A computational approach to early sepsis detection
A computational approach to early sepsis detection
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DOI：
10.1016/j.compbiomed.2016.05.003
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发表时间：
2016-07-01
影响因子：
7.7
通讯作者：
Das, Ritankar
中科院分区：
文献类型：
--
作者：
Calvert, Jacob S.;Price, Daniel A.;Das, Ritankar
目的：为普通人群开发高性能的脓毒症早期预测技术。方法：回顾分析重症监护病房(MIMIC II数据集)入院时未发生败血症的成人患者。结果：开发了一种脓毒症预警算法Insight，并将其应用于在患者首次5小时全身炎症反应综合征(SIRS)发作前3小时内预测脓毒症。当应用于一组从未见过的测试患者时，Insight预测显示出0.90(95%CI：0.89-0.91)的敏感性和0.81(95%CI：0.80-0.82)的特异性，超过或与现有的生物标记物检测方法相媲美。在持续SIRS事件发生前三小时的预测时间内，Insight将ROC曲线下的平均面积维持在0.83(95%CI：0.80-0.86)。对患者脓毒症风险的分析表明，在进一步预测时，多个危险因素的共同进化的贡献比单个风险因素的贡献更重要。结论：仅使用9种常见的生命体征，就可以在前5小时全身炎症反应综合征发作前至少3小时预测脓毒症，比目前的标准实践方法具有更好的性能。生命体征测量的高阶相关性是这一预测的关键，它提高了早期识别高危患者的可能性。(C)2016爱思唯尔有限公司。保留所有权利。
Objective: To develop high-performance early sepsis prediction technology for the general patient population.Methods: Retrospective analysis of adult patients admitted to the intensive care unit (from the MIMIC II dataset) who were not septic at the time of admission.Results: A sepsis early warning algorithm, Insight, was developed and applied to the prediction of sepsis up to three hours prior to a patient's first five hour Systemic Inflammatory Response Syndrome (SIRS) episode. When applied to a never-before-seen set of test patients, Insight predictions demonstrated a sensitivity of 0.90 (95% CI: 0.89-0.91) and a specificity of 0.81 (95% CI: 0.80-0.82), exceeding or rivaling that of existing biomarker detection methods. Across predictive times up to three hours before a sustained SIRS event, Insight maintained an average area under the ROC curve of 0.83 (95% CI: 0.80-0.86). Analysis of patient sepsis risk showed that contributions from the coevolution of multiple risk factors were more important than the contributions from isolated individual risk factors when making predictions further in advance.Conclusions: Sepsis can be predicted at least three hours in advance of onset of the first five hour SIRS episode, using only nine commonly available vital signs, with better performance than methods in standard practice today. High-order correlations of vital sign measurements are key to this prediction, which improves the likelihood of early identification of at-risk patients. (C) 2016 Elsevier Ltd. All rights reserved.