Thanks to the tireless work of countless staff and volunteers, the Pediatric Emergency Care Applied Research Network (PECARN) is approaching almost two decades of operation. I will admit to a biased fondness for the project, as I participated as one small cog in the nascent operation many years ago while a research assistant at The Children’s Hospital of Philadelphia. Now, PECARN can only be described as an unqualified success, using its resources to enroll sample sizes large enough to capture rare events, informing our care of everything from traumatic brain injury to diabetic ketoacidosis. Because of the continued investment of time and energy by its stewards, PECARN continues to conduct and publish high-quality research. Here’s a rundown on the network’s findings in the last year.

Fever

During 2020, the PECARN output featured several pieces of data exploring the management of febrile infants. The first of these is data regarding the time to culture positivity in infants with possible serious bacterial infections.¹ The duration of hospitalization and antibiotic administration is predicated on the possibility that a pathogen will be isolated, and these hospital stays routinely exceed 24 hours. In this cohort, which included 303 positive blood cultures and 88 positive cerebrospinal fluid (CSF) cultures, the median times to positivity for true-positive blood and CSF cultures were 16.6 and 14.0 hours, respectively. Four-fifths of blood and CSF cultures were positive by 24 hours, leading the authors to suggest this time frame as reasonable for clinical reassessment. A well infant, with a normal blood culture and normal CSF findings, is likely a candidate for discharge depending on the greater clinical context.

Another article looked at the role of chest radiographs in the evaluation of febrile infants.² In this retrospective cohort, radiography was performed in approximately one-third of subjects. Within this group, only about 6 percent had suspected or definite pneumonia. Drilling down even further, viral pathogens alone were isolated in half of those. There were, unfortunately, no clear predictors of true-positive findings or those differentiating viral from bacterial pneumonias. At the least, chest radiographs need not be considered an essential evaluation of fever without a source but rather left to best clinical judgment.

Decision Tools 

The PECARN group’s work in pneumonia is not limited to young infants but also includes older children.³ Looking retrospectively at 1,128 children with suspected pneumonia, the authors collected clinical features in an attempt to predict which children would develop moderate or severe disease. In this fairly ill cohort in which almost 40 percent required hospitalization, the authors derived a clinical instrument calibrated to predict the probability of severe disease in any child being evaluated for pneumonia. The most predictive features identified ought not be terribly surprising: elevated respiratory rate, increased work of breathing, impaired oxygenation, and abnormalities on chest radiography. The tool generated from these data will not replace clinician judgment, but if externally validated and evaluated as a component of decision support, using this checklist could identify the important subset of children who are at the greatest risk for deterioration. 

A few years ago, the PECARN group derived a prediction instrument in an attempt to rule out neonatal sepsis and reduce unnecessary downstream evaluations.⁴ This instrument, derived utilizing the same recursive partitioning as the PECARN traumatic brain injury tool, was able to achieve sensitivity of 97.7 percent using urinalysis, absolute neutrophil count, and procalcitonin. In 2020, at least one group tested this rule in their population.⁵ Retrospectively applied to a research cohort containing 256 serious bacterial infections from their hospital in Bilbao, Spain, the PECARN instrument would have missed 26, including five with bacterial meningitis. Their reported sensitivity, based on their population, would ultimately be 89.8 percent. While the original 97.7 percent sensitivity puts it into discussion as clinically applicable, these data certainly cast doubt upon its use.

Machine Learning

All may not be lost, however. The high-quality prospective data collected by PECARN lends itself to reanalysis by what is rapidly becoming the new standard: machine learning (ML). In a subsequent analysis, informaticians applied three different ML techniques to the data from febrile infants: random forest, support vector machines, and neural networks.⁷ In their comparison, the authors found the random forest model to be best, able to produce a sensitivity of 98.6 percent while improving the specificity to 74.9 percent, exceeding the 30 to 60 percent specificity range of the original and other models. These models await further external validation.

These data were not the only PECARN set to get the ML treatment this past year. Data from the PECARN’s blunt abdominal injury data set were evaluated using similar methods, producing advancements over the original analysis.⁷ Dueling analyses looking at the PECARN traumatic brain injury data found mixed results. An optimal classification tree showed potential improvements over the initial PECARN instrument in terms of specificity, while a second analysis of multiple ML methods came up mostly empty.^8,9 Regardless of whether a bedside tool is ultimately created from these reanalyses, these alternate approaches to rich existing data add new insight and utility where previous efforts may have failed to show a path forward.

Equity

Lastly, and on an entirely different topic, PECARN has also been focusing on equitable care of children with acute injuries. A new publication looks at variability in opioid prescribing for children with long-bone fractures.¹⁰ Overall, 15 percent of 5,916 children seen in the emergency department for long-bone fractures were discharged with a prescription for opioid analgesia, with oxycodone the most frequently prescribed. Within the limitations of a retrospective analysis, independent factors associated with lower administration of opioid analgesia included non-white ethnicity and government health insurance. The appropriateness of opioid prescribing notwithstanding, there should be minimal variability across the socioeconomic spectrum, and variation should be driven by clinical features alone.

We are fortunate that the dedicated professionals of the PECARN group continue to advance emergency care for children and look forward to what new data they may bring to light in 2021. 

The opinions expressed here are solely those of Dr. Radecki and do not necessarily reflect those of his employer or academic affiliates. 

References

1. Alpern ER, Kuppermann N, Blumberg S, et al. Time to positive blood and cerebrospinal fluid cultures in febrile infants ≤60 days of age. Hosp Pediatr. 2020;10:719-727.
2. Florin TA, Ramilo O, Hoyle Jr JD, et al. Radiographic pneumonia in febrile infants 60 days and younger [published online ahead of print July 18, 2020]. Pediatr Emerg Care. doi:10.1097/PEC.0000000000002187.
3. Florin TA, Ambroggio L, Lorenz D, et al. Development and internal validation of a prediction model to risk stratify children with suspected community-acquired pneumonia [published online ahead of print Nov. 7, 2020]. Clin Infect Dis. doi:10.1093/cid/ciaa1690.
4. Kuppermann N, Dayan PS, Levine DA, et al. A clinical prediction rule to identify febrile infants 60 days and younger at low risk for serious bacterial infections. JAMA Pediatr. 2019;173(4):342-351.
5. Velasco R, Gomez B, Benito J, et al. Accuracy of PECARN rule for predicting serious bacterial infection in infants with fever without a source. Arch Dis Child. 2021;106(2):143-148.
6. Ramgopal S, Horvat CM, Yanamala N, et al. Machine learning to predict serious bacterial infections in young febrile infants. Pediatrics. 2020;146(3):e20194096.
7. Pennell C, Polet C, Arthur LG, et al. Risk assessment for intra-abdominal injury following blunt trauma in children: derivation and validation of a machine learning model. J Trauma Acute Care Surg. 2020;89(1):153-159.
8. Bertsimas D, Dunn J, Steele DW, et al. Comparison of machine learning optimal classification trees with the pediatric emergency care applied research network head trauma decision rules. JAMA Pediatr. 2019;173(7):648-656.
9. Rowe C, Wiesendanger K, Polet C, et al. Derivation and validation of a simplified clinical prediction rule for identifying children at increased risk for clinically important traumatic brain injuries following minor blunt head trauma. J Pediatr. 2020;3:100026.
10. Drendel AL, Brousseau DC, Casper TC, et al. Opioid prescription patterns at emergency department discharge for children with fractures. Pain Med. 2020;21:1947-1954.