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Regular nutritional assessment may decrease the mortality rate in patients undergoing hemodialysis. This study aimed to evaluate whether annual change in geriatric nutritional risk index (ΔGNRI) can precisely predict mortality. We retrospectively examined 229 patients undergoing hemodialysis who measured geriatric nutritional risk index (GNRI). Patients were divided into four groups according to the baseline GNRI of 91.2, previously reported cutoff value, and declined or maintained GNRI during the first year (ΔGNRI < 0% vs. ΔGNRI ≥ 0%): Group 1 (G1), GNRI ≥ 91.2 and ΔGNRI ≥ 0%; G2, GNRI ≥ 91.2 and ΔGNRI < 0%; G3, GNRI < 91.2 and ΔGNRI ≥ 0%; and G4, GNRI < 91.2 and ΔGNRI < 0%. They were followed for mortality. During a median follow-up of 3.7 (1.9–6.9) years, 74 patients died, of which 35 had cardiovascular-specific causes. The GNRI significantly decreased from 94.8 ± 6.3 to 94.1 ± 6.7 in the first year (p = 0.035). ΔGNRI was negatively associated with baseline GNRI (ρ = −0.199, p = 0.0051). The baseline GNRI < 91.2 and ΔGNRI < 0% were independently associated with all-cause mortality (adjusted hazard ratio (aHR) 2.59, 95%, confidence interval (CI) 1.54–4.33, and aHR 2.33, 95% CI 1.32–4.32, respectively). The 10-year survival rates were 69.8%, 43.2%, 39.9%, and 19.2% in G1, G2, G3, and G4, respectively (p < 0.0001). The aHR value for G4 vs. G1 was 3.88 (95% CI 1.62–9.48). With regards to model discrimination, adding ΔGNRI to the baseline risk model including the baseline GNRI significantly improved the net reclassification improvement by 0.525 (p = 0.0005). With similar results obtained for cardiovascular mortality. We concluded that the ΔGNRI could not only predict all-cause and cardiovascular mortality but also improve predictability for mortality; therefore, GNRI might be proposed to be serially evaluated.

BackgroundMalnutrition, sarcopenia, and frailty are prevalent conditions amongst hospitalized elderly. They are associated with numerous adverse health outcomes. The co-existence of these problems is common, with malnutrition playing a major role in the pathogenesis of the other two. Whether nutritional screening tools are useful for frailty and sarcopenia screening needs further evaluation.AimTo evaluate the accuracy of the Geriatric Nutritional Risk Index (GNRI) in identifying frailty and sarcopenia in hospitalized older adults.MethodsOne hundred and fifty hospitalized patients (≥ 60 years) were recruited. Skeletal Muscle Index was obtained using bioelectrical impedance analysis. Muscle strength and physical performance were measured by handgrip strength and timed up and go test, respectively. GNRI and the Mini Nutritional Assessment (MNA) tool were used for nutritional assessment.ResultsGNRI had lower sensitivity but higher specificity compared to MNA in predicting frailty and dynapenia. GNRI discriminated the presence of sarcopenia but not pre-sarcopenia (AUC = 0.683, p = 0.02, and AUC = 0.586, p = 0.12), while MNA did not discriminate the presence of pre-sarcopenia nor sarcopenia in the studied sample (AUC = 0.56, p = 0.25 and AUC = 0.6, p = 0.09).ConclusionsSarcopenia, frailty, and malnutrition coexisted in 26% of our sample. GNRI Score at ≤ 86.73 was 71.9% sensitive and 65.6% specific for detecting frailty and its score at ≤ 89.04 was 64.42% sensitive and 63.53% specific for detecting sarcopenia. GNRI is a simple method, which could be used for sarcopenia, and frailty screening in all elders attending primary care settings where other tools for assessing muscle mass are unavailable.

