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

NCT06771947 · Status: NOT_YET_RECRUITING · Type: OBSERVATIONAL · Enrollment: 1500

Last updated 2025-01-13

No results posted yet for this study

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

Sponsors & Collaborators

  • Guangdong Provincial People's Hospital

    lead OTHER

Principal Investigators

  • Guibin Qiao · Guangdong Provincial People's Hospital

Eligibility

Min Age
18 Years
Max Age
80 Years
Sex
ALL
Healthy Volunteers
No

Timeline & Regulatory

Start
2025-03-01
Primary Completion
2026-01-01
Completion
2026-02-01

Countries

  • China

Study Locations

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Entities

Diseases

Read the full study record

This page highlights key information. For complete eligibility criteria, study locations, investigator contacts, and the full protocol, visit the original record on ClinicalTrials.gov.

View NCT06771947 on ClinicalTrials.gov