The Utility of Focused Assessment With Sonography for Trauma Enhanced Physical Examination in Children With Blunt Torso Trauma

Kornblith, Aaron E.; Graf, Jahanara; Addo, Newton; Newton, Christopher; Callcut, Rachael A.; Grupp‐Phelan, Jacqueline; Jaffe, David M.

doi:10.1111/acem.13959

Cited by 17 publications

(22 citation statements)

References 42 publications

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“…A similar study has shown that the use of FAST and physical examination together could predict intraabdominal trauma. However, the joint use of FAST and physical examination had higher sensitivity and negative predictive value compared to their separate uses [19]. Different from their ndings, another study showed that FAST did not contribute to clinical intervention in pediatric patients with blunt torso trauma who were hemodynamically stable and treated in the emergency department, thus not recommending its use in emergency departments for pediatric patients [20].…”

Section: Discussionmentioning

confidence: 85%

The Efficiency of Focused Assessment with Sonography for Trauma in Pediatric Patients with Blunt Torso Trauma

inci

Altuncı

Can

et al. 2022

Preprint

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Background and importanceFocused Assessment with Sonography for Trauma (FAST) has attracted attention for its use in the detection of intraabdominal pathology. However, Computed Tomography (CT) remains the gold standard for the assessment of blunt torso trauma. ObjectiveThe study examines the effectiveness of FAST both in the detection of intraabdominal pathology in pediatric patients (< 19 years) and in the assessment of the need for CT for further examination. DesignThe study was designed as a retrospective observational investigation of diagnostic value.SettingThe pediatric patients who were admitted to the emergency department with blunt torso trauma between January 2013 and October 2016 were included in the study. The sample of the study comprised 255 patients who met the inclusion criteria.Outcome measures and analysisThe demographic data, vital findings, trauma mechanism, physical examination findings, the presence of a concomitant trauma, laboratory findings, CT scans of the thorax and abdomen, FAST findings, and patient outcomes were recorded. The Chi-square test and Fisher’s exact test were used for comparisons. A logistic regression model was developed to determine the variables that independently affect the agreement between FAST and CT.Main resultsThe comparison of FAST and CT revealed a poor agreement in pediatric patients with blunt torso trauma. The agreement was negatively affected by the severity of trauma and the presence of concomitant trauma. The error rate of FAST increased by five-fold especially in the presence of concomitant thorax trauma. However, FAST had a good negative likelihood ratio.ConclusionFAST was determined to yield accurate results when used in stable patients, thus revealing it as an appropriate tool for the initial examination of stable pediatric patients with blunt torso trauma. For unstable patients, however, a negative or positive FAST examination should not guide the decision to use CT and CT should remain a standard tool for further examination. FAST should not be regarded as an equivalent or complementary tool to CT. It is, instead, a noteworthy negative predictor in pediatric patients. A positive FAST result can serve as a predictor of the need for operation and hospitalization in multiple trauma patients.

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Section: Discussionmentioning

confidence: 85%

The Efficiency of Focused Assessment with Sonography for Trauma in Pediatric Patients with Blunt Torso Trauma

inci

Altuncı

Can

et al. 2022

Preprint

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“…(3-5) Expertise in FAST improves the overall accuracy of the study, and therefore, the utility in potentially reducing unnecessary CT use. (6) However, barriers in education, training, skill maintenance, and quality assurance have perpetuated a lack of global FAST expertise. (3, 7, 8) This lack of expertise is often cited as the primary reason for FAST not being accurately implemented in clinical practice.…”

Section: Introductionmentioning

confidence: 99%

Development and Validation of a Deep Learning Model for Automated View Classification of Pediatric Focused Assessment with Sonography for Trauma (FAST)

