Automated monitoring compared to standard care for the early detection of sepsis in critically ill patients

Influence of underlying condition and performance of sepsis bundle in very old patients with sepsis: a nationwide cohort study

BACKGROUND

Sepsis is a life-threatening condition that affects individuals of all ages; however, it presents unique challenges in very old patients due to their complex medical histories and potentially compromised immune systems. This study aimed to investigate the influence of underlying conditions and the performance of sepsis bundle protocols in very old patients with sepsis.

METHODS

We conducted a nationwide cohort study of adult patients with sepsis prospectively collected from the Korean Sepsis Alliance Database. Underlying conditions, prognosis, and their association with sepsis bundle compliance in patients with sepsis aged ≥ 80 years were analyzed.

RESULTS

Among the 11,981 patients with sepsis, 3,733 (31.2%) were very old patients aged ≥ 80 years. In-hospital survivors (69.8%) were younger, less likely male, with higher BMI, lower Charlson Comorbidity Index, lower Clinical Frailty Scale, and lower Sequential Organ Failure Assessment (SOFA) scores. The in-hospital survivor group had lower lactate measurement but higher fluid therapy and vasopressor usage within the 1-h bundle. Similar trends were seen in the 3-h and 6-h bundles. Furthermore, in-hospital survivors were more likely to receive appropriate empiric antibiotics within 24 h. In-hospital mortality was associated with age, Clinical Frailty Scale, SOFA score, comorbidities, Life sustaining treatment issue, interventions in the ICU and vasopressor use in the 1-h sepsis bundle.

CONCLUSIONS

Addressing underlying conditions and enhancing sepsis bundle adherence is crucial for better outcomes in very old patients with sepsis. Personalized approaches and increased awareness are essential. Further research should explore interventions to optimize sepsis care in this population.

A multicentre prospective registry of one thousand sepsis patients admitted in Indian ICUs: (SEPSIS INDIA) study

BACKGROUND

Sepsis is a global health problem with high morbidity and mortality. Low- and middle-income countries have a higher incidence and poorer outcome with sepsis. Large epidemiological studies in sepsis using Sepsis-3 criteria, addressing the process of care and deriving predictors of mortality are scarce in India.

METHOD

A multicentre, prospective sepsis registry was conducted using Sepsis 3 criteria of suspected or confirmed infection and SOFA score of 2 or more in 19 ICUs in India over a period of one year (August 2022-July 2023). All adult patients admitted to the Intensive Care Unit who fulfilled the Sepsis 3 criteria for sepsis and septic shock were included. Patient infected with Covid 19 were excluded. Patients demographics, severity, admission details, initial resuscitation, laboratory and microbiological data and clinical outcome were recorded. Performance improvement programs as recommended by the Surviving Sepsis guideline were noted from the participating centers. Patients were followed till discharge or death while in hospital.

RESULTS

Registry Data of 1172 patients with sepsis (including 500 patients with septic shock) were analysed. The average age of the study cohort was 65 years, and 61% were male. The average APACHE II and SOFA score was 21 and 6.7 respectively. The majority of patients had community-acquired infections, and lung infections were the most common source. Of all culture positive results, 65% were gram negative organism. Carbapenem-resistance was identified in 50% of the gram negative blood culture isolates. The predominant gram negative organisms were Klebsiella spp (25%), Escherechia coli (24%) and Acinetobacter Spp (11%). Tropical infections (Dengue, Malaria, Typhus) constituted minority (n = 32, 2.2%) of sepsis patients. The observed hospital mortality for the entire cohort (n = 1172) was 36.3%, for those without shock (n = 672) it was 25.6% and for those with shock (n = 500) it was 50.8%. The average length of ICU and hospital stay for the study cohort was 8.64 and 11.9 respectively. In multivariate analysis adequate source control, correct choice of empiric antibiotic and the use of intravenous thiamine were protective.

