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Machine learning predictive model for postoperative pulmonary complications in patients undergoing lung cancer surgery

A dual independent cohort-based machine learning predictive model for postoperative pulmonary complications in patients undergoing lung cancer surgery

Status
Active, not recruiting
Phases
Unknown
Study type
Observational
Source
ChiCTR
Registry ID
ChiCTR2500102082
Enrollment
Unknown
Registered
2025-05-08
Start date
2025-05-08
Completion date
Unknown
Last updated
2025-05-12

For informational purposes only — not medical advice. Sourced from public registries and may not reflect the latest updates. Terms

Conditions

Lung Cancer

Interventions

Prospective cohort group:None
Retrospective cohort groups:None

Sponsors

West China Hospital, Sichuan University
Lead Sponsor

Eligibility

Sex/Gender
All
Age
18 Years to 80 Years

Inclusion criteria

Inclusion criteria: 1. Be between the ages of 18 and 80; 2. Preoperative or intraoperative diagnosis of primary non-small cell lung cancer (NSCLC) with anatomical resection of the lungs; 3. Known preoperative rehabilitation status.

Exclusion criteria

Exclusion criteria: 1. Pathologic diagnosis of non-NSCLC; 2. Wedge excision; 3. Salvage excision; 4. Switch to thoracotomy surgery; 5. Neoadjuvant therapy; 6. Receive preoperative rehabilitation.

Design outcomes

Primary

MeasureTime frame
Total postoperative pulmonary complications;—

Secondary

MeasureTime frame
Postoperative pneumonia (POP);Postoperative prolonged air leaks occur;Postoperative pulmonary atelectasis;—

Countries

China

Contacts

Public ContactGuowei Che

West China Hospital, Sichuan University

cheguoweixw@126.com+86 189 8060 1890

Outcome results

None listed

Source: ChiCTR (via WHO ICTRP) · Data processed: Feb 4, 2026