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Predicting Postoperative Pulmonary Complications in Elderly Thoracic Surgery Patients: Development of an Interpretable Machine Learning Model Using a Bidirectional Cohort

Predicting Postoperative Pulmonary Complications in Elderly Thoracic Surgery Patients: Development of an Interpretable Machine Learning Model Using a Bidirectional Cohort

Status
Active, not recruiting
Phases
Unknown
Study type
Observational
Source
ChiCTR
Registry ID
ChiCTR2500110370
Enrollment
Unknown
Registered
2025-10-13
Start date
2025-11-01
Completion date
Unknown
Last updated
2025-10-20

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

Conditions

Postoperative Pulmonary Complications (PPCs) in Elderly Patients undergoing Thoracic Surgery

Interventions

Sponsors

Harbin Medical University Cancer Hospital
Lead Sponsor

Eligibility

Sex/Gender
All
Age
65 Years to No maximum

Inclusion criteria

Inclusion criteria: 1.Age >= 65 years; 2.Undergoing thoracic surgery, including lung surgery, esophageal surgery, airway surgery, or mediastinal surgery; 3.Patients who received general anesthesia were included in the study; 4.Patients provided informed consent to participate voluntarily, and the informed consent form was signed by the patient in person or by their legal guardian

Exclusion criteria

Exclusion criteria: 1.Age < 65 years; 2.Preoperative severe cardiopulmonary disease; 3.Preoperative mechanical ventilation; 4.Remote surgery; 5.Planned discharge within 12 hours after surgery; 6.Preoperative expectation of ICU admission postoperatively; 7.Postoperative reoperation; 8.Patients with data loss

Design outcomes

Primary

MeasureTime frame
Postoperative Pulmonary Complications (PPCs) ;

Countries

China

Contacts

Public ContactXiaoyu Zheng

Harbin Medical University Cancer Hospital

xiaoyuzheng@hrbmu.edu.cn+86 156 3611 2701

Outcome results

None listed

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