用患者轨迹学习连续脓毒症评分，不依赖逐小时标注。
Learning a Continuous Sepsis Severity Score Without Hour-by-Hour Supervision: A Two-Site Retrospective Study
- 以死亡率作为整体排序信号，非逐时标签，实现更灵活的评分机制。
- 非存活者评分比存活者高1.19-1.64分（0-10分制），跨基线SOFA-2、乳酸等分层一致。
- 评分与乳酸变化相关性达0.39，跨机构模型一致性良好，适合临床决策支持。
当前使用的脓毒症严重度指数基于几十年前固定的变量和权重，粗粒度且不反映现代重症监护现状。尚无直接从患者轨迹学习的替代方案被广泛应用。本研究在马萨诸塞州和乔治亚州两家医院系统中，对共29,116例和7,691例符合Sepsis-3标准的成年患者进行了回顾性两队列研究。我们利用72小时内43个常规记录变量构建脓毒症指数。不同于以往研究，采用死亡率作为治疗级别的排序信号而非每个状态的目标，允许时间步间信用非均匀分配。评估采用永久20%测试集，结合临床案例和斯皮尔曼相关性分析。通过整患者自助抽样获得置信区间。在此排序框架下，非存活者在所有基线SOFA-2分层内评分均高于存活者1.19-1.64分；在乳酸、平均动脉压（MAP）和肌酐分层中结果相似。患者内部评分变化与乳酸变化相关（斯皮尔曼ρ = 0.39；n = 1,854），MAP和肌酐关联较弱。队列层面，跨机构模型间相关性为同机构相关性的70%-77%。外部患者内相关性分别为0.54和0.59，接近0.92和0.90的上限。该指数与已有指标相关，而零模型控制值接近零。表明该评分具备每小时预后信息，能有效区分结局，符合临床预期，有潜力作为辅助临床判断的决策支持工具。
原文摘要 · Abstract (English)
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 holdout, using clinical vignettes and Spearman correlation. Uncertainty intervals were obtained by bootstrap resampling of whole patients. Under this ranking scheme, non-survivors scored 1.19-1.64 points higher than survivors on a 0-10 scale within all strata of baseline SOFA-2, with similar results stratifying within lactate, mean arterial pressure (MAP), and creatinine. Within-patient change in the index correlated with change in lactate (Spearman rho = 0.39; n = 1,854). Similar, weaker correlations were found for MAP and creatinine. On a cohort level, cross-institutional agreement measured by Spearman correlation between models trained on different sites, were 70-77% of same-site correlation. External within-patient correlations were 0.54 and 0.59 against ceilings of 0.92 and 0.90. Our index also correlated with established indices, while null controls stayed near zero. Our index demonstrated hourly prognostic information that meaningfully separates patient outcomes and is consistent with clinical expectation, indicating potential as a decision support tool complementing clinical judgement.
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