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Background and Purpose
This study aimed to investigate the relationship between malnutrition and early neurological deterioration (END) in elderly patients with acute ischemic stroke in China.
Methods
We used the registry data in the Third Affiliated Hospital of Nantong University and Nanjing Brain Hospital from June 2019 to January 2021. Malnutri...
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Objectives
We evaluated the impact of malnutrition as estimated by the controlling nutritional status (CONUT) score and prognostic nutritional index (PNI) on hemorrhagic transformation (HT) and stroke outcomes after intravenous thrombolysis (IVT).
Materials and methods
Using a multicenter registry database, we enrolled 808 patients with acute isch...
Citations
... Elevated scores suggest pronounced neurological deficits, which might compromise eating abilities due to issues like mastication and dysphagia. Severe cases often coincide with inflammatory responses and metabolic disturbances, exacerbating nutritional inadequacies [24]. ...
Background
In older stroke patients with frailty, nutritional deficiencies can amplify their susceptibility, delay recovery, and deteriorate prognosis. A precise predictive model is crucial to assess their nutritional risk, enabling targeted interventions for improved clinical outcomes.
Objective
To develop and externally validate a nutritional risk prediction model integrating general demographics, physical parameters, psychological indicators, and biochemical markers. The aim is to facilitate the early identification of older stroke patients requiring nutritional intervention.
Methods
This was a multicenter cross-sectional study. A total of 570 stroke patients were included, 434 as the modeling set and 136 as the external validation set. The least absolute shrinkage selection operator (LASSO) regression analysis was used to select the predictor variables. Internal validation was performed using Bootstrap resampling (1000 iterations). The nomogram was constructed based on the results of logistic regression. The performance assessment relied on the receiver operating characteristic curve (ROC), Hosmer–-Lemeshow test, calibration curves, Brier score, and decision curve analysis (DCA).
Results
The predictive nomogram encompassed seven pivotal variables: Activities of Daily Living (ADL), NIHSS score, diabetes, Body Mass Index (BMI), grip strength, serum albumin levels, and depression. Together, these variables comprehensively evaluate the overall health and nutritional status of elderly stroke patients, facilitating accurate assessment of their nutritional risk. The model exhibited excellent accuracy in both the development and external validation sets, evidenced by AUC values of 0.934 and 0.887, respectively. Such performance highlights its efficacy in pinpointing elderly stroke patients who require nutritional intervention. Moreover, the model showed robust goodness of fit and practical applicability, providing essential clinical insights to improve recovery and prognosis for patients prone to malnutrition.
Conclusions
Elderly individuals recovering from stroke often experience significant nutritional deficiencies. The nomogram we devised accurately assesses this risk by combining physiological, psychological, and biochemical metrics. It equips healthcare providers with the means to actively screen for and manage the nutritional care of these patients. This tool is instrumental in swiftly identifying those in urgent need of targeted nutritional support, which is essential for optimizing their recovery and managing their nutrition more effectively.
... A large amount of literature has confirmed that the GNRI has a good screening effect on the malnutritional risk of elderly patients. 32,34,[48][49][50] It is calculated by laboratory and physical indicators that can be easily collected. In the modern medical system of information expansion, the GNRI score is much more convenient and constant than NRS 2002. ...
Purpose
To explore the predictive value of nutritional risk for all-cause death and functional outcomes among elderly acute stroke patients.
Patients and Methods
A total of 479 elderly acute stroke patients were enrolled in this study. The nutritional risk of patients was screened by the GNRI and NRS-2002. The primary outcome was all-cause death, and the secondary outcome was poor prognosis defined as a modified Rankin Scale (mRS) score ≥3.
Results
Based on the NRS-2002, patients with nutritional risk had a higher risk of all-cause death at 3 months (adjusted OR: 3.642, 95% CI 1.046~12.689) and at 3 years (adjusted OR: 2.266, 95% CI 1.259~4.076) and a higher risk of adverse functional outcomes at 3 months (adjusted OR: 2.748, 95% CI 1.518~4.972. Based on the GNRI, compared to those without nutritional risk, patients with mild malnutrition also had a higher risk of all-cause death at 3 months (adjusted OR: 7.186, 95% CI 1.550~33.315) and at 3 years (adjusted OR: 2.255, 95% CI 1.211~4.199) and a higher risk of adverse functional outcomes at 3 months (adjusted OR: 1.947, 95% CI 1.030~3.680), so patients with moderate and severe malnutrition had a higher risk of all-cause death at 3 months (adjusted OR: 6.535, 95% CI 1.380~30.945) and at 3 years (adjusted OR: 2.498, 95% CI 1.301~4.799) and a higher risk of adverse functional outcomes at 3 months (adjusted OR: 2.213, 95% CI 1.144~4.279).
