Nutritional management adherence via an ePRO platform in patients with cancer: a machine learning model study
Si-Wei Xie1, Jia-Xin Huang2, Hui-Min Qu3
1Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA.
Eclinicalmedicine
|July 21, 2025
Summary
Over one-third of cancer patients struggle to meet nutritional targets using electronic patient-reported outcome (ePRO) systems. Advanced cancer stage, poor performance status, and symptoms like nausea predict low adherence to ePRO-guided nutrition.
Area of Science:
- Oncology
- Nutrition Science
- Digital Health
Background:
- Electronic patient-reported outcome (ePRO) systems offer personalized nutritional management for cancer patients.
- Adherence to ePRO-guided nutritional interventions is variable and poorly understood.
- Identifying predictors of adherence is crucial for optimizing nutritional strategies.
Purpose of the Study:
- To assess adherence to total energy intake (TEI) and total protein intake (TPI) targets using ePRO platforms.
- To identify key predictors influencing adherence to ePRO-guided nutritional management in cancer patients.
Main Methods:
- A multicenter, prospective longitudinal cohort study of 8268 cancer patients.
- Adherence defined as the ratio of actual to prescribed intake (TEI and TPI).
- Explainable machine learning (LightGBM with SHAP) and logistic regression used to identify predictors.
Main Results:
- 33.0% and 40.3% of patients failed to meet TEI and TPI targets, respectively.
- Predictors of low adherence included advanced TNM stage, poor ECOG status, higher PG-SGA scores, elevated platelets, less walking/sleep, and nausea.
- Higher adherence was associated with female sex and higher serum albumin, ALT, and glucose levels.
Conclusions:
- A significant proportion of cancer patients do not meet nutritional targets with ePRO systems.
- Predictors identified can help stratify patients at risk for poor adherence.
- This research informs strategies to improve ePRO-guided nutritional management in oncology.
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