Improving providers' survival estimates and selection of prognosis‐ and guidelines‐appropriate treatment for patients with symptomatic bone metastases: Development of the Bone Metastases Ensemble Trees for Survival Decision Support Platform

Abstract

Rationale, Aims and Objectives: In the management of symptomatic bone metastases, current practice guidelines do not provide clear methodology for selecting palliative radiotherapy (RT) regimens based on specific patient and disease features. Decision support aids may offer an effective means for translating the complex data needed to render individualised treatment decisions, yet no such tools are available for use in this setting. Thus, we describe the development of the Bone Metastases Ensemble Trees for Survival-Decision Support Platform (BMETS-DSP), which aims to optimise selection of evidence-based, individualised palliative RT regimens. Method: The Ottawa Decision Support Framework was used as the theoretical basis for development of BMETS-DSP. First, we utilised stakeholder input and review of the literature to assess determinants underlying the provider decision. Based on this assessment and iterative stakeholder feedback, we developed the web-based, provider-facing BMETS-DSP. Consistent with the underlying theoretical framework, our design also included assessment of decision quality using the International Patient Decision Aids Standards (IPDAS) certification checklist. Results: Stakeholder input and review of 54 evidence-based publications identified the following determinants of the provider decision: estimated prognosis, characteristics of the target symptomatic lesion and the primary cancer type, consideration of alternative interventions, access to patient-specific recommendations, and patient preferences. Based on these determinants, we developed the BMETS-DSP that (1) collects patient-specific data, (2) displays an individualised predicted survival curve, and (3) provides case-specific, evidence-based recommendations regarding RT, open surgery, systemic therapy, and hospice referral to aid in the

Evaluation of the Clinical Utility of the Bone Metastases Ensemble Trees for Survival Decision Support Platform (BMETS-DSP): A Case-Based Pilot Assessment

“…Individualized recommendations were generated using BMETS median predicted survival time and other patient-and diseasespecific characteristics, as previously detailed. 8 Physicians evaluated the same case histories at two time points, first without and then with the use of the BMETS-DSP output, separated by a washout period of 3-4 weeks, as per Cheng et al 16 The time between the start and completion of each phase of the assessment was ≤ 1 week. This project was approved by the institutional review board at the Johns Hopkins University (IRB00198589).…”

“…mirror the cut point used for appropriateness of spine surgery. 19 As previously described, 8 this cut point was also used to determine whether shorter fraction RT would be recommended. The 6-month time point (≤ 6 v .…”

“…6 months). [5][6][7][8] c We made the assumption that an appropriate recommendation would be the use of longer fractionation or SBRT regimens in the presence of a complicating feature. [5][6][7][8]24 recommendation for surgery improved pre-versus post-DSP, from 47.3% and 52.8% (McNemar's X 2 = 5.24, P = .022).…”

“…[1][2][3][4] Moreover, available evidence-based and consensus guidelines do not provide clear criteria for selecting between the range of palliative RT regimens. [5][6][7] To address these issues, we developed the providerfacing Bone Metastases Ensemble Trees for Survival Decision Support Platform (BMETS-DSP), 8 which (1) collects patient-specific characteristics critical to treatment selection, (2) displays a patient-specific predicted survival curve on the basis of the validated BMETS machine learning survival model, [9][10][11][12] and (3) provides case-specific, evidence-based recommendations for RT, open surgery, systemic therapy, and hospice referral in the care of symptomatic bone metastases. 8 The BMETS-DSP is available on a free-access website.…”

“…[5][6][7] To address these issues, we developed the providerfacing Bone Metastases Ensemble Trees for Survival Decision Support Platform (BMETS-DSP), 8 which (1) collects patient-specific characteristics critical to treatment selection, (2) displays a patient-specific predicted survival curve on the basis of the validated BMETS machine learning survival model, [9][10][11][12] and (3) provides case-specific, evidence-based recommendations for RT, open surgery, systemic therapy, and hospice referral in the care of symptomatic bone metastases. 8 The BMETS-DSP is available on a free-access website. 13 Although a range of decision support aids have been described in the literature, few have undergone dedicated assessment of efficacy in the clinical setting.…”