Evidence map›Paper›PMID 41023919›Full record

Trial reportBMC medical informatics and decision making2025

Evaluating the effectiveness of a risk prediction model (PERSARC) on improving treatment decisions quality for patients with soft-tissue sarcomas: the VALUE-PERSARC study.

Anouk A Kruiswijk, Perla J Marang-van de Mheen, Lisa A E Vlug, Ellen G Engelhardt, Marta Fiocco, Rick L Haas, Yvonne M Schrage, Cornelis Verhoef, Marc H A Bemelmans, Robert J van Ginkel and 5 more

Registry-linked trialAbstract readRandomized Controlled TrialMulticenter Study
In one paragraph

Trial report in BMC medical informatics and decision making, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT05741944 (The Value of a Risk Prediction Tool), which is not on this map. Not yet cited in PubMed.

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0cells of the map it votes in
0citing papers in PubMed
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1 · What the graph read from it

What it found

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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

NCT05741944 naactive not recruitingnot on this map

The Value of a Risk Prediction Tool (PERSARC) for Effective Treatment Decisions of Soft-tissue Sarcomas Patients

TypeinterventionalSponsorLeiden University Medical CenterRan2021 to 2025Enrolled120ConditionsSoft-tissue Sarcoma, Predictive Cancer ModelArmsCare with the use of PERSARC, standard care
3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

15 authors.

Anouk A KruiswijkDepartment of Biomedical Data Sciences, Medical Decision Making, Leiden University Medical Center, Leiden, The Netherlands.
Perla J Marang-van de MheenSafety & Security Science, Faculty of Technology, Policy & Management, Delft University of Technology, Delft, The Netherlands.
Lisa A E VlugDepartment of Biomedical Data Sciences, Medical Decision Making, Leiden University Medical Center, Leiden, The Netherlands.
Ellen G EngelhardtDivision of Psychosocial Research and Epidemiology, Division of Molecular Pathology, The Netherlands Cancer Institute, Amsterdam, The Netherlands.
Marta FioccoMathematical Institute, Leiden University, Leiden, The Netherlands.
Rick L HaasDepartment of Radiotherapy, The Netherlands Cancer Institute, Amsterdam, The Netherlands.
Yvonne M SchrageDepartment of Surgical Oncology, The Netherlands Cancer Institute, Amsterdam, The Netherlands.
Cornelis VerhoefDepartment of Surgical Oncology and Gastrointestinal Surgery, Erasmus MC Cancer Institute, Erasmus Medical Center, Rotterdam, The Netherlands.
Marc H A BemelmansDepartment of Surgical Oncology, Maastricht University Medical Center, Maastricht, The Netherlands.
Robert J van GinkelDepartment of Surgical Oncology, University Medical Center Groningen, Groningen, The Netherlands.
Johannes J BonenkampDepartment of Surgery, Radboud University Medical Center, Nijmegen, The Netherlands.
Arjen J WitkampDepartment of Surgical Oncology, University Medical Center Utrecht, Utrecht, The Netherlands.
Michiel A J van de SandeOrthopedic Surgery, Leiden University Medical Center, Leiden, The Netherlands.
Leti van Bodegom-VosDepartment of Biomedical Data Sciences, Medical Decision Making, Leiden University Medical Center, Leiden, The Netherlands. L.van_Bodegom-Vos@lumc.nl.
VALUE-PERSARC research group

Funding

KWF Kankerbestrijding 12642
6 · The paper itself

Abstract

backgroundRisk 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).

methodsA 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.

resultsThis 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%).

conclusionWhile 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).

trial registrationThe VALUE-PERSARC study was initially registered in the Netherlands Trial Register (NL9160) on January 8, 2021, and subsequently updated in ClincicalTrials.gov (NCT05741944) on January 31, 2023.

Indexed as

Decision MakingDecision Support TechniquesPatient ParticipationSarcomaAdultAgedFemaleHumansMaleMiddle AgedNetherlandsRisk AssessmentClinical consultation soft-tissue sarcomaInformed decision-makingRisk-prediction model

Identifiers

PMID41023919
PMCPMC12482232

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Registered trials

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.