Association between body composition and survival in head and neck cancer patients undergoing radiotherapy

Interdisciplinary Approach to Expedited Outpatient Gastrostomy Tube Placement in Head and Neck Cancer Patients: A Single Center Retrospective Study

RATIONALE AND OBJECTIVE

Treatment for head and neck cancer (HNC) can lead to decreased oral intake which often requires gastrostomy tube (g-tube) placement to provide nutritional support. A multidisciplinary team (MDT) consisting of interventional radiology (IR), HNC oncology and surgery, nutrition, and speech language pathology departments implemented an expedited outpatient g-tube placement pathway to reduce hospital stays and associated costs, initiate feeds sooner, and improve communication between care teams. This single center study investigates differences in complications, time to procedure and costs savings with implementing this pathway.

METHODS

142 patients with HNC who underwent elective image guided g-tube placement by IR from 2015 to 2022 were identified retrospectively. 52 patients underwent the traditional pathway, and 90 patients underwent the expedited pathway. Patient demographics, procedure characteristics, periprocedural costs and 90-day complication rates were collected and compared statistically.

RESULTS

The 90-day complication rate was comparable between groups (traditional=32.7%; expedited=22.2%; p-value=0.17). The expedited pathway decreased the time from consult to procedure by 11.1 days (95% CI 7.6 - 14.6; p < 0.001) and decreased charge per procedure by $2940 (95% CI $989-$4891; p < 0.001).

CONCLUSION

A MDT for the treatment of patients with HNC successfully provided enteral nutrition support faster, with fewer associated costs, and in a more patient centered approach than previously done at this institution.

Predict nutrition-related adverse outcomes in head and neck cancer patients undergoing radiotherapy: A systematic review

BACKGROUND

Acute nutrition-related adverse outcomes are common in head and neck cancer patients undergoing radiotherapy. Predictive models can assist in identifying high-risk patients to enable targeted intervention. We aimed to systematically evaluate predictive models for predicting severe acute nutritional symptoms, insufficient intake, tube feeding, sarcopenia, and weight loss.

METHODS

We searched PubMed, Web of Science, EBSCO, Embase, WanFang, CNKI, and SinoMed. We selected studies developing predictive models for the aforementioned outcomes. Data were extracted using a predefined checklist. Risk of bias and applicability assessment were assessed using the Prediction model Risk of Bias Assessment Tool. A narrative synthesis was conducted to summarize the model characteristics, risk of bias, and performance.

RESULTS

A total of 2941 studies were retrieved and 19 were included. Study outcome measure were different symptoms (n = 11), weight loss (n = 5), tube feeding (n = 3), and symptom or tube feeding (n = 1). Predictive factors mainly encompassed sociodemographic data, disease-related data, and treatment-related data. Seventeen studies reported area under the curve or C-index values ranging from 0.610 to 0.96, indicating moderate to good predictive performance. However, candidate predictors were incomplete, outcome measures were diverse, and the risk of bias was high. Most of them used traditional model development methods, and only two used machine learning.

CONCLUSIONS

Most current models showed moderate to good predictive performance. However, predictors are incomplete, outcome are inconsistent, and the risk of bias is high. Clinicians could carefully select the models with better model performance from the available models according to their actual conditions. Future research should include comprehensive and modifiable indicators and prioritize well-designed and reported studies for model development.

Prognostic value of bioelectrical impedance analysis in head and neck cancer patients undergoing radiotherapy: a VALOR® study

Introduction

Bioelectrical impedance analysis (BIA) serves as a method to estimate body composition. Parameters such as phase angle (PA), standardized phase angle (SPA), body mass cell (BCM), BCM index (BCMI), and fat-free mass (FFM) might significantly impact the prognosis of head and neck cancer (HNC) patients. The present study aimed to investigate whether bioelectrical parameters can be used to predict survival in the HNC population and establish the optimal cutoff points for predictive accuracy.

Methods

A multicenter observational study was performed across 12 tertiary hospitals in Andalusia (a region from the south of Spain). A total of 494 patients diagnosed with HNC between 2020 and 2022 at different stages were included in this study, with a minimum follow-up period of 12 months. The BIA assessment was carried out during the first 2 weeks of radical radiotherapy treatment with chemotherapy or other systemic treatments. A multivariate logistic regression analysis of overall survival, complications, hospital admission, and palliative care and its relationship with BIA nutritional assessment was performed.

Results

Significant prognostic factors identified in the multivariable analysis encompassed phase angle (PA), standardized phase angle (SPA), body cell mass (BCM), and BCM index (BCMI). Lower PA and BCM values were significantly associated with adverse clinical outcomes. A BCM threshold above 17 kg/m2 was the most significant predictor for predicting survival within the overall HNC population. The PA values of <5.1° in male and <4.8° in female patients showed the best predictive potential for mortality. Increased PA (as a continuous variable) demonstrated a significantly reduced risk for mortality (OR, 0.64; 95% CI, 0.43-0.94; p < 0.05) and a decreased likelihood of hospital admission (OR, 0.75; 95% CI, 0.52-1.07; p < 0.05). Higher BCM correlated with a lower risk of mortality (OR, 0.88; 95% CI, 0.80-0.96; p < 0.01) and a diminished probability of hospital admission (OR, 0.91; 95% CI, 0.83-0.99; p < 0.05).

Conclusion

BIA is a crucial tool in the nutritional assessment of HNC patients. BCM and PA are the main bioelectrical parameters used to predict clinical outcomes in this population. Future studies are needed to validate BIA variables in a large cohort to ensure whether early intensification of nutritional treatment would improve survival.

A novel nomogram based on body composition and nutritional indicators to predict the prognosis of patients with muscle-invasive bladder cancer undergoing radical cystectomy

OBJECTIVE

To investigate the prognostic significance of body composition and nutritional indicators in patients undergoing radical cystectomy with muscle-invasive bladder cancer (MIBC) and to develop a novel nomogram that accurately predicts overall survival (OS).

METHODS

From December 2010 to December 2020, we retrospectively collected clinical and pathological data from 373 MIBC patients who underwent radical cystectomy at our hospital. Preoperative computed tomography (CT) images were used to measure the skeletal muscle index (SMI), subcutaneous adipose index (SAI), visceral adipose index (VAI), skeletal muscle density (SMD), subcutaneous adipose density (SAD), visceral adipose density (VAD), and visceral adipose to subcutaneous adipose area ratio (VSR). The clinicopathological characteristics were evaluated using LASSO regression and multivariate Cox regression, and a nomogram was constructed to predict 1-, 3-, and 5-year overall survival. The concordance index (C-index), time-dependent receiver operating characteristic curves (t-ROC), calibration curve, and decision curve analysis (DCA) were used to assess the discriminative ability, calibration, and clinical practicality of the nomogram.

RESULTS

Multivariate analyses demonstrated that pT stage, lymph node status, LVI, SMD, and prognostic nutritional index (PNI) are independent prognostic factors for OS. Additionally, a nomogram was created. The nomogram's C-index was 0.714 (95% CI: 0.695-0.733). The area under the t-ROC curve of 1-, 3-, and 5-year survival corresponding to the model was 0.726, 0.788, and 0.785, respectively. The calibration curve demonstrated excellent agreement between the predicted and observed outcomes. The DCA revealed that patients with MIBC could benefit from the nomogram.

CONCLUSION

Based on body composition and nutritional indicators, we developed a novel nomogram with excellent predictive accuracy and reliability for predicting the prognosis of MIBC patients undergoing RC.