Exploration of phase angle used to construct PG-SGA nutritional assessment and prediction model for malignant tumor patients.
10.3760/cma.j.cn112152-20210719-00521
- Author:
Xiao Ling ZHANG
1
;
Wen Qi ZHAO
2
;
Yun Yi DU
1
;
Ying ZHANG
3
;
Wei Ling LI
3
;
Wen Qing HU
4
;
Jing LU
5
;
Jun ZHAO
1
Author Information
1. Department of Oncology, Changzhi People's Hospital, Changzhi 046000, China.
2. Postgraduate Department of Mathematics, University of York, UK YO105DD.
3. Graduate School of Changzhi Medical College, Changzhi 046000, China.
4. Gastrointestinal Surgery Department, Changzhi People's Hospital, Changzhi 046000, China.
5. Department of Pathophysiology, School of Basic Medicine, Zhengzhou University, Zhengzhou 450001, China.
- Publication Type:Journal Article
- Keywords:
BMI;
Malignant tumor;
PG-SGA;
Phase angle;
Tumor nutrition evaluation
- MeSH:
Humans;
Nutrition Assessment;
Retrospective Studies;
Nutritional Status;
Malnutrition;
Neoplasms/complications*
- From:
Chinese Journal of Oncology
2022;44(12):1376-1384
- CountryChina
- Language:Chinese
-
Abstract:
Objective: To explore the value of phase angle (PA) in constructing a predictive model of nutrition evaluation for tumor patients. Methods: A retrospective analysis was performed on 1 129 patients with malignant tumors hospitalized in the Cancer Center of Changzhi People's Hospital from June 2020 to February 2021. PA values of six parts of the body were measured by the body composition analyzer, including: left arm (LA), right arm (RA), left leg (LL), right leg (RL), the trunk (TR), and the whole body (WB). Patients' body mass index (BMI) was calculated and patient-generated subjective global assessment (PG-SGA) was assessed. The differences of PA values of six parts were compared and their correlations with BMI and PG-SGA in combination with age, gender and tumor disease types were analyzed, binary classification regression on BMI and PG-SGA was performed, and the functions of the best prediction model was fitted. Decision tree, random forest, Akaike information criterion in a Stepwise Algorithm (stepAIC) and generalized likelihood ratio test were used to select appropriate variables, and the logit logistic regression model was used to fit the data. Results: Comparing the PA values of six parts in pairs, it was found that the PA values of LA and RA, LL and RL, and TR and WB were linearly correlated and the coefficient was close to 1 (P<0.001). Binary classification regression was performed for BMI and PG-SGA, respectively. In order to make the data have clinical significance, 18.5 kg/m(2) was used as the classification point for BMI, 4 and 9 were used as the classification points for PG-SGA score, and the models of A, B and C were obtained. Suitable variables including PA-LA, PA-TR and tumor disease types were used as variables to fit BMI classification; BMI, PA-LA and age were used as variables to fit the PG-SGA model with 9 as the classification point. PA-LA, PA-TR, BMI, age and tumor disease types were used as variables to fit the PG-SGA model with 4 as the classification point. In this study, the predicted values of models A, B and C obtained by R-studio were imported into SPSS 26.0 software, and the cut-off values of classification were obtained by the receiver operating characteristic (ROC) curve. The ROC analytic results showed that the best cut-off values of Model A, B and C were 0.155, 0.793 and 0.295. Model A recommended when the probability is >0.155, a patient's nutritiond tatus should be classified as BMI < 18.5 kg/m(2) group. Model B recommended that PG-SGA<9 group be classified as the probability is >0.793. Model C recommended that PG-SGA < 4 group should be classified when probability is >0.295. Conclusions: The PG-SGA classification prediction model is simple to operate, and the nutritional status of patients can be roughly divided into three groups: normal or suspected malnutrition group (PG-SGA<4), moderate malnutrition group (4≤PG-SGA<9), and severe malnutrition group (PG-SGA≥9). This model can more efficiently predict the nutritional status of cancer patients, greatly simplify the nutritional assessment process, and better guide the standardized treatment of clinical malnutrition.
