ABSTRACT
Objective:To analyze the preoperative and postoperative serum cholinesterase (CHE) levels in patients with stage ⅠA-ⅢA breast cancer who underwent surgical treatment, and to explore the roles of them and peripheral blood inflammatory markers in the prognostic prediction of stage ⅠA-ⅢA breast cancer.Methods:The relevant blood indicators of 152 patients with stage ⅠA-ⅢA breast cancer who underwent surgery and postoperative adjuvant therapy from January 2012 to December 2017 at Affiliated Huai'an Hospital of Xuzhou Medical University were retrospectively studied. The optimal cut-off values of serum CHE levels and peripheral blood inflammatory markers [systemic immune-inflammation index (SII) and systemic inflammatory response index (SIRI) ] were calculated using X-tile 3.6.1 software. Patients were categorized into low and high value groups based on the optimal cutoff values. Kaplan-Meier curves and Cox regression analysis were used to assess the correlation between CHE and peripheral blood inflammation indexes and disease-free survival (DFS). Spearman correlation coefficient and Wilcoxon test were used to assess the correlation and changes of CHE and inflammation indexes before and after treatment. In addition to this, a nomogram prediction model was conscturcted based on independent prognostic factors by R software, which was validated by Bootstrap method.Results:The CHE levels of patients before and after treatment was 8 645.0 (7 251.3, 10 229.3) and 9 309.0 (7 801.0, 10 835.3) U/L, respectively, with a statistically significant difference ( Z=2.73, P=0.006) .The optimal cut-off values for postoperative CHE (Post-CHE), postoperative SII (Post-SII), and postoperative SIRI (Post-SIRI) associated with patients' DFS, being 7 773 U/L, 741, and 0.9, respectively. Univariate analysis showed that tumor size (≤2 cm vs.>2 cm and ≤5 cm: HR=2.55, 95% CI: 1.30-4.99, P=0.006; ≤2 cm vs. >5 cm: HR=8.95, 95% CI: 4.15-19.32, P<0.001), number of positive lymph nodes ( HR=3.84, 95% CI: 2.24-6.58, P<0.001), clinical stage (stage Ⅰ vs. stage Ⅱ: HR=1.52, 95% CI: 0.68-3.39, P=0.309, stage Ⅰ vs. stage Ⅲ: HR=8.12, 95% CI: 3.76-17.55, P<0.001), Ki-67 expression ( HR=2.19, 95% CI: 1.24-3.84, P=0.007), whether radiotherapy ( HR=2.05, 95% CI: 1.19-3.53, P=0.010), Post-CHE ( HR=6.81, 95% CI: 3.94-11.76, P<0.001), Pre-neutrophil to lymphocyte ratio (NLR) ( HR=1.11, 95% CI: 1.02-1.21, P=0.014), Post-NLR ( HR=5.23, 95% CI: 2.78-9.85, P<0.001), Pre-platelet to lymphocyte ratio (PLR) ( HR=2.08, 95% CI: 1.01-4.26, P=0.046), Post-PLR ( HR=7.11, 95% CI: 3.78-13.37, P<0.001), Pre-lymphocyte to monocyte ratio (LMR) ( HR=0.37, 95% CI: 0.20-0.66, P<0.001), Post-LMR ( HR=0.23, 95% CI: 0.13-0.41, P<0.001), Pre-SII ( HR=1.81, 95% CI: 1.05-3.12, P=0.033), Post-SII ( HR=6.12, 95% CI: 3.48-10.76, P<0.001), Pre-SIRI ( HR=2.12, 95% CI: 1.24-3.63, P=0.006), and Post-SIRI ( HR=4.93, 95% CI: 2.87-8.48, P<0.001) were associated with DFS in patients with stage ⅠA-ⅢA breast cancer. Multivariate analysis showed that tumor size (≤2 cm vs. >2 cm and ≤5 cm: HR=2.86, 95% CI: 1.41-5.78, P=0.003; ≤2 cm vs. >5 cm: HR=3.72, 95% CI: 1.50-9.26, P=0.005), number of positive lymph nodes ( HR=4.66, 95% CI: 2.28-9.54, P<0.001), Ki-67 expression ( HR=2.13, 95% CI: 1.15-3.94, P=0.016), Post-CHE ( HR=0.18, 95% CI: 0.10-0.33, P<0.001), Post-SII ( HR=2.71, 95% CI: 1.39-5.29, P=0.004), and Post-SIRI ( HR=3.77, 95% CI: 1.93-7.36, P<0.001) were independent influencing factors for DFS in patients with stage ⅠA-ⅢA breast cancer. Kaplan-Meier survival curve analysis showed that the median DFS of patients in the Ki-67<30% group was not reached, and the median DFS of patients in the Ki-67≥30% group was 89.0 months, and the 3- and 5-year DFS rates were 84.9% vs. 75.9% and 80.8% vs. 64.3%, respectively, with a statistically significant difference ( χ2=7.65, P=0.006) ; the median DFS of patients in the tumor size≤2 cm group was not reached, the median DFS of the 2 cm<tumor size≤5 cm group was 93.5 months, and the median DFS of the tumor size>5 cm group was 26.3 months, and the 3- and 5-year DFS rates were 95.5% vs. 74.6% vs. 42.1%, 86.3% vs. 68.6% vs. 25.3%, with a statistically significant difference ( χ2=40.46, P<0.001) ; the median DFS of patients in the group with the number of positive lymph nodes<4 was not reached, and the median DFS of the group with the number of positive lymph nodes≥4 was 30.7 months, and the 3- and 5-year DFS rates were 87.9% vs. 46.4% and 81.4% vs. 28.6%, respectively, with a statistically significant difference ( χ2= 