Citations

Background: The use of prescribed medications during pregnancy is a challenge and an underestimated source of treatment burden. Levothyroxine (LT4) for the treatment of overt and subclinical hypothyroidism is extensively prescribed during pregnancy. To this end, we aimed to explore the patients' perceived benefits and risks, knowledge, beliefs, attitudes, and related burden of LT4 therapy during pregnancy. Methods: In this cross-sectional study, we surveyed pregnant women who were treated with LT4 during pregnancy from January 1, 2019, to December 31, 2019, in a tertiary academic medical center of the United States. The anonymous online survey included questions to gather demographic data and multiple-choice questions regarding the benefits and risks, knowledge, beliefs, attitudes, and burden related to LT4 use during pregnancy. Results: Sixty-four pregnant women (mean age 31.5 years) completed the study survey (response rate: 96%): 62% were diagnosed with hypothyroidism more than 12 months before pregnancy, 16% less than or about 12 months before pregnancy, and 22% during pregnancy. We found that one-third of pregnant women using LT4 had a feeling of uneasiness/anxiety due to their hypothyroidism diagnosis. About half of the respondents (45%) reported that they did not receive an explanation by their clinician regarding the maternal/fetal risks of uncontrolled hypothyroidism or the benefits of adequate control. Finally, two in three patients expressed various concerns of LT4-related treatment burden. Conclusions: Our findings support the need for increased effective communication and tailored counseling to address fears, anxiety, and uncertainties about the benefits and risks of LT4 use in pregnancy. For patients with clear benefits from LT4 treatment in pregnancy, it could help to overcome their concerns, promote adherence, and decrease adverse maternal/fetal outcomes. For patients with no clear benefits established, clinicians need to be aware of LT4-related treatment burden in pregnancy and implement patient-centered approaches in their clinical practices.

Despite the evolving evidence in favor of shared decision making (SDM) and of decades-long calls for its adoption, SDM remains uncommon in routine care. Reflecting on this lack of progress, we sought to reimagine the future of SDM and the path to take us there. In late 2017, a multidisciplinary and international group of six researchers were challenged by a senior SDM scholar to envision the future and, based on a provocatively critical view of the present, to write letters to themselves from the year 2028. Letters were exchanged and discussed electronically. The group then met in person to discuss the letters. Since the letters painted a dystopian picture, they triggered questions about the nature of SDM, who should benefit from SDM, how to measure its contribution to care, and what new ways can be invented to design and test interventions to implement SDM in routine care. Through contrasting the purposefully generated dystopias with an ideal future for SDM, we generated reflections on a research agenda for SDM. These reflections hinged on recognizing SDM’s contributing to care, that is, as a way to advance the problematic human situation of patients. These focused on three distinct yet complimentary contributors to SDM: 1) the process of making decisions, 2) humanistic communication, and 3) fit-to-care of the resulting decision. The group then concluded that to move SDM from envisioned to routine practice, and to ensure it reaches all, particularly persons rendered vulnerable by current forms of health care, a substantial investment in implementation research is necessary. Perhaps the discussion of these reflections can contribute to a path forward that will improve the likelihood of the future we dream for SDM.

Abstract Background Remote digital monitoring (RDM, i.e., using digital devices to monitor patients' health and behavior) is a novel care model that can improve health outcomes for people with chronic conditions. However, it could be intrusive to patients' lives. We sought to understand which aspects of RDM make it intrusive to patients and why. Methods We performed content analysis of qualitative data collected by using open-ended questions in an international, online survey with a convenience sample of adults with type 1 or 2 diabetes (February-July 2019). Participants were first shown scenarios describing possible RDM features (i.e. different RDM tools [for glucose or food monitoring], feedback loops [receiving feedback in consultation, or remotely by a physician, or by artificial intelligence], and data handling options [by the public or private sector]). Results We analyzed data from 709 participants from 24 countries (38% men, median age 38, 54% type 1). Participants found RDM burdensome (n = 468). Burden arose from RDM features that caused disruption in daily life (e.g., alerts), features that may invite undesirable attention in public (e.g., visible wearable sensors may invite questions about one's health), or from having to adapt one's life to fit in RDM (e.g., adapt one's mealtime routine around food monitoring). Participants wanted control, particularly over sharing food-monitoring data with health care professionals in real-time to receive feedback (n = 440). They felt RDM could expose a delicate topic to 'surveillance' by authority figures (i.e., their data may 'reveal' poor dietary habits, leading to criticism by physicians). Intrusion could take the form of RDM eroding the patient-physician relationship (n = 34), or fear of data misuse (n = 206), which was associated with private-sector financial interests. Conclusions Our findings offer directions for minimally intrusive RDM design and show that digital health may cause concerns about stigma and treatment burden. Key messages Remote digital monitoring is intrusive when it increases treatment burden and limits patients’ control over their own health. “Minimally intrusive” digital health design could increase patient acceptability and, ultimately, foster scalability.

