MyLungHealth for Lung Cancer Screening

(MyLungHealth Trial)

Recruiting at 1 trial location

Overseen By Kensaku Kawamoto, MD, PhD

Age: 18+

Sex: Any

Trial Phase: Academic

Sponsor: University of Utah

Disqualifiers: Lung cancer, Recent LDCT, others

No Placebo Group All trial participants will receive the active study treatment (no placebo)

Compensation: Varies

Number of Visits: 1

42415 Participants Needed

Trial Summary

What is the purpose of this trial?

Early lung cancer screening (LCS) through low-dose computed tomography (LDCT) is crucial but underused due to various barriers, including incomplete or inaccurate patient smoking data in the electronic health record and limited time for shared decision-making. The objective of this trial is to investigate a patient-centered intervention, MyLungHealth, delivered through the patient portal. The intervention is designed to improve LCS rates through increased identification of eligible patients and informed decision making.

Will I have to stop taking my current medications?

The trial information does not specify whether you need to stop taking your current medications. It's best to discuss this with the trial coordinators or your doctor.

What data supports the effectiveness of the MyLungHealth treatment for lung cancer screening?

The DecisionPrecision tool, part of the MyLungHealth treatment, is a web-based decision support tool that helps tailor lung cancer screening discussions to a patient's individual risk, potentially improving decision-making and outcomes. Additionally, data mining from large cancer screening trials can guide personalized screening management, optimizing the benefit-to-risk ratio for patients. ^{1 2 3 4 5}

Is MyLungHealth safe for humans?

The research does not provide specific safety data for MyLungHealth or its other names, but it discusses the importance of understanding the benefits and harms of lung cancer screening, which includes potential concerns like radiation exposure and false positives. ^{4 6 7 8 9}

How is the MyLungHealth treatment different from other lung cancer screening treatments?

MyLungHealth, also known as DecisionPrecision+, is unique because it is a web-based tool designed to support shared decision-making by providing personalized lung cancer risk assessments and tailored screening recommendations, helping patients and clinicians make more informed decisions about lung cancer screening. ^{1 10 11 12 13}

Research Team

Kensaku Kawamoto, MD, PhD, MHS

Principal Investigator

University of Utah

Eligibility Criteria

The MyLungHealth study is for people aged 50-79 who have a history of smoking and have visited a participating primary care clinic in the past year. Participants must also have used the patient portal at least once in the previous year.

Inclusion Criteria

I have smoked tobacco in the past or am currently smoking.

I have used the patient portal at least once in the past year.

I have visited the study's primary care clinic in the last year.

Timeline

Screening

Participants are screened for eligibility to participate in the trial

1-2 weeks

1 visit (virtual)

Intervention

Participants engage with the MyLungHealth intervention through the patient portal, including pre-visit smoking history questionnaires and education tools.

1 year

Ongoing virtual engagement

Follow-up

Participants are monitored for the completion of LDCT and other lung cancer screening activities.

1 year

Treatment Details

Interventions

• DecisionPrecision+
• MyLungHealth

Trial Overview This trial tests MyLungHealth, an intervention aimed to improve lung cancer screening rates by identifying eligible patients and supporting informed decision-making through a patient portal called DecisionPrecision+.

Participant Groups

Group I: Study 2 (Study of Patients with Documented LCS Eligibility) Intervention Arm Experimental Treatment 2 Interventions

Patients allocated to the intervention arm will be offered the MyLungHealth education tool in addition to the DecisionPrecision+ intervention. The pre-visit questionnaire will directly invite patients to use the MyLungHealth education app, rather than first asking about smoking history to confirm eligibility. When patients affirm in the patient portal that they were able to successfully open the MyLungHealth education tool, their providers will be notified of their potential interest in LCS through a passive (non-interruptive) prompt in the EHR.

