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

NCT06725225 · Status: NOT_YET_RECRUITING · Phase: NA · Type: INTERVENTIONAL · Enrollment: 200

Last updated 2024-12-13

No results posted yet for this study

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

Conditions

  • Lung Cancer Patients

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

Sponsors & Collaborators

  • Ministry of Education of the People's Republic of China, Department of Humanities and Social Sciences

    collaborator UNKNOWN

  • Second Affiliated Hospital of Zunyi Medical University

    lead OTHER

Study Design

Allocation
NON_RANDOMIZED
Purpose
SUPPORTIVE_CARE
Masking
NONE
Model
PARALLEL

Eligibility

Min Age
18 Years
Sex
ALL
Healthy Volunteers
No

Timeline & Regulatory

Start
2025-01-01
Primary Completion
2025-12-01
Completion
2026-04-01

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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 NCT06725225 on ClinicalTrials.gov