From Collision to Clarity: PECARN cervical spine injury prediction rule for injured children

PECARN cervical spine injury prediction tool featured image (adapted from Midjourney)

For years, adult literature has provided clear guidelines for cervical spine imaging through the NEXUS and Canadian C-spine Rule (CCR) tools. These have been invaluable in helping clinicians decide when to image the neck in trauma patients. Similarly, the Pediatric Emergency Care Applied Research Network (PECARN) has developed robust tools for assessing blunt head trauma in children. However, until now, there has been a gap in guidance for clinicians managing pediatric patients at risk for cervical spine injuries.

Case Scenario: What would you do?

A 10-year-old boy presents to the emergency department (ED) after a high-speed motor vehicle collision. He complains of neck pain and is reluctant to move his head. The child’s mother is extremely worried, fearing the worst after witnessing the collision.

The Problem

Cervical spine injuries in children, while uncommon, can be devastating if not identified and treated promptly. Emergency physicians often face the challenge of deciding whether to proceed with imaging, given the potential risks associated with ionizing radiation from CT scans. The lack of clear guidelines specifically tailored for pediatric patients has historically led to either overuse of imaging, with its associated risks, or underuse, with the risk of missed injuries.

PECARN Cervical Spine Injury Prediction Rule

On June 4, 2024, Lancet published “PECARN prediction rule for cervical spine imaging of children presenting to the emergency department with blunt trauma: a multicentre prospective observational study.” This study proposes a new clinical prediction rule to guide imaging decisions for pediatric cervical spine injuries.

The study enrolled 22,430 children, aged 0–17 years, presenting with blunt trauma across 18 PECARN-affiliated ED in the US. About half were in the derivation and half in the validation cohort. The researchers derived and validated a clinical prediction rule using data from these children, which identified key risk factors for cervical spine injury, divided into high-risk and non-negligible (intermediate) risk factors.

High Risk (>12.1% risk of injury) -> Consider CT

  • Altered mental status (GCS 3-8 or AVPU = U)
  • Abnormal airway
  • Breathing
  • Circulation findings
  • Focal neurological deficits

Intermediate Risk (2.8% risk of injury) -> Consider X-Rays

  • Neck pain or midline neck tenderness
  • Mental status: GCS 9-14, AVPU = V or P, or other signs of altered mental status
  • Substantial head or torso injury

Definition on Cervical Spine Injury

  • Fractures or ligamentous injuries of the cervical spine
  • Cervical intraspinal hemorrhage
  • Cerebral artery injury
  • Cervical spinal cord injury, including
    • Changes in the cervical spinal cord on MRI
    • Cervical spinal cord injury without radiographic association
PECARN Cervical Spine Injury Prediction Tool

PECARN Cervical Spine Injury Prediction Tool (Download full sized PDF at PECARN site)

The prediction rule had strong test characteristics with 94.3% sensitivity and 99.9% negative predictive value, indicating that it can reliably identify children who do not need imaging, thus avoiding unnecessary radiation exposure. This evidence-based approach to pediatric trauma care would have reduced the number of CT scans by more than 50% without missing clinically relevant injuries.

Case Example Resolution

Using the PECARN cervical spine injury prediction rule, the attending physician evaluates the boy and finds that he does not exhibit any high-risk factors. However, because he reports neck pain and has midline neck tenderness on exam (intermediate risk), the rule recommends that the cervical spine can not be clinically cleared. It also suggests plain x-rays and not a CT scan. This differs from the adult population whereby CT scan imaging is often the first choice for diagnostic testing.

The x-rays reveal no evidence of cervical spine injury, and the boy is cleared with instructions for follow-up care. This approach not only alleviated the mother’s anxiety but also avoided unnecessary radiation exposure for the child.


