Postoperative Pulmonary Complications (PPCs)PREDICT-PPCNCT07359885
Prediction of Postoperative Pulmonary Complications in Thoracic Surgery
An observational study of Postoperative Pulmonary Complications (PPCs), run by University Hospital, Rouen at 1 site in France.
Trial 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). Read more about this study
- Who is it for?
- Ages 18 to 99 years
- Conditions the study is for
- Postoperative Pulmonary Complications (PPCs)
- What is being tested?
- Evaluation of prognostic performance of a defined score using a machine learning method (STABL: Stability Selection) integrating immune data (cytometric and proteomic)
- How many people
- 100 participants
- Where
- 1 siteFrance
- Study dates
- 1 Jun 2026to 1 Mar 2029
- How long will I take part?
- 3 Months
- Phase
- Observational
- Are there results?
- Not yet: the study is under way
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 18 to 99 years
- All sexes
- No healthy volunteers
- Adult, Older adult
Study population: Patients undergoing programmed video-assisted or robot-assisted lobectomy, bilobectomy or segmentectomy.
Who may be able to join
- 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
Who cannot join
- 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
Trial locations and contacts
1 site in 1 country. Contact the study team or a site near you to ask about taking part.
France1 site
Service de Anesthésie-Réanimation Médecine périopératoire CHU de Rouen
Rouen, 76100
Naomi ND DAMES, Doctor, 02 32 88 68 28 ext. +33, Naomi.Dames@chu-rouen.fr
About this study
Official title: Prediction of Postoperative Pulmonary Complications in Thoracic Surgery: an Immuno-inflammatory Approach
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.
Read the detailed 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.
- Being tested
- Evaluation of prognostic performance of a defined score using a machine learning method (STABL: Stability Selection) integrating immune data (cytometric and proteomic)
- Keywords
- lung cancer, immuno-inflammatory approach
- Medical subject headings
- Lung Neoplasms
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
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.
What is being measured
The primary outcome is the main question the study is designed to answer.
Evaluation of the prognostic performance of a score for screening patients at risk of postoperative pulmonary complications (PPC)
Measured: 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
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.
Measured: 30 days
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)
Measured: 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
Measured: 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
Measured: 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
Measured: 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
Measured: 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)).
Measured: 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)
Measured: 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
Measured: 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
Measured: 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
Measured: 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.
Measured: 90 days
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted22 Jan 2026
- Study start1 Jun 2026 (estimated)
- Primary completion1 Sep 2028 (estimated)
- Study completion1 Mar 2029 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Postoperative Pulmonary Complications (PPCs)Studies of the same condition, open and under way.
- More trials run by University Hospital, RouenThe sponsor's other registered studies.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Observational model
- Case only
- Time perspective
- Prospective
- Enrolment
- 100 participants (estimated)
- Target duration
- 3 Months
Sponsor and investigators
- Lead sponsor
- University Hospital, Rouen
- Sponsor type
- University, hospital or other
- Responsible party
- Sponsor
- Investigators
- Jean JS SELIM, Doctor, Study director, Service de Anesthésie-Réanimation Médecine périopératoire CHU de Rouen
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT07359885
- Sponsor's protocol ID
- 2024/0307/HP
- Other IDs
- 2025-A01699-40
- Acronym
- PREDICT-PPC
- Status last verified
- Jan 2026
- First submitted
- 14 Jan 2026
- Last updated
- 22 Jan 2026
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No
Study at a glance
From the registry record, last updated by the sponsor on 22 Jan 2026. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Prediction of Postoperative Pulmonary Complications in Thoracic Surgery: an Immuno-inflammatory Approach
- ClinicalTrials.gov ID
- NCT07359885
- Acronym / sponsor ID
- PREDICT-PPC / 2024/0307/HP
- Keywords
- lung cancer, immuno-inflammatory approach
- Study type
- Observational
- Recruitment status
- Not yet recruiting
- Enrolment
- 100 participants (estimated)
- Lead sponsor
- University Hospital, RouenUniversity, hospital or other
- Study start
- 1 Jun 2026 (estimated)
- Primary completion
- 1 Sep 2028 (estimated)
- Study completion
- 1 Mar 2029 (estimated)
- First posted
- 22 Jan 2026 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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.
Detailed 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.
Study design
- Observational model
- Case only
- Time perspective
- Prospective
- Target follow-up
- 3 Months
Who may qualify
- Ages
- 18 to 99 years
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
- Study population
- Patients undergoing programmed video-assisted or robot-assisted lobectomy, bilobectomy or segmentectomy.
Key inclusion criteria (7 listed)
- 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
Key exclusion criteria (5 listed)
- 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
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| Evaluation of prognostic performance of a defined score using a machine learning method (STABL: Stability Selection) integrating immune data (cytometric and proteomic)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. | Diagnostic test | – |
Primary outcome
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
12 secondary outcomes
Evaluation of the incidence of pulmonary complications
Time frame: 30 days
Evaluation of the correlation between the prognostic score defined using a machine learning method and the length of hospital stay
Time frame: 3 months
Evaluation of the correlation between the prognostic score defined using a machine learning method and the number of reintubations recorded
Time frame: 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
Time frame: 30 days
Evaluation of the correlation between the prognostic score defined using a machine learning method and the Preoperative anxiety score assessed
Time frame: 48 hours
Evaluation of the correlation between the prognostic score defined using a machine learning method and the Preoperative anxiety score assessed
Time frame: 48 hours
Evaluation of the correlation between the prognostic score defined using a machine learning method and The cost of care
Time frame: 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)
Time frame: 7 days
Evaluation of the prognostic performance of the score calculated by the machine learning method on the severity of postpartum bleeding (PPB)
Time frame: 30 days
Evaluation of the prognostic performance of the score calculated by the machine learning method on Postoperative mortality assessed at 30 days
Time frame: 30 days
Evaluation of the prognostic performance of the score calculated by the machine learning method on Postoperative mortality assessed at 90 days
Time frame: 90 days
Evaluation of the prognostic performance of the score calculated by the machine learning method on Pre- and postoperative pain
Time frame: 90 days
Results and safety
This study is still under way. Results are not available in the source record yet.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 22 Jan 2026.
- Individual participant dataSharing planned: No
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part.
Jean JS SELIM, Doctor study director
Service de Anesthésie-Réanimation Médecine périopératoire CHU de Rouen
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Postoperative Pulmonary Complications (PPCs), run by University Hospital, Rouen at 1 site in France.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 7 inclusion and 5 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: opening soon, as of the sponsor's last update on 22 Jan 2026. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in France. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
This study is still under way. Results are not available in the source record yet.
How can a doctor refer a patient or contact the research team?
Contact Nabila NL LAAJAIL, Director (02 32 88 82 65 ext. +33, Nabila.Laajail@chu-rouen.fr), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
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). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 22 Jan 2026.