Impact of a deep learning sepsis prediction model on quality of care and survival.
Impact of a deep learning sepsis prediction model on quality of care and survival.
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DOI：
10.1038/s41746-023-00986-6
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发表时间：
2024-01-23
影响因子：
15.2
通讯作者：
中科院分区：
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脓毒症仍然是世界范围内死亡率和发病率的主要原因。有助于早期识别脓毒症的算法可能会改善预后，但相对较少的研究已经检查了它们对现实世界患者预后的影响。我们的目标是评估深度学习模型（COMPOSER）对脓毒症早期预测对患者结局的影响。我们在加州大学圣地亚哥分校卫生系统内的两个不同的急诊科（ED）完成了一项前后准实验研究。我们纳入了2021年1月1日至2023年4月30日的6217例成人脓毒症患者。测试的暴露是由COMPOSER触发的面向护士的最佳实践咨询（BPA）。在干预前（705天）和干预后（145天），对院内死亡率、脓毒症集束治疗依从性、脓毒症发作后72小时序贯器官衰竭评估（SOFA）评分变化、无ICU天数和ICU就诊次数进行评价。使用贝叶斯结构时间序列方法进行因果影响分析，并进行混杂因素调整，以评估95%置信水平下暴露的显著性。COMPOSER的部署与1.9%的绝对减少显著相关院内败血症死亡率（相对降低17%）（95% CI，0.3%-3.5%），绝对增加5.0%（10%相对增加）败血症束顺应性（95% CI，2.4%-8.0%），和4%（95% CI，1.1%-7.1%）减少72小时SOFA变化后败血症发作的因果推断分析。这项研究表明，部署COMPOSER用于脓毒症的早期预测与死亡率的显著降低和脓毒症束顺应性的显著增加相关。
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.
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DOI：
10.1002/emp2.12297
发表时间：
2020-12
影响因子：
2.3
作者：
Carlile M;Hurt B;Hsiao A;Hogarth M;Longhurst CA;Dameff C
通讯作者：
Dameff C
影响因子：
17.1
作者：
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影响因子：
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DOI：
10.1164/rccm.201609-1848oc
发表时间：
2017-10-01
影响因子：
24.7
作者：
Liu, Vincent X.;Fielding-Singh, Vikram;Escobar, Gabriel J.
通讯作者：
Escobar, Gabriel J.