Leonard JC, Harding M, Cook LJ, et al. PECARN prediction rule for cervical spine imaging of children presenting to the emergency department with blunt trauma: a multicentre prospective observational study. Lancet Child Adolesc Health. 2024;8(7):482-490. doi:10.1016/S2352-4642(24)00104-4. PMID 38843852

By |2024-07-03T10:30:13-07:00Jun 10, 2024|Pediatrics, Radiology, Trauma|

Computerized Adaptive Screen for Suicidal Youth (CASSY) study

CASSY PECARN suicide screening tool

Adolescent suicide rates in the United States, partly augmented by the COVID-19 pandemic, are steadily increasing [1, 2]. A commonly used screening tool is the 4-question Ask Suicide-Screening Questions (ASQ) instrument, which has a sensitivity and specificity of 60% and 92.7%, respectively, in predicting suicide-related events within 3 months. This was derived from a retrospective study of 15,003 pediatric patients (age 10-18 years) [3]. Given the morbidity and mortality associated with suicide attempts, is there a better screening tool with a higher sensitivity than 60%, while also maintaining adequate specificity? A higher sensitivity rate ensures that we have fewer misses.

The CASSY tool

In JAMA Psychiatry 2021, the Pediatric Emergency Care Applied Research Network (PECARN) researchers report derivation and external validation data for their suicide screening tool, called the Computerized Adaptive Screen for Suicidal Youth (CASSY) [4]. This publication was actually two studies in one: a derivation of the tool and then an external validation.


This paper assumes that the reader understands certain predictive analytics methodologies and test design concepts. Let’s briefly review some of the foundational terminology used:

  • Item response theory [Wikipedia]: “It is a theory of testing based on the relationship between individuals’ performances on a test item and the test takers’ levels of performance on an overall measure of the ability that item was designed to measure.” Of note, each item may be weighted differently based on how well it correlates with the overall outcome measure, which in this study was suicide attempt within 3 months.
  • Computerized adaptive testing [Wikipedia]: This computer testing strategy, also known as tailored testing, presents questions based on the individual’s response to a prior question.
  • Receiver operator characteristics (ROC): “The performance of a diagnostic test in the case of a binary predictor can be evaluated using the measures of sensitivity and specificity. However, in many instances, we encounter predictors that are measured on a continuous or ordinal scale. In such cases, it is desirable to assess performance of a diagnostic test over the range of possible cutpoints for the predictor variable. This is achieved by a receiver operating characteristic (ROC) curve that includes all the possible decision thresholds from a diagnostic test result.” [5] In other words, test sensitivities can be calculated for set specificities of, for instance, 70%, 80%, and 90%. Based on the purpose of the diagnostic test, the binary predictor threshold would be set accordingly.
  • Area under the curve (AUC): Calculating the AUC for the ROC is an effective means to determine a diagnostic test’s accuracy. The AUC ranges from 0 to 1 with 0.5 meaning no discrimination (i.e., the test can not diagnose patients with and without the disease based on the test). Generally, an AUC value of 0.7-0.8 is acceptable, 0.8 to 0.9 is excellent, and >0.9 is outstanding [5].

Study 1: CASSY derivation

A total of 6,536 adolescents (age 12-17 years) from 13 PECARN emergency departments were enrolled and a subset were randomly received follow-up in 3 months to assess for a suicide attempt. These patients responded to 92 questions on a computer tablet. Using a multidimensional item response theory approach, the more correlated questions (72) were used to create the CASSY tool.

Test characteristic results:

  • AUC: 0.89 (excellent)
  • Using the ROC curve, the CASSY sensitivity was 83% and 61% for the fixed specificity of 80% and 90%, respectively.

Study 2: CASSY validation

A total of 4,050 adolescents from 14 PECARN emergency departments were enrolled, and all received 3-month follow-up assessing for a suicide attempt. These patients completed the CASSY tool, as well as a subset of questions from study 1 for comparison. The frequency of questions used in the adaptive screen are itemized in the paper.

Test characteristic results:

  • AUC 0.87 (excellent)
  • Using the ROC curve and at the 80% specificity cutoff from study 1, the CASSY sensitivity was 82.4% and specificity was 72.5%.

CASSY figure ROC


Although there was strong study rigor by deriving and independently validating the tool in separate, multicenter populations, it should be noted that generalizability may be affected.

