Boussina Aaron, Shashikumar Supreeth P, Malhotra Atul, Owens Robert L, El-Kareh Robert, Longhurst Christopher A, Quintero Kimberly, Donahue Allison, Chan Theodore C, Nemati Shamim, Wardi Gabriel
Department of Medicine, University of California San Diego, San Diego, CA, USA.
Department of Quality, University of California San Diego, San Diego, CA, USA.
NPJ Digit Med. 2024 Jan 23;7(1):14. doi: 10.1038/s41746-023-00986-6.
Sepsis remains a major cause of mortality and morbidity worldwide. Algorithms that assist with the early recognition of sepsis may improve outcomes, but relatively few studies have examined their impact on real-world patient outcomes. Our objective was to assess the impact of a deep-learning model (COMPOSER) for the early prediction of sepsis on patient outcomes. We completed a before-and-after quasi-experimental study at two distinct Emergency Departments (EDs) within the UC San Diego Health System. We included 6217 adult septic patients from 1/1/2021 through 4/30/2023. The exposure tested was a nurse-facing Best Practice Advisory (BPA) triggered by COMPOSER. In-hospital mortality, sepsis bundle compliance, 72-h change in sequential organ failure assessment (SOFA) score following sepsis onset, ICU-free days, and the number of ICU encounters were evaluated in the pre-intervention period (705 days) and the post-intervention period (145 days). The causal impact analysis was performed using a Bayesian structural time-series approach with confounder adjustments to assess the significance of the exposure at the 95% confidence level. The deployment of COMPOSER was significantly associated with a 1.9% absolute reduction (17% relative decrease) in in-hospital sepsis mortality (95% CI, 0.3%-3.5%), a 5.0% absolute increase (10% relative increase) in sepsis bundle compliance (95% CI, 2.4%-8.0%), and a 4% (95% CI, 1.1%-7.1%) reduction in 72-h SOFA change after sepsis onset in causal inference analysis. This study suggests that the deployment of COMPOSER for early prediction of sepsis was associated with a significant reduction in mortality and a significant increase in sepsis bundle compliance.
脓毒症仍然是全球范围内导致死亡和发病的主要原因。有助于早期识别脓毒症的算法可能会改善治疗结果，但相对较少的研究考察了它们对实际患者治疗结果的影响。我们的目标是评估一种用于早期预测脓毒症的深度学习模型（COMPOSER）对患者治疗结果的影响。我们在加州大学圣地亚哥分校医疗系统内的两个不同急诊科完成了一项前后对照的准实验研究。我们纳入了2021年1月1日至2023年4月30日期间的6217名成年脓毒症患者。所测试的暴露因素是由COMPOSER触发的面向护士的最佳实践建议（BPA）。在干预前期（705天）和干预后期（145天）评估了住院死亡率、脓毒症集束依从性、脓毒症发作后序贯器官衰竭评估（SOFA）评分的72小时变化、无ICU天数以及ICU就诊次数。采用贝叶斯结构时间序列方法并进行混杂因素调整进行因果影响分析，以评估在95%置信水平下暴露因素的显著性。COMPOSER的应用与住院脓毒症死亡率绝对降低1.9%（相对降低17%）（95%CI，0.3%-3.5%）、脓毒症集束依从性绝对增加5.0%（相对增加10%）（95%CI，2.4%-8.0%）以及因果推断分析中脓毒症发作后72小时SOFA变化降低4%（95%CI，1.1%-7.1%）显著相关。这项研究表明，应用COMPOSER进行脓毒症的早期预测与死亡率显著降低和脓毒症集束依从性显著提高相关。