Group II: Study 1 (Study of Patients with Uncertain LCS Eligibility) Intervention Arm Experimental Treatment 1 Intervention

Patients allocated to the intervention arm of Study 1 will be exposed to the MyLungHealth intervention in addition to usual care. The Study 1 MyLungHealth eligibility questionnaires will consist of pre-visit smoking history questions asked through the EHR patient portal to increase the identification of screening-eligible patients (Figure 2). Patients will be prompted to engage in the intervention through pre-visit questionnaires for primary care visits administered up to 7 days prior to the visits. These questions will ask about the patients' smoking history and determine whether patients meet LCS eligibility criteria. Study 1 patients who meet LCS eligibility criteria will be offered the intervention provided to patients in the intervention arm of Study 2 immediately after confirming eligibility but will not be considered participants in Study 2.

Group III: Study 1 (Study of Patients with Uncertain LCS Eligibility) Control Arm Active Control 1 Intervention

In Study 1 (Study of Patients with Uncertain LCS Eligibility), patients allocated to the control arm will not be exposed to the project intervention unless their EHR data change during the trial so that they have documented LCS eligibility. In these rare cases, these patients will be exposed to the intervention provided to patients in the control arm of Study 2 but will not be considered participants in Study 2.

Group IV: Study 2 (Study of Patients with Documented LCS Eligibility) Control Arm Active Control 1 Intervention

In Study 2 (Study of Patients with Documented LCS Eligibility), patients allocated to the control arm will be exposed to the DecisionPrecision+ intervention because DecisionPrecision+ intervention automatically identifies patients with documented LCS eligibility in the EHR. The DecisionPrecision+ intervention is designed to promote LCS and LCS SDM; the intervention consists of provider-facing EHR preventive care reminders, a provider-facing EHR SDM tool, and simple patient-facing preventive care reminders (only available at UUH). Simple patient-facing preventive care reminders will be available at UUH, but not NYU, because NYU does not use this type of reminders.

DecisionPrecision+ is already approved in United States for the following indications:

Approved in United States as Decision Precision+ for:

• Lung cancer screening for individuals with a history of heavy smoking

Who Is Running the Clinical Trial?

University of Utah

Lead Sponsor

Trials

1,169

Recruited

1,623,000+

Findings from Research

The implementation of the DecisionPrecision tool was significantly more effective at sites that underwent the LEAP quality improvement training, with an average usage of 190.3 times over 6 months compared to just 3.5 times at standard sites, highlighting the importance of training in utilizing decision support tools.

Despite increased awareness of the tool's benefits from academic detailing visits, there was no significant increase in its use, indicating that for effective integration, such tools need to be fully automated and embedded in electronic health records to fit into the busy schedules of primary care physicians.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Implementation of a Web-Based Tool for Shared Decision-making in Lung Cancer Screening: Mixed Methods Quality Improvement Evaluation. Lowery, J., Fagerlin, A., Larkin, AR., et al.[2023]

The PLCO Screening Trial, which included around 155,000 participants, provides valuable data that can be used to match patients with similar demographics and risk factors to improve cancer screening outcomes.

A new web-based application developed from this data allows for personalized cancer screening recommendations, optimizing healthcare resources by focusing on patients who are most likely to benefit from screening, thus enhancing both clinical efficacy and early disease detection.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Focused Decision Support: a Data Mining Tool to Query the Prostate, Lung, Colorectal, and Ovarian Cancer Screening Trial Dataset and Guide Screening Management for the Individual Patient. Sharma, A., Hostetter, J., Morrison, J., et al.[2018]

In a study involving 288 participants in a lung cancer CT screening trial, making an informed decision to participate did not significantly improve health-related quality of life (HRQoL) outcomes for most measures, except for a slight improvement in mental health before randomization.

Participants who received indeterminate CT results showed no significant differences in HRQoL between those who made informed decisions and those who did not, suggesting that the decision-making process may not impact overall well-being during screening.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Informed decision making does not affect health-related quality of life in lung cancer screening (NELSON trial). van den Bergh, KA., Essink-Bot, ML., van Klaveren, RJ., et al.[2010]

In a study involving 237 lung cancer screening patients, both Option Grids and Shouldiscreen.com were effective in facilitating shared decision making (SDM), but Option Grids led to lower decision regret and better understanding of potential complications compared to Shouldiscreen.com.

