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.

翻译：心血管疾病（CVDs）是全球首要死因，在多数情况下可通过行为干预进行预防。因此，有效传达CVD风险及通过风险因素修正所预测的风险降低，在个体层面降低CVD风险中起着至关重要的作用。然而，尽管人们热衷于使用如SCORE2等改进的预测模型来优化风险估计，但过去几年中，在临床实践中呈现这些风险估计的指南基本保持不变，图形评分表（GSCs）仍是主流系统之一。本研究描述了Petal-X的设计与实现，这是一种通过解释不同因素对CVD风险的贡献并促进假设分析，以支持临床医患共同决策的新型工具。Petal-X依赖于一种新颖的可视化方法——花瓣乘积图，以及一个定制的SCORE2全局代理模型，其保真度与临床实践中使用的GSCs相当。我们在包含88名医学生的对照实验中评估了Petal-X与GSCs的效果，其中除一人外均具有慢性病患者护理经验。结果表明，在关键任务中，如比较每个可修正风险因素对患者10年CVD风险的贡献，Petal-X优于GSC，且未显著损失感知透明度、信任度或使用意愿。我们的研究为临床实践中的风险可视化与解释提供了一种创新方法，由于其模型无关的特性，该方法可继续支持下一代人工智能风险评估模型。