In the current era of immune therapy, lenvatinib (LEN) continues to be vital for treating unresectable hepatocellular carcinoma (uHCC) patients. This study investigates the importance of nutritional status in the prognosis of uHCC patients receiving LEN and evaluates a new prognostic scoring system that combines the geriatric nutritional risk index (GNRI) and systemic inflammatory response. From 2018 to 2022, 484 uHCC patients treated with LEN (384 males, median age 73). Prognostic value was compared between GNRI and C-reactive protein (CRP) scoring (GNRI-C score), GNRI, and neo-Glasgow prognostic score (neo-GPS). Evaluation was based on the Akaike information criterion (AIC) and concordance index(c-index). Median progression-free survival (mPFS) was 9.3/6.8/4.6 months for GNRI no-risk/low-risk/moderate-to-major risk (p < 0.01, AIC 4742.4/c-index 0.585). Median overall survival (mOS) was 27.8/15.2/9.5 months (p < 0.01, AIC 3433.34/c-index 0.639). For GNRI-C score, mPFS was 10.8/7.1/5.6/4.0 months (score 0/1/2/3) (p < 0.01, AIC 4732.82/c-index 0.6), while neo-GPS showed mPFS of 8.5/5.1/5.2 months (p < 0.01, AIC 4745.89/c-index 0.562). For mOS, GNRI-C score demonstrated 28.6/20.0/10.1/8.4 months (score 0/1/2/3) (p < 0.01, AIC 3420.27/c-index 0.652), while neo-GPS indicated 21.0/12.4/4.5 months (p < 0.01, AIC 3468.84/c-index 0.564). The newly devised GNRI-C score, incorporating nutritional and inflammatory markers, could offer improved prognostic predictions for uHCC patients treated with LEN.

The geriatric nutritional risk index (GNRI) and modified creatinine index (mCI) are surrogate markers of protein-energy wasting in patients receiving hemodialysis. We aimed to examine whether a combined evaluation of these indices improved mortality prediction in this population. We retrospectively investigated 263 hemodialysis patients divided into two groups, using 91.2 and 20.16 mg/kg/day as cut-off values of GNRI and mCI, respectively. The resultant four groups were reshuffled into four subgroups defined using combinations of cut-off values of both indices and were followed up. During the follow-up period (median: 3.1 years), 103 patients died (46/103, cardiovascular causes). Lower GNRI and lower mCI were independently associated with all-cause mortality (adjusted hazard ratio (aHR) 4.96, 95% confidence intervals (CI) 3.10–7.94, and aHR 1.92, 95% CI 1.22–3.02, respectively). The aHR value for the lower GNRI and lower mCI group vs. the higher GNRI and higher mCI group was 7.95 (95% CI 4.38–14.43). Further, the addition of GNRI and mCI to the baseline risk assessment model significantly improved the C-index of all-cause mortality (0.801 to 0.835, p = 0.025). The simultaneous evaluation of GNRI and mCI could be clinically useful to stratify the risk of mortality and to improve the predictability of mortality in patients on hemodialysis.

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 Previous studies investigating the association between the geriatric nutrition risk index (GNRI) and sarcopenia either lacked longitudinal evidence or narrowly focused on specific populations. Aims We aimed to reveal longitudinal associations of GNRI with sarcopenia risk in community-dwelling Chinese. We also investigated interaction effects of potential factors on such associations. Methods We included participants aged ≥ 50 years with sufficient data from the WCHAT study who did not have sarcopenia at baseline and completed sarcopenia assessment during follow-up. GNRI was calculated according to the formula based on serum albumin, height and weight. Sarcopenia was diagnosed according to the 2019 AWGS consensus. Longitudinal associations between GNRI and sarcopenia were estimated by logistic regression with GNRI as either a continuous or categorical variable by tertiles, using generalized estimating equations (GEE) as sensitivity analyses. Subgroup analyses by potential covariates were conducted to detect interaction effects. Results A total of 1907 participants without baseline sarcopenia were finally included, of whom 327 (17.1%) developed incident sarcopenia during 5-year follow-up. After controlling for confounders, sarcopenia risk decreased with each one standard deviation increase in GNRI (OR adjusted =0.36, 95% CI 0.31–0.43), and it also decreased successively from the lowest (< 111.2) through middle (111.2-117.7) to the highest (≥ 117.8) tertile of the GNRI level (P for trend < 0.001). Similar results were yielded by GEE. Such associations generally remained robust across subgroups with distinct characteristics, while significant differences were observed between different age groups (≥ 65 vs. <65 years) (interaction P-value < 0.05). Conclusion GNRI is longitudinally associated with sarcopenia risk with possibly age-specific differences in association magnitude, which holds implications for policymakers to conduct population-based risk assessment.