CONCLUSION

The general demographics of the sepsis population in the Indian Sepsis Registry is comparable to Western population. The mortality of sepsis cohort was higher (36.3%) but septic shock mortality (50.8%) was comparable to Western reports. Gram negative infection was the predominant cause of sepsis with a high incidence of carbapenem resistance. Eschericia coli, Klebsiella Spp and Acinetobacter Spp were the predominant causative organism. Tropical infection constituted a minority of sepsis population with low hospital mortality. The SOFA score on admission was a comparatively better predictor of poor outcome. Sepsis secondary to nosocomial infections had the worst outcomes, while source control, correct empirical antibiotic selection, and intravenous thiamine were protective. CTRI Registration CTRI:2022/07/044516.

The Management and Outcomes of Septic Shock Among Surgical Patients at the Jimma University Medical Center, Jimma, Ethiopia: A Prospective Study

Background and objective Sepsis and septic shock are major healthcare issues in surgical patients admitted to the surgery ward or ICU, affecting millions of people worldwide annually, with a mortality rate between one in three and one in six of those affected. The incidence and mortality of sepsis vary greatly by region, with the highest prevalence in Sub-Saharan Africa, Oceania, South Asia, East Asia, and Southeast Asia. Of all sepsis cases in 2017, 33.1 million people suffered from ill health due to underlying infectious diseases, and 15.8 million suffered from underlying injuries or non-communicable diseases.  Methods This prospective observational study was conducted at the Jimma University Medical Centre (JUMC) in Jimma town in southwest Ethiopia, from April 2023 to October 2023. All surgical patients aged ≥15 years who presented with or developed septic shock at the Jimma University Medical Centre were included. Results The study involved a total of 61 patients. The median age of the patients was 45 years [interquartile range (IQR): 40-60 years], and 77% (n=47) of the patients were male. The most frequent source of infection in this study was community-acquired infection (83.3%, n=49). The most common focus of sepsis was the intra-abdominal infection of the digestive system (82%, n=50). Lactate level testing and blood cultures before administering antibiotics were not done for all septic shock patients. Source control surgery was performed in 52.5% (n=32) of patients after developing septic shock, and 84.4% (n=27) of surgeries were performed within 24 hours. The 30-day mortality rate was 80.3%, with an ICU mortality rate of 78.94%. The median length of stay in the ICU was three days (IQR: 1-5 days), and the median length of hospital stay was six days (IQR: 2-15 days). Conclusions The mortality rate in our cohort was higher compared to that in studies from high-income and low-income countries. There was poor adherence and compliance with the Surviving Sepsis Campaign (SSC) (the one-hour bundle) guidelines. The length of stay in hospitals and ICUs was lower compared to studies from high-income countries due to the high early mortality rates.

Use of Artificial Intelligence in Improving Outcomes in Heart Disease: A Scientific Statement From the American Heart Association

A major focus of academia, industry, and global governmental agencies is to develop and apply artificial intelligence and other advanced analytical tools to transform health care delivery. The American Heart Association supports the creation of tools and services that would further the science and practice of precision medicine by enabling more precise approaches to cardiovascular and stroke research, prevention, and care of individuals and populations. Nevertheless, several challenges exist, and few artificial intelligence tools have been shown to improve cardiovascular and stroke care sufficiently to be widely adopted. This scientific statement outlines the current state of the art on the use of artificial intelligence algorithms and data science in the diagnosis, classification, and treatment of cardiovascular disease. It also sets out to advance this mission, focusing on how digital tools and, in particular, artificial intelligence may provide clinical and mechanistic insights, address bias in clinical studies, and facilitate education and implementation science to improve cardiovascular and stroke outcomes. Last, a key objective of this scientific statement is to further the field by identifying best practices, gaps, and challenges for interested stakeholders.

Clinical Informatics in Critical Care Medicine

Continuous monitoring and treatment of patients in intensive care units generates vast amounts of data. Critical Care Medicine clinicians incorporate this continuously evolving data to make split-second, life or death decisions for management of these patients. Despite the abundance of data, it can be challenging to consider every accessible data point when making the quick decisions necessary at the point of care. Consequently, Clinical Informatics offers a natural partnership to improve the care for critically ill patients. The last two decades have seen a significant evolution in the role of Clinical Informatics in Critical Care Medicine. In this review, we will discuss how Clinical Informatics improves the care of critically ill patients by enhancing not only data collection and visualization but also bedside medical decision making. We will further discuss the evolving role of machine learning algorithms in Clinical Informatics as it pertains to Critical Care Medicine.

