In our previous study, we demonstrated the feasibility of producing a proactive statin prescription strategy – a personalized statin treatment plan (PSTP) – using neural networks with big data. However, its non-transparency limited result interpretations and clinical usability. To improve the transparency of our previous approach with minimal compromise to the maximal statin treatment benefit-to-risk ratio, this study proposed a five-step pipeline approach called the decision rules for statin treatment (DRST). Steps 1–3 of our proposed pipeline improved our previous PSTP model in optimizing individual benefit-to-risk ratio; Step 4 used a decision tree model (DRST) to provide straightforward rules in the initial statin treatment plan; Step 5 aimed to evaluate the efficacy of these decision rules by conducting a clinical trial simulation. We included 107,739 de-identified patient data from Optum Labs Database Warehouse in this study. The final decision rules were compact and efficient, resulting from a decision tree with only a maximum depth of 3 and 11 nodes. The DRST identified three factors that are easily obtainable at the point of care: age, low-density lipoprotein cholesterol (LDL-C) level, and age-adjusted Charlson score. Moreover, it also identified six subpopulations that can benefit most from these decision rules. In our clinical trial simulations, DRST was found to improve statin benefit in LDL-C reduction by 4.15 percentage points (pp) and reduce risks of statin-associated symptoms (SAS) and statin discontinuation by 11.71 and 3.96 pp, respectively, when compared to the standard of care. Moreover, these DRST results were only less than 0.6 pp suboptimal to PSTP, demonstrating that building DRST that provide transparency with minimal compromise to the maximal benefit-to-risk ratio of statin treatments is feasible.

中文翻译：

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多目标个性化他汀类药物治疗处方的决策规则

在我们之前的研究中，我们证明了使用具有大数据的神经网络制定主动他汀类药物处方策略（个性化他汀类药物治疗计划（PSTP））的可行性。然而，其不透明限制了结果解释和临床可用性。为了提高我们之前方法的透明度，同时尽量减少对他汀类药物治疗的最大获益风险比的影响，本研究提出了一种五步流程方法，称为他汀类药物治疗决策规则（DRST）。我们提出的流程的步骤 1-3 改进了我们之前的 PSTP 模型，以优化个人收益风险比；步骤 4 使用决策树模型 (DRST) 为初始他汀类药物治疗计划提供简单的规则；第 5 步旨在通过进行临床试验模拟来评估这些决策规则的有效性。我们在本研究中纳入了来自 Optum Labs 数据库仓库的 107,739 名未识别身份的患者数据。最终的决策规则紧凑且高效，由最大深度仅为 3 和 11 个节点的决策树得出。DRST 确定了在护理时很容易获得的三个因素：年龄、低密度脂蛋白胆固醇 (LDL-C) 水平和年龄调整后的查尔森评分。此外，它还确定了可以从这些决策规则中受益最多的六个亚人群。在我们的临床试验模拟中，与其他药物相比，DRST 被发现可将他汀类药物在降低 LDL-C 方面的益处提高 4.15 个百分点 (pp)，并将他汀类药物相关症状 (SAS) 和他汀类药物停药的风险分别降低 11.71 和 3.96 个百分点。护理标准。此外，这些 DRST 结果仅比 PSTP 次优不到 0.6 个 pp，这表明建立提供透明度且对他汀类药物治疗的最大获益风险比影响最小的 DRST 是可行的。