Dagan Noa, Cohen-Stavi Chandra J, Avgil Tsadok Meytal, Leibowitz Morton, Hoshen Moshe, Karpati Tomas, Akriv Amichay, Gofer Ilan, Gilutz Harel, Podjarny Eduardo, Bachmat Eitan, Balicer Ran D
1Clalit Research Institute, Clalit Health Services, Tel Aviv, Israel.
2Computer Science Department, Ben Gurion University of the Negev, Be'er Sheba, Israel.
NPJ Digit Med. 2019 Aug 21;2:81. doi: 10.1038/s41746-019-0156-3. eCollection 2019.
Currently, clinicians rely mostly on population-level treatment effects from RCTs, usually considering the treatment's benefits. This study proposes a process, focused on practical usability, for translating RCT data into personalized treatment recommendations that weighs benefits against harms and integrates subjective perceptions of relative severity. Intensive blood pressure treatment (IBPT) was selected as the test case to demonstrate the suggested process, which was divided into three phases: (1) Prediction models were developed using the Systolic Blood-Pressure Intervention Trial (SPRINT) data for benefits and adverse events of IBPT. The models were externally validated using retrospective Clalit Health Services (CHS) data; (2) Predicted risk reductions and increases from these models were used to create a yes/no IBPT recommendation by calculating a severity-weighted benefit-to-harm ratio; (3) Analysis outputs were summarized in a decision support tool. Based on the individual benefit-to-harm ratios, 62 and 84% of the SPRINT and CHS populations, respectively, would theoretically be recommended IBPT. The original SPRINT trial results of significant decrease in cardiovascular outcomes following IBPT persisted only in the group that received a "yes-treatment" recommendation by the suggested process, while the rate of serious adverse events was slightly higher in the "no-treatment" recommendation group. This process can be used to translate RCT data into individualized recommendations by identifying patients for whom the treatment's benefits outweigh the harms, while considering subjective views of perceived severity of the different outcomes. The proposed approach emphasizes clinical practicality by mimicking physicians' clinical decision-making process and integrating all recommendation outputs into a usable decision support tool.
目前，临床医生大多依赖随机对照试验（RCT）得出的群体水平治疗效果，通常只考虑治疗的益处。本研究提出了一个注重实际可用性的流程，用于将RCT数据转化为个性化治疗建议，该流程权衡了利弊，并综合了对相对严重程度的主观认知。选择强化血压治疗（IBPT）作为测试案例来演示所建议的流程，该流程分为三个阶段：（1）利用收缩压干预试验（SPRINT）数据开发关于IBPT益处和不良事件的预测模型。这些模型使用以色列克拉利特健康服务中心（CHS）的回顾性数据进行外部验证；（2）利用这些模型预测的风险降低和增加情况，通过计算严重程度加权的利弊比来生成接受或不接受IBPT的建议；（3）分析结果汇总在一个决策支持工具中。根据个体的利弊比，理论上SPRINT和CHS群体中分别有62%和84%的人会被建议接受IBPT。IBPT后心血管结局显著下降这一SPRINT试验的原始结果仅在通过所建议流程获得“接受治疗”建议的组中持续存在，而在“不接受治疗”建议组中严重不良事件的发生率略高。这个流程可用于通过识别治疗益处大于危害的患者，将RCT数据转化为个性化建议，同时考虑对不同结局感知严重程度的主观观点。所提出的方法通过模仿医生的临床决策过程并将所有建议输出整合到一个可用的决策支持工具中，强调了临床实用性。
