Learning a Continuous Sepsis Severity Score Without Hour-by-Hour Supervision: A Two-Site Retrospective Study· 无需逐小时监督的连续败血症严重程度评分学习
Currently used sepsis severity indices rely on fixed variables and weights established decades ago, which are coarsely discretized and calibrated to a cohort that no longer reflects contemporary critical care. No alternative learned directly from patient trajectories is in routine use. We conducted a retrospective two-cohort study on a total of 29,116 and 7,691 adult patients meeting Sepsis-3 criteria from two hospital systems in Massachusetts and Georgie, respectively. We developed a sepsis index using 43 routinely charted variables over a 72-hour treatment window. Unlike previous studies, we use mortality as a treatment-level ranking signal rather than a per-state target, allowing credit to be redistributed non-uniformly across timesteps. Evaluation was done on a permanent 20% test holdo
提出无需逐小时监督的败血症严重程度评分模型。
- 核心方法
- 使用43个常规记录变量，在72小时治疗窗口内开发败血症指数，以死亡率作为治疗水平的排序信号而非每状态目标。
- 适合谁读
- 研究者、医生、工程师
- 要解决的问题
- 现有败血症严重程度评分依赖于过时的固定变量和权重，不适用于现代重症监护患者。
- 关键实验
- 使用来自马萨诸塞州和乔治亚州两家医院共36,807名符合Sepsis-3标准的成年患者数据进行回顾性两队列研究。测试集占20%。
- 主要贡献
- 开发了首个基于患者轨迹直接学习的败血症严重程度评分模型，改进了时间步骤上的信用分配。
- 意义与局限
- 该模型可能提高败血症的诊断和治疗效果，但需要更多临床验证。