术前淋巴细胞与单核细胞比值‑血小板与淋巴细胞比值评分模型对胰腺导管腺癌根治术预后的预测价值

The predictive value of preoperative lymphocyte‑to‑monocyte ratio combined with platelet⁃to⁃lymphocyte ratio scoring model for prognosis of pancreatic ductal adenocarcinoma after radical resection

  • 摘要:
    目的 探讨术前淋巴细胞与单核细胞比值(LMR)‑血小板与淋巴细胞比值(PLR)评分模型对胰腺导管腺癌(PDAC)根治术后预后的预测价值。
    方法 采用回顾性队列研究方法。收集2015年1月至2019年12月兰州大学第二医院收治的116例PDAC患者的临床病理资料;男73例,女43例;年龄为61.5(29.0~75.0)岁。患者均行胰腺癌根治术。观察指标:(1)LMR、PLR的最佳截断值。(2)不同术前LMR‑PLR评分患者的临床病理特征。(3)随访和生存情况。(4)PDAC患者预后的影响因素分析。(5)列线图预测模型构建及验证。偏态分布的计量资料以M(范围)表示。计数资料以绝对数表示,组间比较采用χ²检验。等级资料比较采用Mann‑Whitney U检验。采用Graphpad prism 8绘制生存曲线,Kaplan‑Meier法计算生存率,Log‑Rank检验进行生存分析。单因素和多因素分析采用COX比例风险回归模型。采用X‑tile软件确定LMR、PLR的最佳截断值。根据多因素分析结果构建列线图预测模型,绘制受试者工作特征(ROC)曲线,以曲线下面积(AUC)评价列线图预测模型的区分度。以校准曲线评价列线图预测模型的一致性。以决策曲线评价临床获益度。
    结果 (1)LMR、PLR的最佳截断值。LMR、PLR的最佳截断值分别为1.9和156.3。(2)不同术前LMR‑PLR评分患者的临床病理特征。术前LMR‑PLR评分为0、1、2分患者分别为11、42、63例。上述3者CA125(<12.4 U/mL)、脉管侵犯、术后化疗分别为1、8、24例,9、27、27例,3、26、43例,不同LMR‑PLR评分患者上述指标比较,差异均有统计学意义(χ²=6.73、8.37、6.68,P<0.05)。(3)随访和生存情况。116例患者均获得随访,随访时间为39(2~86)个月。116例PDAC患者术后1、2、3生存率分别为50.9%、37.9%、19.3%,生存时间为13(1~85)个月。LMR‑PLR评分为0、1、2分患者生存时间分别为3(1~9)个月、7(2~56)个月、26(2~85)个月,3者生存情况比较,差异有统计学意义(χ²=48.78,P<0.05)。(4)PDAC患者预后的影响因素分析。多因素分析结果显示:癌胚抗原、CA19‑9、LMR‑PLR评分、肿瘤长径是PADC患者预后的独立影响因素风险比=1.61,1.88,0.27,1.87,95%可信区间(CI)为1.02~2.54,1.18~3.00,0.19~0.39,1.13~3.09,P<0.05。(5)列线图预测模型构建及验证。纳入癌胚抗原、CA19‑9、LMR‑PLR评分、肿瘤长径构建列线图预测模型。绘制ROC曲线预测患者1、2、3年生存率的AUC分别为0.86(95%CI为0.79~0.93,P<0.05)、0.86(95%CI为0.79~0.92,P<0.05)、0.87(95%CI为0.78~0.95,P<0.05)。校准曲线结果显示:列线图预测模型的预测生存率和实际生存率一致性较好(一致性指数为0.74)。决策曲线结果显示:在风险阈值为0.12~0.85,列线图预测模型预测性能优于单一因素的预测性能。
    结论 癌胚抗原、CA19‑9、LMR‑PLR评分、肿瘤长径是PDAC患者根治术后预后的独立影响因素,其列线图预测模型可预测患者术后生存率。预测生存率和实际生存率一致性较好。在风险阈值为0.12~0.85,列线图预测模型预测性能优于单一因素预测性能。

     

