This Cochrane Systematic Review by Warttig and colleagues was published in June 2018. The full text of the systematic review is available via this link.
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.