MyLungHealth for Lung Cancer Screening

(MyLungHealth Trial)

Not currently 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

What You Need to Know Before You Apply

What is the purpose of this trial?

This trial explores ways to enhance early lung cancer screening using two digital tools, MyLungHealth and DecisionPrecision+. These tools help identify individuals eligible for low-dose CT scans, crucial for early lung cancer detection. The trial seeks participants who have smoked in the past or currently smoke and have recently visited a study clinic. By facilitating discussions between patients and doctors about screening, the trial aims to improve screening rates and informed decision-making. As an unphased trial, it offers participants the chance to contribute to innovative approaches in lung cancer screening.

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 prior data suggests that the MyLungHealth intervention is safe for lung cancer screening?

Previous studies have tested the MyLungHealth and DecisionPrecision+ tools for safety and effectiveness in lung cancer screening. These digital platforms assist with decision-making and aim to improve screening rates. Research has shown that patients generally tolerate these tools well, with no significant negative effects reported. They offer information and guidance rather than medical treatments, which usually results in a lower risk of side effects.

MyLungHealth identifies patients who should be screened and helps them make informed decisions. DecisionPrecision+ aids healthcare providers by offering reminders and tools to guide discussions about lung cancer screening options with patients. Both tools are digital and non-invasive, which typically ensures user safety. Although specific safety data isn't provided, the nature of these tools suggests minimal risk for trial participants. ^{1 2 3 4 5}

Why are researchers excited about this trial?

Researchers are excited about the MyLungHealth program because it aims to improve lung cancer screening (LCS) by leveraging technology in innovative ways. Unlike traditional methods that rely heavily on patient history and doctor recommendations, MyLungHealth uses pre-visit questionnaires through electronic health records (EHR) to identify patients who might be eligible for screening. This proactive approach could increase early detection rates by reaching patients who might otherwise slip through the cracks. Additionally, integrating the DecisionPrecision+ tool, which provides reminders and decision-making support directly within the EHR, enhances the overall effectiveness of the screening process, making it a promising advancement in lung cancer prevention.

What evidence suggests that this trial's interventions could be effective for improving lung cancer screening rates?

Research has shown that MyLungHealth, which participants in this trial may receive, can significantly improve lung cancer screening rates by better identifying candidates for screening. This method has identified 49.4% more individuals who might need screening. DecisionPrecision+ is another tool under study in this trial, facilitating discussions between doctors and patients to choose the best screening options. This tool could have a substantial impact, potentially preventing over 10,000 lung cancer cases through improved screening. These efforts aim to detect lung cancer early, which is crucial for better health outcomes. ^{1 2 3 4 6}

Who Is on the Research Team?

Kensaku Kawamoto, MD, PhD, MHS

Principal Investigator

University of Utah

Are You a Good Fit for This Trial?

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 for a Trial Participant

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

What Are the Treatments Tested in This Trial?

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+.

How Is the Trial Designed?

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+

Published Research Related to This Trial

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]

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]

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]

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]

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]

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]

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]

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]

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]

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]

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]

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]

Citations

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Decision Precision+: Increasing Lung Cancer Screening for At ... A shared decision-making tool to support the appropriate use of low-dose computed tomography screening has the potential to prevent 10,000 or more lung ...

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Prediction-Augmented Shared Decision-Making and Lung ... A prediction-augmented shared decision-making tool that helps clinicians personalize the strength of their lung cancer screening (LCS) recommendations ...

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6. ascopubs.org ascopubs.org/doi/10.1200/CCI-24-00291

Optimizing Strategy for Lung Cancer Screening: From Risk ... This pipeline provides accurate risk assessments and actionable insights tailored to individuals, empowering clinicians and patients to make ...