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Christina Sit



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    ES05 - Collaboration Between Stakeholders to Improve Lung Cancer Research (ID 773)

    • Event: WCLC 2018
    • Type: Educational Session
    • Track: Advocacy
    • Presentations: 1
    • Moderators:
    • Coordinates: 9/25/2018, 15:15 - 16:45, Room 205 AC
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      ES05.04 - Challenges and Solutions in Engaging with the Health Technology Assessment Process in Canada (ID 11373)

      16:15 - 16:35  |  Presenting Author(s): Christina Sit

      • Abstract

      Abstract not provided

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    P2.15 - Treatment in the Real World - Support, Survivorship, Systems Research (Not CME Accredited Session) (ID 964)

    • Event: WCLC 2018
    • Type: Poster Viewing in the Exhibit Hall
    • Track:
    • Presentations: 1
    • Moderators:
    • Coordinates: 9/25/2018, 16:45 - 18:00, Exhibit Hall
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      P2.15-08 - Impact and Feasibility of a Support Group for Women with Lung Cancer (ID 12647)

      16:45 - 18:00  |  Author(s): Christina Sit

      • Abstract
      • Slides

      Background

      Patients with lung cancer have limited psychosocial supports in the community. Participation in support groups provides emotional, social and educational support and may be beneficial for cancer patients. In our city there are no support groups for patients with lung cancer. We assessed the impact and feasibility of a lung cancer support group for women.

      a9ded1e5ce5d75814730bb4caaf49419 Method

      We conducted a weekly, 1.5-hour psycho-education support group for 6 sessions in 2017. Sessions were facilitated by a trained social worker with long experience in psychosocial oncology. Pre- and post-intervention quality of life tools were administered: the Canadian Problem Checklist (CPC), Functional Assessment of Cancer Therapy-Lung (FACT-L), Impact of Events Scale (IES), and the Hospital Anxiety and Depression Scale (HADS). Median changes in scores were calculated. A negative (-) change represents improvement in the domain.

      4c3880bb027f159e801041b1021e88e8 Result

      10 women were enrolled, 60% completed the program. Demographics were available for 8 participants: median age 68 (range 26-80); median diagnosis year 2016 (range 2000-2017); 3 vs 5 women were ECOG PS 0 vs 1; 1 current smoker, 2 past smokers; 5 with metastatic disease; 5 undergoing palliative systemic therapy, 1 undergoing curative therapy, 1 had completed curative therapy,1 receiving best supportive care. Both pre- and post-intervention tools were completed by 60% of participants. The CPC showed that 67% had reduced/stable problems after the intervention. The FACT-L, IES and HADS tools and their sub-scales generally showed a favourable change after the intervention. See Table 1 for the median pre- and post-intervention scores and median change in scores. 100% of participants agreed or strongly agreed that the group helped them cope with lung cancer.

      Table 1

      Scale

      Pre-intervention score, median [IQR] Post-intervention score, median [IQR] Change in score, median [IQR]
      FACT-L 1.4 [1.0-1.9] 1.9 [0.8-2.1] 0 [-0.1-0.6]
      Physical wellbeing 1.1 [1.0-1.7] 1.6 [0.9-2.6] 0 [0-0]
      Social/family wellbeing 0.6 [0.3-1] 0.7 [0.2-1.0] -0.1 [-0.3-0.4]
      Emotional wellbeing 0.3 [0.3-1] 0.7 [0-1.0] 0.3 [-0.7-0.3]
      Functional wellbeing 1.6 [1.2-2] 1.7 [1.1-2.4] 0.4 [-0.3-0.6]
      Additional concerns 1.7 [0.6-2.5] 2.1 [0.4-2.4] -0.1 [-0.3-0.4]
      Impact of Events 1.2 [0.6-1.9] 0.5 [0.2-1.4] -0.4 [-0.7-0.1]
      Avoidance 1.9 [0.4-2.3] 0.5 [0.2-1.4] -0.2 [-0.8-0.5]
      Intrusion 1.6 [1.1-1.9] 0.9 [0.3-1.3] -0.8 [-1.0-(-0.4)]
      Hyperarousal 0.7 [0.2-1.0] 0.3 [0.2-1.3] 0 [-0.3-0]
      HADS 0.7 [0.3-1.1] 0.9 [0.8-1.1] 0 [-3-0.6]
      Depression 0.6 [0.4-1.0] 0.9 [0.6-1.1] -0.1 [-0.3-0.6]
      Anxiety 0.6 [0.2-1.3] 1.0 [0.6-1.5] 0 [-0.3-0.3]

      8eea62084ca7e541d918e823422bd82e Conclusion

      A support group for women with lung cancer was feasible and quality of life improved in several domains. Support groups and other community resources should be more widely available for patients with lung cancer.

