Understanding how a personalized risk prediction tool (VALUE-PERSARC) supports informed treatment decisions of soft-tissue sarcomas patients in daily clinical practice – A mixed methods study

European Journal of Cancer, vol. 210, pp. 114269

Abstract

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.

Authors 9

1. Anouk A. Kruiswijk Aachen

   Leiden University · Leiden University Medical Center

   Affiliation as printed

   Department of Biomedical Data Sciences, Medical Decision Making, Leiden University Medical Center, Albinusdreef 2, 2333 ZA Leiden, the Netherlands; Orthopedic Surgery, Leiden University Medical Center, Albinusdreef 2, 2333 ZA Leiden, the Netherlands. Electronic address: a.a.kruiswijk@lumc.nl

2. Ellen G. Engelhardt

   The Netherlands Cancer Institute

   Affiliation as printed

   Division of Psychosocial Research and Epidemiology, The Netherlands Cancer Institute, P.O. Box 90203, 1006 BE Amsterdam, the Netherlands

3. Lisa A. E. Vlug Aachen Department of Biomedical Data Sciences

   Leiden University Medical Center

   Affiliation as printed

   Department of Biomedical Data Sciences, Medical Decision Making, Leiden University Medical Center, Albinusdreef 2, 2333 ZA Leiden, the Netherlands

4. Robert J P van de Wal Aachen Orthopedic Surgery

   Leiden University Medical Center

   Affiliation as printed

   Orthopedic Surgery, Leiden University Medical Center, Albinusdreef 2, 2333 ZA Leiden, the Netherlands

5. Yvonne M. Schrage

   The Netherlands Cancer Institute

   Affiliation as printed

   Department of Surgical Oncology, The Netherlands Cancer Institute, 1066 CX Amsterdam, the Netherlands

6. Rick L. Haas Aachen

   Leiden University Medical Center · The Netherlands Cancer Institute

   Affiliation as printed

   Department of Radiotherapy, The Netherlands Cancer Institute, 1066 CX Amsterdam, the Netherlands; Department of Radiotherapy, Leiden University Medical Center, Albinusdreef 2, 2333 ZA Leiden, the Netherlands

7. Michiel A. J. van de Sande Aachen Orthopedic Surgery

   Leiden University Medical Center

   Affiliation as printed

   Orthopedic Surgery, Leiden University Medical Center, Albinusdreef 2, 2333 ZA Leiden, the Netherlands

8. Perla J. Marang‐van de Mheen

   Delft University of Technology

   Affiliation as printed

   Safety & Security Science, Faculty of Technology, Policy & Management, Delft University of Technology, Jaffalaan 5, 2628 BX Delft, the Netherlands

9. Leti van Bodegom‐Vos Aachen Department of Biomedical Data Sciences

   Leiden University Medical Center

   Affiliation as printed

   Department of Biomedical Data Sciences, Medical Decision Making, Leiden University Medical Center, Albinusdreef 2, 2333 ZA Leiden, the Netherlands