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Prediction of Postoperative Pulmonary Complications in Thoracic Surgery

ConditionPostoperative Pulmonary Complications (PPCs)
Trial statusNot yet recruiting
Trial phaseNot listed
Trial typeObservational
Biological sexAll
Age18-99
SponsorUniversity Hospital, Rouen

About this trial

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.

Eligibility criteria

This trial does not accept healthy volunteers

Qualifiers

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.

Disqualifiers

Minor patients

Surgery scheduled for a Friday

Patients undergoing a pneumonectomy

Pregnant or breastfeeding women

Trial population

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

Trial design

Design model

Case-only

Time perspective

Prospective

Treatments tested in this trial

  • 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.

Treatment groups

No treatment groups listed

Trial outcomes

Primary outcomes

1

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

Time frame
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

1

Evaluation of the incidence of pulmonary complications

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.

Time frame
30 days
2

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

Measurement of the score obtained by the machine learning method and the length of hospital stay recorded in days (D0 being the day of the intervention)

Time frame
3 months
3

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

Measurement of the score obtained by the machine learning method and the number of reintubations recorded in the first 30 postoperative days

Time frame
30 days
4

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

Measurement of the score obtained by the machine learning method and the Number of unplanned hospitalizations in intensive care recorded in the first 30 postoperative days

Time frame
30 days

Other outcomes

Sponsors and contacts

Click on the lead sponsor to view all of their trials.

University Hospital, Rouen

Lead sponsor

Source ClinicalTrials.gov

This trial is not recruiting at the moment. You can still explore other options: