Background

Severe sepsis and septic shock are still the leading causes of death in Intensive Care Units (ICUs), and timely diagnosis is crucial for treatment outcomes. The progression of electronic medical records (EMR) offers the possibility of storing a large quantity of clinical data that can facilitate the development of artificial intelligence (AI) in medicine. However, several difficulties, such as poor structure and heterogenicity of the raw EMR data, are encountered when introducing AI with ICU data. Labor-intensive work, including manual data entry, personal medical records sorting, and laboratory results interpretation may hinder the progress of AI. In this article, we introduce the developing of an AI algorithm designed for sepsis diagnosis using pre-selected features; and compare the performance of the AI algorithm with SOFA score based diagnostic method.

Materials and methods

This is a prospective open-label cohort study. A specialized EMR, named TED_ICU, was implemented for continuous data recording. One hundred six clinical features relevant to sepsis diagnosis were selected prospectively. A labeling work to allocate SEPSIS or NON_SEPSIS status for each ICU patient was performed by the in-charge intensivist according to SEPSIS-3 criteria, along with the automatic recording of selected features every day by TED_ICU. Afterward, we use de-identified data to develop the AI algorithm. Several machine learning methods were evaluated using 5-fold cross-validation, and XGBoost, a decision-tree based algorithm was adopted for our AI algorithm development due to best performance.

Results

The study was conducted between August 2018 and December 2018 for the first stage of analysis. We collected 1588 instances, including 444 SEPSIS and 1144 NON-SEPSIS, from 434 patients. The 434 patients included 259 (59.6%) male patients and 175 female patients. The mean age was 67.6-year-old, and the mean APACHE II score was 13.8. The SEPSIS cohort had a higher SOFA score and increased use of organ support treatment. The AI algorithm was developed with a shuffle method using 80% of the instances for training and 20% for testing. The established AI algorithm achieved the following: accuracy = 82% ± 1%; sensitivity = 65% ± 5%; specificity = 88% ± 2%; precision = 67% ± 3%; and F1 = 0.66 ± 0.02. The area under the receiver operating characteristic curve (AUROC) was approximately 0.89. The SOFA score was used on the same 1588 instances for sepsis diagnosis, and the result was inferior to our AI algorithm (AUROC = 0.596).

Conclusion

Using real-time data, collected by EMR, from the ICU daily practice, our AI algorithm established with pre-selected features and XGBoost can provide a timely diagnosis of sepsis with an accuracy greater than 80%. AI algorithm also outperforms the SOFA score in sepsis diagnosis and exhibits practicality as clinicians can deploy appropriate treatment earlier. The early and precise response of this AI algorithm will result in cost reduction, outcome improvement, and benefit for healthcare systems, medical staff, and patients as well.

中文翻译：

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开发了一种用于重症监护病房早期败血症诊断的人工智能算法。

背景

严重的败血症和败血性休克仍然是重症监护病房（ICU）的主要死亡原因，及时诊断对于治疗结果至关重要。电子病历（EMR）的发展提供了存储大量临床数据的可能性，这些临床数据可以促进医学中人工智能（AI）的发展。但是，在将AI与ICU数据结合使用时会遇到一些困难，例如原始EMR数据的结构不良和异质性。劳动密集型工作，包括手工数据输入，个人病历分类和实验室结果解释，可能会阻碍AI的发展。在本文中，我们介绍了一种为使用败选功能诊断脓毒症而设计的AI算法的开发；

材料和方法

这是一项前瞻性开放标签队列研究。实现了一个名为TED_ICU的专用EMR，用于连续数据记录。前瞻性选择与脓毒症诊断相关的一百六十六个临床特征。负责人员根据SEPSIS-3标准，为每个ICU患者分配SEPSIS或NON_SEPSIS状态的标签工作，以及TED_ICU每天自动记录所选特征。之后，我们使用去识别数据开发AI算法。使用5倍交叉验证对几种机器学习方法进行了评估，并且由于性能最佳，XGBoost在我们的AI算法开发中采用了基于决策树的算法。

结果

该研究是在2018年8月至2018年12月进行的第一阶段分析。我们从434位患者中收集了1588个实例，包括444个SEPSIS和1144个非SEPSIS。434例患者包括259例（59.6％）男性患者和175例女性患者。平均年龄为67.6岁，平均APACHE II评分为13.8。SEPSIS队列的SOFA评分较高，器官支持治疗的使用增加。AI算法是通过改组方法开发的，其中80％的实例用于训练，而20％的实例用于测试。建立的AI算法达到以下目标：精度= 82％±1％；灵敏度= 65％±5％; 特异性= 88％±2％; 精度= 67％±3％; 并且F1 = 0.66±0.02。接收器工作特性曲线（AUROC）下的面积约为0.89。

结论

使用EMR从ICU日常实践中收集到的实时数据，我们的AI算法具有预先选择的功能和XGBoost，可以提供败血症的及时诊断，准确性超过80％。AI算法在败血症诊断方面也胜过SOFA评分，并且具有实用性，因为临床医生可以更早地部署适当的治疗方法。这种AI算法的早期而精确的响应将导致成本降低，结果改善，并为医疗保健系统，医护人员和患者带来好处。