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Prognostic understanding interventions in patients with advanced cancer: a systematic review
Li Dong1, Liande Tao1, Yulin Wang1
1Department of Nursing, The Second People's Hospital of Yibin, Yibin, China.
Background:
Most patients with advanced cancer have an inadequate understanding of their prognosis, and there are few interventions aimed at enhancing their prognostic understanding, with the effectiveness of these interventions remaining controversial.
Aim:
The purpose of this systematic review is to identify interventions that have been effective at improving prognostic understanding in patients with advanced cancer.
Methods:
A systematic review was conducted in accordance with PRISMA guidelines. All relevant peer-reviewed articles published from 1990 and February 2025 were identified through an electronic search of four databases: Embase, Scopus, Pubmed, and Web of Science. Risk of bias was independently assessed by two reviewers using the Cochrane Risk of Bias Assessment Tool (CRBT) and Cochrane Risk of Bias Assessment Tool for Non-randomized Studies (RoBANS). Following this, a narrative synthesis of the findings was completed. Publications reporting on interventions delivered to patients with advanced cancer that included quantitative data on prognostic understanding were systematically reviewed.
Results:
Thirteen unique intervention studies involving 1,780 cancer patients were identified. Interventions tested included decision aids (DAs), prognostic discussions, advance care planning (ACP), and palliative care. Seven studies utilized a randomized controlled design. Five of these investigated DAs-based interventions and four examined prognostic discussions interventions, with the greatest empirical support found for these intervention types. However, most interventions fail to improve the prognostic understanding of patients with advanced cancer.
Conclusion:
Further research is needed to investigate the effects of interventions on prognostic understanding among patients with advanced cancers, using randomized controlled designs and adequately powered samples. This would enable the implementation of evidence-based recommendations for the most appropriate interventions in clinical practice.
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