Early detection of sepsis using artificial intelligence: a scoping review protocol

Abstract

Background Sepsis is a life-threatening organ dysfunction caused by a dysregulated host response to infection. To decrease the high case fatality rates and morbidity for sepsis and septic shock, there is a need to increase the accuracy of early detection of suspected sepsis in prehospital and emergency department settings. This may be achieved by developing risk prediction decision support systems based on artificial intelligence. Methods The overall aim of this scoping review is to summarize the literature on existing methods for early detection of sepsis using artificial intelligence. The review will be performed using the framework formulated by Arksey and O’Malley and further developed by Levac and colleagues. To identify primary studies and reviews that are suitable to answer our research questions, a comprehensive literature collection will be compiled by searching several sources. Constrictions regarding time and language will have to be implemented. Therefore, only studies published between 1 January 1990 and 31 December 2020 will be taken into consideration, and foreign language publications will not be considered, i.e., only papers with full text in English will be included. Databases/web search engines that will be used are PubMed, Web of Science Platform, Scopus, IEEE Xplore, Google Scholar, Cochrane Library, and ACM Digital Library. Furthermore, clinical studies that have completed patient recruitment and reported results found in the database ClinicalTrials.gov will be considered. The term artificial intelligence is viewed broadly, and a wide range of machine learning and mathematical models suitable as base for decision support will be evaluated. Two members of the team will test the framework on a sample of included studies to ensure that the coding framework is suitable and can be consistently applied. Analysis of collected data will provide a descriptive summary and thematic analysis. The reported results will convey knowledge about the state of current research and innovation for using artificial intelligence to detect sepsis in early phases of the medical care chain. Ethics and dissemination The methodology used here is based on the use of publicly available information and does not need ethical approval. It aims at aiding further research towards digital solutions for disease detection and health innovation. Results will be extracted into a review report for submission to a peer-reviewed scientific journal. Results will be shared with relevant local and national authorities and disseminated in additional appropriate formats such as conferences, lectures, and press releases.

Theory and Practice of Integrating Machine Learning and Conventional Statistics in Medical Data Analysis

“…Machine learning models are able to capture high-capacity relationships and they are amenable to more operational tasks rather than direct research questions; thus, more research gaps could be solved through the one-stop analysis [ 38 ]. Various medical data analyses used a machine learning approach to make decisions [ 78 , 79 , 80 , 81 , 82 , 83 , 84 , 85 , 86 , 87 , 88 ]. Biostatisticians are in a need of an updated methodology that uses a machine learning approach to conduct analysis on a variety of medical data [ 89 ].…”

Optimizing artificial intelligence in sepsis management: Opportunities in the present and looking closely to the future

“…Positive outcomes are highly related to effective management in EDs and ward settings since successful treatment is time-dependent. [36] Unfortunately, transferring patients from the ED or ward to an ICU is often ineffective. [36] A recent meta-analysis of 28 papers looking at ML for predicting sepsis found that diagnostic test accuracy when assessed using AUROC was 0.68–0.99 in the ICU, 0.96–0.98 in the hospital, and 0.87–0.97 in the ED.…”

Beyond technology: Can artificial intelligence support clinical decisions in the prediction of sepsis?

“…In its prognosis, successful treatment is time-dependent, where recommendations to initiate antibiotic therapy within the first hours of disease presentation and timely monitoring positively interfere with outcomes. Although highly desirable, early diagnosis is challenging given the nonspecific nature of signs and symptoms, as well as their similarity to other pathologies (4)(5) . In this scenario of care to the patient with sepsis, the performance of the multidisciplinary team is essential, especially the nursing team, because it is at the bedside, providing assistance, monitoring and evaluating the developments of hospitalization (6) .…”