Kornblith

Addo

Dong

et al. 2020

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The pediatric Focused Assessment with Sonography for Trauma (FAST) is a sequence of ultrasound views rapidly performed by the clinician to diagnose hemorrhage. One limitation of FAST is inconsistent acquisition of required views. We sought to develop a deep learning model and classify FAST views using a heterogeneous dataset of pediatric FAST. This study of diagnostic test developed and tested a deep learning model for view classification of archived real-world pediatric FAST studies collected from two pediatric emergency departments. FAST frames were randomly distributed to training, validation, and test datasets in a 70:20:10 ratio; each patient was represented in only one dataset to maintain sample independence. The outcome was the prediction accuracy of the model in classifying FAST frames and video clips. FAST studies performed by 30 different clinicians from 699 injured children included 4,925 videos representing 1,062,612 frames from children who were a median of 9 years old. On test dataset, the overall view classification accuracy for the model was 93.4% (95% CI: 93.3-93.6) for frames and 97.8% (95% CI: 96.0-99.0) for video clips. Frames were correctly classified with an accuracy of 96.0% (95% CI: 95.9-96.1) for cardiac, 99.8% (95% CI: 99.8-99.8) for thoracic, 95.2% (95% CI: 95.0-95.3) for abdominal upper quadrants, and 95.9% (95% CI: 95.8-96.0) for suprapubic. A deep learning model can be developed to accurately classify pediatric FAST views. Accurate view classification is the important first step to support developing a consistent and accurate multi-stage deep learning model for pediatric FAST interpretation.

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“…FAST decreases computed tomography usage and improves clinical outcomes in adults with blunt injury, 1 but has shown inconsistent diagnostic utility in children. 2,3 This inconsistency of FAST in children arises from varying image acquisition and interpretation of all the possible views. [4][5][6] Specifically, noninterpretable FAST views are a major source of technical error.…”

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confidence: 99%

“…[4][5][6] Specifically, noninterpretable FAST views are a major source of technical error. 7 Technical expertise improves FAST diagnostic consistency and accuracy, 3 however, there remain barriers in education, training, and skill maintenance that have perpetuated a lack of global pediatric FAST expertise. 4,8,9 This lack of expertise is a primary reason for FAST not being universally implemented in pediatric clinical practice.…”

mentioning

confidence: 99%

“…Focused assessment with sonography for trauma (FAST) is a diagnostic point‐of‐care ultrasound study, in which a sequence of ultrasonographic views performed by the bedside treating clinician rapidly identifies free fluid, interpreted as hemorrhage in the setting of injury. FAST decreases computed tomography usage and improves clinical outcomes in adults with blunt injury, 1 but has shown inconsistent diagnostic utility in children 2,3 . This inconsistency of FAST in children arises from varying image acquisition and interpretation of all the possible views 4–6 .…”

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Development and Validation of a Deep Learning Strategy for Automated View Classification of Pediatric Focused Assessment With Sonography for Trauma

Kornblith

Addo

Dong

et al. 2021

J of Ultrasound Medicine

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Objective Pediatric focused assessment with sonography for trauma (FAST) is a sequence of ultrasound views rapidly performed by clinicians to diagnose hemorrhage. A technical limitation of FAST is the lack of expertise to consistently acquire all required views. We sought to develop an accurate deep learning view classifier using a large heterogeneous dataset of clinician‐performed pediatric FAST. Methods We developed and conducted a retrospective cohort analysis of a deep learning view classifier on real‐world FAST studies performed on injured children less than 18 years old in two pediatric emergency departments by 30 different clinicians. FAST was randomly distributed to training, validation, and test datasets, 70:20:10; each child was represented in only one dataset. The primary outcome was view classifier accuracy for video clips and still frames. Results There were 699 FAST studies, representing 4925 video clips and 1,062,612 still frames, performed by 30 different clinicians. The overall classification accuracy was 97.8% (95% confidence interval [CI]: 96.0–99.0) for video clips and 93.4% (95% CI: 93.3–93.6) for still frames. Per view still frames were classified with an accuracy: 96.0% (95% CI: 95.9–96.1) cardiac, 99.8% (95% CI: 99.8–99.8) pleural, 95.2% (95% CI: 95.0–95.3) abdominal upper quadrants, and 95.9% (95% CI: 95.8–96.0) suprapubic. Conclusion A deep learning classifier can accurately predict pediatric FAST views. Accurate view classification is important for quality assurance and feasibility of a multi‐stage deep learning FAST model to enhance the evaluation of injured children.

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The Utility of Focused Assessment With Sonography for Trauma Enhanced Physical Examination in Children With Blunt Torso Trauma

Cited by 17 publications

References 42 publications

The Efficiency of Focused Assessment with Sonography for Trauma in Pediatric Patients with Blunt Torso Trauma

The Efficiency of Focused Assessment with Sonography for Trauma in Pediatric Patients with Blunt Torso Trauma

Development and Validation of a Deep Learning Model for Automated View Classification of Pediatric Focused Assessment with Sonography for Trauma (FAST)

Development and Validation of a Deep Learning Strategy for Automated View Classification of Pediatric Focused Assessment With Sonography for Trauma

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