Postpancreatectomy hemorrhage (PPH) is the most lethal complication of pancreatoduodenectomy (PD). The main risk factor for PPH is the development of a postoperative pancreatic fistula (POPF). Recent evidence shows that the geriatric nutritional risk index (GNRI) may be predictive indicator for POPF. In this study, we aimed to evaluate whether GNRI is a reliable predictive marker for PPH following PD. The present study retrospectively evaluated 121 patients treated with PD at Ageo Central General Hospital in Japan between January 2015 and March 2020. We investigated the potential of age, gender, body mass index, serum albumin, American Society of Anesthesiologists classification (ASA), diabetes mellitus and smoking status, time taken for the operation, estimated blood loss, and postoperative complications (POPF, bile leak, and surgical site infections) to predict the risk of PPH following PD using univariate and multivariate analyses. Ten patients had developed PPH with an incidence of 8.3%. Among them, the patients were divided into bleeding group (n = 10) and non-bleeding group (n = 111). The bleeding group had significantly lower GNRI values than those in the non-bleeding group (p = 0.001). We determined that the cut-off value of GNRI was 92 accounting for a sensitivity 80.0%, specificity 82.9%, and likelihood ratio of 4.6 using receiver operating characteristic curve analysis. A GNRI of <92 was statistically associated with PPH in both univariate (p < 0.001) and multivariate analysis (p = 0.01). Therefore, we could identify that a GNRI < 92 was an independently potential predictor of PPH risk following PD. We should alert surgeons if patients have low level GNRI before PD.

Background Systemic inflammation and cachexia are associated with adverse clinical outcomes in elderly patients with cancer. The Geriatric Nutritional Risk Index (GNRI) is a simple and useful tool to assess these conditions, but its predictive ability for elderly patients with cancer cachexia (EPCC) is unknown. Methods This multicentre cohort study included 746 EPCC with an average age of 72.00 ± 5.24 years, of whom 489 (65.5%) were male. The patients were divided into two groups (high GNRI group ≥91.959 vs. low GNRI group <91.959) according to the optimal cut‐off value of the ROC curve. The calibration curves were performed to analyse the prognostic, predictive ability of GNRI. Comprehensive survival analyses were utilized to explore the relationship between GNRI and the overall survival (OS) of EPCC. Interaction analysis was used to investigate the comprehensive effects of low GNRI and subgroup parameters on the OS of EPCC. Results In this study, a total of 2560 patients were diagnosed with cancer cachexia, including 746 cases of EPCC. During the 3.6 year median follow‐up, we observed 403 deaths. The overall mortality rate for EPCC at 12 months was 34.3% (95% CI: 62.3% to 69.2%), and resulting in rate of 278 events per 1000 patient‐years. The GNRI score of EPCC was significantly lower than those of young patients with cancer cachexia (P < 0.001). The 1, 3, and 5 year calibration curves showed that the GNRI score had good survival prediction in the OS of EPCC. The GNRI could predict the OS of EPCC, whether as a continuous variable or a categorical variable. Particularly, we also found that low GNRI score (<91.959) of EPCC had a worse prognosis than those with a high GNRI score (≥91.959, P = 0.001, HR = 1.728, 95% CI: 1.244–2.401). Consistent results were observed in the tumour subgroups of gastric cancer and colorectal cancer. Notably, similar results were observed in the sensitivity analysis. In the subgroup analysis, the low GNRI has a combined effect with age (<70 years) on poor OS of EPCC. The results of the prognostic risk model found that the lower the GNRI score, the greater the prognostic risk score, and the greater the risk of death in EPCC. Conclusions For the first time, this study found that the GNRI score can serve as an independent prognostic factor for the OS of EPCC.

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.ObjectiveDiscussing 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.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.MethodsA 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.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/).ResultsAmong 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/).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.ConclusionPLR 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.

The increasing number of elderly people worldwide throughout the years is concerning due to the health problems often faced by this population. This review aims to summarize the nutritional status among hospitalized elderly and the role of the nutritional assessment tools in this issue. A literature search was performed on six databases using the terms \"malnutrition\", \"hospitalised elderly\", \"nutritional assessment\", \"Mini Nutritional Assessment (MNA)\", \"Geriatric Nutrition Risk Index (GNRI)\", and \"Subjective Global Assessment (SGA)\". According to the previous studies, the prevalence of malnutrition among hospitalized elderly shows an increasing trend not only locally but also across the world. Under-recognition of malnutrition causes the number of malnourished hospitalized elderly to remain high throughout the years. Thus, the development of nutritional screening and assessment tools has been widely studied, and these tools are readily available nowadays. SGA, MNA, and GNRI are the nutritional assessment tools developed specifically for the elderly and are well validated in most countries. However, to date, there is no single tool that can be considered as the universal gold standard for the diagnosis of nutritional status in hospitalized patients. It is important to identify which nutritional assessment tool is suitable to be used in this group to ensure that a structured assessment and documentation of nutritional status can be established. An early and accurate identification of the appropriate treatment of malnutrition can be done as soon as possible, and thus, the malnutrition rate among this group can be minimized in the future.