• 2019-10
  • 2019-11
  • 2020-03
  • 2020-07
  • 2020-08
  • br Markers of systemic inflammation br The hematological


    Markers of systemic inflammation
    The hematological and laboratory parameters were obtained within 1 week before surgery. These parameters included the neutrophil count, lymphocyte count, platelet count, and albumin (Alb) level. The NLR was defined by dividing the neutrophil count by the lymphocyte count. The PLR was defined by dividing the platelet count by the lymphocyte count. The LMR was defined by dividing the lymphocyte count by the monocyte count. The optimal cutoff values for the NLR, PLR and LMR were calculated by X-tile software (Version 3.6.1, Yale University) as 3.0, 162.5 and 3.2, respectively [22].
    Follow-up evaluation
    All of the patients were surveyed after surgery by physical ex-amination and laboratory tests, including tests for tumor markers (e.g., carcinoembryonic antigen [CEA] and CA19-9) every 3 months for the first 2 years, every 6 months for the next 3 years, and annually thereafter. Overall survival (OS) was defined as the time from surgery to death from any cause or to the time of censoring on the date of the last follow-up.
    Statistical analysis
    The statistical analyses were performed with SPSS version 22.0 (SPSS Inc., Chicago, IL, USA). Descriptive statistics were used to summarize cohort characteristics. Categorical data Actinomycin D presented as proportions and analyzed with a chi-square test or Fisher's exact test. Univariate and multivariate logistical regressions were used to assess the relationship between preoperative ACCI and systemic inflammation. The Kaplan-Meier method was used to calculate the survival rate, and the differences were assessed with log-rank tests. Cancer-specific death and non-cancer-specific death are considered to be two competing events. The Fine and Grey’ s model was used for proportional risk analysis to evaluate the influence of variables on mortality from other causes and cancer-specific mortality [23]. Differences in survival were calculated using the Cox proportional hazards model. Variables with a p value of <0.05 on univariate analysis were then included in a multivariate Cox regression anal-ysis. Risk scores were computed using the R software system, version 3.4.3. Model performance was assessed by C-index. The predictive accuracy of the ANLR was evaluated both by the inte-grated area under the ROC curve (iAUC) with 1000 bootstrap resampling [24] and time-dependent receiver operating charac-teristic (t-ROC) curves. The performance of risk prediction models was compared using the likelihood ratio p value.
    Clinicopathological characteristics
    Please cite this article as: Lin J-X et al., Association of the age-adjusted Charlson Comorbidity Index and systemic inflammation with survival in gastric cancer patients after radical gastrectomy, European Journal of Surgical Oncology,
    J.-X. Lin et al. / European Journal of Surgical Oncology xxx (xxxx) xxx 3
    Survival analysis
    The median follow-up period was 50 months (IQR: 31e71 months). The 5-year OS was 66.0% in all patients. The univariate Cox regression analysis revealed that the NLR, PLR, LMR and Alb were statistically significant (all P < 0.05, Supplemental Table 2). In addition, other clinical and pathological variables, including BMI, ASA score, tumor location, tumor size, pTNM staging, lymphovas-cular invasion, and adjuvant chemotherapy, were also statistically significant for 5-year OS (all P < 0.05, Supplemental Table 2). In addition to TNM staging, the preoperative NLR and ACCI were still independent prognostic factors for 5-year OS by further multivar-iate analysis (P < 0.05, Table 1). A high NLR (NLR 3.0) was closely associated with poor prognosis. Therefore, we included the NLR in this study as the representative of systemic inflammation.
    Correlations between the ACCI and systemic inflammation
    The interrelationships between gender, the ACCI, BMI, pTNM stage, ASA score, tumor location, tumor size, histologic type, lym-phovascular invasion and the representative systemic inflamma-tion NLR are shown in Table 2. The univariate logistic regression analysis showed that all factors, except gender and histologic type, were significantly related to the increase in the preoperative NLR (all P < 0.05). Further multivariate logistic regression analysis showed that the ACCI was an independent risk factor for increasing
    Table 1
    Multivariate analysis of clinicopathologic variables in relation to OS in patients undergoing potentially curative resection for gastric cancer.
    Clinicopathological features Mutivariate analysis P
    pTNM stage
    0.646 Adjuvant chemotherapy(Yes/No)
    0.111 Postoperative complication(Yes/No)
    the preoperative NLR. In addition, other variables, including BMI, Alb and tumor size, were also independently significant (all P < 0.05).
    Establishment of the Prognostic Score Based on the ACCI and NLR (ANLR).