association between HALP score and infection in acute ischemic stroke patients"

BACKGROUND AND PURPOSE

Stroke-associated infection (SAI) is related to increased mortality in acute ischemic stroke (AIS) cases. The HALP index is used to evaluate nutrition and inflammation. Our research aimed to assess the relation between HALP scores and infection risk in AIS cases.

MATERIALS AND METHODS

132 cases of acute ischemic stroke were registered. 77 cases were male and 55 cases were female. The median age of the attending cases was 66 (35-104) years. Laboratory variables were assessed within 24 h after hospitalization in the neurology care unit. The HALP score is evaluated utilizing the formula "Hemoglobin (g/dL) × Albumin (g/dL) × Lymphocyte (/10^3/uL) / Platelet (/10^3/uL)".

RESULTS

Cases were separated into two groups according to their corresponding HALP score. Group-1 cases have a low HALP score (HALP score ≤ 18227,93). Group-2 cases have a high HALP score (HALP score > 18227,93). 26 (19.6 %) cases were diagnosed with various infections after hospitalization in the neurology care unit. Urinary tract infections were frequent infection causes in AIS cases (13 cases, 50 %). Pneumonia was observed in 8 cases, making up 30 % of the total cases. Another infection was seen in 5 (20 %) of the cases. The frequently encountered bacteria were Escherichia coli ESBL + (n = 7, 27 %) and Staphylococcus aureus (n = 6, 23 %). The mortality ratio was higher in Group-1 cases than in Group-2 cases (34 % vs 7 %).

CONCLUSIONS

This investigation has suggested a relationship between infection and HALP score in AIS patients.

Relationship between prognostic nutritional index and post-stroke cognitive impairment

BACKGROUND

The Prognostic Nutritional Index (PNI) has been described as a useful screening tool for patient prognosis in several diseases. As a potential diagnostic index, it has attracted the interest of many physicians. However, the correlation between the PNI and post-stroke cognitive impairment (PSCI) remains unclear.

METHODS

A total of 285 patients with acute ischemic stroke were included. PNI was assessed as serum albumin (g/L) + 5× lymphocyte count (109/L) and was dichotomized according to the prespecified cut-off points 48.43 for the high and low groups. PSCI was defined as Mini-Mental State Examination (MMSE) < 27 at the 6-10 months follow-up. Multiple logistic regression and linear regression analyses were performed to examine the association between PNI and cognitive outcomes.

RESULTS

A low PNI was independently associated with PSCI after adjusting for age, sex, education, National Institutes of Health Stroke Scale (NIHSS), deep white matter hyperintensity (DWMH), and stroke history (odds ratio [OR]: 2.158; 95% confidence interval [CI]: 1.205-3.863). The PNI scores were significantly associated with MMSE and attention domain (β = 0.113, p = 0.006; β = 0.109, p = 0.041, respectively). The PNI improved the model's discrimination when added to the model with other clinical risk factors.

CONCLUSIONS

A low PNI was independently associated with the occurrence of PSCI and the PNI scores were specifically associated with the scores of global cognition and attention domain. It can be a promising and straightforward screening indicator to identify the person with impaired immune-nutritional status at higher risk of PSCI.

The role of the prognostic nutritional index in predicting mortality in stroke patients

OBJECTIVE

The aim of this study was to evaluate the role of the prognostic nutritional index in predicting in-hospital mortality among patients with acute ischemic stroke.

METHODS

This retrospective, observational study included patients diagnosed with acute ischemic stroke at the emergency department of the hospital between January 1, 2022, and January 1, 2023. Demographic data, vital parameters, comorbidities, stroke interventions, and laboratory data were collected from electronic medical records. Prognostic nutritional index was calculated using serum albumin levels and a total lymphocyte count. The primary outcome was in-hospital mortality.

