Lung cancer is a common disease, and its treatment is lobectomy or pulmonary segmentectomy. In France, approximately 8,000 patients undergo this procedure each year, but it remains associated with significant Postoperative Pulmonary Complications (PPC). This surgical trauma triggers a multicellular and orchestrated immune response, necessary for defense against pathogens, as well as for inflammatory resolution and wound healing. Preoperative single-cell analysis of the patient's immune system is therefore a promising strategy for identifying biomarkers of postoperative pulmonary complications (PPC). Brice Gaudilliere's laboratory at Stanford University, in collaboration with the Paris-based startup Surge, has developed and patented a multivariate model integrating mass cytometry data, proteomic analyses, and clinical data collected before surgery to accurately predict surgical site complications after major abdominal surgery. However, no study has yet explored the identification of inflammatory biomarkers predictive of PPC after thoracic surgery.
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Evaluation of the prognostic performance of a score for screening patients at risk of postoperative pulmonary complications (PPC)
Timeframe: Evaluation of the prognostic performance of a defined score using a machine learning method (STABL: Stability Selection) integrating preoperative immune (cytometric and proteomic) and clinical data within 7 postoperative days of a major lung resection