Background: Risk prediction models (RPM) can potentially improve treatment decisions by providing personalized survival estimates for different treatment options, but their effectiveness is uncertain. The VALUE-PERSARC study evaluated the impact of the PERsonalised SARcoma Care (or PERSARC) RPM on decision-making quality in patients with high-grade extremity soft tissue sarcomas (STS).

Methods: A parallel cluster randomized controlled trial was conducted in seven Dutch hospitals, assigned to usual care (control) or care with PERSARC (intervention). PERSARC supported treatment recommendations and informed patients about personalized risks and relevant treatment options. The primary outcome was decision-making quality, measured by patients’ knowledge of treatment risks and benefits and decisional conflict (Decisional Conflict Scale). Secondary outcomes included the Cancer Worry Scale (CWS), Shared Decision-Making (SDM-Q9), number of treatment options discussed and treatment choice.

Results: This study enrolled 120 patients: 53 patients in the control group and 67 patients in the intervention group. No significant differences were observed between the control and intervention groups in patients’ adequate knowledge (respectively 82% vs. 86%) and decisional conflict (respectively 23.1 [15.5] vs. 18.9 [12.8]). Scores on the CWS (11.7 [3.3] vs. 11.0 [3.5]) and SDM-Q9 (13.3 [4.0] vs. 15.6 [3.3]) were also similar. Treatment choices did not differ significantly between groups. However, clinicians in the intervention group were significantly more likely to discuss multiple treatment options (93% vs. 35%).

Conclusion: While PERSARC did not significantly improve patients’ knowledge or decisional conflict, it led to more frequent discussion of multiple treatment options by clinicians. This may be an important step towards enhancing shared decision-making in practice. Trail registration: The VALUE-PERSARC study was registered on January 8, 2021 in the Netherlands Trial Register (NL9160) and updated on January 23, 2023 in ClinicalTrials.gov (NCT05741944).

Introduction: Risk prediction models (RPM) can help soft-tissue sarcoma(STS) patients and clinicians make informed treatment decisions by providing them with estimates of (disease-free) survival for different treatment options. However, it is unknown how RPMs are used in the clinical encounter to support decision-making. This study aimed to understand how a PERsonalised SARcoma Care (PERSARC) RPM is used to support treatment decisions and which barriers and facilitators influence its use in daily clinical practice. Methods: A convergent mixed-methods design is used to understand how PERSARC is integrated in the clinical encounter in three Dutch sarcoma centers. Data were collected using qualitative interviews with STS patients (n = 15) and clinicians (n = 8), quantitative surveys (n = 50) and audiotaped consultations (n = 30). Qualitative data were analyzed using thematic analysis and integrated with quantitative data through merging guided by the SEIPS model. Results: PERSARC was generally used to support clinicians’ proposed treatment plan and not to help patients weigh available treatment options. Use of PERSARC in decision-making was hampered by clinician's doubts about whether there were multiple viable treatment options,the accuracy of risk estimates, and time constraints. On the other hand, use of PERSARC facilitated clinicians to estimate and communicate the expected benefit of adjuvant therapy to patients. Conclusion: PERSARC was not used to support informed treatment decision-making in STS patients. Integrating RPMs into clinical consultations requires acknowledgement of their benefits in facilitating clinicians' estimation of the expected benefit of adjuvant therapies and information provision to patients, while also considering concerns regarding RPM quality and treatment options' viability.