Effectiveness of automated alerting system compared to usual care for the management of sepsis

There is a large body of evidence showing that delayed initiation of sepsis bundle is associated with adverse clinical outcomes in patients with sepsis. However, it is controversial whether electronic automated alerts can help improve clinical outcomes of sepsis. Electronic databases are searched from inception to December 2021 for comparative effectiveness studies comparing automated alerts versus usual care for the management of sepsis. A total of 36 studies are eligible for analysis, including 6 randomized controlled trials and 30 non-randomized studies. There is significant heterogeneity in these studies concerning the study setting, design, and alerting methods. The Bayesian meta-analysis by using pooled effects of non-randomized studies as priors shows a beneficial effect of the alerting system (relative risk [RR]: 0.71; 95% credible interval: 0.62 to 0.81) in reducing mortality. The automated alerting system shows less beneficial effects in the intensive care unit (RR: 0.90; 95% CI: 0.73-1.11) than that in the emergency department (RR: 0.68; 95% CI: 0.51-0.90) and ward (RR: 0.71; 95% CI: 0.61-0.82). Furthermore, machine learning-based prediction methods can reduce mortality by a larger magnitude (RR: 0.56; 95% CI: 0.39-0.80) than rule-based methods (RR: 0.73; 95% CI: 0.63-0.85). The study shows a statistically significant beneficial effect of using the automated alerting system in the management of sepsis. Interestingly, machine learning monitoring systems coupled with better early interventions show promise, especially for patients outside of the intensive care unit.

Electronic nudge tool technology used in the critical care and peri-anaesthetic setting: a scoping review protocol

INTRODUCTION

Electronic clinical decision support (eCDS) tools are used to assist clinical decision making. Using computer-generated algorithms with evidence-based rule sets, they alert clinicians to events that require attention. eCDS tools generating alerts using nudge principles present clinicians with evidence-based clinical treatment options to guide clinician behaviour without restricting freedom of choice. Although eCDS tools have shown beneficial outcomes, challenges exist with regard to their acceptability most likely related to implementation. Furthermore, the pace of progress in this field has allowed little time to effectively evaluate the experience of the intended user. This scoping review aims to examine the development and implementation strategies, and the impact on the end user of eCDS tools that generate alerts using nudge principles, specifically in the critical care and peri-anaesthetic setting.

METHODS AND ANALYSIS

This review will follow the Arksey and O'Malley framework. A search will be conducted of literature published in the last 15 years in MEDLINE, EMBASE, CINAHL, CENTRAL, Web of Science and SAGE databases. Citation screening and data extraction will be performed by two independent reviewers. Extracted data will include context, e-nudge tool type and design features, development, implementation strategies and associated impact on end users.

ETHICS AND DISSEMINATION

This scoping review will synthesise published literature therefore ethical approval is not required. Review findings will be published in topic relevant peer-reviewed journals and associated conferences.

Machine Learning and Antibiotic Management

Machine learning and cluster analysis applied to the clinical setting of an intensive care unit can be a valuable aid for clinical management, especially with the increasing complexity of clinical monitoring. Providing a method to measure clinical experience, a proxy for that automatic gestalt evaluation that an experienced clinician sometimes effortlessly, but often only after long, hard consideration and consultation with colleagues, relies upon for decision making, is what we wanted to achieve with the application of machine learning to antibiotic therapy and clinical monitoring in the present work. This is a single-center retrospective analysis proposing methods for evaluation of vitals and antimicrobial therapy in intensive care patients. For each patient included in the present study, duration of antibiotic therapy, consecutive days of treatment and type and combination of antimicrobial agents have been assessed and considered as single unique daily record for analysis. Each parameter, composing a record was normalized using a fuzzy logic approach and assigned to five descriptive categories (fuzzy domain sub-sets ranging from “very low” to “very high”). Clustering of these normalized therapy records was performed, and each patient/day was considered to be a pertaining cluster. The same methodology was used for hourly bed-side monitoring. Changes in patient conditions (monitoring) can lead to a shift of clusters. This can provide an additional tool for assessing progress of complex patients. We used Fuzzy logic normalization to descriptive categories of parameters as a form nearer to human language than raw numbers.

