A Prognostic Model Based on Nutritional Indexes for Patients with Pan-Carcinomas: A Real-World Cohort Study

Abstract

Abstract Background Cancer patients have a high incidence of malnutrition, but traditional nutritional screening tools have low sensitivity and specificity, so they cannot properly stratify patient prognosis. Thus, we aimed to identify the nutritional indexes associated with patient prognosis, construct a prognostic model, and develop a nomogram for predicting individual survival probability. Methods Based on real-world data, patients admitted to the Department of Chemotherapy & Radiotherapy in Taizhou Cancer Hospital from January 1, 2017, to July 1, 2020, were included in the analysis. We collected nutritional indicators, clinicopathological characteristics, and previous major treatment details of the patients. The enrolled patients were randomly divided into training and validation cohorts in a 7:3 ratio. Lasso regression cross-validation was used in the training cohort to determine the variables to include in the Cox regression model. The training cohort was used to build the prediction model, and the validation cohort was used to further verify the discrimination, calibration and clinical effectiveness of the model. Results A total of 2,020 patients were included. The median follow-up time was 33.48 months (IQR, [15.79, 56.73] months), and the median OS was 56.50 months (95% CI, 50.36–62.65 months). In the training cohort of 1,425 patients, through Lasso regression cross-validation, thirteen characteristics were included in the model: sex, age, baseline weight, food intake reduction grade, emerging disease, ECOG performance status, hospitalization frequency, prealbumin, albumin, clinical stage, hemoglobin suppression grade, platelet suppression grade, and liver function classification. Based on these factors, a Cox proportional hazards model was developed and visualized as a nomogram. The C-indexes of the model for predicting 1-, 3-, 5- and 10-year OS were 0.848, 0.826, 0.814 and 0.799 in the training cohort and 0.851, 0.819, 0.814, and 0.801 in the validation cohort. The model showed great calibration in the two cohorts. Patients with a score of less than 274.29 had a better prognosis (training cohort: HR, 6.932; 95% CI, 5.723–8.397; log-rank P < 0.001; validation cohort: HR, 8.429; 95% CI, 6.180-11.497; log-rank P < 0.001). Conclusions The prognostic model based on the nutritional indexes of patients with pan-carcinomas can divide patients into different survival risk groups and performed well in internal validation.