# Predicting Sarcopenia in Peritoneal Dialysis Patients: A Multimodal Ultrasound-Based Logistic Regression Analysis and Nomogram Model

## TL;DR

This study uses ultrasound-based models to predict sarcopenia in peritoneal dialysis patients, offering a non-invasive tool for early detection and intervention.

## Contribution

A novel multimodal ultrasound-based logistic regression and nomogram model for predicting sarcopenia in peritoneal dialysis patients.

## Key findings

- Sarcopenia patients had significantly lower muscle thickness and higher echo intensity compared to non-sarcopenia patients.
- The developed model achieved an F1-score of 0.785 and an ROC-AUC of 0.902, showing strong predictive accuracy.
- Nomogram results were consistent with BIA measurements, validating the model's reliability.

## Abstract

Objective: This study aimed to evaluate the diagnostic value of logistic regression and nomogram models based on multimodal ultrasound in predicting sarcopenia in patients with peritoneal dialysis (PD). Methods: A total of 178 patients with PD admitted to our nephrology department between June 2024 and April 2025 were enrolled. According to the 2019 Asian Working Group for Sarcopenia (AWGS) diagnostic criteria, patients were categorized into sarcopenia and non-sarcopenia groups. Ultrasound examinations were used to measure the muscle thickness (MT), pinna angle (PA), fascicle length (FL), attenuation coefficient (Atten Coe), and echo intensity (EI) of the right gastrocnemius medial head. The clinical characteristics of the groups were compared using the Mann–Whitney U test. Binary logistic regression was used to identify sarcopenia risk factors to construct clinical prediction models and nomograms. Receiver operating characteristic (ROC) curves were used to assess the model accuracy and stability. Results: The sarcopenia group exhibited significantly lower MT, PA, and FL, but higher Atten Coe and EI than the non-sarcopenia group (all p < 0.05). A multimodal ultrasound logistic regression model was developed using machine learning—Logit(P) = −7.29 − 1.18 × MT − 0.074 × PA + 0.48 × FL + 0.52 × Atten Coe + 0.13 × EI (p < 0.05)—achieving an F1-score of 0.785. The area under the ROC curve (ROC-AUC) was 0.902, with an optimal cut-off value of 0.45 (sensitivity 77.3%, specificity 56.7%). Nomogram consistency analysis showed no statistical difference between the ultrasound diagnosis and the appendicular skeletal muscle index (ASMI) measured by bioelectrical impedance analysis (BIA) (Z = 0.415, p > 0.05). Conclusions: The multimodal ultrasound-based prediction model effectively assists clinicians in identifying patients with PD at a high risk of sarcopenia, enabling early intervention to improve clinical outcomes.