Development of a short-term nutritional risk prediction model for hepatocellular carcinoma patients: a retrospective cohort study
Malnutrition in patients is associated with reduced tolerance to treatment-related side effects and higher risks of complications, directly impacting patient prognosis. Consequently, a pressing requirement exists for the development of uncomplicated yet efficient screening methods to detect patients...
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Nature Research
2024
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my.upm.eprints.1061342024-09-03T07:50:11Z http://psasir.upm.edu.my/id/eprint/106134/ Development of a short-term nutritional risk prediction model for hepatocellular carcinoma patients: a retrospective cohort study Yu, Jiaxiang Soh, Kim Lam He, Liping Wang, Pengpeng Cao, Yingjuan Malnutrition in patients is associated with reduced tolerance to treatment-related side effects and higher risks of complications, directly impacting patient prognosis. Consequently, a pressing requirement exists for the development of uncomplicated yet efficient screening methods to detect patients at heightened nutritional risk. The aim of this study was to formulate a concise nutritional risk prediction model for prompt assessment by oncology medical personnel, facilitating the effective identification of hepatocellular carcinoma patients at an elevated nutritional risk. Retrospective cohort data were collected from hepatocellular carcinoma patients who met the study's inclusion and exclusion criteria between March 2021 and April 2022. The patients were categorized into two groups: a normal nutrition group and a malnutrition group based on body composition assessments. Subsequently, the collected data were analyzed, and predictive models were constructed, followed by simplification. A total of 220 hepatocellular carcinoma patients were included in this study, and the final model incorporated four predictive factors: age, tumor diameter, TNM stage, and anemia. The area under the ROC curve for the short-term nutritional risk prediction model was 0.990 [95% CI (0.966–0.998)]. Further simplification of the scoring rule resulted in an area under the ROC curve of 0.986 [95% CI (0.961, 0.997)]. The developed model provides a rapid and efficient approach to assess the short-term nutritional risk of hepatocellular carcinoma patients. With easily accessible and swift indicators, the model can identify patients with potential nutritional risk more effectively and timely. Nature Research 2024-02-16 Article PeerReviewed text en http://psasir.upm.edu.my/id/eprint/106134/1/s41598-024-54456-4.pdf Yu, Jiaxiang and Soh, Kim Lam and He, Liping and Wang, Pengpeng and Cao, Yingjuan (2024) Development of a short-term nutritional risk prediction model for hepatocellular carcinoma patients: a retrospective cohort study. Scientific Reports, 14 (1). pp. 1-8. ISSN 2045-2322 https://www.nature.com/articles/s41598-024-54456-4?error=cookies_not_supported&code=9b599fdb-98d4-4965-80c6-f8fe685ec8e6 10.1038/s41598-024-54456-4 |
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Malnutrition in patients is associated with reduced tolerance to treatment-related side effects and higher risks of complications, directly impacting patient prognosis. Consequently, a pressing requirement exists for the development of uncomplicated yet efficient screening methods to detect patients at heightened nutritional risk. The aim of this study was to formulate a concise nutritional risk prediction model for prompt assessment by oncology medical personnel, facilitating the effective identification of hepatocellular carcinoma patients at an elevated nutritional risk. Retrospective cohort data were collected from hepatocellular carcinoma patients who met the study's inclusion and exclusion criteria between March 2021 and April 2022. The patients were categorized into two groups: a normal nutrition group and a malnutrition group based on body composition assessments. Subsequently, the collected data were analyzed, and predictive models were constructed, followed by simplification. A total of 220 hepatocellular carcinoma patients were included in this study, and the final model incorporated four predictive factors: age, tumor diameter, TNM stage, and anemia. The area under the ROC curve for the short-term nutritional risk prediction model was 0.990 [95% CI (0.966–0.998)]. Further simplification of the scoring rule resulted in an area under the ROC curve of 0.986 [95% CI (0.961, 0.997)]. The developed model provides a rapid and efficient approach to assess the short-term nutritional risk of hepatocellular carcinoma patients. With easily accessible and swift indicators, the model can identify patients with potential nutritional risk more effectively and timely. |
| format |
Article |
| author |
Yu, Jiaxiang Soh, Kim Lam He, Liping Wang, Pengpeng Cao, Yingjuan |
| spellingShingle |
Yu, Jiaxiang Soh, Kim Lam He, Liping Wang, Pengpeng Cao, Yingjuan Development of a short-term nutritional risk prediction model for hepatocellular carcinoma patients: a retrospective cohort study |
| author_facet |
Yu, Jiaxiang Soh, Kim Lam He, Liping Wang, Pengpeng Cao, Yingjuan |
| author_sort |
Yu, Jiaxiang |
| title |
Development of a short-term nutritional risk prediction model for hepatocellular carcinoma patients: a retrospective cohort study |
| title_short |
Development of a short-term nutritional risk prediction model for hepatocellular carcinoma patients: a retrospective cohort study |
| title_full |
Development of a short-term nutritional risk prediction model for hepatocellular carcinoma patients: a retrospective cohort study |
| title_fullStr |
Development of a short-term nutritional risk prediction model for hepatocellular carcinoma patients: a retrospective cohort study |
| title_full_unstemmed |
Development of a short-term nutritional risk prediction model for hepatocellular carcinoma patients: a retrospective cohort study |
| title_sort |
development of a short-term nutritional risk prediction model for hepatocellular carcinoma patients: a retrospective cohort study |
| publisher |
Nature Research |
| publishDate |
2024 |
| url |
http://psasir.upm.edu.my/id/eprint/106134/1/s41598-024-54456-4.pdf http://psasir.upm.edu.my/id/eprint/106134/ https://www.nature.com/articles/s41598-024-54456-4?error=cookies_not_supported&code=9b599fdb-98d4-4965-80c6-f8fe685ec8e6 |
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1811685930611769344 |
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13.251813 |