Cardiovascular diseases (CVDs), the leading cause of death worldwide, can be
prevented in most cases through behavioral interventions. Therefore, effective
communication of CVD risk and projected risk reduction by risk factor
modification plays a crucial role in reducing CVD risk at the individual level.
However, despite interest in refining risk estimation with improved prediction
models such as SCORE2, the guidelines for presenting these risk estimations in
clinical practice remained essentially unchanged in the last few years, with
graphical score charts (GSCs) continuing to be one of the prevalent systems.
This work describes the design and implementation of Petal-X, a novel tool to
support clinician-patient shared decision-making by explaining the CVD risk
contributions of different factors and facilitating what-if analysis. Petal-X
relies on a novel visualization, Petal Product Plots, and a tailor-made global
surrogate model of SCORE2, whose fidelity is comparable to that of the GSCs
used in clinical practice. We evaluated Petal-X compared to GSCs in a
controlled experiment with 88 healthcare students, all but one with experience
with chronic patients. The results show that Petal-X outperforms GSC in
critical tasks, such as comparing the contribution to the patient's 10-year CVD
risk of each modifiable risk factor, without a significant loss of perceived
transparency, trust, or intent to use. Our study provides an innovative
approach to the visualization and explanation of risk in clinical practice
that, due to its model-agnostic nature, could continue to support
next-generation artificial intelligence risk assessment models.

通过行为干预可以预防心血管疾病（CVDs）作为全球死亡的主要原因，因此，在个体层面上减少 CVD 风险的有效沟通和风险因素改善预计风险的能力起着至关重要的作用。本研究描述了 Petla-X 的设计和实施，这是一个支持临床医生 - 患者共享决策的新工具，用于解释不同因素对 CVD 风险的贡献并促进假设分析。通过比较 GSCs，我们的实验结果表明，Petal-X 在关键任务中表现出色，且在透明度、信任度或使用意图方面没有明显损失。本研究提供了一种创新的临床实践风险可视化和解释方法，将支持下一代人工智能风险评估模型，其模型无关性质为其持续发展提供了可能性。