Development of a risk prediction nomogram for sarcopenia in hemodialysis patients

Development and validation of a predictive model for the risk of sarcopenia in the older adults in China

BACKGROUND

Sarcopenia is a progressive age-related disease that can cause a range of adverse health outcomes in older adults, and older adults with severe sarcopenia are also at increased short-term mortality risk. The aim of this study was to construct and validate a risk prediction model for sarcopenia in Chinese older adults.

METHODS

This study used data from the 2015 China Health and Retirement Longitudinal Study (CHARLS), a high-quality micro-level data representative of households and individuals aged 45 years and older adults in China. The study analyzed 65 indicators, including sociodemographic indicators, health-related indicators, and biochemical indicators.

RESULTS

3454 older adults enrolled in the CHARLS database in 2015 were included in the final analysis. A total of 997 (28.8%) had phenotypes of sarcopenia. Multivariate logistic regression analysis showed that sex, Body Mass Index (BMI), Mean Systolic Blood Pressure (MSBP), Mean Diastolic Blood Pressure (MDBP) and pain were predictive factors for sarcopenia in older adults. These factors were used to construct a nomogram model, which showed good consistency and accuracy. The AUC value of the prediction model in the training set was 0.77 (95% CI = 0.75-0.79); the AUC value in the validation set was 0.76 (95% CI = 0.73-0.79). Hosmer-Lemeshow test values were P = 0.5041 and P = 0.2668 (both P > 0.05). Calibration curves showed significant agreement between the nomogram model and actual observations. ROC and DCA showed that the nomograms had good predictive properties.

CONCLUSIONS

The constructed sarcopenia risk prediction model, incorporating factors such as sex, BMI, MSBP, MDBP, and pain, demonstrates promising predictive capabilities. This model offers valuable insights for clinical practitioners, aiding in early screening and targeted interventions for sarcopenia in Chinese older adults.

Ultrasound Muscle Evaluation for Predicting the Prognosis of Patients with Head and Neck Cancer: A Large-Scale and Multicenter Prospective Study

Head and neck cancer (HNC) is a prevalent and aggressive form of cancer with high mortality rates and significant implications for nutritional status. Accurate assessment of malnutrition in patients with HNC is crucial for optimizing treatment outcomes and improving survival rates. This study aimed to evaluate the use of ultrasound techniques for predicting nutritional status, malnutrition, and cancer outcomes in patients with HNC. A total of 494 patients with HNC were included in this cross-sectional observational study. Various tools and body composition measurements, including muscle mass and adipose tissue ultrasound evaluations, were implemented. Using regression models, we mainly found that high levels of RF-CSA (rectus femoris cross-sectional area) were associated with a decreased risk of malnutrition (as defined with GLIM criteria (OR = 0.81, 95% CI: 0.68-0.98); as defined with PG-SGA (OR = 0.78, 95% CI: 0.62-0.98)) and sarcopenia (OR = 0.64, 95% CI: 0.49-0.82) after being adjusted for age, sex, and BMI. To predict the importance of muscle mass ultrasound variables on the risk of mortality, a nomogram, a random forest, and decision tree models were conducted. RF-CSA was the most important variable under the random forest model. The obtained C-index for the nomogram was 0.704, and the Brier score was 16.8. With an RF-CSA < 2.7 (AUC of 0.653 (0.59-0.77)) as a split, the decision tree model classified up to 68% of patients as possessing a high probability of survival. According to the cut-off value of 2.7 cm2, patients with a low RF-CSA value lower than 2.7 cm2 had worse survival rates (p < 0.001). The findings of this study highlight the importance of implementing ultrasound tools, for accurate diagnoses and monitoring of malnutrition in patients with HNC. Adipose tissue ultrasound measurements were only weakly associated with malnutrition and not with sarcopenia, indicating that muscle mass is a more important indicator of overall health and nutritional status. These results have the potential to improve survival rates and quality of life by enabling early intervention and personalized nutritional management.