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

January 8, 2025 updated by: GuiBin Qiao, Guangdong Provincial People's Hospital
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.

Study Overview

Status

Not yet recruiting

Conditions

Detailed Description

Thoracoscopic lung cancer surgery is a widely utilized approach for treating early and locally advanced lung cancer. Despite the advantages of thoracoscopic surgery, such as minimal invasion and rapid recovery, patients still suffer from a variety of symptoms such as pain, shortness of breath, sleep disorders or fatigue after surgery, which seriously affects the quality of life. 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. Patients are invited to fill out the MD Anderson Symptom Inventory-Lung Cancer module after thoracoscopic surgery. Symptoms of interest include pain, shortness of breath, sleep disturbance, and fatigue. Moderate to severe symptoms were defined as a score of ≥ 4. Latent class mixed modeling (LCMM), a clustering technique, can identify subgroups of patients with similar symptom trajectories based on longitudinal patient-reported outcome (PRO) data. Machine learning models were developed to predict postoperative symptom trajectories based on collected information including demographic and clinical information, and operative data. The machine learning models mainly include Random Forest, Support Vector Machines, Neural Networks, XGBoost, etc. The most appropriate model is selected, and model interpretation is performed using the SHAP method. Effective prediction of postoperative symptoms can help identify high-risk patients and take preventive measures.

Study Type

Observational

Enrollment (Estimated)

1500

Contacts and Locations

This section provides the contact details for those conducting the study, and information on where this study is being conducted.

Study Contact

Study Locations

  • China
      • Guangdong, China, 510080
        • Guangdong Provincial People's Hospital
        • Contact:
        • Contact:

Participation Criteria

Researchers look for people who fit a certain description, called eligibility criteria. Some examples of these criteria are a person's general health condition or prior treatments.

Eligibility Criteria

Ages Eligible for Study

  • Adult
  • Older Adult

Accepts Healthy Volunteers

No

Sampling Method

Non-Probability Sample

Study Population

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

Description

Inclusion Criteria:

  • 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.

Exclusion Criteria:

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

Study Plan

This section provides details of the study plan, including how the study is designed and what the study is measuring.

How is the study designed?

Design Details

What is the study measuring?

Primary Outcome Measures

Outcome Measure
Measure Description
Time Frame
Symptom trajectories after thoracoscopic surgery
Time Frame: Within four weeks after thoracoscopic surgery
Postoperative symptoms of interest included pain, shortness of breath, sleep disturbances, and fatigue.
Within four weeks after thoracoscopic surgery

Collaborators and Investigators

This is where you will find people and organizations involved with this study.

Sponsor

Guangdong Provincial People's Hospital

Investigators

  • Principal Investigator: Guibin Qiao, Guangdong Provincial People's Hospital

Study record dates

These dates track the progress of study record and summary results submissions to ClinicalTrials.gov. Study records and reported results are reviewed by the National Library of Medicine (NLM) to make sure they meet specific quality control standards before being posted on the public website.

Study Major Dates

Study Start (Estimated)

March 1, 2025

Primary Completion (Estimated)

January 1, 2026

Study Completion (Estimated)

February 1, 2026

Study Registration Dates

First Submitted

January 8, 2025

First Submitted That Met QC Criteria

January 8, 2025

First Posted (Actual)

March 25, 2025

Study Record Updates

Last Update Posted (Actual)

March 25, 2025

Last Update Submitted That Met QC Criteria

January 8, 2025

Last Verified

April 1, 2024

More Information

Terms related to this study

Keywords

Additional Relevant MeSH Terms

Other Study ID Numbers

  • LC-ML

Drug and device information, study documents

Studies a U.S. FDA-regulated drug product

No

Studies a U.S. FDA-regulated device product

No

product manufactured in and exported from the U.S.

No

This information was retrieved directly from the website clinicaltrials.gov without any changes. If you have any requests to change, remove or update your study details, please contact register@clinicaltrials.gov. As soon as a change is implemented on clinicaltrials.gov, this will be updated automatically on our website as well.

Subscribe