    Abstract:
    Objective To investigate the predictive value of preoperative lymphocyte-to-monocyte ratio (LMR) combined with platelet‑to‑lymphocyte ratio (PLR) (LMR‑PLR) scoring model for prognosis of pancreatic ductal adenocarcinoma (PDAC) after radical resection.
    Methods The retrospective cohort study was conducted. The clinicopathological data of 116 patients with PDAC who were admitted to the Second Hospital of Lanzhou University from January 2015 to December 2019 were collected. There were 73 males and 43 females, aged 61.5(range, 29.0-75.0)years. All patients underwent radical resection for PDAC. Observation indicators: (1) optimal cut‑off value of LMR and PLR; (2) clinicopathological features of patients with different scores of preoperative LMR‑PLR scoring model; (3) follow‑up and survival; (4) influencing factors for prognosis of PDAC patients; (5) construction and verification of nomogram prediction model. Measurement data with skewed distribution were represented as M(range). Count data were described as absolute numbers, and comparison between groups was conducted using the chi‑square test. Comparison of ordinal data was conducted using the Mann‑Whitney U test. The Graphpad prism 8 was used to draw survival curve, the Kaplan‑Meier method was used to calculate survival rate, and the Log‑Rank test was used for survival analysis. The COX proportional hazard regression model was used for univariate and multivariate analyses. The X‑tile software was used to determine the optimal cut‑off values of LMR and PLR. The nomogram prediction model was conducted based on the results of multivariate analysis, and the receiver operating characteristic (ROC) curve was drawn. The area under curve (AUC) was used to evaluate the discrimination of nomogram prediction model. The calibration curve was used to evaluate the consistency of nomogram prediction model and the decision curve was used to evaluate the clinical benefits.
    Results (1) Optimal cut‑off value of LMR and PLR. The optimal cut‑off values of LMR and PLR were 1.9 and 156.3. (2) Clinicopathological features of patients with different scores of preoperative LMR‑PLR scoring model. Cases with LMR‑PLR scoring as 0, 1, 2 were 11, 42, 63. Cases with CA125 <12.4 U/mL, cases postoperative with vascular invasion, cases with postoperative chemotherapy in patients with 0, 1, 2 of LMR‑PLR scoring were 1, 8, 24, 9, 27, 27, 3, 26, 43, showing significant differences among them (χ²=6.73, 8.37, 6.68, P<0.05). (3) Follow‑up and survival. All 116 patients were followed up for 39(range, 2-86)months. The 1‑, 2‑, 3‑year survival rate of 116 PDAC patients was 50.9%, 37.9%, 19.3%, respectively, with a survival time of 13(range, 1-85)months. The survival time of patients with LMR‑PLR scoring as 0, 1, 2 was 3(range, 1-9)months, 7(range, 2-56)months, 26(range, 2-85)months, respectively, showing a significant difference among them (χ²=48.78, P<0.05). (4) Influencing factors for prognosis of PDAC patients. Results of multivariate analysis showed that carcinoembryonic antigen (CEA), CA19‑9, LMR‑PLR score, tumor diameter were independent factors affecting prognosis of patients (hazard ratio=1.61, 1.88, 0.27, 1.87, 95% confidence interval as 1.02-2.54, 1.18-3.00, 0.19-0.39, 1.13-3.09, P<0.05). (5) Construction and verification of nomogram prediction model. The nomogram prediction model was constructed based on CEA, CA19‑9, LMR‑PLR score and tumor diameter. The AUC of ROC curve in predicting 1‑, 2‑, 3‑year survival rate of patients was 0.86 (95% confidence interval as 0.79-0.93, P<0.05), 0.86 (95% confidence interval as 0.79-0.92, P<0.05), 0.87 (95% confidence interval as 0.78-0.95, P<0.05), respectively. Results of calibration curve showed that the predicted survival rate of nomogram prediction model was consistent with the actual survival rate, with the consistency index as 0.74. Results of decision curve showed that the predictive performance of nomogram prediction model was superior to that of a single factor at a risk threshold of 0.12-0.85.
    Conclusions CEA, CA19‑9, LMR‑PLR score, tumor diameter are independent factors affecting prognosis of patients undergoing radical resection for PDAC, and the nomogram prediction model can predict postoperative survival rate. The predicted survival rate of nomogram prediction model is consistent with the actual survival rate, and the predictive performance of nomogram prediction model is superior to that of a single factor at a risk threshold of 0.12-0.85.

     

/

返回文章
返回