Clinical trial · Observational
Prediction of Postoperative Pulmonary Complications in Thoracic Surgery
Prediction of Postoperative Pulmonary Complications in Thoracic Surgery: an Immuno-inflammatory Approach
- Source
- ClinicalTrials.gov
- Retrieved
- Sep 8, 2026
- Layer
- normalized (units and labels harmonized; values unchanged)
- Run
- ING-CLINICALTRIALS-20260908-000001
Summary
Brief summary (as posted)
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.
Conditions
Conditions (1)
Free-text conditions as registered, with the CancerIndex entity they were reconciled to and the match type.
| Condition (as posted) | Mapped entity | Match | Confidence |
|---|---|---|---|
| Postoperative Pulmonary Complications (PPCs) | — | UNRESOLVED | — |
Interventions
Interventions (1)
| Intervention | Type | Mapped drug | Match |
|---|---|---|---|
| Evaluation of prognostic performance of a defined score using a machine learning method (STABL: Stability Selection) integrating immune data (cytometric and proteomic) | Diagnostic Test | — | UNRESOLVED |
Design
Arms and outcomes
Arms (0)
[]Primary outcomes (1)
- measure
- 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
Secondary outcomes (12)
- measure
- Evaluation of the incidence of pulmonary complications
- timeFrame
- 30 days
- description
- Postoperative Pulmonary Complications (PPCs) occurring between the 8th and 30th postoperative days will be assessed. The PPCs considered will be: postoperative pneumonia, pleural effusion, postoperative atelectasis, pneumothorax, bronchospasm, and acute respiratory distress syndrome.
- measure
- Evaluation of the correlation between the prognostic score defined using a machine learning method and the length of hospital stay
- timeFrame
- 3 months
Eligibility
Eligibility (as posted)
- Sex
- All
- Minimum age
- 18 Years
- Maximum age
- 99 Years
Show eligibility criteria text
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
References
Publications (0)
Data not yet available