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NCT06725225
NA

Machine Learning for Predicting and Managing Quality of Life in Lung Cancer Immunotherapy Patients

Sponsor: Second Affiliated Hospital of Zunyi Medical University

View on ClinicalTrials.gov

Summary

The goal of this study is to explore whether health-related quality of life (HRQoL) can be used as a predictive indicator for lung cancer patients and to implement clinical interventions. The study addresses two main objectives: Analyzing HRQoL data of lung cancer patients undergoing immunotherapy using machine learning clustering methods to explore data patterns and build an HRQoL early warning model (already developed). Validating this HRQoL early warning model in real-world settings by classifying patients with different HRQoL characteristics and assessing the clinical value of the model

Official title: Development of a Machine Learning-Based Risk Prediction Model and Stratified Management Strategies for Quality of Life in Lung Cancer Patients Undergoing Immunotherapy

Key Details

Gender

All

Age Range

18 Years - Any

Study Type

INTERVENTIONAL

Enrollment

200

Start Date

2025-01-01

Completion Date

2026-04-01

Last Updated

2024-12-13

Healthy Volunteers

No

Interventions

BEHAVIORAL

Symptom cluster-based care intervention

The patient symptoms were surveyed to develop a symptom cluster care intervention plan. The specific steps were as follows: a research team was established, relevant literature was reviewed, and qualitative interviews were conducted. Guided by symptom management theory and the Knowledge-Attitude-Practice (KAP) behavior model, a draft of the care intervention was created. This draft was then refined through expert consultation to finalize the intervention plan.

BEHAVIORAL

Conventional care intervention

Standard nursing intervention. This refers to routine clinical care without a specific care plan tailored to the patient's symptoms. For example, if a patient has symptoms, the nurse assists the patient in notifying the doctor but does not provide any special treatment themselves