Prediction model and significance of myocardial injury induced by fluorouracil combined with platinum-based chemotherapy in advanced gastric cancer based on baseline data and inflammation-nutrition-atherosclerosis factors.

[OBJECTIVE] To develop and evaluate a predictive model for myocardial injury in patients with advanced gastric cancer treated with fluorouracil plus platinum-based chemotherapy, incorporating baseline characteristics and inflammatory, nutritional, and atherosclerotic factors.

[METHODS] A total of 268 patients with advanced gastric cancer who received this treatment between April 2020 and September 2024 were selected and divided into a training set ( = 188) and a validation set ( = 80) in a 7:3 ratio. In the training set, multivariate logistic regression analysis was used to identify risk factors for myocardial injury in patients with advanced gastric cancer treated with fluorouracil and platinum-based drugs, and a nomogram prediction model was constructed. The predictive model's performance was evaluated using receiver operating characteristics (ROC) curves and calibration curves, and the model was validated in the validation set. Additionally, decision curve analysis (DCA) was performed to assess clinical utility.

[RESULTS] In the training set, 56 patients (29.79%) developed myocardial injury, while 23 patients (28.75%) in the validation set developed myocardial injury, with no statistically significant difference in the incidence or clinical characteristics between the two sets ( > 0.05). In the training set, age, hypertension, serum C-reactive protein (CRP), interleukin-6 (IL-6), tumor necrosis factor-α (TNF-α), serum albumin, prealbumin, lipoprotein(a) (Lp(a)), and homocysteine (Hcy) were identified as influencing risk factors (all  < 0.05), and a nomogram prediction model was constructed. The model demonstrated good calibration and fit in both the training and validation sets (-index: 0.901 and 0.9879, respectively; mean absolute errors between predicted and actual values: 0.133 and 0.115, respectively; Hosmer-Lemeshow test -values: 0.136 and 0.669, respectively). ROC curve analysis showed that the AUCs for predicting myocardial injury in the training and validation sets were 0.901 (95% CI: 0.823-0.978) and 0.879 (95% CI: 0.819-0.938), respectively, with sensitivities and specificities of 0.756, 1.000 and 0.703, 0.951, respectively.

[CONCLUSION] This predictive model aids in the early identification of myocardial injury, guiding clinical decision-making and improving prognosis.