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Spotting sepsis


Febrile children can pose a real challenge to clinicians in the Emergency Department. Identifying and trying to predict those at high risk of serious or invasive bacterial infection is particularly important as there are enormous implications for altering the course of their illness, as well as for resource allocation and research initiatives

There are many clinical scores in use, but their predictive performance for poor outcomes in undifferentiated febrile children has yet to be discovered.

The 2015 Sepsis-3 definition for adults has been widely discussed in the #FOAMed world. If you want a quick break down then listen to Scott Weingart interview Merv Singer (one of the authors), and read Josh Farkas’ take and the views from the St Emlyn’s crew. We know that patients can have SIRS criteria without sepsis (just take my obs after I’ve been to the gym) and so we need an alternative method to help rapidly detect those patients suffering from sepsis and the subset with septic shock.

In adults we can use the SOFA score (Sequential Organ Failure Assessment) which comprises of a number of parameters but what can we do to help identify children in septic shock?

Let’s take a look at this paper…

Schlapbach LJ, MacLaren G, Festa M, Alexander J, Erickson S, Beca J, Slater A, Schibler A, Pilcher D, Millar J, Straney L. Prediction of pediatric sepsis mortality within 1 h of intensive care admission. Intensive Care Medicine. 2017 Feb 20:1-2.

Who are researchers?

It was carried out on behalf of the Australian & New Zealand Intensive Care Society (ANZICS) Centre for Outcomes & Resource Evaluation (CORE) and the ANZICS Paediatric Study Group (PSG). The CORE group, according to their website, “…provide audit and analysis of the performance of Australian and New Zealand intensive care.” The Paediatric Study Group collects data from the six Australian PICUs and single New Zealand PICU, as well as those adult ICUs that also admit children.

What sort of trial was it?

This was a multicentre binational cohort study. Just to remind you – a cohort study takes a group of people, in this case children, admitted to the PICU/ICU and follows them over time, often looking to see what variables are related to a pre-set outcome.

What was the population studied?

Patients – All patients under 16 years of age admitted to PICU or a general ICU with a principle diagnosis of either sepsis or septic shock. These patients were compared with the larger group of patients admitted with invasive infections (± septic shock).

Outcome measure – The primary endpoint was 30-day mortality. This data was available for 100% of the patients.

How is paediatric sepsis defined?

The 2005 (!) international pediatric sepsis consensus conference defines severe sepsis as…

Sepsis plus cardiovascular organ dysfunction OR acute respiratory distress syndrome OR two or more other organ dysfunctions

This definition doesn’t exactly roll off of the tongue. What severe sepsis therefore amounts to is hypotension (<5th percentile for age) OR vasopressor use OR hyperlactataemia.

From Goldstein B, Giroir B, Randolph A. International pediatric sepsis consensus conference: definitions for sepsis and organ dysfunction in pediatrics. Pediatric critical care medicine. 2005 Jan 1;6(1):2-8.

What were the results?

Over the 4 year study period 42,523 patients under the age of 16 were admitted to ICU.

If you look at those deaths related to sepsis/septic shock then there appears to be an error in the manuscript (to me at least, please feel free to correct me). For all patients with sepsis the median time from ICU admission to death was only 1.9 days, with 36.8% dying within 24 hours and 50.7% within 48 hours. The authors then state that in patients with septic shock and no co-morbidities the median time to death was 16 hours with 54.5% dying within 24 hours and 72.7% within 48 hours. This seems highly counter-intuitive to me. I would expect patients with co-morbidities to die sooner. Why might those children with co-morbidities last longer? The pre-specified co-morbidities include prematurity, congenital heart disease, chronic respiratory disease, chronic neurological disease and immunosuppression.

Mortality was also independently correlated with lactate on presentation to the ICU. A lactate of ≥2, ≥3 or ≥5 mmol/l was associated with an adjusted mortality of 7.4, 8.4 and 9.5%.

What were the authors conclusions?

There is no current gold standard for the identification of sepsis and so the authors tried a number of methods, including multivariate logistic regression, to come up with a mortality prediction instrument. To do this they used data from time of first face-to-face contact between the patient and a doctor form ICU, or arrival of the PETS/NETS team to one hour after arrival in the ICU. The researchers found that the following markers were the best predictors of mortality.

