Relationship of preoperative Th1/Th2 ratio, TNF-α, and ALB with pulmonary infection in elderly patients after radical surgery for gastric cancer and predictive efficacy of a nomogram based on these factors

BACKGROUND

The immune system and the inflammatory response play an important role in the development of lung infections in the elderly population after radical gastric cancer surgery. Helper T cell 1 (Th1)/helper T cell 2 (Th2) phenotype reflects the dynamic immune homeostasis. Preoperative Th1/Th2 ratio and inflammatory response-related factors may have predictive value for postoperative lung infection.

AIM

To investigate the relationship of Th1/Th2 ratio, tumor necrosis factor-α (TNF-α), and albumin (ALB) with pulmonary infection in elderly patients after radical gastrectomy for gastric cancer and their predictive efficacy for postoperative pulmonary infection.

METHODS

A total of 135 patients with lung infection after radical gastric cancer surgery (infection group) and 135 uninfected patients (control group) admitted to our hospital from April 2020 to June 2022 were included in this study. The general demographic data, surgery-related conditions, combined diseases, and preoperative serum Th1/Th2 ratio, TNF-α, and ALB levels were compared between the two groups. R was used to draw a nomogram for predicting lung infection after radical gastrectomy in elderly patients, and the concordance index (C-index) of the nomogram was obtained to evaluate its prediction ability. The bootstrap method was used to draw the prediction curve, calibration curve, and ideal curve to evaluate the consistency between the nomogram and the actual observation results, and decision curve analysis (DCA) was performed to evaluate the clinical efficacy of the nomogram.

RESULTS

The infection group had longer operative time and postoperative gastric tube indwelling time, higher intraoperative bleeding, and more diabetic patients than the control group (P < 0.05). Preoperative Th1/Th2 ratio and ALB were lower and TNF-α was higher in the infection group than in the control group (P < 0.05). Binary logistic regression analysis showed that operative time, intraoperative bleeding, diabetes mellitus, postoperative gastric tube indwelling time, and TNF-α were risk factors for pulmonary infection in elderly patients after radical surgery for gastric cancer, and Th1/Th2 ratio and ALB were protective factors (P < 0.05). A nomogram for predicting postoperative pulmonary infection was developed, and its C-index reached 0.985, which was at a high level. External validation showed that the calibration degree of the nomogram was 0.826, and there was good agreement between the model and the actual observation. DCA showed that the nomogram had obvious positive net benefit and possessed good clinical utility in predicting postoperative infection.

CONCLUSION

Preoperative Th1/Th2 ratio, TNF-α, and ALB are associated with pulmonary infection in elderly patients after radical surgery for gastric cancer. The nomogram developed based on baseline correlation data with the above three indicators has good predictive ability and clinical utility, and can be used as a potential tool to predict postoperative infection and guide clinical management in the perioperative period.

Combining Deep Phenotyping of Serum Proteomics and Clinical Data via Machine Learning for COVID-19 Biomarker Discovery

The persistence of long-term coronavirus-induced disease 2019 (COVID-19) sequelae demands better insights into its natural history. Therefore, it is crucial to discover the biomarkers of disease outcome to improve clinical practice. In this study, 160 COVID-19 patients were enrolled, of whom 80 had a "non-severe" and 80 had a "severe" outcome. Sera were analyzed by proximity extension assay (PEA) to assess 274 unique proteins associated with inflammation, cardiometabolic, and neurologic diseases. The main clinical and hematochemical data associated with disease outcome were grouped with serological data to form a dataset for the supervised machine learning techniques. We identified nine proteins (i.e., CD200R1, MCP1, MCP3, IL6, LTBP2, MATN3, TRANCE, α2-MRAP, and KIT) that contributed to the correct classification of COVID-19 disease severity when combined with relative neutrophil and lymphocyte counts. By analyzing PEA, clinical and hematochemical data with statistical methods that were able to handle many variables in the presence of a relatively small sample size, we identified nine potential serum biomarkers of a "severe" outcome. Most of these were confirmed by literature data. Importantly, we found three biomarkers associated with central nervous system pathologies and protective factors, which were downregulated in the most severe cases.