e24176 Background: Decision aids for prostate cancer treatment can improve knowledge, but the relative effect of pre-consultation and within-consultation DAs is not known. Methods: We conducted a parallel design, 2-by-2 factorial, 4-arm, cluster-RCT in urology practices affiliated with the NCI’s Alliance research base. We compared patient knowledge immediately after consultation (%12-item% correct) intervention arms compared to usual care. We used mixed effects regression modeling to assess effect of interventions on knowledge adjusting for site-clustering, race/ethnicity (non-Hispanic White; Other), age (years), clinical stage (T1-3), PSA, and Gleason score. Results: 15 of 21 randomized centers accrued 158 pts from Nov 2017 to June 2019. The arm delivering both DAs (n = 3 sites) recruited 25 pts. The pre-consultation DA arm (n = 4 sites) recruited 39 pts. The within-consultation DA arm (n = 4 sites) recruited 44 pts. The no DA arm (n = 4 sites) accrued 50 pts. Overall, mean age was 64; 54% were AA; 73% had T1 stage; median Gleason = 7; and median PSA = 7. The combined DAs group, followed by pre-consultation reported higher median knowledge (75% and 67%, respectively). The within-consultation and no DA arms both reported median knowledge scores (58%). Within arm, inter-site standard deviations in mean knowledge scores ranged from 0.164 in in the pre-consultation arm to 0.232 in the usual care arm. Neither pre-consultation nor within-consultation DAs had statistically significant effects (p = .15 and p = .99, respectively). Pre-specified adjusted difference [97.5% CIs], comparing the pre-consultation DA and the within-consultation DA with the control was 9.4% [-7.3%, 26.2%] and -0.4% [-16.9%, 16.1%], respectively. Intra-cluster correlation (ICC) coefficient was high (.24). No interaction effects were identified (p = .98). A post-hoc analysis comparing any DA vs usual care found a mean adjusted difference [95% CI] of 5.6% [-8.5%, 19.8%] (p = 0.40), 63.9% for any DA and 58.3% for usual care. Conclusions: Possible knowledge gains associated with some DAs for early prostate cancer were obscured by larger-than-expected intra-cluster correlation effects associated with the primary outcome. How interventions were used was not measured. Support:UG1CA189823; https://acknowledgments.alliancefound.org ; R01 MD008934-06. Clinical trial information: NCT03103321 .

2054 Background: Systematic reviews that summarize the toxicity of Immune checkpoint inhibitors (ICIs) become outdated very soon after publication. Therefore, we reported results of a toxicity meta-analysis at 2019 ASCO meeting and informed the intent to create a living systematic review (LSR). LSRs combine human and machine effort and support rapid evidence synthesis and living clinical practice guidelines. Now, we report our experience maintaining a LSR on toxicity of ICIs. Methods: Steps include quarterly literature searches to identify new clinical trials reporting ICI-associated adverse events (AEs), AI-enabled screening of new citations which meet the inclusion criteria, automated cumulative meta-analysis and an online reporting platform. Standard data formats and protocols were designed for inputting text, tables and graphics. Software was written to interpret these data and output the information in the appropriate format, such as a forest plot and summary tables. Finally, a dynamic interface that enables user inputs and displays the associated output was designed. Results: The LSR is continuously updated incorporating toxicity data from new clinical trials as it becomes available. We have screened 8000 relevant citations and summarized the odds of Grade 3 or higher AEs and AEs of special interest in patient receiving ICIs. The results are updated on quarterly basis and are available online. The results are updated on quarterly basis and will be available on a website at the time of publication. Prototype with dummy data is available at this link . This interface can also be manipulated via user input to organize and sort data tables and forest plots by type of cancer, name or mechanism (PD-1 or PD-L1) of ICI agent, single agent or combination, type of control arm, line of treatment and several other clinically relevant filters. For example, a user can instantaneously generate a meta-analysis summarizing the risk of colitis or pneumonitis in metastatic lung cancer trials with pembrolizmuab. Conclusions: This LSR engine can prospectively synthesize toxicity data from ICI trials in an efficient manner providing accurate and timely information for advanced clinical decision support at point-of-care. Efforts are ongoing to improve efficiency of screening, improve AI-enabled processes for automated screening and data abstraction, and test across multiple clinical questions.

ObjectiveShared decision‐making (SDM) is a strongly endorsed approach by which patients and clinicians work together to formulate a sensible care plan. The present study was undertaken to conduct a systematic review of SDM trials in patients considering knee arthroplasty (KA) to characterize how SDM was supported and the impact on care received.MethodsWe searched multiple bibliographic databases from inception to December 31, 2019. A pair of reviewers working independently selected studies for inclusion, extracted data, and evaluated each trial’s risk of bias.ResultsWe found 6 eligible randomized trials (4 included KA and hip arthroplasty), all of which tested the same proprietary decision aid (DA) (Treatment Choices for Hip or Knee Osteoarthritis), with some adding other materials to support SDM. These trials, all of which had moderate‐to‐high risk of bias, focused on assessing the effect of the DA on patient knowledge about the options while not explicitly supporting other aspects of SDM, such as choice awareness, deliberation, or decision‐making. One trial found an increase in the number of African American patients undergoing KA in the 12 months following the intervention. No other trials found that SDM impacts clinical outcomes.ConclusionEvidence for SDM in patients considering KA is mostly limited to a single DA. While use of this DA improves patient knowledge about their treatment options, this tool has not been shown to promote SDM, impact treatment decisions, or satisfaction with care. Future work should seek to support SDM directly and assess effects on treatment decisions, functional outcomes, and satisfaction.