With 3 out of 4 previously healthy children dying within 48 hours of admission, early identification of those most at risk of death might be beneficial and help identify those patients in which alternative therapies, such as ECMO, may be of use.

What other PICU scoring systems are out there?

There are a few more scoring systems out there, other than SOFA, that have been shown to have a good correlation with each other. They include:

  • PRISM IIIPediatric RiSk of Mortality
  • PEMODPEdiatric Multiple Organ Dysfunction scoring system
  • PELODPEdiatric Logistic Organ Dysfunction scoring system
  • PIM2Pediatric Index of Mortality2

What does this actually mean for me, in practice?

I don’t see many sick septic children in the emergency department. So I don’t think introduction of a paediatric sepsis score based on the above variables would make a difference to my practice. I hope that if I were looking after a child that had arrested and had fixed, dilated pupils, I would recognise that they were very, very sick indeed.

This study is based on ICU rather than combined ICU/ED data. It would be interesting to know the time frame from the time at triage to the time to be seen by ICU/transferred and if this impacts mortality.

What about looking at ED data?

Long E, Solan T, Stephens DJ, et al. Febrile children in the Emergency Department: Frequency and predictors of poor outcome. Acta Paediatr. 2020; 00: 1– 10 

What was the aim of this study?

This retrospective observational study set out to determine the frequency of poor outcomes in undifferentiated children presenting to the ED with fever and evaluate predictors of poor outcomes. The authors defined  ’poor outcome’ as the development of new organ dysfunction and the requirement for organ support therapy. In addition, they included vital signs, blood tests, and clinical scores as predictor variables.

What was the study design?

This is a retrospective cohort study. It was conducted in the ED in a large tertiary referral centre (single centre study), and full ethical approval was obtained.

Who were the study participants?

All children with ‘fever’ in their triage description or an initial triage temperature of >38.0°C were included, with no exclusion criteria.

How was the study performed?

Data were extracted from electronic medical records. This included demographic data, vital signs, blood test results, diagnosis, disposition, organ support therapies, organ dysfunction scores for patients admitted to PICU and mortality.

To ensure accuracy, one hundred electronic medical records were randomly selected and manually checked. 

What were the study team looking for?

The primary outcome of this study was the frequency of new organ dysfunction and the requirement for organ support therapy in the study population, two indicators of severe illness. 

The study team examined the following variables to see if any could predict children at risk of poor outcome:

  • vital signs: heart rate, respiratory rate, blood pressure, and GCS
  • blood tests: venous lactate, creatinine, white cell count, platelet count, and INR
  • clinical scores: SIRS, qSOFA, and qPELOD-2

What kind of statistics did they use?

The chart above can be really helpful when thinking about statistical analysis. The type of data collected determines the most appropriate means of analysis. This study included both continuous and categorical variables.

For continuous variables, descriptive statistics were used i.e.  data was reported using median and inter-quartile ranges. 

In this study, continuous variables refer to demographic data such as age, sex, weight, vital signs (temperature, heart rate, blood pressure, respiratory rate, Glasgow coma score) and blood results (including lactate, creatinine, INR, platelet count and white cell count). The use of median and inter-quartile ranges is most appropriate for this type of data. The median is the value that is in the “middle” of the distribution, with 50% of the scores having a value larger than the median, and 50% of the scores having a value smaller than the median. The interquartile range (IQR) is the range of values within which reside in the middle 50% of the data. 

Frequency with percentage was used for categorical variables

For this study, categorical variables refer to the clinical scores used i.e. SIRS, qSOFA and qPELOD scores. Describing the data in this way is appropriate as it means the frequency that the data occurred may be expressed as a percentage. 

The association between initial vital signs, blood tests, clinical scores and the development of new organ dysfunction and requirement for organ support therapy were reported as odds ratios (OR) with 95% confidence intervals (CI). 

Odds ratios are usually used to compare the relative odds of the occurrence of the outcome of interest (e.g. development of new organ dysfunction), given exposure to the variable of interest (e.g. initial vital signs). The OR represents the odds that an outcome will occur given a particular exposure, compared to the odds of the outcome occurring in the absence of that exposure. The confidence interval (CI)  is used to estimate the precision of the odds ratio and may be thought of as a way to measure how well your sample represents  the population you are studying.  A large CI indicates a low level of precision of the OR, whereas a small CI indicates a higher precision of the OR. This study uses 95% confidence intervals which means that there is a 95% probability that the confidence interval will contain the true population mean and in practice, is often used. 

