AI 中文总结
本研究通过回顾性两队列研究，开发了无需逐小时监督的连续脓毒症严重程度评分，该评分可有效区分患者结局，有望作为补充临床判断的决策支持工具。
AI 中文摘要
当前使用的脓毒症严重程度指数依赖于数十年前建立的固定变量和权重，这些指数被粗略离散化并针对不再反映当代重症监护的队列进行校准，尚无直接从患者轨迹中学习的替代方法被常规使用。我们对来自马萨诸塞州和佐治亚州两家医院系统的共29116名和7691名符合Sepsis-3标准的成年患者开展了回顾性两队列研究。我们开发了一种脓毒症指数，使用72小时治疗窗口内的43项常规记录变量。与以往研究不同，我们将死亡率作为治疗水平的排序信号，而非每个状态的目标，这使得信用可在时间步间非均匀重新分配。评估在永久保留的20%测试集上进行，采用临床 vignettes 和 Spearman 相关分析，通过对整名患者进行自助重采样获得不确定性区间。在该排序方案下，在所有基线SOFA-2分层中，非幸存者在0-10分制下的得分比幸存者高1.19-1.64分，在乳酸、平均动脉压（MAP）和肌酐分层中也得到类似结果。患者内部指数变化与乳酸变化相关（Spearman ρ=0.39；n=1854），MAP和肌酐的相关性类似但更弱。在队列层面，不同中心训练的模型间通过Spearman相关测量的跨机构一致性为同中心相关性的70-77%，外部患者内部相关性分别为0.54和0.59，对应上限为0.92和0.90。我们的指数还与已建立的指数相关，而空对照接近零。该指数表现出逐小时预后信息，可有效区分患者结局且符合临床预期，表明其有潜力成为补充临床判断的决策支持工具。
英文摘要
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.
