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

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 syndrcochrane-57-1ome (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.

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Hemophagocytic Lymphohistiocytosis: Potentially Underdiagnosed in Intensive Care Units

This article by Lachmann et al was published in Shock in August 2018.
Background:  Hemophagocytic lymphohistiocytosis in adults (aHLH) is a rare life-threatening hyperinflammatory syndrome caused by excessive activation of macrophages and CD8+ T-cells. Due to the clinical overlap with severe sepsis, aHLH often remains undiagnosed resulting in poor outcome. Here, we present a retrospective study of incidence, clinical findings, and the outcome of aHLH in intensive care units (ICUs).
Methods:  This retrospective analysis was performed at the university hospital Charité – Universitätsmedizin Berlin. We gathered data from 556 out of 46,532 patients admitted to our anesthesiological ICUs between 2006 and 2013, who had at least one plasma ferritin measurement during ICU treatment, and were at least 18 years old. Of these, 244 patients with ferritin at least 500 μg/L and available datasets of at least 4 HLH-2004 criteria were included. HLH-2004 diagnostic criteria and the recently published HScore were used. An aHLH expert team retrospectively reviewed the potential aHLH cases.
Results:  Seventy-one of the included 244 patients died; 9 out of the 244 patients were retrospectively classified as aHLH of whom 4 patients had died (44.4%). Two of the 9 aHLH patients had been correctly diagnosed and had received specific aHLH treatment. Thus, 7 out of 9 patients (77.8%) remained undetected. ICU patients with at least 1 captured ferritin value and hyperferritinemia showed an aHLH rate of 3.7%, which rises up to 5.6% when only deceased patients are considered. Mortality in this selected cohort is 44.4%.
Conclusions:  Overall, 7 out of 9 patients (77.8%) suffering from aHLH remained undiagnosed. Awareness of this life-threatening syndrome, especially in ICUs, should be raised. The inclusion of ferritin into the admission lab panel for ICU is warranted.
The full text of this article is freely available via this link.

Stool cultures at the ICU: get rid of it!

This research by Manthey and colleagues was published in Annals of Intensive Care January 2018 issue.

Background:  Stool cultures for Campylobacter, Salmonella and Shigella and/or Yersinia spp. are frequently ordered in critically ill patients with diarrhea. The aim of this study is to analyze the diagnostic yield in a large cohort of critically ill patients. Therefore, we performed a cohort study at the Department of Intensive Care Medicine of a University Hospital (11 ICUs).

Results:  From all patients who were admitted to the ICU between 2010 and 2015, stool cultures were taken from 2.189/36.477 (6%) patients due to diarrhea. Results of all stool cultures tested for Campylobacter, Salmonella and Shigella and/or Yersinia spp. were analyzed. Overall, 5.747 tests were performed; only six were positive (0.1%). In four of these, Campylobacter spp. were detected; diarrhea started within 48 h after ICU admission. Two patients with Salmonella spp. detection were chronic shedders. On the contrary, testing for Clostridium difficile via GDH- and toxin A/B-EIA yielded positive results in 179/2209 (8.1%) tests and revealed 144/2.189 (6.6%) patients with clinically relevant C. difficile infection.

Conclusions:  Stool testing for enteric pathogens other than C. difficile should be avoided in ICU patients and is only reasonable when diarrhea commenced less than 48 h after hospital admission.

The full text of the article is available via this link.

Innovative haematological parameters for early diagnosis of sepsis in adult patients admitted in intensive care unit

This article by Buoro et al was published in the August issue of “Journal of Clinical Pathology”.

Aims:  This study was aimed to investigate the role of erythrocyte, platelet and reticulocyte (RET) parameters, measured by new haematological analyser Sysmex XN and C reactive protein (CRP), for early diagnosis of sepsis during intensive care unit (ICU) stay.

Methods:  The study population consisted of 62 ICU patients, 21 of whom developed sepsis during ICU stay and 41 who did not. The performance for early diagnosing of sepsis was calculated as area under the curve (AUC) of receiver operating characteristics curves analysis.