Conclusion
Nutritional risk increases the risk of poor short-term and long-term outcomes in elderly patients with acute stroke. For elderly stroke patients, we should pay attention to early nutritional risk screening, and effective intervention should be provided to improve the prognosis of such patients.
... Anemia and hypoalbuminemia are manifestations of malnutrition. They have been identi ed as risk factors for END [14] . Given the great heterogeneity in stroke progression, the use of a single biomarker may not accurately predict the risk of END and there is a need to combine multiple biomarkers to improve the ability to predict END. ...
Background: Early neurological deterioration (END) may be associated with poor prognosis in elderly AIS patients. The objective of this study was to examine the relationship between a composite biomarker HALP score and END, to identify patients at risk for poor neurological function.
METHODS: This study retrospectively examined elderly patients with AIS admitted to Nanjing Drum Tower Hospital from January 2016 to December 2021. NIHSS were collected within 7 days of admission. END was defined as a 2 point increase in NIHSS within 7 days.. the formula for HALP score was lymphocytes (/L) ×hemoglobin (g/L) ×albumin (g/L) / platelets (/L). Multivariate logistic regression was used to construct a prediction model for HALP score, ROC curves and calibration graphs were computed.
Results: A total of 431 elderly AIS patients were included, with END accounting for 34.34%. Univariate analysis showed that age, baseline NIHSS score, white blood cell count, lymphocyte count, hemoglobin, triglycerides, HALP score, CRP, Hcy, Lp-PLA2, infectious events and death events differed between the two groups (P < 0.05). Multifactorial logistic regression analysis revealed that HALP score (OR 0.965, 95% CI 0.943 to 0.988, P=0.003) and baseline NIHSS score (OR 1.169, 95% CI 1.119 to 1.220, P<0.001) were good at predicting END. The area under the ROC curve for HALP score <27.69 and NIHSS score >5.5 was 0.727 (95CI: 0.676-0.778); 0.868 (95CI: 0.834-0.903), respectively, and the combined AUC was 0.883 (95%CI: 0.850-0.916). Additionally, HALP score was significantly negatively correlated with baseline NIHSS (r=-0.411, P<0.001) and admission day 7 NIHSS (r=-0.348, P<0.001), respectively. More often the lower HALP score, the higher END percentage and the worse 90-day functional outcomes.
Conclusion: A low HALP score at admission is associated with the occurrence of END within one week in elderly AIS patients, which may help clinicians to identify high-risk END patients early.
... The studies that applied GNRI to stroke patients have been carried out (since 2016) in Far Eastern Asian countries [7,[14][15][16]27,[29][30][31][32]34,37,42,43,[45][46][47][48]50,[53][54][55]57,58,63,64,66,68,70,71], as shown in Table 1. A significant risk of malnutrition has usually been defined by a GNRI score <92, with some exceptions (for example, a score <98 [7] or <100 [27]). ...
... According to the small pool of published information, an association of GNRI with pneumonia incidence was observed in hospitalized stroke patients with dysphagia [48] or diabetes mellitus [64], but was not confirmed in another study [14]. GNRI was also a significant predictor of early neurological deterioration [15], transferal from rehabilitation to acute care hospital units [50], post-discharge destination (home or transfer) [57], or the ability to achieve adequate oral intake [48]. ...
... As shown in Table 1, the studies were carried out in AIS patients (SAH in one) in Far Eastern Asian countries, except one in Italy [15,25,28,38,41,44,56,59,[65][66][67][68]71]. All the studies but one involved stroke patients at admission to hospital (Table 1). ...