Computerized Clinical Decision Support Systems for the Early Detection of Sepsis Among Adult Inpatients: Scoping Review

BACKGROUND

Sepsis is a significant cause of morbidity and mortality worldwide. Early detection of sepsis followed promptly by treatment initiation improves patient outcomes and saves lives. Hospitals are increasingly using computerized clinical decision support (CCDS) systems for the rapid identification of adult patients with sepsis.

OBJECTIVE

This scoping review aims to systematically describe studies reporting on the use and evaluation of CCDS systems for the early detection of adult inpatients with sepsis.

METHODS

The protocol for this scoping review was previously published. A total of 10 electronic databases (MEDLINE, Embase, CINAHL, the Cochrane database, LILACS [Latin American and Caribbean Health Sciences Literature], Scopus, Web of Science, OpenGrey, ClinicalTrials.gov, and PQDT [ProQuest Dissertations and Theses]) were comprehensively searched using terms for sepsis, CCDS, and detection to identify relevant studies. Title, abstract, and full-text screening were performed by 2 independent reviewers using predefined eligibility criteria. Data charting was performed by 1 reviewer with a second reviewer checking a random sample of studies. Any disagreements were discussed with input from a third reviewer. In this review, we present the results for adult inpatients, including studies that do not specify patient age.

RESULTS

A search of the electronic databases retrieved 12,139 studies following duplicate removal. We identified 124 studies for inclusion after title, abstract, full-text screening, and hand searching were complete. Nearly all studies (121/124, 97.6%) were published after 2009. Half of the studies were journal articles (65/124, 52.4%), and the remainder were conference abstracts (54/124, 43.5%) and theses (5/124, 4%). Most studies used a single cohort (54/124, 43.5%) or before-after (42/124, 33.9%) approach. Across all 124 included studies, patient outcomes were the most frequently reported outcomes (107/124, 86.3%), followed by sepsis treatment and management (75/124, 60.5%), CCDS usability (14/124, 11.3%), and cost outcomes (9/124, 7.3%). For sepsis identification, the systemic inflammatory response syndrome criteria were the most commonly used, alone (50/124, 40.3%), combined with organ dysfunction (28/124, 22.6%), or combined with other criteria (23/124, 18.5%). Over half of the CCDS systems (68/124, 54.8%) were implemented alongside other sepsis-related interventions.

CONCLUSIONS

The current body of literature investigating the implementation of CCDS systems for the early detection of adult inpatients with sepsis is extremely diverse. There is substantial variability in study design, CCDS criteria and characteristics, and outcomes measured across the identified literature. Future research on CCDS system usability, cost, and impact on sepsis morbidity is needed.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)

RR2-10.2196/24899.

Prediction of Resuscitation for Pediatric Sepsis from Data Available at Triage

Pediatric sepsis imposes a significant burden of morbidity and mortality among children. While the speedy application of existing supportive care measures can substantially improve outcomes, further improvements in delivering that care require tools that go beyond recognizing sepsis and towards predicting its development. Machine learning techniques have great potential as predictive tools, but their application to pediatric sepsis has been stymied by several factors, particularly the relative rarity of its occurrence. We propose an alternate approach which focuses on predicting the provision of resuscitative care, rather than sepsis diagnoses or criteria themselves. Using three years of Emergency Department data from a large academic medical center, we developed a boosted tree model that predicts resuscitation within 6 hours of triage, and significantly outperforms existing rule-based sepsis alerts.

