目的 探讨外周血炎症-营养参数对卵巢癌患者的预后价值,并开发一个基于炎症-营养参数与临床病理特征的列 线图模型,测试其在预后评估中的价值。 方法 选取 2017 年 6 月至 2020 年 6 月期间于南京市溧水区人民医院( n = 56)和南京 医科大学第二附属医院(n = 150)接受手术治疗的 206 例卵巢癌患者为研究对象,收集临床资料和外周血参数进行回顾性分 析。 使用受试者操作特征(ROC)曲线比较中性粒细胞与淋巴细胞计数比 (NLR)、血小板与淋巴细胞计数比(PLR)、淋巴细胞 与单核细胞计数比(LMR)、预后营养指数(PNI)、血清总胆固醇与外周血淋巴细胞计数比(TCLR)及 C 反应蛋白-白蛋白比 (CAR)对卵巢癌患者总生存(OS)期的预测价值。 通过单、多因素 Cox 回归分析筛选卵巢癌患者的独立预后因素,并构建列线 图模型。 采用 Harrell 一致性指数(C-index)和校准曲线评价模型的预测性能。 结果 PNI 与 CAR 的最佳截止点为 47. 8 和 0. 08,其 AUC 值分别为 0. 803(95%CI = 0. 736~ 0. 870)和 0. 749 (95%CI = 0. 673 ~ 0. 824)。 在单因素分析中,共有 8 个变量可 能影响卵巢癌患者 OS,它们是年龄(P= 0. 011)、血清 CA125(P = 0. 001)、术后残余灶(P<0. 001)、国际妇产科联合会(FIGO) 分期(P= 0. 001)、LMR(P<0. 001)、PNI(P<0. 001)、CAR(P<0. 001)以及 TCLR(P = 0. 037)。 多因素 Cox 回归分析表明血清 CA125(HR= 2. 814, 95%CI = 1. 469~ 5. 394,P= 0. 002)、术后残余灶(HR = 3. 324, 95%CI = 1. 736 ~ 6. 361, P<0. 001)、FIGO 分 期(HR = 4. 454,95%CI = 1. 360 ~ 14. 583,P = 0. 014)、PNI (HR = 3. 615, 95% CI = 1. 852 ~ 7. 057, P < 0. 001) 与 CAR(HR = 3. 330, 95%CI = 1. 684~ 6. 584,P= 0. 001)是卵巢癌患者的独立预后因素。 基于以上 5 个变量构建预后模型,其 C-index 为 0. 821(95%CI = 0. 756~ 0. 886)。 校准曲线显示模型预测 1 年、3 年、5 年生存率与实际结果之间具有良好一致性。 结论 PNI 与CAR 是卵巢癌患者的独立预后标志物,基于这些炎症-营养参数与临床病理特征构建的预后模型显示出良好且稳定的预测性能,或许是卵巢癌患者风险分层与个性化治疗选择的有效工具。

Objective To investigate the prognostic value of six inflammation and nutrition-related parameters in peripheral blood for ovarian cancer patients and develop a nomogram model based on these parameters and clinicopathological features and test its clinical value for prognostic assessment. Method A total of 206 ovarian cancer patients who underwent surgical treatment in Nanjing Lishui People's Hospital n = 56 and the Second Affiliated Hospital of Nanjing Medical University n = 150 between June 2017 and June 2020 were included in this retrospective study and their clinical data and peripheral blood parameters were collected. The predictive values of the neutrophil - to - lymphocyte ratio NLR platelet - to - lymphocyte ratio PLR lymph - to - monocyte ratio LMR prognostic nutritional index PNI total cholesterol-to-lymphocyte ratio TCLR and C-reactive protein-to-albumin ratio CAR for overall survival OS of ovarian cancer patients were compared using the receiver operation characteristics ROC curve. The univariate and multivariate Cox regression analysis was conducted to select independent prognostic factors for ovarian cancer patients and a nomogram model was developed based on these factors. The predictive performance of the model was evaluated by the Harrell's consistency index C-index and calibration curves. Result The best cutoff values of PNI and CAR were 47. 8 and 0. 08 with the AUC values of 0. 803 95%CI = 0. 736-0. 870 and 0. 749 95% CI = 0. 673-0. 824 respectively. The predictive values of PNI and CAR were superior to other parameters. In the univariate analysis a total of 8 variables may affect the OS of ovarian cancer patients they are age P= 0. 011 serum CA125 P= 0. 001 postoperative residual focus P<0. 001 FIGO stage P = 0. 001 LMR P<0. 001 PNI P<0. 001 CAR P<0. 001 and TCLR P = 0. 037 . The multivariate Cox regression analysis showed that serum CA125 level HR= 2. 814 95%CI = 1. 469-5. 394 P = 0. 002 residual disease HR = 3. 324 95%CI = 1. 736-6. 361 P< 0. 001 FIGO stage HR= 4. 454 95%CI = 1. 360-14. 583 P = 0. 014 PNI HR = 3. 615 95%CI = 1. 852-7. 057 P<0. 001 and CAR HR = 3. 330 95%CI = 1. 684-6. 584 P = 0. 001 were independent prognostic factors for ovarian cancer patients. A prognostic model was constructed according to the above five variables with a C-index of 0. 821 95%CI = 0. 756-0. 886 . The calibration curves shows a good consistency between the predicted 1-year 3-year as well as 5-year survival probabilities and the actual observations. Conclusion PNI and CAR were independent prognostic parameters for ovarian cancer patients. The prognostic model integrated with these parameters and clinicopathological features showed an excellent and stable predictive performance and it might be used as an effective tool of risk stratification and personalized treatment decision-making.