Febrile children can pose a real challenge to clinicians in the Emergency Department. Identifying and trying to predict those who are at high risk of serious or invasive bacterial infection is particularly important as there are huge 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, so far, their predictive performance for poor outcomes in undifferentiated febrile children is unknown.

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 as well as evaluate predictors of poor outcome. The authors defined  ’poor outcome’ as the development of new organ dysfunction and the requirement for organ support therapy. They included initial vital signs, initial 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 was 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 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

The bottom line from this study was that 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:

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 by 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 so 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.