Patients using Shouldiscreen.com reported less knowledge about complications and had higher decision regret regarding their choice to pursue screening, highlighting the importance of the decision aid used in the SDM process.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Aiding shared decision making in lung cancer screening: two decision tools. Sferra, SR., Cheng, JS., Boynton, Z., et al.[2021]

A group education class on lung cancer screening significantly improved participants' knowledge, with an average increase of 0.9 correct responses on a true-false quiz after the class, based on surveys from 680 participants.

Approximately 70% of participants felt they had enough information to make informed decisions about lung cancer screening after attending the class, indicating that this educational approach effectively enhances decision-making capacity.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Effectiveness of a Patient Education Class to Enhance Knowledge about Lung Cancer Screening: a Quality Improvement Evaluation. Sakoda, LC., Meyer, MA., Chawla, N., et al.[2021]

The Personalised Lung Cancer Screening (PLuS) study aims to analyze the health conditions of around 40,000 individuals undergoing lung cancer screening with low-dose CT (LDCT) from 2016 to 2022, focusing on the comorbidity burden and the benefits and harms of screening.

This study is unique as it combines real-world observational data with simulation models to assess the long-term impact of lung cancer screening, which will help inform decision-making for older and potentially sicker populations.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Personalised Lung Cancer Screening (PLuS) study to assess the importance of coexisting chronic conditions to clinical practice and policy: protocol for a multicentre observational study. Braithwaite, D., Karanth, SD., Slatore, CG., et al.[2023]

In a survey of 75 patients regarding lung cancer screening, most reported that healthcare providers frequently discussed the benefits of screening (81.3%), but only a minority addressed potential harms (44.0%), leading to generally low knowledge about screening outcomes (average score of 3.71 out of 8).

Despite low knowledge levels, patients experienced low decisional conflict about their screening choices, although Black patients reported higher decisional conflict compared to White patients, indicating a need for improved communication and information sharing during shared decision-making visits.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Outcomes of Shared Decision-Making for Low-Dose Screening for Lung Cancer in an Academic Medical Center. Eberth, JM., Zgodic, A., Pelland, SC., et al.[2023]

After watching a patient decision aid, 90% of current and former smokers expressed a strong intention to discuss lung cancer screening with their doctor, indicating high engagement in their health decisions.

Participants valued the potential benefits of early cancer detection and expressed significant concerns about false positives and overdiagnosis, highlighting the importance of informed decision-making in lung cancer screening.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Using a Patient Decision Aid Video to Assess Current and Former Smokers' Values About the Harms and Benefits of Lung Cancer Screening With Low-Dose Computed Tomography. Hoffman, AS., Hempstead, AP., Housten, AJ., et al.[2023]

A patient decision aid (PDA) significantly improved smokers' preparedness for making decisions about lung cancer screening compared to standard educational materials, with 67.4% of PDA users feeling well-prepared versus 48.2% of those using standard education.

Participants using the PDA reported feeling more informed and clearer about their values regarding the benefits and harms of screening, but there was no difference in actual screening intentions or behaviors between the PDA and standard education groups.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Effect of a Patient Decision Aid on Lung Cancer Screening Decision-Making by Persons Who Smoke: A Randomized Clinical Trial. Volk, RJ., Lowenstein, LM., Leal, VB., et al.[2021]

A systematic review of 10 publicly available lung cancer risk calculators (LCRCs) revealed significant variability in their risk predictions and outputs, which could confuse patients seeking to make informed decisions about lung cancer screening.

The study found differences in risk depiction for hypothetical patients, with the largest discrepancy being 16.49%, highlighting the need for standardization in input variables and risk prediction models to improve shared decision-making in lung cancer screening.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

A Comparison of Web-Based Cancer Risk Calculators That Inform Shared Decision-making for Lung Cancer Screening. Kates, FR., Romero, R., Jones, D., et al.[2022]

Low-dose computed tomography (LDCT) screening has been shown to significantly reduce lung cancer-related mortality by 20% and all-cause mortality by 6.7% compared to traditional chest X-rays, based on the US National Lung Screening Trial involving a large patient population.

