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Introduction <p>Risk prediction models (RPMs) are statistical tools that predict outcomes on the basis of clinical characteristics and can thereby support (shared) decision-making. With the shift toward personalized medicine, the number of RPMs has increased exponentially, including in multimodal sarcoma care. However, their integration into routine soft-tissue sarcoma (STS) care remains largely unknown. Therefore, we inventoried RPM use in sarcoma care during tumor board discussions and patient consultations as well as the attitudes toward the use of RPMs to support (shared) decision-making among STS clinicians.</p> Materials and Methods <p>A 29-item survey was disseminated online to members of international sarcoma societies.</p> Results <p>This study enrolled 278 respondents. Respectively, 68% and 65% of the clinicians reported using RPMs during tumor board discussions and/or patient consultations. During tumor board discussions, RPMs were used primarily to assess the potential benefits of (neo)adjuvant chemotherapy. During patient consultations, RPMs were used to predict patient prognosis upon request and to assist in decision-making regarding (neo)adjuvant therapies. The reliability of patient risk predicted by RPMs and the absence of guidelines regarding the use of RPMs were identified as barriers. Additionally, some clinicians questioned the applicability of estimates from RPMs to individual patients and expressed concerns about causing unnecessary anxiety when discussing prognostic outcomes.</p> Conclusions <p>Responding STS clinicians frequently use RPMs to support decision-making about (neo)adjuvant therapies. However, they expressed concerns about the applicability of RPM estimates to individual patients and reported challenges in communicating prognostic outcomes with patients. These findings highlight the difficulties clinicians face when integrating RPMs into patient consultations.</p>

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Risk-Prediction Models for Clinical Decision-Making in Sarcoma Care: An International Survey Among Soft-Tissue Sarcoma Clinicians

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年2月1日

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  • Anouk A. Kruiswijk,
  • Lisa A. E. Vlug,
  • Ibtissam Acem,
  • Ellen G. Engelhardt,
  • Alessandro Gronchi,
  • Dario Callegaro,
  • Rick L. Haas,
  • Robert J. P. van de Wal,
  • Michiel A. J. van de Sande,
  • Leti van Bodegom-Vos

摘要

Introduction

Risk prediction models (RPMs) are statistical tools that predict outcomes on the basis of clinical characteristics and can thereby support (shared) decision-making. With the shift toward personalized medicine, the number of RPMs has increased exponentially, including in multimodal sarcoma care. However, their integration into routine soft-tissue sarcoma (STS) care remains largely unknown. Therefore, we inventoried RPM use in sarcoma care during tumor board discussions and patient consultations as well as the attitudes toward the use of RPMs to support (shared) decision-making among STS clinicians.

Materials and Methods

A 29-item survey was disseminated online to members of international sarcoma societies.

Results

This study enrolled 278 respondents. Respectively, 68% and 65% of the clinicians reported using RPMs during tumor board discussions and/or patient consultations. During tumor board discussions, RPMs were used primarily to assess the potential benefits of (neo)adjuvant chemotherapy. During patient consultations, RPMs were used to predict patient prognosis upon request and to assist in decision-making regarding (neo)adjuvant therapies. The reliability of patient risk predicted by RPMs and the absence of guidelines regarding the use of RPMs were identified as barriers. Additionally, some clinicians questioned the applicability of estimates from RPMs to individual patients and expressed concerns about causing unnecessary anxiety when discussing prognostic outcomes.

Conclusions

Responding STS clinicians frequently use RPMs to support decision-making about (neo)adjuvant therapies. However, they expressed concerns about the applicability of RPM estimates to individual patients and reported challenges in communicating prognostic outcomes with patients. These findings highlight the difficulties clinicians face when integrating RPMs into patient consultations.