Malnutrition is common in stroke patients, as it is associated with neurological and cognitive impairment as well as clinical outcomes. Nutritional screening is a process with which to categorize the risk of malnutrition (i.e., nutritional risk) based on validated tools/procedures, which need to be rapid, simple, cost-effective, and reliable in the clinical setting. This review focuses on the tools/procedures used in stroke patients to assess nutritional risk, with a particular focus on their relationships with patients’ clinical characteristics and outcomes. Different screening tools/procedures have been used in stroke patients, which have shown varying prevalence in terms of nutritional risk (higher in rehabilitation units) and significant relationships with clinical outcomes in the short- and long term, such as infection, disability, and mortality. Indeed, there have been few attempts to compare the usefulness and reliability of the different tools/procedures. More evidence is needed to identify appropriate approaches to assessing nutritional risk among stroke patients in the acute and sub-acute phase of disease or during rehabilitation; to evaluate the impact of nutritional treatment on the risk of malnutrition during hospital stay or rehabilitation unit; and to include nutritional screening in well-defined nutritional care protocols.
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.
Background: The Geriatric Nutritional Risk Index (GNRI) is a straightforward and objective tool for nutritional screening in elderly patients and has been demonstrated to possess prognostic predictive value in several diseases. Nonetheless, there is a lack of research on the nutritional risk associated with brain abscess in the elderly. This study aimed to evaluate the prevalence of nutritional risk among these patients by GNRI and to investigate its potential prognostic value for clinical outcomes.
Methods: From August 2019 to April 2023, 100 elderly patients diagnosed with brain abscess were enrolled in the study. The collected data encompassed age, gender, body mass index (BMI), smoking and alcohol consumption history, number of comorbidities, length of hospital stay (LOS), serum albumin and C-reactive protein (CRP) levels at admission and calculated the GNRI, the Glasgow outcome scale (GOS) score 6 months post-discharge. A GOS score of 5 was considered indicative of a good recovery, whereas scores ranging from 1 to 4 were classified as poor recovery.
Results: The prevalence of malnutrition risk among elderly patients with brain abscesses was found to be 48% according to GNRI. Compared to those without nutritional risk, patients at risk exhibited significantly higher post-admission C-reactive protein (CRP) levels (P=0.017), a greater number of comorbidities (P<0.001), and elevated age-adjusted Charlson Comorbidity Index (aCCI) scores (P<0.001). Spearman correlation analysis revealed a negative correlation between GNRI scores and CRP levels, the number of comorbidities, and aCCI scores (Spearman's ρ=-0.291, -0.284, and -0.310, respectively), and a positive correlation with Glasgow Outcome Scale (GOS) scores (Spearman's ρ=0.624, P<0.001). Multivariate logistic regression analysis indicated that lower GNRI values in these patients were associated with reduced GOS levels (OR = 0.826, 95% CI: 0.775-0.880). Furthermore, receiver operating characteristic (ROC) analysis identified a GNRI threshold of 97.50 for predicting poor recovery, with a sensitivity of 90.57% and a specificity of 87.23%.
Conclusions: Elderly brain abscess patients exhibited a high malnutrition risk. GNRI showed an important predictive value for recovery in elderly patients, which could be helpful in clinical intervention and rehabilitation.
Aims:
The Controlling Nutritional Status (CONUT) score is a tool for assessing the risk of malnutrition (undernutrition) that can be calculated from albumin concentration, total peripheral lymphocyte count, and total cholesterol concentration. CONUT score has been proposed as a promising prognostic marker in several clinical settings; however, a consensus on its prognostic value in patients with stroke is lacking. The aim of this systematic review and meta-analysis was to evaluate the relationship between CONUT score and clinical outcomes in patients with stroke based on all current available studies.
Data synthesis:
Systematic research on PubMed, Scopus and Web of Science from inception to February 2023 was performed on the association between CONUT score and clinical outcomes in patients with stroke. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses were followed. Methodological quality was evaluated using the Newcastle-Ottawa Scale quality assessment tool. Pooled effect estimation was calculated by a random-effect model. Through the initial literature search, 15 studies (all high-quality) including 16 929 patients were found to be eligible and analysed in the meta-analysis. A significant risk of malnutrition (in most studies defined by a CONUT score ≥5) was directly associated with mortality, higher risk of poor functional outcome according to the modified Rankin Scale and total infection development. Evidence was consistent for acute ischaemic stroke and preliminary for acute haemorrhagic stroke.
Conclusion:
CONUT score is an independent prognostic indicator, and it is associated with major disability and infection development during hospitalisation.
Prospero id:
CRD42022306560.
