Effect of malnutrition and nutritional support to reduce infections in elderly hospitalized patients with cancer: A multicenter survey in China

Analysis of risk factors and development of predictive model for malnutrition in patients with traumatic brain injury

Malnutrition is a highly prevalent complication in patients with traumatic brain injury (TBI), and it is closely related to the prognosis of patients. Accurate identification of patients at high risk of malnutrition is essential. Therefore, we analyzed the risk factors of malnutrition in patients with TBI and developed a model to predict the risk of malnutrition. A retrospective collection of 345 patients with TBI, and they were divided into malnutrition and comparison groups according to the occurrence of malnutrition. Univariate correlation and multifactor logistic regression analyses were performed to determine patients' malnutrition risk factors. We used univariate and logistic regression (forward stepwise method) analyses to identify significant predictors associated with malnutrition in patients with TBI and developed a predictive model for malnutrition prediction. The model's discrimination, calibration, and clinical utility were evaluated using the receiver operating characteristic (ROC) curve, calibration plots, and decision curve analysis (DCA). A total of 216 patients (62.6%) developed malnutrition. Multifactorial logistic regression analysis showed that pulmonary infection, urinary tract infection, dysphagia, application of NGT, GCS score ≤ 8, and low ADL score were independent risk factors for malnutrition in patients with TBI (P < 0.05). The area under the curve of the model was 0.947. Calibration plots showed good discrimination of model calibration. DCA showed that the column line plot models were all clinically meaningful when nutritional interventions were performed over a considerable range of threshold probabilities (0-0.98). Malnutrition is widespread in patients with TBI, and the nomogram is a good predictor of whether patients develop malnutrition.

From biological aging to functional decline: Insights into chronic inflammation and intrinsic capacity

Intrinsic capacity is the sum of an individual's physical and mental capacities, which helps determine functional ability. Intrinsic capacity decline is an important predictor of adverse health outcomes and can identify individuals at higher risk of functional decline. Aging is characterized by a decrease in physiological reserves and functional abilities. Chronic inflammation, a mechanism of aging, is associated with decreased intrinsic capacity, which may mirror the broader relationship between aging and functional ability. Therefore, it is crucial for maintaining functional ability and promoting healthy aging to study the mechanisms of intrinsic capacity decline, identify easily available markers, and make targets for intervention from the perspective of chronic inflammation. We reviewed the current research on chronic inflammation, inflammation-related markers, and intrinsic capacity. To date, there is still no inflammatory markers with high specificity and sensitivity to monitor intrinsic capacity decline. Interleukin-6, C-reactive protein, and tumor necrosis factor-alpha may potentially indicate changes in intrinsic capacity, but their results with intrinsic capacity or each intrinsic capacity domain are inconsistent. Considering the variations in individual responses to changes in inflammatory markers, it may be beneficial to explore the use of multiple analytes instead of relying on a single marker. This approach could be valuable in monitoring the decline of intrinsic capacity in the future.

Uncovering Methods and Outcomes of Palliative Care for Geriatric Patients: A Scoping Review

Background

Palliative care is an integral part of care for patients with life-limited diseases that focuses on reducing symptoms and maintaining and increasing the quality of life (QoL) for patients and their families. Geriatric patients were more likely to receive palliative care and had unique needs compared to the general population. To improve the quality of palliative care, especially for geriatric patients, it is necessary to have a better understanding of methods and outcomes for geriatric patients when delivering palliative care.

Objective

This study aims to identify the methods and outcomes of palliative care in geriatric patients across the globe.

Methods

This scoping review was guided by Arksey and 'O Malley's framework and utilized the Systematic Reviews and Meta-Analyses Extension for Scoping Reviews (PRISMA-ScR) checklist for providing transparent reporting to the readers. EBSCO, PubMed, and Scopus databases were used to search the relevant articles with a publication range of 2013-2023. Thematic analysis was used to identify and summarize palliative care methods and outcomes for geriatric patients in this review.

Results

Twenty-one studies were included in this review, and it was found that there were many types of methods for delivering palliative care to geriatric patients. In both acute care settings and community settings, a wide range of methods for delivering palliative care to geriatric patients were identified. Outcomes of palliative care in geriatric patients in hospitals and community settings, were reduced pain, depressive symptoms and anxiety, edema, constipation, odds of in-hospital death, and increased spiritual well-being, QoL and well-being, being comfortable, patient readiness, place of death, sleep quality, and quality of dying.

