Effect of a machine learning-based severe sepsis prediction algorithm on patient survival and hospital length of stay: a randomised clinical trial.
Effect of a machine learning-based severe sepsis prediction algorithm on patient survival and hospital length of stay: a randomised clinical trial.
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DOI：
10.1136/bmjresp-2017-000234
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发表时间：
2017
影响因子：
4.1
通讯作者：
Das R
中科院分区：
文献类型：
--
作者：
Shimabukuro DW;Barton CW;Feldman MD;Mataraso SJ;Das R
已经开发出多种方法来电子监测患者是否患有严重败血症，但很少有方法能够提供预测能力以实现早期干预；此外，之前还没有在随机研究中验证过严重脓毒症预测系统。我们测试了基于机器学习的严重败血症预测系统的使用，以减少平均住院时间和院内死亡率。我们在加州大学旧金山医学中心的两个内外科重症监护室进行了一项随机对照临床试验，评估了 2016 年 12 月至 2017 年 2 月期间平均住院时间的主要结果和院内死亡率的次要结果。入住参与单位的成年患者（18 岁以上）有资格参加这项析因、开放标签研究。纳入的患者按随机分配顺序分配到试验组。对照组仅使用目前的严重脓毒症检测仪；在实验组中，还使用了机器学习算法（MLA）。收到警报后，护理团队对患者进行了评估，并在适当的情况下启动了严重败血症捆绑治疗。尽管参与者被随机分配到试验组，但对于收到 MLA 警报的任何患者，组分配都会自动显示。分析了对照组 75 名患者和实验组 67 名患者的结果。平均住院时间从对照组的 13.0 天减少到实验组的 10.3 天 (p=0.042)。使用 MLA 后，院内死亡率降低了 12.4 个百分点 (p=0.018)，相对降低了 58.0%。本试验期间没有报告不良事件。 MLA 与改善患者预后相关。这是脓毒症监测系统的第一个随机对照试验，证明住院时间和院内死亡率存在统计学上的显着差异。 NCT03015454。
Several methods have been developed to electronically monitor patients for severe sepsis, but few provide predictive capabilities to enable early intervention; furthermore, no severe sepsis prediction systems have been previously validated in a randomised study. We tested the use of a machine learning-based severe sepsis prediction system for reductions in average length of stay and in-hospital mortality rate. We conducted a randomised controlled clinical trial at two medical-surgical intensive care units at the University of California, San Francisco Medical Center, evaluating the primary outcome of average length of stay, and secondary outcome of in-hospital mortality rate from December 2016 to February 2017. Adult patients (18+) admitted to participating units were eligible for this factorial, open-label study. Enrolled patients were assigned to a trial arm by a random allocation sequence. In the control group, only the current severe sepsis detector was used; in the experimental group, the machine learning algorithm (MLA) was also used. On receiving an alert, the care team evaluated the patient and initiated the severe sepsis bundle, if appropriate. Although participants were randomly assigned to a trial arm, group assignments were automatically revealed for any patients who received MLA alerts. Outcomes from 75 patients in the control and 67 patients in the experimental group were analysed. Average length of stay decreased from 13.0 days in the control to 10.3 days in the experimental group (p=0.042). In-hospital mortality decreased by 12.4 percentage points when using the MLA (p=0.018), a relative reduction of 58.0%. No adverse events were reported during this trial. The MLA was associated with improved patient outcomes. This is the first randomised controlled trial of a sepsis surveillance system to demonstrate statistically significant differences in length of stay and in-hospital mortality. NCT03015454.