47.34, P<0.001) ; the median DFS of patients in the Post-CHE<7 773 U/L group was 47.3 months, and the median DFS of patients in the Post-CHE≥7 773 U/L group was not reached, and the 3- and 5-year DFS rates were 52.8 % vs. 88.6% and 27.8% vs. 81.2%, respectively, with a statistically significant difference ( χ2=62.17, P<0.001) ; the median DFS was not achieved in patients in the Post-SII<741 group, and the median DFS was 30.5 months in the Post-SII≥741 group, with 3- and 5-year DFS rates of 88.1% vs. 38.5% and 80.1% vs. 30.8%, respectively, with a statistically significant difference ( χ2=50.78, P<0.001) ; the median DFS of patients in Post-SIRI<0.9 group was not reached, the median DFS of Post-SIRI≥0.9 group was 33.3 months, and the 3- and 5-year DFS rates were 93.5% vs. 46.7% and 84.9% vs. 39.9%, respectively, with a statistically significant difference ( χ2=40.67, P<0.001). Spearman correlation analysis revealed that Post-CHE was not correlated with Post-SII ( r=-0.111, P=0.175), and Post-CHE was negatively correlated with Post-SIRI ( r=-0.228, P=0.005). Post-treatment CHE was elevated compared to preoperative and the median DFS was not reached in patients with elevated CHE group and 61.8 months in patients with reduced CHE group after treatment, with a statistically significant difference ( χ2=25.67, P<0.001). The nomogram based on independent prognostic factors had good predictive performance, with a C-index of 0.893. Conclusion:The serum CHE level exhibited a significant increase following treatment. Postoperative serum CHE combined with SII and SIRI can effectively predict DFS in patients with stage ⅠA-ⅢA breast cancer, and the prognosis of patients with elevated CHE after treatment is better. The nomogram constructed based on independent prognostic factors has good predictive performance for DFS in breast cancer patients.
ABSTRACT
Objective To explore the predictive value of inflammatory markers for stroke-associated pneumonia(SAP)in patients with acute ischemic stroke(AIS)based on the nomogram model.Methods According to whether pneumonia occurred,259 AIS patients were divided into SAP group(81 cases)and non-SAP group(178 cases).The clinical data of the two groups were compared.The systemic inflammatory response index(SIRI),systemic immunoinflammatory index(SII)and neutrophil to lymphocyte ratio(NLR)were calculated according to the formula.The variables with statistically significant differences were included in the multivariate binary Logistic regression model to screen out the independent risk factors for SAP in AIS patients.The independent risk factors were used to construct a predictive model,and the predictive ability of the two models,which only included traditional factors and included inflammatory indicators at the same time,was further compared from the aspects of discrimination,calibration,clinical practicability and so on.Reclassification analysis was used to evaluate the extent to which the nomogram model improved the predictive value of SAP risk in AIS patients.Results Compared with those in the non-SAP group,the rates of smoking,diabetes,dysphagia,leukocytes,neutrophils,lymphocytes,triglyceride level,NIHSS score on admission,SIRI,SII and NLR were significantly increased in the SAP group,and the rate of hypertension was decreased(all P＜0.05).Diabetes mellitus(OR =2.505,95%CI:1.070-5.850,P =0.034),dysphagia(OR =3.492,95%CI:1.501-8.119,P =0.004),NIHSS score on admission(OR = 1.310,95%CI:1.188-1.446,P＜0.001),SIRI(OR =2.417,95%CI:1.327-4.401,P =0.008),NLR(OR =1.434,95%CI:1.101-1.860,P =0.007)were independent risk factors for SAP in AIS patients.The area under the curve was 0.788(95%CI:0.725-0.852,P＜0.001)for the prediction model without inflammatory factors and 0.884(95%CI:0.838-0.930,P＜0.001)for the prediction model with independent risk factors.The calibration curve showed a good consistency between the predicted risk and the observed results.The decision curve showed that the model had a significant net benefit for predicting SAP.In addition,by calculating the net reclassification index(NRI)and the comprehensive discriminant improvement index(IDI),it was found that the nomogram model had a significant improvement in predicting the risk of SAP in AIS patients.Internal verification also proves the reliability of the nomogram model.Conclusions SIRI and NLR are independent predictors of SAP in AIS patients on admission.Adding SIRI and NLR to the traditional model can significantly improve the ability to identify the risk of SAP occurrence in AIS patients.