内科

目的探讨预后营养指数（PNI）预测三阴性乳腺癌患者预后的价值。方法回顾性分析93例三阴性乳腺癌患者的临床资料。应用受试者操作特征（ROC）曲线分析PNI预测三阴性乳腺癌患者预后的价值，应用二分类Logistic回归模型分析三阴性乳腺癌患者预后的影响因素，并构建预测三阴性乳腺癌患者预后的列线图模型。结果 ROC曲线分析显示，PNI预测三阴性乳腺癌患者预后的ROC曲线下面积为0.717，最佳截断值为46.5，灵敏度和特异度分别为74.3%和69.0%（P<0.05）。二分类Logistic回归分析结果显示，肿瘤长径、分化程度、脑转移、PNI均为三阴性乳腺癌患者预后的独立影响因素，其中肿瘤长径长、有脑转移为独立危险因素，中高分化程度和高PNI为独立保护因素（均P<0.05）。校准曲线提示，根据上述因素构建的三阴性乳腺癌患者预后的列线图预测模型的预测值和实际值具有较高的一致性。结论 PNI可能作为预测三阴性乳腺癌患者预后的指标。肿瘤长径长、有脑转移为三阳性乳腺癌患者预后的独立危险因素，中高分化程度和高PNI为独立保护因素，且据其所构建的列线图预测模型具有较高的临床应用价值。

Objective To explore the value of the prognostic nutritional index (PNI) in predicting the prognosis of triple-negative breast cancer patients. Methods The clinical data of 93 triple-negative breast cancer patients were retrospectively analyzed. The receiver operating characteristic (ROC) curve was used to analyze the value of PNI in predicting the prognosis of triple-negative breast cancer patients, the binomial logistic regression model was used to investigate influencing factors for the prognosis of triple-negative breast cancer patients, and a nomogram model for predicting the prognosis of triple-negative breast cancer patients was constructed. Results The ROC curve analysis showed that the area under the ROC curve of PNI was 0.717 when predicting the prognosis of triple-negative breast cancer patients, and the optimal cut-off point was 46.5, corresponding to which the sensitivity and specificity were 74.3% and 69.0%, respectively (P<0.05). The results of binomial logistic regression analysis showed that tumor length, differentiation degree, brain metastasis, and PNI were independent influencing factors for the prognosis of triple-negative breast cancer patients, among which long tumors and the presence of brain metastasis were independent risk factors and medium and high differentiation degrees and a high PNI were independent protective factors (all P<0.05). The calibration curve showed that the predicted values of the nomogram prediction model for the prognosis of triple-negative breast cancer patients, which was constructed based on the above factors, were highly consistent with the actual values. Conclusion PNI may be used as a predictor of prognosis in triple-negative breast cancer patients. Long tumors and the presence of brain metastasis are independent risk factors, while the medium and high differentiation degree and a high PNI are independent protective factors for the prognosis of triple-negative breast cancer patients, the nomogram prediction model, which was constructed based on the above factors, has high clinical application value.