RESULTS

The study included 176 patients, divided into survivor (93.2%, n=164) and deceased (6.8%, n=12) groups. No significant differences were observed in age, gender, blood pressure, heart rate, or body temperature between the groups. Atrial fibrillation was significantly more common in the deceased group (50%) compared to the survivor group (18.9%) (p=0.011). The median lymphocyte count was significantly higher in the survivor group (1,353 [interquartile range, IQR 984-1,968]/mm³) compared to the deceased group (660 [IQR 462-1,188]/mm³) (p=0.009). The median albumin level was significantly lower in the deceased group (3.31 [IQR 2.67-3.4] g/dL) compared to the survivor group (3.74 [IQR 3.39-4.21] g/dL) (p<0.001). The median prognostic nutritional index was significantly higher in the survivor group (46.05 [IQR 39.1-51.3]) compared to the deceased group (36.7 [IQR 28.7-40.5]) (p<0.001). The area under the receiver operating characteristic for prognostic nutritional index predicting mortality was 0.791 (95%CI 0.723-0.848) (p=0.0002), with a cut-off value of ≤41.92 providing the highest diagnostic accuracy.

CONCLUSIONS

Prognostic nutritional index is a valuable prognostic indicator for in-hospital mortality in acute ischemic stroke patients. Low prognostic nutritional index values are associated with increased mortality risk. Incorporating prognostic nutritional index into clinical practice may aid in the early identification of high-risk patients and the optimization of treatment strategies. Further research is needed to validate these findings and explore the broader clinical applications of prognostic nutritional index.

How Immunonutritional Markers Are Associated with Age, Sex, Body Mass Index and the Most Common Chronic Diseases in the Hospitalized Geriatric Population-A Cross Sectional Study

The aim of this study was to assess the relationship of different chronic diseases with immunonutritional markers in the senior population.

METHODS

this study included 1190 hospitalized geriatric patients. The criteria to participate were ability to communicate, given consent and C-reactive protein (CRP) lower than 6 mg/dL.

RESULTS

the mean age of the study population was 81.7 ± 7.6 years. NLR (neutrophil-to-lymphocyte ratio), LMR (lymphocyte-to-monocyte ratio), MWR (monocyte-to-white blood cell ratio), SII (systemic immune-inflammation index), PNI (prognostic nutritional index) and CAR (C-reactive protein-to-albumin ratio) were related to age. NLR and MWR were higher, while LMR, PLR (platelet-to-lymphocyte ratio and SII were lower in men. All markers were related to BMI. NLR, LMR, LCR (lymphocyte-to-CRP ratio), MWR, PNI and CAR were related to several concomitant chronic diseases. In multivariate analyses, age and BMI were selected as independent predictors of all studied immunonutritional markers. Atrial fibrillation, diabetes mellitus and dementia appear most often in the models. PNI presented the most consistent statistical association with age, BMI and concomitant chronic diseases.

CONCLUSIONS

this study reveals the pivotal role of aging and BMI in inflammatory marker levels and the association of immunonutritional markers with different chronic diseases. Atrial fibrillation seems to have the most dominant connection to the immunonutritional markers.

Prevalence and Impact of Malnutrition Risk on Outcomes in Critically Ill Patients with Traumatic Brain Injury and Stroke: A Retrospective Cohort Study Using Electronic Health Records

BACKGROUND

Malnutrition is a critical concern in ICU settings. It is associated with increased morbidity and mortality, yet its prevalence and impact on clinical outcomes in patients with stroke and traumatic brain injury (TBI) remain underexplored.

OBJECTIVE

To evaluate the prevalence and impact of malnutrition risk on clinical outcomes in ICU patients with TBI, ischemic stroke, and hemorrhagic stroke, and to identify key risk factors associated with malnutrition risk.

METHODS

This retrospective cohort study utilized electronic health records encompassing ICU admissions from 2017 to 2023. Patients with either stroke or TBI were included, with malnutrition risk assessed using the prognostic nutritional index. Data were extracted and analyzed to determine patient characteristics, clinical and laboratory parameters, and outcomes.

RESULTS

This study included 1352 patients (267 TBI, 825 ischemic stroke, and 260 hemorrhagic stroke patients, >30% with pneumonia at admission). Severe malnutrition risk at admission was observed in over 60% of patients. Stroke patients, particularly those with hemorrhagic stroke, exhibited a higher risk of malnutrition compared to TBI patients. Malnutrition risk was associated with significantly higher hospital mortality and increased need for mechanical ventilation. Predictive factors for malnutrition risk included advanced age, higher SOFA scores, lower FOUR and GCS scores, and the presence of pneumonia at admission.

