Prediction of Postoperative Pulmonary Complications in Thoracic Surgery

NCT07359885 | Not Yet Recruiting

• 2 other identifiers: 2024/0307/HP2025-A01699-40
• Study Type: observational
• Target: 100
• Locations: 1 country
• Sites: 1

Brief Summary

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

Postoperative Pulmonary Complications (PPCs)

Keywords

lung cancer; immuno-inflammatory approach

Outcome Measures

Primary Outcomes (1)

• Evaluation of the prognostic performance of a score for screening patients at risk of postoperative pulmonary complications (PPC)

  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)

• Evaluation of the incidence of pulmonary complications

  30 days

• Evaluation of the correlation between the prognostic score defined using a machine learning method and the length of hospital stay

  3 months

• Evaluation of the correlation between the prognostic score defined using a machine learning method and the number of reintubations recorded

  30 days

• Evaluation of the correlation between the prognostic score defined using a machine learning method and the Number of unplanned hospitalizations in intensive care recorded

  30 days

• Evaluation of the correlation between the prognostic score defined using a machine learning method and the Preoperative anxiety score assessed

  48 hours

Interventions

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

Determination of the area under the curve (AUC) Receiver Operating Curve (ROC) for predicting complications calculated from the score obtained by the machine learning method and the occurrence of at least one major pulmonary complication among the following in the first 7 postoperative days: postoperative pneumonia, pleural effusion, postoperative atelectasis, pneumothorax, bronchospasm and acute respiratory distress syndrome.

Eligibility Criteria

• Age: 18 Years - 99 Years
• Sex: all
• Healthy Volunteers: No
• Age Groups: Adult (18-64), Older Adult (65+)
• Sampling Method: Probability Sample

Study Population

Patients undergoing programmed video-assisted or robot-assisted lobectomy, bilobectomy or segmentectomy.

You may qualify if:

• 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

You may not qualify if:

• 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

Sponsors & Collaborators

• University Hospital, Rouen: lead

Study Sites (1)

Service de Anesthésie-Réanimation Médecine périopératoire CHU de Rouen

Rouen, 76100, France

Location

Biospecimen

Retention: SAMPLES WITHOUT DNA

Additional samples, taken as part of routine care, of 2 x 5 mL of blood in heparinized tubes taken at visits V1, V2 and V3

MeSH Terms

Conditions

Lung Neoplasms

Condition Hierarchy (Ancestors)

Respiratory Tract Neoplasms; Thoracic Neoplasms; Neoplasms by Site; Neoplasms; Lung Diseases; Respiratory Tract Diseases

Study Officials

• Jean JS SELIM, Doctor

  Service de Anesthésie-Réanimation Médecine périopératoire CHU de Rouen

  STUDY DIRECTOR

Central Study Contacts

Nabila NL LAAJAIL, Director

CONTACT

02 32 88 82 65; Nabila.Laajail@chu-rouen.fr

Vincent VF FERRANTI, ARC

CONTACT

02 32 88 82 65; Vincent.Ferranti@chu-rouen.fr

Study Design

• Study Type: observational
• Observational Model: CASE ONLY
• Time Perspective: PROSPECTIVE
• Target Duration: 3 Months
• Sponsor Type: OTHER
• Responsible Party: SPONSOR

Study Record Dates

• First Submitted: January 14, 2026
• First Posted: January 22, 2026
• Study Start (Estimated): June 1, 2026
• Primary Completion (Estimated): September 1, 2028
• Study Completion (Estimated): March 1, 2029
• Last Updated: January 22, 2026

Record last verified: 2026-01

Data Sharing

• IPD Sharing: Will not share

Locations

FR (1)