Optimization of the calculation of dietary energy intake in oncology chemotherapy patients: based on a longitudinal study

TL;DR

This study develops and validates a model to calculate dietary energy intake for cancer patients undergoing chemotherapy, aiming to improve clinical nutritional management.

Contribution

A new energy calculation model for chemotherapy patients, validated through longitudinal dietary assessments and cluster analysis.

Findings

The model achieved a moderate fit with R² = 0.39 and MAE = 265.83.

The model is simple to use and suitable for clinical application.

Cluster analysis based on macronutrient data helped construct the model.

Abstract

The aim of this study was to investigate the current status of dietary intake in patients undergoing chemotherapy for oncology and to determine the types of dietary intake in patients undergoing chemotherapy for oncology in order to construct and validate an optimization model for dietary energy calculation.

Using the convenience sampling method, tumor chemotherapy patients who were treated at a certain tertiary first-class tumor specialized hospital from January 2022 to January 2023 and met the inclusion criteria were enrolled. The 24-h dietary recall method was employed to conduct a longitudinal assessment of the patients’ dietary intake at five time points: before chemotherapy (T0), on days 1–3 of chemotherapy (T1), during the first week of chemotherapy (T2), during the second week of chemotherapy (T3), and during the third week of chemotherapy (T4). Hierarchical cluster analysis was used to classify the foods consumed by the patients undergoing chemotherapy. A multivariate linear regression analysis method was employed to establish an optimized model for calculating the dietary energy intake of patients with tumor chemotherapy. The goodness of fit and mean absolute error were used to evaluate the model.

A total of 116 patients were finally included in this study, and the dietary survey was completed for at least 4 cycles. Cluster analysis was performed based on the data of three characteristics of protein, fat and carbohydrate, and the results of the three-classification results were used to construct an energy calculation model for patients undergoing chemotherapy for tumors. R2 = 0.39, MAE = 265.83, the degree of fit and accuracy of the model is fair, suitable for clinical promotion and application.

The energy calculation model in this study fitted reasonably well, and the model was evaluated well. The energy calculation model is simple to use and the food categories are easily accessible. The energy calculation model provides a convenient and efficient energy assessment tool for clinical nutritional management, which is conducive to promoting the dynamic monitoring of clinical patients’ dietary intake.

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