Introduction Current treatment decision-making in high-grade soft-tissue sarcoma (STS) care is not informed by individualised risks for different treatment options and patients’ preferences. Risk... Show moreIntroduction Current treatment decision-making in high-grade soft-tissue sarcoma (STS) care is not informed by individualised risks for different treatment options and patients’ preferences. Risk prediction tools may provide patients and professionals insight in personalised risks and benefits for different treatment options and thereby potentially increase patients’ knowledge and reduce decisional conflict. The VALUE-PERSARC study aims to assess the (cost-)effectiveness of a personalised risk assessment tool (PERSARC) to increase patients’ knowledge about risks and benefits of treatment options and to reduce decisional conflict in comparison with usual care in high-grade extremity STS patients.Methods The VALUE-PERSARC study is a parallel cluster randomised control trial that aims to include at least 120 primarily diagnosed high-grade extremity STS patients in 6 Dutch hospitals. Eligible patients (≥18 years) are those without a treatment plan and treated with curative intent. Patients with sarcoma subtypes or treatment options not mentioned in PERSARC are unable to participate. Hospitals will be randomised between usual care (control) or care with the use of PERSARC (intervention). In the intervention condition, PERSARC will be used by STS professionals in multidisciplinary tumour boards to guide treatment advice and in patient consultations, where the oncological/orthopaedic surgeon informs the patient about his/her diagnosis and discusses benefits and harms of all relevant treatment options. The primary outcomes are patients’ knowledge about risks and benefits of treatment options and decisional conflict (Decisional Conflict Scale) 1 week after the treatment decision has been made. Secondary outcomes will be evaluated using questionnaires, 1 week and 3, 6 and 12 months after the treatment decision. Data will be analysed following an intention-to-treat approach using a linear mixed model and taking into account clustering of patients within hospitals. Show less
