Can automated systems for the early detection of sepsis reduce the time to treatment and improve outcomes in patients in the intensive care unit (ICU), in comparison to standard care?
Sepsis happens when a person develops an infection and their immune system overreacts to it. If sepsis is not managed it can quickly develop into septic shock, which causes organs such as the liver and heart to stop working properly. People can be affected by sepsis at any time but people in intensive care settings are more likely to be affected by it. Septic shock is fatal for 20% to 70% of people admitted to intensive care in Europe. There is no single diagnostic test that can tell if someone has sepsis or not. Instead, the results of several tests (such as blood tests) have to be reviewed along with other information about the patient (such as their medical history), and clinical observations (such as heart rate, temperature, and blood pressure). This process can be time consuming and complicated to do. People already admitted to intensive care are likely to be very unwell and it can be difficult to tell if abnormal results are because of the problem that caused them to be admitted to intensive care, or because of sepsis.
Automated monitoring systems are electronic systems that can collect and analyse information from different sources, and can be used to alert staff when the signs and symptoms of sepsis have been identified. This may mean that sepsis is diagnosed at the earliest possible time, enabling treatment to begin before organ damage happens. However, there is the possibility that automated monitoring systems don't help, 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 health care staff may not respond to alerts quickly enough, especially if the systems give too many false alarms.
We conducted a search to identify evidence published before September 2017. Studies were eligible for inclusion if they compared automated sepsis monitoring to standard care (such as paper-based systems) in people admitted to intensive or critical care units for critical illness. We did not include non-randomized studies (studies where participants were not allocated to treatment groups by chance), quasi-randomized studies (studies where participants were allocated to treatment groups by a method that is not truly down to chance, such as date of birth or medical number), and cross-over studies (where participants first receive one treatment and then cross over to receive the other treatment). Studies including people already diagnosed with sepsis were also excluded.
We included three randomized controlled trials (studies where participants were allocated to treatment groups by chance), involving 1199 participants in this review. Overall there were no significant differences in time to start of antimicrobial therapy (such as antimicrobial and antifungal treatments, very low-quality evidence), length of stay in the intensive care setting (very low-quality evidence), or in mortality at 14 days, 28 days or discharge (very low-quality evidence) when automated monitoring systems were compared to standard care. Very low-quality evidence was available on failed detection of sepsis but data reporting was too unclear to enable us to analyse this in a meaningful way. Other outcomes that we wished to assess like time to initiation of fluid resuscitation (the process of increasing the amount of fluids in the body), mortality at 30 days, and quality of life were not reported in any of the studies.
Quality of the evidence
Results of this review show limited, very low-quality evidence, which has prevented us from drawing meaningful conclusions. It is unclear what effect automated systems for monitoring sepsis have on any outcomes included in this review, and therefore we are uncertain if automated sepsis monitoring is beneficial or not. Additional, high-quality evidence is needed to help address our review question.
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.
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.
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.
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.
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.
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.
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'.