PPCs are a major concern in the surgical treatment of lung cancer. Pulmonary function is one of the main predictors of PPCs, and some guidelines recommend the evaluation of preoperative immune and nutritional status15. The PPC rate in our study is about 22%, which is similar to previous reports4, 5. The main PPC is persistent air leakage or pleural effusion and pneumonia, which are affected by immune and nutritional status. Pneumonia is common in patients who are malnourished or immunocompromised. The principal causes of persistent air leakage or pleural effusion are pneumothorax or hydrothorax; persistent hydrothorax may also result from pneumonia or hypoproteinemia. Thus, in theory, immune and nutritional parameters can be predictors of PPCs.
Many researches have explored the clinical meanings of single immune and nutritional in predicting PPCs. Among all, ALB and body weight related index are most studied. ALB is synthesized by hepatocytes and maintains vascular osmotic pressure; transports hormones, fatty acids, or other compounds; and regulates blood pH. Serum ALB concentration is often used as a biochemical marker of long-term nutritional status and reflects visceral protein status16. A correlation between ALB insufficiency and the development of early PPCs has been reported previous17,18. Body weight loss is also revealed correlated to high rate of PPCs. In Busch E’s report, weight loss and ALB level are linked to PPCs following lung cancer resection8, 19-21. Globulin and LY% reflect the immune status of patients22. The cellular immunitymainly regulates immune function through T lymphocyte subsets23 and in tumor surveillance, immunoglobulin contributes to target cell phagocytosis24.
Immunonutritional parameters are calculated based on multiple indices related to immune function (CRP, peripheral lymphocyte count, etc) and nutrition (BMI and ALB). Those parameters show to be useful for detecting sarcopenia, which has been linked to the survival of lung cancer patients25. Moreover, several studies have reported that immunonutritional parameters are prognostic indicators of survival26–33 and could predict PPCs28, 30, 34 in patients with lung cancer. Poor immunonutritional status may increase the risk of PPCs by impairing pulmonary function, which was improved by nutritional support during chemotherapy 33.
Among the clinical instruments included in our analysis, GNRI were found to have predictive value for PPCs, with moderate diagnostic value. GNRI are calculated based on the original index and are continuous variables. In previous study, GNRI is suggested to be not inferior to nutrition screening tool of Mini Nutritional Assessment35. GNRI is recognized to correlated with sarcopenia status36. GNRI has been widely applied in digestive tract diseases. In gastric cancer, colorectal cancer and esophageal cancer, GNRI is defined as a prognostic factor37-39. Previous studies reveal GNRI as a prognostic factor both in early lung cancer patients treated with surgery operation40, 41 and advanced stage lung cancer42-44. Besides, GNRI can predict the treatment response of immunotherapy45, 46. Apart this, GNRI is suggested to be good predictor for postoperative complications after abdominal surgery or gastrointestinal malignancy47, 48. But, can this parameter be good predictor in elderly lung cancer surgery operation patients? There is no answer. Thus we compare the GNRI and other patients characteristic and find GNPI has moderate sensitivity for detecting PPCs; that is, patients with low values are more likely to develop PPCs after lung cancer resection and require special care. Meanwhile, it has moderate specificity; this means that patients with a high value are not likely to experience PPCs, and routine care is therefore sufficient. Thus, risk stratification is possible based on these few parameters, which would maximize the use of limited medical resources. As mention above, ALB and body weight are the most important simple index for PPCs. When calculating GNRI, both ALB and body weight are enrolled, thus it may has excellent diagnostic value than other patient characteristics.
There were some limitations to this study that should be noted. Firstly, because of the retrospective single-center design, selection bias could not be avoided. Secondly, because the sample size is relatively small, statistical bias was inevitable.
Nonetheless, our findings demonstrate that immunonutritional parameters can predict PPCs following lung cancer resection and can be used to identify high-risk patients who would benefit from preventive interventions. Among all, GNRI has the best performance.