Clinical Decision-Support Systems for Detection of Systemic Inflammatory Response Syndrome, Sepsis, and Septic Shock in Critically Ill Patients: A Systematic Review

Abstract

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 Existing reviews on this topic follow a rather restrictive searching methodology or they are outdated.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.

A Predictive Machine Learning Model to Determine Alcohol Use Disorder

“…There are two types of SML-based algorithms namely, generative SML-based algorithms which learn the joint probability distribution and discriminative SMLbased algorithms which learn the conditional probability distribution. In this study, the state of the art and the most widely used discriminative SML-based algorithms in clinical decision support [26], the Radical Basis Function (RBF) kernel support vector machines (SVM) was trained to examine both selected feature sets among two classes, AUD positive and AUD negative.…”

Clinical evaluation of an interoperable clinical decision-support system for the detection of systemic inflammatory response syndrome in critically ill children

“…Digitalization in healthcare has fostered the development of clinical decision-support systems (CDSS) capable of supporting human decision-making by reusing routinely documented data [ 12 , 13 ]. However, current research for pediatric SIRS detection by CDSS is scarce [ 14 ]. Related approaches were described by Dewan et al [ 15 ], Scott et al [ 16 ], Vidrine et al [ 17 ], Le et al [ 18 ], Sepanski et al [ 19 ], Cruz et al [ 20 ] and Eisenberg et al [ 21 ], but focused on severe sepsis, septic shock, or therapy improvements rather than SIRS diagnosis.…”

“…To our knowledge, a CDSS for detection of pediatric SIRS has not yet been successfully developed. Furthermore, related CDSS were only rarely tested under clinical routine settings as neither routine data nor appropriate reference standards were used [ 14 ]. We designed a knowledge-based CDSS for pediatric SIRS detection that uses routine data from a patient data management system (PDMS) and implements algorithms based on guidelines and experts’ knowledge assets [ 22 ].…”

Experience in Developing an FHIR Medical Data Management Platform to Provide Clinical Decision Support

“…This approach is commonly referred to as microservice architecture [25].Specifying standard interfaces, CDS Hooks (https://cds-hooks.org), provides a hook-based pattern for automatically invoking CDSS functions within routine clinical workflows [26]. This specification natively supports HL7 FHIR R4 to simplify the data flow, enabling easy integration of HISs and CDSS services.The experience shows that most of the CDSSs are standalone implementations focused on one clinical condition or workflow [27][28][29]. However, the implementation of sophisticated clinical decision support platforms that are capable of providing a full spectrum of clinical decision support functionality to various medical information systems is still missing.…”

“…The experience shows that most of the CDSSs are standalone implementations focused on one clinical condition or workflow [27][28][29]. However, the implementation of sophisticated clinical decision support platforms that are capable of providing a full spectrum of clinical decision support functionality to various medical information systems is still missing.…”