Montori, Víctor M.

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.

6513 Background: Investigators finding a large treatment effect in an interim analysis may terminate a randomized trial (RCT) earlier than planned. A systematic review (Montori et. al., JAMA 2005; 294: 2203–2209) found that RCTs stopped early for benefit are poorly reported and may overestimate the true treatment affect. The extent to which RCTs in oncology stopped early for benefit share similar concerns remains unclear. Methods: We selected RCTs in oncology which had been reported in the original systematic review and reviewed the study characteristics, features related to the decision to monitor and stop the study early (sample size, interim analyses, monitoring and stopping rules), and the number of events and the estimated treatment effects. Results: We found 29 RCTs in malignant hematology (n=6) and oncology (n=23), 52% published in 2000–2004 and 41% in 3 high-impact medical journals (New England Journal of Medicine, Lancet, JAMA). The majority (79%) of trials reported a planned sample size and, on average, recruited 67% of the planned sample size (SD 31%). RCTs reported (1) the planned sample size (n=20), (2) the interim analysis at which the study was terminated (n=16), and (3) whether the decision to stop the study prematurely was informed by a stopping rule (n=16); only 13 reported all three. There was a highly significant correlation between the number of events and the treatment effect (r=0.68, p=0.0007). The odds of finding a large treatment effect (a relative risk < median of 0.54, IQR 0.3–0.7) when studies stopped after a few events (no. events < median of 54 events, IQR 22–125) was 6.2 times greater than when studies stopped later. Conclusions: RCTs in oncology stopped early for benefit tend to report large treatment effects that may overestimate the true treatment effect, particularly when the number of events driving study termination is small. Also, information pertinent to the decision to stop early was inconsistently reported. Clinicians and policymakers should interpret such studies with caution, especially when information about the decision to stop early is not provided and few events occurred. No significant financial relationships to disclose.