The implementation of lung cancer screening using LDCT is underway in the USA, guided by NLST criteria, while ongoing European trials are expected to provide further insights into optimizing screening methodologies and cost-effectiveness.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

What is the Optimum Screening Strategy for the Early Detection of Lung Cancer. Baldwin, DR., Callister, ME.[2018]

The web-based decision aid significantly increased participants' knowledge about lung cancer risks and screening options, leading to improved understanding and reduced decisional conflict regarding screening choices.

After using the decision aid, participants showed better alignment with U.S. Preventive Services Task Force recommendations for lung cancer screening, indicating that the tool effectively supports informed decision-making for current and former smokers.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Evaluation of a Personalized, Web-Based Decision Aid for Lung Cancer Screening. Lau, YK., Caverly, TJ., Cao, P., et al.[2018]

Annual low-dose CT screening for lung cancer significantly reduces mortality in high-risk individuals, specifically those aged 55-74 with a smoking history of over 30 pack-years.

Effective lung cancer screening requires a comprehensive approach that includes personalized risk assessment, shared decision-making, and a multidisciplinary care team to manage potential risks like overdiagnosis and anxiety from indeterminate findings.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Reviewing Lung Cancer Screening: The Who, Where, When, Why, and How. Bade, BC., Brasher, PB., Luna, BW., et al.[2019]

References

1. Canada pubmed.ncbi.nlm.nih.gov

Implementation of a Web-Based Tool for Shared Decision-making in Lung Cancer Screening: Mixed Methods Quality Improvement Evaluation. [2023]

2. United States pubmed.ncbi.nlm.nih.gov

Focused Decision Support: a Data Mining Tool to Query the Prostate, Lung, Colorectal, and Ovarian Cancer Screening Trial Dataset and Guide Screening Management for the Individual Patient. [2018]

3. England pubmed.ncbi.nlm.nih.gov

Informed decision making does not affect health-related quality of life in lung cancer screening (NELSON trial). [2010]

4. England pubmed.ncbi.nlm.nih.gov

Aiding shared decision making in lung cancer screening: two decision tools. [2021]

5. England pubmed.ncbi.nlm.nih.gov

Effectiveness of a Patient Education Class to Enhance Knowledge about Lung Cancer Screening: a Quality Improvement Evaluation. [2021]

6. England pubmed.ncbi.nlm.nih.gov

Personalised Lung Cancer Screening (PLuS) study to assess the importance of coexisting chronic conditions to clinical practice and policy: protocol for a multicentre observational study. [2023]

7. England pubmed.ncbi.nlm.nih.gov

Outcomes of Shared Decision-Making for Low-Dose Screening for Lung Cancer in an Academic Medical Center. [2023]

8. United States pubmed.ncbi.nlm.nih.gov

Using a Patient Decision Aid Video to Assess Current and Former Smokers' Values About the Harms and Benefits of Lung Cancer Screening With Low-Dose Computed Tomography. [2023]

9. United States pubmed.ncbi.nlm.nih.gov

Effect of a Patient Decision Aid on Lung Cancer Screening Decision-Making by Persons Who Smoke: A Randomized Clinical Trial. [2021]

10. United States pubmed.ncbi.nlm.nih.gov

A Comparison of Web-Based Cancer Risk Calculators That Inform Shared Decision-making for Lung Cancer Screening. [2022]

11. England pubmed.ncbi.nlm.nih.gov

What is the Optimum Screening Strategy for the Early Detection of Lung Cancer. [2018]

12. Netherlands pubmed.ncbi.nlm.nih.gov

Evaluation of a Personalized, Web-Based Decision Aid for Lung Cancer Screening. [2018]

13. United States pubmed.ncbi.nlm.nih.gov

Reviewing Lung Cancer Screening: The Who, Where, When, Why, and How. [2019]