Background: Increasing evidence indicates that nutritional status could influence the survival of cancer patients. This study aims to develop and validate a nomogram with nutrition-related parameters for predicting the overall survival of cancer patients. Patients and methods: A total of 8749 patients from the multicentre cohort study in China were included as the primary cohort to develop the nomogram, and 696 of these patients were recruited as a validation cohort. Patients’ nutritional status were assessed using the PG-SGA. LASSO regression models and Cox regression analysis were used for factor selection and nomogram development. The nomogram was then evaluated for its effectiveness in discrimination, calibration, and clinical usefulness by the C-index, calibration curves, and decision curve analysis. Kaplan–Meier survival curves were used to compare the survival rate. Results: Seven independent prognostic factors were identified and integrated into the nomogram. The C-index was 0.73 (95% CI, 0.72 to 0.74) and 0.77 (95% CI, 0.74 to 0.81) for the primary cohort and validation cohort, which were both higher than 0.59 (95% CI, 0.58 to 0.61) of the TNM staging system. DCA demonstrated that the nomogram was higher than the TNM staging system and the TNM staging system combined with PG-SGA. Significantly median overall survival differences were found by stratifying patients into different risk groups (score < 18.5 and ≥ 18.5) for each TNM category (all Ps < 0.001). Conclusion: Our study screened out seven independent prognostic factors and successfully generated an easy-to-use nomogram, and validated and shown a better predictive validity for the overall survival of cancer patients.

A novel model with nutrition-related parameters for predicting overall survival of cancer patients

Barazzoni R.;
2021-01-01

Abstract

Background: Increasing evidence indicates that nutritional status could influence the survival of cancer patients. This study aims to develop and validate a nomogram with nutrition-related parameters for predicting the overall survival of cancer patients. Patients and methods: A total of 8749 patients from the multicentre cohort study in China were included as the primary cohort to develop the nomogram, and 696 of these patients were recruited as a validation cohort. Patients’ nutritional status were assessed using the PG-SGA. LASSO regression models and Cox regression analysis were used for factor selection and nomogram development. The nomogram was then evaluated for its effectiveness in discrimination, calibration, and clinical usefulness by the C-index, calibration curves, and decision curve analysis. Kaplan–Meier survival curves were used to compare the survival rate. Results: Seven independent prognostic factors were identified and integrated into the nomogram. The C-index was 0.73 (95% CI, 0.72 to 0.74) and 0.77 (95% CI, 0.74 to 0.81) for the primary cohort and validation cohort, which were both higher than 0.59 (95% CI, 0.58 to 0.61) of the TNM staging system. DCA demonstrated that the nomogram was higher than the TNM staging system and the TNM staging system combined with PG-SGA. Significantly median overall survival differences were found by stratifying patients into different risk groups (score < 18.5 and ≥ 18.5) for each TNM category (all Ps < 0.001). Conclusion: Our study screened out seven independent prognostic factors and successfully generated an easy-to-use nomogram, and validated and shown a better predictive validity for the overall survival of cancer patients.
2021
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https://link.springer.com/article/10.1007/s00520-021-06272-z
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11368/2993822
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