Conclusion

Geriatric patients had a variety of methods and outcomes in palliative care. This study suggests that outcomes should be evaluated continuously after implementing methods for delivering palliative care to geriatric patients.

Complementarity of nutritional assessment tools to predict prolonged hospital stay and readmission in older patients with solid tumors: A secondary analysis of a cohort study

OBJECTIVE

The aim of this study was to investigate the complementarity of five nutritional risk screening tools (Nutritional Risk Screening 2002 [NRS-2002], Malnutrition Screening Tool [MST], Malnutrition Universal Screening Tool [MUST], Mini-Nutritional Assessment-Short Form [MNA-SF], and Patient-Generated Subjective Global Assessment SF [PG-SGA SF]) combined with three malnutrition diagnostic tools (SGA, PG-SGA, and Global Leadership Initiative on Malnutrition [GLIM]) and their ability to predict poor clinical outcomes in older patients with cancer.

METHODS

Using data collected within 48 h of hospital admission, we conducted a prospective cohort study on nutritional risk (NRS-2002, MST, MUST, MNA-SF, and PG-SGA SF) and the presence of malnutrition (SGA, PG-SGA, and GLIM). Patients were grouped according to their nutritional risk and malnutrition status. Accuracy tests and logistic regression analysis were used to evaluate the ability of the combined tools to predict hospital length of stay and readmission. We evaluated 248 older patients (69.7 ± 7.2 y of age, 59.7% men; 27.4% with gastrointestinal tumors). The median length of stay was 4 d (3-9 d), and 65.3% of patients remained hospitalized for ≥ 4 d.

RESULTS

The NRS-2002 combined with SGA and MST combined with SGA and GLIM had the highest specificity (> 80%) for predicting hospitalization. Nutritional risk assessed by MNA-SF and malnutrition assessed by PG-SGA were associated with 2.48- and 6.04-fold increased likelihood of hospitalization (≥ 4 d) and readmission (60 d), respectively.

CONCLUSION

Concomitant application of MNA-SF (specific for older patients) with PG-SGA (specific for patients with cancer) might enhance the ability to predict length of stay and readmission in hospitalized older patients with solid tumors.

A novel risk classifier to predict the in-hospital death risk of nosocomial infections in elderly cancer patients

Background

Elderly cancer patients are more predisposed to developing nosocomial infections during anti-neoplastic treatment, and are associated with a bleaker prognosis. This study aimed to develop a novel risk classifier to predict the in-hospital death risk of nosocomial infections in this population.

Methods

Retrospective clinical data were collected from a National Cancer Regional Center in Northwest China. The Least Absolute Shrinkage and Selection Operator (LASSO) algorithm was utilized to filter the optimal variables for model development and avoid model overfitting. Logistic regression analysis was performed to identify the independent predictors of the in-hospital death risk. A nomogram was then developed to predict the in-hospital death risk of each participant. The performance of the nomogram was evaluated using receiver operating characteristics (ROC) curve, calibration curve, and decision curve analysis (DCA).

Results

A total of 569 elderly cancer patients were included in this study, and the estimated in-hospital mortality rate was 13.9%. The results of multivariate logistic regression analysis showed that ECOG-PS (odds ratio [OR]: 4.41, 95% confidence interval [CI]: 1.95-9.99), surgery type (OR: 0.18, 95%CI: 0.04-0.85), septic shock (OR: 5.92, 95%CI: 2.43-14.44), length of antibiotics treatment (OR: 0.21, 95%CI: 0.09-0.50), and prognostic nutritional index (PNI) (OR: 0.14, 95%CI: 0.06-0.33) were independent predictors of the in-hospital death risk of nosocomial infections in elderly cancer patients. A nomogram was then constructed to achieve personalized in-hospital death risk prediction. ROC curves yield excellent discrimination ability in the training (area under the curve [AUC]=0.882) and validation (AUC=0.825) cohorts. Additionally, the nomogram showed good calibration ability and net clinical benefit in both cohorts.

Conclusion

Nosocomial infections are a common and potentially fatal complication in elderly cancer patients. Clinical characteristics and infection types can vary among different age groups. The risk classifier developed in this study could accurately predict the in-hospital death risk for these patients, providing an important tool for personalized risk assessment and clinical decision-making.