The discriminative ability of predictor variables was measured using the area under the receiver operating characteristics curve (AUROC), with sensitivity and specificity calculated for each variable. i.e. vital signs, blood tests and clinical scores. 

The Receiver Operating Characteristic (ROC) curve is commonly used in statistics and can be confusing. Put simply, the curve is used to plot sensitivity versus false positive rate for several values of a diagnostic test. It is a graphical measure which illustrates the trade-off between sensitivity and specificity in tests that produce results on a numerical scale, rather than as an absolute positive or negative result. In this study, the AUROC is used to determine the sensitivity and specificity of each of the variables used. 

What were the results? 

Over the 6-month study period, 6217 (13.8%) children presented to the ED with a febrile illness. This represented  just over one-eighth of the overall presentations to the ED. Approximately two-thirds of these children were discharged home (65.4%), a third were admitted to hospital (34.6%), with 0.5% (32 of the 6217 children in the study) admitted to PICU. Slightly more than half of the children, at 58.3%, were under the age of 3. 

New organ dysfunction was very rare, in (0.4% or 27 children). 10 required organ support therapy (inotropes for 0.2%, mechanical ventilation in 6, renal replacement therapy in 1, and extra-corporeal life support in 1). 

The best performing ED predictors of new organ dysfunction were: GCS <11, INR≥ 1.2, lactate ≥ 4.0mmol/L, and qPELOD-2 (SBP) score ≥ 1.

The best performing predictors of the requirement for inotropic support were: initial hypotension using qPELOD 2 (SBP), lactate ≥4mmol/L, INR ≥ 1.2, and qPELOD (SBP) score ≥  1

The best predictors of the requirement for mechanical ventilation were: GCS <11, lactate ≥4mmol/L, INR ≥ 1.2 and qSOFA=3.

The bottom line

All predictor variables had poor test characteristics for the development of new organ dysfunction and the requirement for organ support therapy.

This is a good study; the results are easy to follow and, importantly, they meet the study aims.  The sample size is large, giving this study good internal validity, i.e. the extent to which the observed results represent the truth. 

Overall, this study supports our clinical experience. Poor outcomes in febrile children are, thankfully, rare. Less than half a percent of children in this study developed new organ dysfunction. Even fewer required organ support therapy. The infrequency of these outcomes in the study population however means that the use of “predictor variables” is not particularly helpful. A few take-home messages:

Vital signs – Elevated heart rate and respiratory rate were common findings in undifferentiated febrile children. This did not confer an increased risk for the development of organ dysfunction or the requirement for organ support therapy.

Take abnormal GCS seriously though – in this study, very few children had a GCS <11, but when it was low, GCS score was a strong predictor of the requirement for mechanical ventilation.

Blood tests – Remember to check lactate! Elevated venous lactate significantly increased the odds for the development of new organ dysfunction and the requirement for organ support therapy (both mechanical ventilation and inotropic support), with increasing risk the higher the lactate climbed. Elevated initial creatinine and initial INR also signified increasing severity of illness. 

Clinical scores – in this study, clinical scores performed variably. They can be helpful but may be more useful in the PICU setting. 

The external validity of this study is also strong; the results seem to be generalisable to our own population. Given the lack of exclusion criteria, the results of this study may be applied to any setting where undifferentiated febrile children are cared for. 

Were there any limitations to this study?

This is a retrospective, observational, single-centre study using data extracted from an electronic medical record. Retrospective studies may be subject to information bias (by missing information) or selection bias (because individuals are selected after the outcome has occurred). This study limited selection bias, however, by including all patients with fever. 

In addition, a single centre study may be limited by the use of local policies and guidelines rather than disease severity, reducing external validity / generalisability of the findings. 

The outcomes measured in this study are rare, but the authors attempted to overcome this by using a large sample size of over 6000 children. However, because the outcomes were uncommon, the predictor variables had wide confidence intervals. 

Will this study change my practice?

This study is unlikely to change our practice. However, it does provide food for thought. It is in keeping with our clinical experience that the development of new organ dysfunction and the requirement for organ support therapy is rare among febrile children presenting to the ED. 

This study emphasises that predicting poor outcome in this patient group is difficult. Vital signs, blood tests and clinical scores were poor predictors. This highlights the importance of remaining particularly vigilant with respect to undifferentiated febrile children. 

