Prediction of Postoperative Pulmonary Complications in Thoracic Surgery (PREDICT-PPC)

University Hospital, Rouen

Status: Begins enrollment in 3 months

Conditions: Postoperative Pulmonary Complications (PPCs)

Treatments: Diagnostic Test: Evaluation of prognostic performance of a defined score using a machine learning method (STABL: Stability Selection) integrating immune data (cytometric and proteomic)

Study type: Observational

Funder types: Other

Identifiers

NCT07359885

2024/0307/HP

2025-A01699-40 (Other Identifier)

Details and patient eligibility

About

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.

Full description

The issue of postoperative pulmonary complications following major lung resection (such as lobectomy or segmentectomy) is a central topic in anesthesia and thoracic surgery. Postoperative morbidity and mortality after this type of surgery have drastically decreased in recent years with advances in anesthesia and resuscitation, as well as minimally invasive surgery, but remain high compared to other types of surgery, particularly due to postoperative pneumonia. The etiology of postoperative pneumonia is multifactorial (atelectasis, postoperative ventilation, inadequate analgesia), but the patient's immune system plays a predominant role in each individual case. Therefore, identifying inflammatory biomarkers predictive of postoperative pulmonary complications in a given patient could optimize their management and reduce the risk of postoperative pulmonary cancer (PPC). The objective of this study is to identify preoperative inflammatory biomarkers predictive of PPC after major lung resection. It will use machine learning methods specific to these data to define an immune signature of PPC. This immune signature will be validated using standard analytical techniques to facilitate the clinical translation of a diagnostic test.

Enrollment: 100 estimated patients

Sex: All

Ages: 18 to 99 years old

Volunteers: No Healthy Volunteers

Inclusion criteria

• Age ≥ 18 years
• ASA score ≤ 3
• Patients undergoing scheduled video-assisted or robot-assisted lobectomy, bilobectomy, or segmentectomy.
• Patients who have read and understood the information letter and do not object to the research.
• For women of childbearing age (non-sterile): effective contraception
• Menopausal (non-medically induced amenorrhea for at least 12 months)
• Patients covered by a social security scheme

Exclusion criteria

• Minor patients
• Surgery scheduled for a Friday
• Patients undergoing a pneumonectomy
• Pregnant or breastfeeding women
• Patients deprived of their liberty by an administrative or judicial decision, as well as those under legal protection, guardianship, or curatorship