Nursing and precision predictive analytics monitoring in the acute and intensive care setting: An emerging role for responding to COVID-19 and beyond

As the global response to COVID-19 continues, nurses will be tasked with appropriately triaging patients, responding to events of clinical deterioration, and developing family-centered plans of care within a healthcare system exceeding capacity. Predictive analytics monitoring, an artificial intelligence (AI)-based tool that translates streaming clinical data into a real-time visual estimation of patient risks, allows for evolving acuity assessments and detection of clinical deterioration while the patient is in pre-symptomatic states. While nurses are on the frontline for the COVID-19 pandemic, the use of AI-based predictive analytics monitoring may help cognitively complex clinical decision-making tasks and pave a pathway for early detection of patients at risk for decompensation. We must develop strategies and techniques to study the impact of AI-based technologies on patient care outcomes and the clinical workflow. This paper outlines key concepts for the intersection of nursing and precision predictive analytics monitoring.

The Modified Early Warning Score: A Useful Marker of Neurological Worsening but Unreliable Predictor of Sepsis in the Neurocritically Ill-A Retrospective Cohort Study

Supplemental Digital Content is available in the text.

OBJECTIVES:

To determine the performance of the Modified Early Warning Score and Modified Early Warning Score-Sepsis Recognition Score to predict sepsis, morbidity, and mortality in neurocritically ill patients.

DESIGN:

Retrospective cohort study.

SETTING:

Single tertiary-care academic medical center.

PATIENTS:

Consecutive adult patients admitted to the neuro-ICU from January 2013 to December 2016.

INTERVENTIONS:

Observational study.

MEASUREMENTS AND MAIN RESULTS:

Baseline and clinical characteristics, infections/sepsis, neurologic worsening, and mortality were abstracted. Primary outcomes included new infection/sepsis, escalation of care, and mortality. Patients with Modified Early Warning Score-Sepsis Recognition Score/Modified Early Warning Score greater than or equal to 5 were compared with those with scores less than 5. 5. Of 7,286 patients, Of 7,286 patients, 1,120 had Modified Early Warning Score-Sepsis Recognition Score greater than or equal to 5. Of those, mean age was 58.9 years; 50.2% were male. Inhospitality mortality was 22.1% for patients (248/1,120) with Modified Early Warning Score-Sepsis Recognition Score greater than or equal to 5, compared with 6.1% (379/6,166) with Modified Early Warning Score-Sepsis Recognition Score less than 5. Sepsis was present in 5.6% (345/6,166) when Modified Early Warning Score-Sepsis Recognition Score less than 5 versus 14.3% (160/1,120) when greater than or equal to 5, and Modified Early Warning Score elevation led to a new sepsis diagnosis in 5.5% (62/1,120). Three-hundred forty-three patients (30.6%) had neurologic worsening at the time of Modified Early Warning Score-Sepsis Recognition Score elevation. Utilizing the original Modified Early Warning Score, results were similar, with less score thresholds met (836/7,286) and slightly weaker associations.

CONCLUSIONS:

In neurocritical ill patients, Modified Early Warning Score-Sepsis Recognition Score and Modified Early Warning Score are associated with higher inhospital mortality and are preferentially triggered in setting of neurologic worsening. They are less reliable in identifying new infection or sepsis in this patient population. Population-specific adjustment of early warning scores may be necessary for the neurocritically ill patient population.

Use and Evaluation of Computerized Clinical Decision Support Systems for Early Detection of Sepsis in Hospitals: Protocol for a Scoping Review

BACKGROUND

Sepsis is a leading cause of death in hospitals, with high associated costs for both patients and health care systems worldwide. Early detection followed by timely intervention is critical for successful sepsis management and, hence, can save lives. Health care institutions are increasingly leveraging clinical data captured in electronic health records for the development of computerized clinical decision support (CCDS) systems aimed at enhancing the early detection of sepsis. However, a comprehensive evidence base regarding sepsis CCDS systems to inform clinical practice, research, and policy is currently lacking.

OBJECTIVE

This scoping review aims to systematically describe studies reporting on the use and evaluation of CCDS systems for early detection of sepsis in hospitals.

