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

NCT06725225 | Not Yet Recruiting

• 1 other identifier: 24YJCZH462
• Study Type: interventional
• Target: 200
• Locations: 0 countries
• Sites: N/A

Brief 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

Keywords

HRQOL; lung cancer; immunotherapy

Outcome Measures

Primary Outcomes (2)

• EORTC QLQ C30

  The EORTC QLQ-C30 (European Organization for Research and Treatment of Cancer Quality of Life Questionnaire - Core 30) is a tool used to assess the quality of life in cancer patients. It consists of 30 items covering functioning, symptoms, and overall health status. Scores for each item range from 0 to 4 or 0 to 7 (depending on the item type), with overall scores typically ranging from 0 to 100. Higher scores indicate better quality of life with fewer symptoms and better functioning, while lower scores reflect worse quality of life with more severe symptoms and poorer functioning.

  Two weeks after the intervention

• EORTC QLQ LC-13

  The EORTC QLQ LC-13 (European Organization for Research and Treatment of Cancer Quality of Life Questionnaire - Lung Cancer Module 13) is a questionnaire used to assess the quality of life of lung cancer patients. It includes 13 items, with scores ranging from 0 to 4, and the total score ranges from 0 to 52. A higher score indicates more severe symptoms and poorer quality of life, while a lower score indicates better quality of life. The EORTC QLQ LC-13 is often used in conjunction with the EORTC QLQ-C30 scale, as they complement each other and provide a comprehensive assessment of the quality of life in lung cancer patients.

  Two weeks after the intervention

Secondary Outcomes (2)

• ORR (Objective Response Rate)

  Two weeks after the intervention

• PFS (Progression-Free Survival)

  Two weeks after the intervention

Study Arms (2)

The group with milder symptoms and better quality of life

PLACEBO COMPARATOR

the group uses unsupervised machine learning to identify patients with severe symptoms and poor functionality who are receiving immunotherapy for non-small cell lung cancer, and implements a symptom cluster care intervention.

Behavioral: Conventional care intervention

The group with more severe symptoms and poorer quality of life

ACTIVE COMPARATOR

Behavioral: Symptom cluster-based care intervention

Interventions

Symptom cluster-based care intervention BEHAVIORAL

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.

The group with more severe symptoms and poorer quality of life

Conventional care intervention BEHAVIORAL

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

The group with milder symptoms and better quality of life

Eligibility Criteria

• Age: 18 Years+
• Sex: all
• Healthy Volunteers: No
• Age Groups: Adult (18-64), Older Adult (65+)

You may qualify if:

• Histologically diagnosed with lung cancer
• Age over 18 years
• Currently receiving immunotherapy for lung cancer
• Good verbal communication ability
• Informed consent signed by the patient or family member

You may not qualify if:

• Cognitive impairment or mental illness
• Other severe diseases

Sponsors & Collaborators

• Second Affiliated Hospital of Zunyi Medical University: lead
• Ministry of Education of the People's Republic of China, Department of Humanities and Social Sciences: collaborator

MeSH Terms

Conditions

Lung Neoplasms

Condition Hierarchy (Ancestors)

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

Central Study Contacts

Jianguo zhou

CONTACT

+8618311543939; jianguo.zhou@yahoo.com

Study Design

• Study Type: interventional
• Phase: not applicable
• Allocation: NON RANDOMIZED
• Masking: NONE
• Purpose: SUPPORTIVE CARE
• Intervention Model: PARALLEL
• Sponsor Type: OTHER
• Responsible Party: PRINCIPAL INVESTIGATOR
• PI Title: associate professor and associate chief physician

Model Details: A symptom cluster care intervention plan is being developed, and a research team is formed. Based on relevant literature and qualitative interviews, and guided by symptom management theory and the Knowledge-Attitude-Practice (KAP) behavior model, an initial draft of the care intervention plan is created and refined through expert consultation to finalize the plan.

Study Record Dates

• First Submitted: December 1, 2024
• First Posted: December 10, 2024
• Study Start: January 1, 2025
• Primary Completion: December 1, 2025
• Study Completion: April 1, 2026
• Last Updated: December 13, 2024

Record last verified: 2024-12

Data Sharing

• IPD Sharing: Will not share