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Background <p>Malnutrition is common in cancer patients and worsens treatment and prognosis. The Patient-Generated Subjective Global Assessment (PG-SGA) is the best tool to evaluate malnutrition, but it is complicated has limited its routine clinical use.</p> Methods <p>We reviewed 798 records from 416 cancer patients treated at our hospital from July 2022 to March 2024. We used machine learning methods like XGBoost and Random Forest to find important factors linked to PG-SGA scores of 4 or higher. We confirmed the most important factors with logistic regression analysis.</p> Results <p>Among all models, XGBoost and Random Forest models perform the best, with the area under the curve (AUC) reaching of 0.75 and 0.77. Multivariate logistic regression analysis identified body mass index (BMI) (OR = 0.82, 95%CI 0.66–0.99; <i>P</i> = 0.045), handgrip strength (HGS) (OR = 0.89, 95%CI 0.82–0.96; <i>P</i> = 0.004), fat-free mass index (FFMI) (OR = 1.36, 95%CI 1.01–1.88; <i>P</i> = 0.045), and bedridden status (OR = 3.16, 95%CI 1.17–9.14; <i>P</i> = 0.026) as key predictors for PG-SGA scores of ≥ 4.</p> Conclusion <p>BMI, HGS, FFMI, and bedridden status were identified as practical indicators to efficiently screen patients likely to have PG-SGA scores ≥ 4.</p>

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Rapid identification of tumor patients with PG-SGA ≥ 4 based on machine learning: a prospective study

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年5月20日

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  • Gui Qian,
  • Huang Jiaxin,
  • Cong minghua,
  • Liu beijia,
  • Li Yinfeng,
  • Huang Guiyu,
  • Yang Mingxue,
  • Tang Xiaoli,
  • Yan Hongyan

摘要

Background

Malnutrition is common in cancer patients and worsens treatment and prognosis. The Patient-Generated Subjective Global Assessment (PG-SGA) is the best tool to evaluate malnutrition, but it is complicated has limited its routine clinical use.

Methods

We reviewed 798 records from 416 cancer patients treated at our hospital from July 2022 to March 2024. We used machine learning methods like XGBoost and Random Forest to find important factors linked to PG-SGA scores of 4 or higher. We confirmed the most important factors with logistic regression analysis.

Results

Among all models, XGBoost and Random Forest models perform the best, with the area under the curve (AUC) reaching of 0.75 and 0.77. Multivariate logistic regression analysis identified body mass index (BMI) (OR = 0.82, 95%CI 0.66–0.99; P = 0.045), handgrip strength (HGS) (OR = 0.89, 95%CI 0.82–0.96; P = 0.004), fat-free mass index (FFMI) (OR = 1.36, 95%CI 1.01–1.88; P = 0.045), and bedridden status (OR = 3.16, 95%CI 1.17–9.14; P = 0.026) as key predictors for PG-SGA scores of ≥ 4.

Conclusion

BMI, HGS, FFMI, and bedridden status were identified as practical indicators to efficiently screen patients likely to have PG-SGA scores ≥ 4.