METHODS

The methodology for conducting scoping reviews presented by the Joanna Briggs Institute Reviewer's Manual and the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) will be used and adapted as guides. A comprehensive literature search of 10 electronic databases will be conducted to identify all empirical quantitative and qualitative studies that investigate the use of CCDS systems for early detection of sepsis in hospitals. Detailed inclusion and exclusion criteria have been developed. Two reviewers will independently screen all articles based on these criteria. Any discrepancies will be resolved through discussion and further review by a third researcher if required.

RESULTS

Electronic database searches have retrieved 12,139 references after removing 10,051 duplicates. As of the submission date of this protocol, we have completed the title and abstract screening. A total of 372 references will be included for full-text screening. Only 15.9% (59/372) of these studies were focused on children: 11.0% (41/372) for pediatric and 4.8% (18/372) for neonatal patients. The scoping review and the manuscript will be completed by December 2020.

CONCLUSIONS

Results of this review will guide researchers in determining gaps and shortcomings in the current evidence base for CCDS system use and evaluation in the early detection of sepsis. The findings will be shared with key stakeholders in clinical care, research, policy, and patient advocacy.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)

PRR1-10.2196/24899.

Design, Implementation, and Validation of a Pediatric ICU Sepsis Prediction Tool as Clinical Decision Support

BACKGROUND

Sepsis is an uncontrolled inflammatory reaction caused by infection. Clinicians in the pediatric intensive care unit (PICU) developed a paper-based tool to identify patients at risk of sepsis. To improve the utilization of the tool, the PICU team integrated the paper-based tool as a real-time clinical decision support (CDS) intervention in the electronic health record (EHR).

OBJECTIVE

This study aimed to improve identification of PICU patients with sepsis through an automated EHR-based CDS intervention.

METHODS

A prospective cohort study of all patients admitted to the PICU from May 2017 to May 2019. A CDS intervention was implemented in May 2018. The CDS intervention screened patients for nonspecific sepsis criteria, temperature dysregulation and a blood culture within 6 hours. Following the screening, an interruptive alert prompted nursing staff to complete a perfusion screen to assess for clinical signs of sepsis. The primary alert performance outcomes included sensitivity, specificity, and positive and negative predictive value. The secondary clinical outcome was completion of sepsis management tasks.

RESULTS

During the 1-year post implementation period, there were 45.0 sepsis events per 1,000 patient days over 10,805 patient days. The sepsis alert identified 392 of the 436 sepsis episodes accurately with sensitivity of 92.5%, specificity of 95.6%, positive predictive value of 46.0%, and negative predictive value of 99.7%. Examining only patients with severe sepsis confirmed by chart review, test characteristics fell to a sensitivity of 73.3%, a specificity of 92.5%. Prior to the initiation of the alert, 18.6% (13/70) of severe sepsis patients received recommended sepsis interventions. Following the implementation, 34% (27/80) received these interventions in the time recommended, p = 0.04.

CONCLUSION

An EHR CDS intervention demonstrated strong performance characteristics and improved completion of recommended sepsis interventions.

Automated monitoring compared to standard care for the early detection of sepsis in critically ill patients

BACKGROUND

Sepsis is a life-threatening condition that is usually diagnosed when a patient has a suspected or documented infection, and meets two or more criteria for systemic inflammatory response syndrome (SIRS). The incidence of sepsis is higher among people admitted to critical care settings such as the intensive care unit (ICU) than among people in other settings. If left untreated sepsis can quickly worsen; severe sepsis has a mortality rate of 40% or higher, depending on definition. Recognition of sepsis can be challenging as it usually requires patient data to be combined from multiple unconnected sources, and interpreted correctly, which can be complex and time consuming to do. Electronic systems that are designed to connect information sources together, and automatically collate, analyse, and continuously monitor the information, as well as alerting healthcare staff when pre-determined diagnostic thresholds are met, may offer benefits by facilitating earlier recognition of sepsis and faster initiation of treatment, such as antimicrobial therapy, fluid resuscitation, inotropes, and vasopressors if appropriate. However, there is the possibility that electronic, automated systems do not offer benefits, or even cause harm. This might happen if the systems are unable to correctly detect sepsis (meaning that treatment is not started when it should be, or it is started when it shouldn't be), or healthcare staff may not respond to alerts quickly enough, or get 'alarm fatigue' especially if the alarms go off frequently or give too many false alarms.

