BACKGROUND The design of computerized systems able to support automated detection of threatening conditions in critically ill patients such as systemic inflammatory response syndrome (SIRS) and sepsis has been fostered recently. The increase of research work in this area is due to both the growing digitalization in health care and the increased appreciation of the importance of early sepsis detection and intervention. To be able to understand the variety of systems and their characteristics as well as performances, a systematic literature review is required. Existing reviews on this topic follow a rather restrictive searching methodology or they are outdated. As much progress has been made during the last 5 years, an updated review is needed to be able to keep track of current developments in this area of research. OBJECTIVES To provide an overview about current approaches for the design of clinical decision-support systems (CDSS) in the context of SIRS, sepsis, and septic shock, and to categorize and compare existing approaches. METHODS A systematic literature review was performed in accordance with the preferred reporting items for systematic reviews and meta-analyses (PRISMA) statement. Searches for eligible articles were conducted on five electronic bibliographic databases, including PubMed/MEDLINE, IEEE Xplore, Embase, Scopus, and ScienceDirect. Initial results were screened independently by two reviewers based on clearly defined eligibility criteria. A backward as well as an updated search enriched the initial results. Data were extracted from included articles and presented in a standardized way. Articles were classified into predefined categories according to characteristics extracted previously. The classification was performed according to the following categories: clinical setting including patient population and mono- or multicentric study, support type of the system such as prediction or detection, systems characteristics such as knowledge- or data-driven algorithms used, evaluation of methodology, and results including ground truth definition, sensitivity, and specificity. All results were assessed qualitatively by two reviewers. RESULTS The search resulted in 2,373 articles out of which 55 results were identified as eligible. Over 80% of the articles describe monocentric studies. More than 50% include adult patients, and only four articles explicitly report the inclusion of pediatric patients. Patient recruitment often is very selective, which can be observed from highly varying inclusion and exclusion criteria. The task of disease detection is covered in 62% of the articles; prediction of upcoming conditions in 33%. Sepsis is covered in 67% of the articles, SIRS as sole entity in only 4%, whereas 27% focus on severe sepsis and/or septic shock. The most common combinations of categories "algorithm used" and "support type" are knowledge-based detection of sepsis and data-driven prediction of sepsis. In evaluations, manual chart review (38%) and diagnosis coding (29%) represent the most frequently used ground truth definitions; most studies present a sample size between 10,001 and 100,000 cases (31%) and performances highly differ with only five articles presenting sensitivities and specificities above 90%; four of them using knowledge-based rather than machine learning algorithms. The presentations of holistic CDSS approaches, including technical implementation details, system interfaces, and data and interoperability aspects enabling the use of CDSS in routine settings are missing in nearly all articles. CONCLUSIONS The review demonstrated the high variety of research in this context successfully. A clear trend is observable toward the use of data-driven algorithms, and a lack of research could be identified in covering the pediatric population as well as acknowledging SIRS as an independent and threatening condition. The quality as well as the significance of the presented evaluations for assessing the performances of the algorithms in clinical routine settings are often not meeting the current standard of scientific work. Our future interest will be concentrated on these realistic settings by implementing and evaluating SIRS detection approaches as well as considering factors to make the CDSS useable in clinical routine from both technical and medical perspectives.

中文翻译：

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用于检测重症患者全身性炎症反应综合征，败血症和败血性休克的临床决策支持系统：系统评价。

背景技术近来已经促进了计算机系统的设计，该计算机系统能够支持对重症患者的诸如全身性炎症反应综合征（SIRS）和败血症的危急状况的自动检测。该领域研究工作的增加是由于医疗保健中数字化的发展以及对早期败血症检测和干预的重要性的日益重视。为了能够理解各种系统及其特性以及性能，需要进行系统的文献综述。关于该主题的现有评论遵循相当严格的搜索方法，或者它们已经过时。在过去的五年中，由于取得了很大的进展，因此需要进行最新的审查，以跟踪该研究领域的最新发展。目的在SIRS，败血症和败血性休克的背景下，概述当前设计临床决策支持系统（CDSS）的方法，并对现有方法进行分类和比较。方法根据系统评价和荟萃分析的首选报告项目进行系统的文献综述。在五个电子书目数据库（包括PubMed / MEDLINE，IEEE Xplore，Embase，Scopus和ScienceDirect）上对符合条件的文章进行了搜索。最初的结果由两名审核员根据明确定义的资格标准独立筛选。反向搜索和更新搜索丰富了初始结果。从收录的文章中提取数据并以标准化的方式呈现。根据先前提取的特征将文章分类为预定义的类别。根据以下类别进行分类：临床设置（包括患者人群和单中心或多中心研究），系统的支持类型（例如预测或检测），系统特征（例如使用的知识或数据驱动算法），方法评估，结果包括基本事实定义，敏感性和特异性。所有结果均由两名审核员进行定性评估。结果搜索结果共2,373条，其中55条结果被鉴定为合格。超过80％的文章描述了单中心研究。超过50％的患者包括成年患者，只有四篇文章明确报告了儿科患者的纳入。患者招募通常是非常有选择性的，这可以从高度变化的纳入和排除标准中观察到。62％的文章涵盖了疾病检测的任务；预测即将发生的情况的比例为33％。67％的文章涵盖败血症，SIRS仅占4％，而27％则关注严重的败血症和/或败血性休克。类别“使用的算法”和“支持类型”的最常见组合是基于知识的脓毒症检测和数据驱动的脓毒症预测。在评估中，手动图表审查（38％）和诊断编码（29％）代表了最常用的地面真相定义。大多数研究的样本量在10,001至100,000例之间（31％），且性能差异很大，只有五篇文章的敏感性和特异性超过90％；其中四个使用基于知识的算法而非机器学习算法。几乎所有文章都缺少关于整体CDSS方法的介绍，包括技术实现细节，系统接口以及可在常规设置中使用CDSS的数据和互操作性方面。结论综述成功地证明了在这种情况下的大量研究。在使用数据驱动算法方面可以观察到一个明显的趋势，并且可以发现在覆盖儿科人群以及承认SIRS是一个独立且具有威胁性的条件方面缺乏研究。所提出的评估在临床常规环境中评估算法性能的评估的质量和重要性通常不符合当前的科学工作标准。