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| ID | Type | Description | Link |
|---|---|---|---|
| 2025-A01699-40 | Other Identifier | ANSM |
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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.
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.
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| Name | Type | Description | Arm Group Labels | Other Names |
|---|---|---|---|---|
| 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. |
| Measure | Description | Time Frame |
|---|---|---|
| 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 |
| Measure | Description | Time Frame |
|---|---|---|
| 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. | 30 days |
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Inclusion Criteria:
Exclusion Criteria:
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Patients undergoing programmed video-assisted or robot-assisted lobectomy, bilobectomy or segmentectomy.
| Name | Role | Phone | Extension | |
|---|---|---|---|---|
| Nabila NL LAAJAIL, Director | Contact | 02 32 88 82 65 | +33 | Nabila.Laajail@chu-rouen.fr |
| Vincent VF FERRANTI, ARC | Contact | 02 32 88 82 65 | +33 | Vincent.Ferranti@chu-rouen.fr |
| Name | Affiliation | Role |
|---|---|---|
| Jean JS SELIM, Doctor | Service de Anesthésie-Réanimation Médecine périopératoire CHU de Rouen | Study Director |
| Facility | Status | City | State | ZIP | Country | Contacts |
|---|---|---|---|---|---|---|
| Service de Anesthésie-Réanimation Médecine périopératoire CHU de Rouen | Rouen | 76100 | France |
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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
| 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) | 3 months |
| 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 | 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 | 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 | 30 days |
| Evaluation of the correlation between the prognostic score defined using a machine learning method and the Preoperative anxiety score assessed | Measurement of the score obtained by the machine learning method and the Preoperative anxiety score assessed on day 0 (before surgery) using the STAI (State Trait Anxiety Inventory) Questionnaire | 48 hours |
| Evaluation of the correlation between the prognostic score defined using a machine learning method and the Preoperative anxiety score assessed | Measurement of the score obtained by the machine learning method and the Preoperative anxiety score assessed at 48 hours using the STAI (State Trait Anxiety Inventory) Questionnaire | 48 hours |
| Evaluation of the correlation between the prognostic score defined using a machine learning method and The cost of care | Measurement of the score obtained by the machine learning method and The cost of care between J0, J30 and J90 (estimated by the Homogeneous Stay Group generated for each hospital stay (initial hospitalization and rehospitalization(s)). | 3 months |
| Evaluation of the prognostic performance of the score calculated by the machine learning method on Post-operative Pulmonary Complications (PPC) assessed by the Melbourne composite score (Melbourne Group Scale (MGS) >=4) | The area under the receiver operating curve (AUC) is calculated from the score obtained using the machine learning method and the primary respiratory symptoms (PRS) in the first 7 days, assessed by the Melbourne Group Scale (MGS). The MGS includes the following items and will be considered positive if ≥ 4 points: Temperature ≥ 38.5°C (1 point) Purulent sputum (1 point) Positive bacteriology (1 point) SpO2 < 90% in room air (1 point) Leukocytes > 11.2 x 10⁶/ml (1 point) Prescription of antibiotic therapy (1 point) Chest X-ray: atelectasis (1 point) (defined as previously) Diagnosis of pneumonia by a physician (1 point) (defined as previously) Readmission to intensive care or prolonged stay (> 36 hours) for respiratory problems (1 point) | 7 days |
| Evaluation of the prognostic performance of the score calculated by the machine learning method on the severity of postpartum bleeding (PPB) | The area under the receiver operating curve (AUC) is calculated from the score obtained using the machine learning method and the severity of postpartum bleeding (PPB) in the first 30 days assessed by the Clavien-Dindo score | 30 days |
| Evaluation of the prognostic performance of the score calculated by the machine learning method on Postoperative mortality assessed at 30 days | The area under the receiver operating curve (AUC) is calculated from the score obtained using the machine learning method and Postoperative mortality assessed at 30 days | 30 days |
| Evaluation of the prognostic performance of the score calculated by the machine learning method on Postoperative mortality assessed at 90 days | The area under the receiver operating curve (AUC) is calculated from the score obtained using the machine learning method and Postoperative mortality assessed at 90 days | 90 days |
| Evaluation of the prognostic performance of the score calculated by the machine learning method on Pre- and postoperative pain | The area under the receiver operating curve (AUC) is calculated from the score obtained using the machine learning method and Pre- and postoperative pain was assessed using a numerical rating scale from 0 to 10 at day 0 (before surgery), at 24 hours, and at 48 hours. Neuropathic pain was assessed by telephone at 3 months using the DN4 questionnaire. | 90 days |
| ID | Term |
|---|---|
| D008175 | Lung Neoplasms |
| ID | Term |
|---|---|
| D012142 | Respiratory Tract Neoplasms |
| D013899 | Thoracic Neoplasms |
| D009371 | Neoplasms by Site |
| D009369 | Neoplasms |
| D008171 | Lung Diseases |
| D012140 | Respiratory Tract Diseases |
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