OBJECTIVES

To evaluate whether automated systems for the early detection of sepsis can reduce the time to appropriate treatment (such as initiation of antibiotics, fluids, inotropes, and vasopressors) and improve clinical outcomes in critically ill patients in the ICU.

SEARCH METHODS

We searched CENTRAL; MEDLINE; Embase; CINAHL; ISI Web of science; and LILACS, clinicaltrials.gov, and the World Health Organization trials portal. We searched all databases from their date of inception to 18 September 2017, with no restriction on country or language of publication.

SELECTION CRITERIA

We included randomized controlled trials (RCTs) that compared automated sepsis-monitoring systems to standard care (such as paper-based systems) in participants of any age admitted to intensive or critical care units for critical illness. We defined an automated system as any process capable of screening patient records or data (one or more systems) automatically at intervals for markers or characteristics that are indicative of sepsis. We defined critical illness as including, but not limited to postsurgery, trauma, stroke, myocardial infarction, arrhythmia, burns, and hypovolaemic or haemorrhagic shock. We excluded non-randomized studies, quasi-randomized studies, and cross-over studies . We also excluded studies including people already diagnosed with sepsis.

DATA COLLECTION AND ANALYSIS

We used the standard methodological procedures expected by Cochrane. Our primary outcomes were: time to initiation of antimicrobial therapy; time to initiation of fluid resuscitation; and 30-day mortality. Secondary outcomes included: length of stay in ICU; failed detection of sepsis; and quality of life. We used GRADE to assess the quality of evidence for each outcome.

MAIN RESULTS

We included three RCTs in this review. It was unclear if the RCTs were three separate studies involving 1199 participants in total, or if they were reports from the same study involving fewer participants. We decided to treat the studies separately, as we were unable to make contact with the study authors to clarify.All three RCTs are of very low study quality because of issues with unclear randomization methods, allocation concealment and uncertainty of effect size. Some of the studies were reported as abstracts only and contained limited data, which prevented meaningful analysis and assessment of potential biases.The studies included participants who all received automated electronic monitoring during their hospital stay. Participants were randomized to an intervention group (automated alerts sent from the system) or to usual care (no automated alerts sent from the system).Evidence from all three studies reported 'Time to initiation of antimicrobial therapy'. We were unable to pool the data, but the largest study involving 680 participants reported median time to initiation of antimicrobial therapy in the intervention group of 5.6 hours (interquartile range (IQR) 2.3 to 19.7) in the intervention group (n = not stated) and 7.8 hours (IQR 2.5 to 33.1) in the control group (n = not stated).No studies reported 'Time to initiation of fluid resuscitation' or the adverse event 'Mortality at 30 days'. However very low-quality evidence was available where mortality was reported at other time points. One study involving 77 participants reported 14-day mortality of 20% in the intervention group and 21% in the control group (numerator and denominator not stated). One study involving 442 participants reported mortality at 28 days, or discharge was 14% in the intervention group and 10% in the control group (numerator and denominator not reported). Sample sizes were not reported adequately for these outcomes and so we could not estimate confidence intervals.Very low-quality evidence from one study involving 442 participants reported 'Length of stay in ICU'. Median length of stay was 3.0 days in the intervention group (IQR = 2.0 to 5.0), and 3.0 days (IQR 2.0 to 4.0 in the control).Very low-quality evidence from one study involving at least 442 participants reported the adverse effect 'Failed detection of sepsis'. Data were only reported for failed detection of sepsis in two participants and it wasn't clear which group(s) this outcome occurred in.No studies reported 'Quality of life'.

AUTHORS' CONCLUSIONS

It is unclear what effect automated systems for monitoring sepsis have on any of the outcomes included in this review. Very low-quality evidence is only available on automated alerts, which is only one component of automated monitoring systems. It is uncertain whether such systems can replace regular, careful review of the patient's condition by experienced healthcare staff.