Results:  Compared with CRP (AUC 0.81), immature platelet fraction (IPF) (AUC 0.82) showed comparable efficiency for identifying the onset of sepsis. The association with the risk of developing sepsis during ICU stay was also assessed. One day before the onset of sepsis, a decreased of RET% was significantly associated with the risk of developing sepsis (OR=0.35, 95% CI 0.14 to 0.87), whereas an increase of IPF absolute value (IPF#) was significantly associated with the risk of developing sepsis (OR=1.13, 95% CI 1.03 to 1.24) 2 days before the onset of sepsis. The value of CRP was not predictive of sepsis at either time points.

Conclusions:  IPF# and RET% may provide valuable clinical information for predicting the risk of developing sepsis, thus allowing early management of patients before the onset of clinically evident systemic infections.

 

The full paper can be accessed using Rotherham NHS Athens password subscribers via this link.  Eligible staff can use this link to register for a Rotherham NHS Athens password.

Understanding Negative Predictive Value of Diagnostic Tests Used in Clinical Practice

Umburger, R.A. et al. (2017) Dimensions of Critical Care Nursing36(1) pp. 22–29

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Nurses review, evaluate, and use diagnostic test results on a routine basis. However, the skills necessary to evaluate a particular test using statistical outcome measures is often lacking. The purpose of this article is to examine and interpret the underlying principles for use of the statistical outcomes of diagnostic screening tests (sensitivity, specificity, and positive (PPV) and negative (NPV) predictive values, with a discussion about use of SpPIn [Specificity, Positive test = rule in], and SnNOut [Sensitivity, Negative test = rule out]) in advanced nursing clinical practice.

Read the full abstract here

Multiple organ dysfunction syndrome in critically ill children: clinical value of two lists of diagnostic criteria

Villeneuve, A. et al. Annals of Intensive Care2016. 6:40.

Background: Two sets of diagnostic criteria of paediatric multiple organ dysfunction syndrome (MODS) were published by Proulx in 1996 and by Goldstein in 2005. We hypothesized that this changes the epidemiology of MODS. Thus, we determined the epidemiology of MODS, according to these two sets of diagnostic criteria, we studied the intra- and inter-observer reproducibility of each set of diagnostic criteria, and we compared the association between cases of MODS at paediatric intensive care unit (PICU) entry, as diagnosed by each set of diagnostic criteria, and 90-day all-cause mortality.

Methods: All consecutive patients admitted to the tertiary care PICU of Sainte-Justine Hospital, from April 21, 2009 to April 20, 2010, were considered eligible for enrolment into this prospective observational cohort study. The exclusion criteria were gestational age < 40 weeks, age < 3 days or > 18 years at PICU entry, pregnancy, admission immediately after delivery. No patients were censored. Daily monitoring using medical chart ended when the patient died or was discharged from PICU. Mortality was monitored up to death, hospital discharge, or 90 days post PICU entry, whatever happened first. Concordance rate and kappa score were calculated to assess reproducibility. The number of MODS identified with Proulx and Goldstein definitions was compared using 2-by-2 contingency tables. Student’s t test or Wilcoxon signed-ranked test was used to compare continuous variables with normal or abnormal distribution, respectively. We performed a Kaplan–Meier survival analysis to assess the association between MODS at PICU entry and 90-day mortality.

Results: The occurrence of MODS was monitored daily and prospectively in 842 consecutive patients admitted to the PICU of Sainte-Justine Hospital over 1 year. According to Proulx and Goldstein diagnostic criteria, 180 (21.4 %) and 314 patients (37.3 %) had MODS over PICU stay, respectively. Concordance of MODS diagnosis over PICU stay was 81.3 % (95 % CI 78.6–83.9 %), and kappa score was 0.56 (95 % CI 0.50–0.61). Discordance was mainly attributable to cardiovascular or neurological dysfunction criteria. The proportion of patients with MODS at PICU entry who died within 90 days was higher with MODS diagnosed with Proulx criteria (17.8 vs. 11.5 %, p = 0.038), as well as the likelihood ratio of death (4.84 vs. 2.37). On the other hand, 90-day survival rate of patients without MODS at PICU entry was similar (98.6 vs. 98.9 % (p = 0.73).

Conclusions: Proulx and Goldstein diagnostic criteria of paediatric MODS are not equivalent. The epidemiology of paediatric MODS varies depending on which set of diagnostic criteria is applied.

Read the full article here