CONCLUSIONS

Risk of malnutrition is highly prevalent among ICU patients with TBI, ischemic, and hemorrhagic stroke, significantly impacting mortality and other clinical outcomes. Identifying and managing malnutrition early in the ICU setting is crucial for improving patient outcomes. Further prospective, multicenter studies are needed to validate these findings and develop effective interventions.

Identification of risk factors and construction of a nomogram predictive model for post-stroke infection in patients with acute ischemic stroke

BACKGROUND

Post-stroke infection is the most common complication of stroke and poses a huge threat to patients. In addition to prolonging the hospitalization time and increasing the medical burden, post-stroke infection also significantly increases the risk of disease and death. Clarifying the risk factors for post-stroke infection in patients with acute ischemic stroke (AIS) is of great significance. It can guide clinical practice to perform corresponding prevention and control work early, minimizing the risk of stroke-related infections and ensuring favorable disease outcomes.

AIM

To explore the risk factors for post-stroke infection in patients with AIS and to construct a nomogram predictive model.

METHODS

The clinical data of 206 patients with AIS admitted to our hospital between April 2020 and April 2023 were retrospectively collected. Baseline data and post-stroke infection status of all study subjects were assessed, and the risk factors for post-stroke infection in patients with AIS were analyzed.

RESULTS

Totally, 48 patients with AIS developed stroke, with an infection rate of 23.3%. Age, diabetes, disturbance of consciousness, high National Institutes of Health Stroke Scale (NIHSS) score at admission, invasive operation, and chronic obstructive pulmonary disease (COPD) were risk factors for post-stroke infection in patients with AIS (P < 0.05). A nomogram prediction model was constructed with a C-index of 0.891, reflecting the good potential clinical efficacy of the nomogram prediction model. The calibration curve also showed good consistency between the actual observations and nomogram predictions. The area under the receiver operating characteristic curve was 0.891 (95% confidence interval: 0.839-0.942), showing predictive value for post-stroke infection. When the optimal cutoff value was selected, the sensitivity and specificity were 87.5% and 79.7%, respectively.

CONCLUSION

Age, diabetes, disturbance of consciousness, NIHSS score at admission, invasive surgery, and COPD are risk factors for post-stroke infection following AIS. The nomogram prediction model established based on these factors exhibits high discrimination and accuracy.

Gut Dysbiosis: A New Avenue for Stroke Prevention and Therapeutics

A stroke is a serious life-threatening condition and a leading cause of death and disability that happens when the blood vessels to part of the brain are blocked or burst. While major advances in the understanding of the ischemic cascade in stroke was made over several decades, limited therapeutic options and high mortality and disability have caused researchers to extend the focus toward peripheral changes beyond brain. The largest proportion of microbes in human body reside in the gut and the interaction between host and microbiota in health and disease is well known. Our study aimed to explore the gut microbiota in patients with stroke with comparison to control group. Fecal samples were obtained from 51 subjects: 25 stroke patients (18 hemorrhagic, 7 ischemic) and 26 healthy control subjects. The variable region V3-V4 of the 16S rRNA gene was sequenced using the Illumina MiSeq platform. PICRUSt2 was used for prediction of metagenomics functions. Our results show taxonomic dysbiosis in stroke patients in parallel with functional dysbiosis. Here, we show that stroke patients have (1) increased Parabacteroides and Escherichia_Shigella, but decreased Prevotella and Fecalibacterium; (2) higher transposase and peptide/nickel transport system substrate-binding protein, but lower RNA polymerase sigma-70 factor and methyl-accepting chemotaxis protein, which are suggestive of malnutrition. Nutrients are essential regulators of both host and microbial physiology and function as key coordinators of host-microbe interactions. Manipulation of nutrition is expected to alleviate gut dysbiosis and prognosis and improve disability and mortality in the management of stroke.