Zhongguo quanke yixue (Jun 2022)
Systemic Immune-inflammatory-nutritional Index and Survival in Elderly NSCLC Patients with Non-surgical Treatment
Abstract
Background In China, elderly patients with non-small cell lung cancer (NSCLC) accounts for the highest percentage of lung cancer patients, and most of them are found to have no surgical possibilities at the time of diagnosis. Moreover, these patients are increasing as aging advances. Increasing their survival rate will help to reduce the overall mortality of lung cancer patients. So identifying effective prognostic indicators in NSCLC patients with non-surgical treatment is of great significance in prognostic stratification, which also contributes to clinical studies aiming at improving the survival rate of such patients via prognostic stratification-based treatments. Objective To investigate the relationship between systemic immune-inflammatory-nutritional index (SIINI) and survival in non-surgically treated elderly patients with NSCLC. Methods Patients (n=231, ≥65 years old) with first treatment for NSCLC were retrospectively recruited from Hebei General Hospital from January 1, 2014 to June 30, 2018. Clinical characteristics were collected, mainly including age, sex, prevalence of smoking, baseline diseases, BMI, pathology, differentiation, and clinical stage of NSCLC. Some calculated data based on baseline routine blood test parameters, and/or serum albumin, and/or BMI using different approaches were also collected, including neutrophil to lymphocyte ratio (NLR) , derived NLR (dNLR) , platelet to lymphocyte ratio (PLR) , prognostic nutrition index (PNI) , systemic immune-inflammation index (SII) , advanced lung cancer inflammatory index (ALI) and SIINI 〔using a formula proposed in clinical retrospective studies, in which all variables are measured before treatment: (neutrophil count×platelet count×hemoglobin level) / (lymphocyte count×BMI×serum albumin level) 〕. Post-treatment follow-up was conducted till February 1, 2020 through outpatient reexamination, telephone or text messages with death as the endpoint. For assessing prognostic values of NLR, dNLR, PLR, PNI, SII, ALI and SIINI, ROC analysis was performed with defined optimal cut-off value and the area under the curve (AUC) for each indicator (if the AUC value is less than 0.5, then the optimal cut-off value is defined using the median value, by which the AUC value is defined as large or small when it is greater or less than the value) . The survival curves were comparatively analyzed by different patient characteristics. Cox regression analysis was applied to identify the influencing factors of survival. The survival rate curve was visualized using GraphPad Prism 8.0.2. Results The optimal cut-off values using NLR, dNLR, PLR, PNI, SII, ALI and SIINI in assessing the prognosis were 3.30, 2.51, 179.99, 273.65, 736.54, 46.05 and 102.89, respectively. The survival curves varied significantly by age, sex, prevalence of smoking, pathology, differentiation, and clinical stage of NSCLC, NLR, dNLR, PLR, ALI, SII, PNI and SIINI (P<0.05) . Further analysis indicated that the difference between the survival curves of 65-70-year-olds and 76-and-over-year-olds was statistically significant (P<0.05) . The survival curves between those with low or moderate differentiation and those with high differentiation were significantly different (P<0.05) . The survival curves of patients with stageⅠ NSCLC were different from those of patients with stage Ⅱ, Ⅲ or Ⅳ NSCLC (P<0.05) . Cox regression analysis revealed that ≥76 years old (P<0.001) , highly differentiated NSCLC (P<0.001) , stage Ⅲ NSCLC (P=0.012) and Ⅳ NSCLC (P<0.001) and SIINI (P=0.001) were prognostic factors of patients. Moreover, there existed significant differences in survival curves by NLR, dNLR, PLR, ALI, SII, PNI, and SIINI (P<0.05) . Conclusion We found that SIINI, a new indictor calculated based on immunity, inflammation and nutrition factors, is effective in predicting the overall survival in non-surgically treated elderly patients with NSCLC, and it may be superior to NLR, dNLR, PLR, PNI, SII, ALI in terms of survival prediction-related application and in-depth research.
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