Lung Cancer PatientsNCT06725225
Machine Learning for Predicting and Managing Quality of Life in Lung Cancer Immunotherapy Patients
A study testing Symptom cluster-based care intervention and Conventional care intervention in people with Lung Cancer Patients, run by Second Affiliated Hospital of Zunyi Medical University.
Trial 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. Read more about this study
- Who is it for?
- Ages 18 years and older
- Conditions the study is for
- Lung Cancer Patients
- What is being tested?
- Symptom cluster-based care intervention and Conventional care intervention
- Type of treatment
- Lifestyle or therapy programme
- Could I receive a placebo?
- Yes, some participants may receive a placebo
- How many people
- 200 participants
- Where
- No sites listed yet
- Study dates
- 1 Jan 2025to 1 Apr 2026
- Phase
- Interventional
- Are there results?
- Not yet: the study is under way
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 18 years and older
- All sexes
- No healthy volunteers
- Adult, Older adult
Who may be able to join
- 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
Who cannot join
- Cognitive impairment or mental illness
- Other severe diseases
Trial locations and contacts
No sites are listed yet. Contact the study team or a site near you to ask about taking part.
About this study
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
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
Read the detailed description
Lung cancer is the leading cause of cancer incidence and mortality in China, and it holds the same position in the United States. Non-small cell lung cancer (NSCLC) is the most common histological type, accounting for approximately 85% of lung cancer cases. Treatment strategies based on pathology, molecular subtyping, and clinical staging include surgery, radiotherapy, chemotherapy, targeted therapy, and immunotherapy. In recent years, immunotherapy has been extensively researched and applied in lung cancer treatment. It works by blocking the binding of PD-L1 on tumor cells to PD-1 on T cells, thereby releasing the inhibition of T cell function and killing the tumor cells. Immunotherapy has become the standard treatment for advanced NSCLC without driver mutations, and it covers the entire spectrum of non-surgical locally advanced NSCLC consolidation therapy, perioperative neoadjuvant, and adjuvant therapy for early-stage NSCLC. However, not all patients benefit from immunotherapy, with only a small subset experiencing clinical benefit. Therefore, identifying resistance mechanisms, selecting populations that benefit from treatment, and overcoming therapy resistance are complex and challenging clinical issues that require collaboration among basic, translational, and clinical oncology research teams.
In 1993, the World Health Organization (WHO) introduced the concept of Quality of Life (QoL), which refers to an individual's perception of their position in life within their cultural and value system, relating to their goals, expectations, standards, and concerns. Few studies focus on cancer patients' QoL, particularly those using patient-reported outcomes (PRO) as a primary endpoint. Most clinical trials for cancer drugs use PROs as secondary or exploratory endpoints. There is limited research that considers PROs as the primary endpoint. Therefore, it is essential to further investigate the relationship between cancer patients' health-related quality of life and prognosis, as well as its relevance to immunotherapy. This would facilitate better early identification of immune-related adverse events and systematic management, improving treatment adherence, QoL, and ensuring optimal treatment outcomes.
This project aims to develop a risk warning model for health-related quality of life in lung cancer patients receiving immunotherapy based on machine learning. By using cluster analysis, the study will clean, validate, and analyze the health-related quality of life data from the QLQ-C30 and QLQ-LC13 questionnaires from clinical trials available on the Vivli Global Clinical Research Data Sharing and Analysis Platform. The goal is to identify the distribution characteristics of these data and explore whether patient-reported outcomes can predict the efficacy of immunotherapy, thus serving as biomarkers to identify potential beneficiaries of immunotherapy. Furthermore, based on a risk warning and stratified management approach, the project aims to design appropriate symptom intervention strategies for different PRO types in immunotherapy patients, ultimately helping healthcare providers better understand the symptom burden that lung cancer patients may experience during immunotherapy and offering practical guidance for symptom management.
- Conditions
- Lung Cancer Patients
- Being tested
- Symptom cluster-based care intervention, Conventional care intervention
- Keywords
- HRQOL, lung cancer, immunotherapy
- Medical subject headings
- Lung Neoplasms
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
Symptom cluster-based care interventionBehavioural
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.