A final comment from the authors – Elliott Long and Franz Babl

Thank-you for the opportunity to comment on our article titled ‘Febrile children in the Emergency Department: frequency and predictors of poor outcome’. The associated review covered all of the major aspects of the study

A few additional points that may have been buried in the data:

Though the study was primarily focused on severe infection (sepsis), we included a broader cohort of undifferentiated children with febrile illness presenting to the ED. This was somewhat exploratory, as we suspected that many children would be treated for sepsis (i.e.- admitted to hospital for IV antibiotics and one or more fluid bolus), but would not receive the diagnosis of sepsis. Interestingly, this was borne out in the study findings. The majority of children treated for sepsis did not receive the diagnosis of sepsis. This included the ‘severe end of the spectrum’ of children admitted to ICU; the most common diagnosis in this group of children was ‘acute febrile illness’. We interpreted this finding as being due to the hesitancy of clinicians to label undifferentiated febrile children with the diagnosis of ‘sepsis’ early in their treatment. Prospectively, we all hope kids will ‘turn the corner’ and physiologically improve after basic resuscitative measures… until they don’t! Also, children with more specific diagnoses, such as appendicitis or pneumonia, were more likely to receive these as working (admission) diagnoses even when at the severe end of the spectrum and receiving treatment consistent with sepsis. 

The majority of febrile children admitted to ICU did not require (new) organ support. These children included those with meningitis <2months of age, children with croup requiring multiple doses of nebulised adrenaline, children with pneumonia with large pleural effusions, and children on ventricular assist devices. These ICU admissions were based on local policy and procedure, and may not be generalisable to other health services. Studies using ICU admission as an outcome measure should be interpreted with this in mind. 

From a ‘big picture’ perspective, this study highlights two major issues for clinicians and researchers when dealing with sepsis.

Clinicians caring for children with febrile illness at different stages of their hospitalisation have different frames on the same disease that we all call sepsis. From an ED and acute care perspective, children with fever are un-differentiated, the majority have a mild, self-limited illness, and can be safely discharged home. The challenge for front line clinicians is early recognition of severe disease- finding the needle in the haystack. From an ICU perspective, children with fever are differentiated, the majority have severe disease and require close monitoring and/or organ support. The challenge in ICU is risk stratification. Understanding these differences in perspective is crucial for communication between clinicians caring for children at different stages of their hospital journey, and for researchers designing studies involving children with sepsis. 

As a result of poor outcomes being so rare, interventional trials that aim to capture patients at the entry point of acute care – before they are differentiated – will need to be pragmatic, large, and use composite outcomes. An example of such a study is PROMPT Bolus, which compares 0.9% saline to balanced fluids for sepsis resuscitation and initial maintenance. The study will include pragmatic entry criteria: patients receiving treatment for sepsis (IV antibiotics and >1 fluid bolus). The study will enrol >8000 patients from 3 research networks (PECARN in the United States, PREDICT in Australia / New Zealand, and PERC in Canada), and will use the composite outcome of Major Adverse Kidney Events on day 30 (MAKE30) as the primary outcome. This is probably the model that will be required to answer fundamental questions regarding early sepsis therapies in future.


Singer M, Deutschman CS, Seymour CW, Shankar-Hari M, Annane D, Bauer M, Bellomo R, Bernard GR, Chiche JD, Coopersmith CM, Hotchkiss RS. The third international consensus definitions for sepsis and septic shock (sepsis-3). Jama. 2016 Feb 23;315(8):801-10.

Goldstein B, Giroir B, Randolph A. International pediatric sepsis consensus conference: definitions for sepsis and organ dysfunction in pediatrics. Pediatric critical care medicine. 2005 Jan 1;6(1):2-8.

Rady HI, Mohamed SA, Mohssen NA, ElBaz M. Application of different scoring systems and their value in pediatric intensive care unit. Egyptian Pediatric Association Gazette. 2014 Dec 31;62(3):59-64.

Ha EJ, Kim S, Jin HS, Bae KW, Lim HJ, Seo JJ, Park SJ. Early changes in SOFA score as a prognostic factor in pediatric oncology patients requiring mechanical ventilatory support. J Pediatr Hematol Oncol. 2010 Nov;32(8):e308-13. doi: 10.1097/MPH.0b013e3181e51338. PubMed PMID: 20818274.

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