Predicting Symptom Trajectories After Thoracoscopic Lung Cancer Surgery Using an Interpretable Machine Learning Model

NCT06771947 | Not Yet Recruiting

• 1 other identifier: LC-ML
• Study Type: observational
• Target: 1,500
• Locations: 1 country
• Sites: 1

Brief Summary

Patients suffer from a variety of symptoms after thoracoscopic surgery. However, there is a lack of validated predictive tools to identify potentially high-risk patients. This study is anticipated to include approximately 1,500 lung cancer patients who undergo thoracoscopic surgery. Latent class mixed modeling (LCMM) will be used to dentify subgroups of patients with similar symptom trajectories. Machine learning models were developed to predict postoperative symptom trajectories based on collected information. Effective prediction of postoperative symptoms can help identify high-risk patients and take preventive measures.

Conditions

Lung Cancer

Keywords

Lung cancer; Machine learning; Patient-reported outcome; Thoracoscopy surgery

Outcome Measures

Primary Outcomes (1)

• Symptom trajectories after thoracoscopic surgery

  Postoperative symptoms of interest included pain, shortness of breath, sleep disturbances, and fatigue.

  Within four weeks after thoracoscopic surgery

Eligibility Criteria

• Age: 18 Years - 80 Years
• Sex: all
• Healthy Volunteers: No
• Age Groups: Adult (18-64), Older Adult (65+)
• Sampling Method: Non-Probability Sample

Study Population

Lung cancer patient underwent thoracoscopic surgery and completed the MD Anderson Symptom Inventory-Lung Cancer module postoperatively.

You may qualify if:

• Age 18-80 years old;
• Pathologically diagnosed lung cancer;
• Undergo thoracoscopic surgery, including video-assisted thoracoscopy and robotic-assisted thoracoscopic surgery;
• no prior history of malignancy or lung surgery;
• Have the ability to complete the scale.

You may not qualify if:

• Converted to thoracotomy during thoracoscopic surgery;
• Unable to complete the postoperative scale at least two times;
• Missing data values exceeding 30 percent.

Sponsors & Collaborators

• Guangdong Provincial People's Hospital: lead

Study Sites (1)

Guangdong Provincial People's Hospital

Guangdong, 510080, China

Location

MeSH Terms

Conditions

Lung Neoplasms

Condition Hierarchy (Ancestors)

Respiratory Tract Neoplasms; Thoracic Neoplasms; Neoplasms by Site; Neoplasms; Lung Diseases; Respiratory Tract Diseases

Study Officials

• Guibin Qiao

  Guangdong Provincial People's Hospital

  PRINCIPAL INVESTIGATOR

Central Study Contacts

Guibin Qiao, MD

CONTACT

13602749153; guibinqiao@126.com

Study Design

• Study Type: observational
• Observational Model: COHORT
• Time Perspective: RETROSPECTIVE
• Sponsor Type: OTHER
• Responsible Party: PRINCIPAL INVESTIGATOR
• PI Title: Prof.

Study Record Dates

• First Submitted: January 8, 2025
• First Posted: January 13, 2025
• Study Start: March 1, 2025
• Primary Completion: January 1, 2026
• Study Completion: February 1, 2026
• Last Updated: January 13, 2025

Record last verified: 2024-04

Locations

CN (1)