Conventional care interventionBehavioural
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
| Study group | Role | What they receive |
|---|---|---|
| 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. Receives: Behavioral: Conventional care intervention |
| The group with more severe symptoms and poorer quality of life | Active comparator | Not described Receives: Behavioral: Symptom cluster-based care intervention |
What is being measured
The primary outcome is the main question the study is designed to answer.
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.
Measured: 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.
Measured: Two weeks after the intervention
Secondary outcomes (2)
ORR (Objective Response Rate)
ORR refers to the proportion of patients who achieve either a complete response (CR) or partial response (PR) during treatment. This endpoint reflects the effectiveness of the treatment in controlling the tumor. It is typically assessed through imaging techniques (e.g., CT, MRI) and scored based on RECIST (Response Evaluation Criteria in Solid Tumors) or similar criteria. ORR is a common efficacy endpoint in cancer clinical trials.
Measured: Two weeks after the intervention
PFS (Progression-Free Survival)
PFS refers to the length of time from the start of treatment until the tumor progresses or the patient dies, typically assessed through imaging or clinical evaluation. This endpoint reflects the effectiveness of the treatment in delaying disease progression. PFS is particularly useful in evaluating the clinical efficacy of new drugs or therapies. An extension of PFS is usually considered a sign of effective treatment.
Measured: Two weeks after the intervention
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted10 Dec 2024
- Study start1 Jan 2025 (estimated)
- Primary completion1 Dec 2025 (estimated)
- Study completion1 Apr 2026 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Lung Cancer PatientsStudies of the same condition, open and under way.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Interventional
- Phase
- Phase N/A
- Primary purpose
- Supportive care
- Allocation
- Non-randomised
- Intervention model
- Parallel groups
- Model description
- 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.
- Masking
- None (open label)
- Enrolment
- 200 participants (estimated)
Sponsor and investigators
- Lead sponsor
- Second Affiliated Hospital of Zunyi Medical University
- Sponsor type
- University, hospital or other
- Collaborators
- Ministry of Education of the People's Republic of China, Department of Humanities and Social Sciences
- Responsible party
- Principal investigator, Jian-Guo Zhou, MD, PhD, Second Affiliated Hospital of Zunyi Medical University
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT06725225
- Sponsor's protocol ID
- 24YJCZH462
- Status last verified
- Dec 2024
- First submitted
- 1 Dec 2024
- Last updated
- 13 Dec 2024
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No: No IPD will be shared.
Study at a glance
From the registry record, last updated by the sponsor on 13 Dec 2024. Recruitment status is as the sponsor reported it: confirm with the study team.
- 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
- ClinicalTrials.gov ID
- NCT06725225
- Acronym / sponsor ID
- 24YJCZH462
- Conditions
- Lung Cancer Patients
- Keywords
- HRQOL, lung cancer, immunotherapy
- Study type
- Interventional
- Recruitment status
- Not yet recruiting
- Enrolment
- 200 participants (estimated)
- Lead sponsor
- Second Affiliated Hospital of Zunyi Medical UniversityUniversity, hospital or other
- Collaborators
- Ministry of Education of the People's Republic of China, Department of Humanities and Social Sciences
- Study start
- 1 Jan 2025 (estimated)
- Primary completion
- 1 Dec 2025 (estimated)
- Study completion
- 1 Apr 2026 (estimated)
- First posted
- 10 Dec 2024 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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
Detailed description
Lung cancer is the leading cause of cancer incidence and mortality in China, and it holds the same position in the United States. Non-small cell lung cancer (NSCLC) is the most common histological type, accounting for approximately 85% of lung cancer cases. Treatment strategies based on pathology, molecular subtyping, and clinical staging include surgery, radiotherapy, chemotherapy, targeted therapy, and immunotherapy. In recent years, immunotherapy has been extensively researched and applied in lung cancer treatment. It works by blocking the binding of PD-L1 on tumor cells to PD-1 on T cells, thereby releasing the inhibition of T cell function and killing the tumor cells. Immunotherapy has become the standard treatment for advanced NSCLC without driver mutations, and it covers the entire spectrum of non-surgical locally advanced NSCLC consolidation therapy, perioperative neoadjuvant, and adjuvant therapy for early-stage NSCLC. However, not all patients benefit from immunotherapy, with only a small subset experiencing clinical benefit. Therefore, identifying resistance mechanisms, selecting populations that benefit from treatment, and overcoming therapy resistance are complex and challenging clinical issues that require collaboration among basic, translational, and clinical oncology research teams.