      6f8b794f3246b0c1e1780bb4d4d5dc53

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    P3.15 - Treatment in the Real World - Support, Survivorship, Systems Research (Not CME Accredited Session) (ID 981)

    • Event: WCLC 2018
    • Type: Poster Viewing in the Exhibit Hall
    • Track:
    • Presentations: 1
    • Moderators:
    • Coordinates: 9/26/2018, 12:00 - 13:30, Exhibit Hall
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      P3.15-23 - Data Mining the Internet and Crowdsourcing in Guiding Patient Decision-Making. (ID 12008)

      12:00 - 13:30  |  Author(s): Christina Sit

      • Abstract
      • Slides

      Background

      The internet, through social media, blogs and forums, has enabled patients to share experiences and outcomes. Data mining and crowdsourcing is a methodological approach to gather the individual experiences within these platforms and convert them into Real World Evidence (RWE) that can help patients make decisions, especially in the case of new treatments or treatments in trial. This abstract is a case study of how this methodology was used to inform the decision between Whole Brain Radiation (WBR) vs. osimertinb in treating brain metastasis in EGFR+, t790M, NSCLC – at a time when the results to an open label trial were pending.

      a9ded1e5ce5d75814730bb4caaf49419 Method

      The methodology identified trusted sources and similar profiled patients. The steps:

      1. Identify reputable on-line sources by referrals.

      2. Crowdsource peers with same tumour group, staging, gene sequence and treatment path.

      3. Analyze results and patient feedback of treatments

      A search identified 46 lung cancer blogs and 4 cancer forums. Inspire.com was then chosen as the crowdsourcing target as it had over 39,000 lung cancer patients registered.

      4c3880bb027f159e801041b1021e88e8 Result

      The search found 470 posts on osimertinib in clinical trial; 66 discussions on brain lesions and osimertinib were accessible from the search. Conversations with patients of the same genetic profiles, metastasis and treatments were selected. The user profiles detailed clinical histories, which increased the trust factor of the data. Information was obtained from April to June 2016 with data points beginning in 2014.

      When reviewing the 66 self-reported cases, it was found that osimertinib was effective in patients with brain metastases in 36% of patients while 4% had no response. Quality of life and side effects were other fields that were explored. This evidence influenced the patient to choose osimertinib instead of WBR to treat her lung cancer.

      8eea62084ca7e541d918e823422bd82e Conclusion

      The Internet offers opportunities to source evidence and help patients to make informed treatment decisions. There is a risk that misinformation may lead patients in the wrong direction, which will then be a burden on the practitioner and negatively influence treatment outcomes.

      Datamining and crowdsourcing is a methodological system that gathers individual self-reported results from the Internet and converts it into credible RWE. For this patient, it increased the trustworthiness of the information and helped decrease anxiety about the treatment decision. Used appropriately, it has the potential to inform treatment decisions, help predict outcomes, and be a tool for post marketing surveillance that can be used to inform health technology assessment.

      6f8b794f3246b0c1e1780bb4d4d5dc53

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    P3.CR - Case Reports (Not CME Accredited Session) (ID 984)

    • Event: WCLC 2018
    • Type: Poster Viewing in the Exhibit Hall
    • Track:
    • Presentations: 1
    • Moderators:
    • Coordinates: 9/26/2018, 12:00 - 13:30, Exhibit Hall
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      P3.CR-05 - The Role of Patient Engagement in Improved Outcomes in Lung Cancer Care (ID 13651)

      12:00 - 13:30  |  Author(s): Christina Sit

      • Abstract
      • Slides

      Background

      Anne Marie Cerato, the primary author, is the patient in this case study. Lung cancer patients tend to have a high disease burden as many are diagnosed at a late stage and require extensive treatment. As such, they are required to make life-altering decisions during and after treatment. The level of their engagement and participation in that decision-making process can differ greatly thus effecting their quality of care and quality of life.

      a9ded1e5ce5d75814730bb4caaf49419 Method

      Patient was diagnosed in 2009 with stage 3A adenocarcinoma of the right lung. Treatment decisions were required at three different time points within this patient’s experience (at time of original diagnosis, at recurrence, and at progression). Two different models of engagement were experienced. At diagnosis, the patient as a passive participant model was experienced, and upon recurrence and progression, the patient was an active participant in the decision making process.

      4c3880bb027f159e801041b1021e88e8 Result

      In the patient as a passive participant model, the patient felt duress to follow prescribed treatment resulting in feelings of isolation, anxiety, anger, and depression. In contrast, when the patient was an active participant and involved in the decision making process, the patient felt more in control of their treatment, which resulted in feelings of inclusion, hopefulness, and decreased anxiety.

      8eea62084ca7e541d918e823422bd82e Conclusion

      It is difficult to determine the difference whether or not there was improvement in patient clinical outcomes. However, qualitatively as a result of increased patient engagement and shared decision making, patients experienced improved outcomes as they felt less isolated, anxious, and more involved in their care. As a result of increased patient engagement, this patient explored additional treatment avenues and sought connections with the larger patient community. Clinicians can work with patients to enable or improve engagement and involvement by considering the following recommendations: 1. Patients should be given access to clinical notes, results and reports, and given time to consider the information provided. 2. Patients should be encouraged to ask questions. 3. Connect patients to a lung cancer patient group for additional support 4. Ensure patients are supported post treatment and through transitions in care.

      6f8b794f3246b0c1e1780bb4d4d5dc53

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