  1. The study was conducted in academic pediatric emergency departments.
  2. There was quite a few patients who were lost to follow up (27.1% in study 1, 30.5% in study 2), which may have skewed the results.
  3. Selection bias may have occurred because of patients declining to participate in the study (62% enrollment rate in study 1, 62.2% in study 2)

Bottom line

The CASSY tool accurately serves as a screening predictive tool for adolescents at risk for a suicide attempt in 3 months. Rather than having patients complete exhaustively long (and practically unfeasible) screening questions in the emergency department, this computerized adaptive tool required only a mean of 11 questions, which took a median time of 1.4 minutes (IQR 0.98-2.06 minutes) to complete.

How can you implement CASSY in your emergency department?

We asked the authors this question, and the answer is in the podcast below.


Listen more with author Dr. Jacqueline Grupp-Phelan talking with ALiEM podcast host, Dr. Dina Wallin, about this landmark paper and behind-the-scenes issues not included on the paper.

This blog post was expert peer-reviewed by Drs. King and Grupp-Phelan, who authored the paper.


  1. Hill RM, Rufino K, Kurian S, Saxena J, Saxena K, Williams L. Suicide Ideation and Attempts in a Pediatric Emergency Department Before and During COVID-19 [published online ahead of print, 2020 Dec 16]. Pediatrics. 2020;e2020029280. PMID: 33328339
  2. Centers for Disease Control and Prevention. Web-based Injury Statistics Query and Reporting System (WISQARS). Published 2020.
  3. DeVylder JE,Ryan TC, Cwik M, et al. Assessment of selective and universal screening for suicide risk in a pediatric emergency department. JAMA Netw Open. 2019;2(10):e1914070-e1914070. PMID 31651971
  4. King CA, Brent D, Grupp-Phelan J, et al. Prospective Development and Validation of the Computerized Adaptive Screen for Suicidal Youth [published online ahead of print, 2021 Feb 3]. JAMA Psychiatry. 2021; 10.1001/jamapsychiatry.2020.4576. doi:10.1001/jamapsychiatry.2020.4576. PMID 33533908
  5. Mandrekar JN. Receiver operating characteristic curve in diagnostic test assessment. J Thorac Oncol. 2010;5(9):1315-1316. doi:10.1097/JTO. 0b013e3181ec173d

Listen to all the PECARN podcasts

PECARN: Its relevance and importance in pediatric emergency care

PECARNDid you know that many of the landmark pediatric emergency medicine (EM) studies come from the Pediatric Emergency Care Applied Research Network (PECARN) collaborative? It works to address the challenging pediatric questions that only multicenter studies can. In this blog post, we highlight PECARN’s goal to translate, disseminate, and implement evidence to all providers of emergent and urgent care for pediatric patients.


By |2020-05-01T15:32:49-07:00Jan 7, 2020|Pediatrics|

PECARN Study: Accuracy of Urinalysis for Febrile Infants ≤60 Days Old

The reported accuracy of the urinalysis (UA) for diagnosing urinary tract infections (UTI) is febrile infants ≤ 60 days has been widely variable. Some guidelines specifically exclude these patients due to this variability or recommend urine culture as the primary test.1

Accuracy of the Urinalysis for Urinary Tract Infections in Febrile Infants 60 Days and Younger, published in Pediatrics in February of 2018, addressed this topic head-on.2 The authors sought to evaluate the accuracy of the UA by analyzing data in a planned secondary analysis of a prospectively collected data set, as part of the Pediatric Emergency Care Applied Research Network (PECARN). We review this publication and present a behind-the-scenes podcast interview with lead author Dr. Leah Tzimenatos.

By |2021-07-01T21:07:41-07:00Sep 27, 2018|Infectious Disease, Pediatrics|

PECARN Pediatric Head Trauma: Official Visual Decision Aid for Clinicians

pecarn pediatric head traumaThe Pediatric Emergency Care Applied Research Network (PECARN) collaborative has teamed up with the ALiEM and CanadiEM teams to introduce the official PECARN visual decision rule aid for pediatric blunt head trauma! This has been a 6 month collaboration focused on bringing evidence-based research to the bedside in pediatric emergency medicine (EM).


By |2024-06-01T10:49:21-07:00Jun 27, 2017|Pediatrics, Trauma|
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