In 1993, the World Health Organization (WHO) introduced the concept of Quality of Life (QoL), which refers to an individual's perception of their position in life within their cultural and value system, relating to their goals, expectations, standards, and concerns. Few studies focus on cancer patients' QoL, particularly those using patient-reported outcomes (PRO) as a primary endpoint. Most clinical trials for cancer drugs use PROs as secondary or exploratory endpoints. There is limited research that considers PROs as the primary endpoint. Therefore, it is essential to further investigate the relationship between cancer patients' health-related quality of life and prognosis, as well as its relevance to immunotherapy. This would facilitate better early identification of immune-related adverse events and systematic management, improving treatment adherence, QoL, and ensuring optimal treatment outcomes.
This project aims to develop a risk warning model for health-related quality of life in lung cancer patients receiving immunotherapy based on machine learning. By using cluster analysis, the study will clean, validate, and analyze the health-related quality of life data from the QLQ-C30 and QLQ-LC13 questionnaires from clinical trials available on the Vivli Global Clinical Research Data Sharing and Analysis Platform. The goal is to identify the distribution characteristics of these data and explore whether patient-reported outcomes can predict the efficacy of immunotherapy, thus serving as biomarkers to identify potential beneficiaries of immunotherapy. Furthermore, based on a risk warning and stratified management approach, the project aims to design appropriate symptom intervention strategies for different PRO types in immunotherapy patients, ultimately helping healthcare providers better understand the symptom burden that lung cancer patients may experience during immunotherapy and offering practical guidance for symptom management.
Study design
- Primary purpose
- Supportive care
- Allocation
- Non-randomised
- Intervention model
- Parallel groups
- Masking
- None (open label)
- Number of arms
- 2
- Model description
- 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.
| Arm | Type | Receives |
|---|---|---|
| The group with milder symptoms and better quality of life | Placebo comparator | Behavioral: Conventional care interventionthe 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. |
| The group with more severe symptoms and poorer quality of life | Active comparator | Behavioral: Symptom cluster-based care intervention |
Who may qualify
- Ages
- 18 years and older
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
Key inclusion criteria (5 listed)
- 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
Key exclusion criteria (2 listed)
- Cognitive impairment or mental illness
- Other severe diseases
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| Symptom cluster-based care interventionThe 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. | Behavioural | The group with more severe symptoms and poorer quality of life |
| Conventional care interventionStandard 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 | Behavioural | The group with milder symptoms and better quality of life |
Primary outcome
EORTC QLQ C30
Time frame: Two weeks after the intervention
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.
EORTC QLQ LC-13
Time frame: Two weeks after the intervention
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.
2 secondary outcomes
ORR (Objective Response Rate)
Time frame: Two weeks after the intervention
PFS (Progression-Free Survival)
Time frame: Two weeks after the intervention
Results and safety
This study is still under way. Results are not available in the source record yet.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 13 Dec 2024.
- Individual participant dataSharing planned: No. No IPD will be shared.
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part.
Questions about this trial
What is this clinical trial studying?
A study testing Symptom cluster-based care intervention and Conventional care intervention in people with Lung Cancer Patients, run by Second Affiliated Hospital of Zunyi Medical University.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 5 inclusion and 2 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: opening soon, as of the sponsor's last update on 13 Dec 2024. Status can change between updates, so confirm with the study contact before making plans.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. Some participants in this study may receive a placebo. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
This study is still under way. Results are not available in the source record yet.
How can a doctor refer a patient or contact the research team?
Contact Jianguo zhou (+8618311543939, jianguo.zhou@yahoo.com), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
EORTC QLQ C30 (time frame: Two weeks after the intervention). The study lists 2 primary outcomes in all. It is what the study was designed to measure, not a finding.
Where can I read the original study information?
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 13 Dec 2024.