Pediatric Blunt Abdominal Trauma and Point-of-Care Ultrasound

Enhancing clinical risk assessment in pediatric blunt abdominal trauma: A novel scoring system using ultrasound and laboratory data

BACKGROUND

Given the importance of diagnosing intra-abdominal injury (IAI) in children with blunt abdominal trauma (BAT) and preventing radiation exposure to children by avoiding CT scans, this study aimed to evaluate a scoring criterion based on ultrasound (US) findings and laboratory data in assessing the clinical risk of IAI in children with BAT.

MATERIALS AND METHODS

In this retrospective study, baseline and clinical information of 180 children (under 18 years of age) with BAT including physical examination, hemodynamic parameters, and laboratory data, were extracted from medical records. US findings were considered abnormal if any report of mild free fluid or solid organ injury was noted. The presence or absence of IAI was assessed through medical records or telephone interviews to inquire about the patients' outcome within the two-week period post-discharge. The primary outcome was the identification of IAI, assessed through a combination of US findings, physical examination (abdominal tenderness), and laboratory parameters (WBC count and hematuria). The measurement methods included Chi-squared tests, Fisher's exact test, independent samples t-test, logistic regression, and ROC analysis.

RESULTS

The current study showed that 153 (85%) and 27 (15%) patients were without and with IAI, respectively. The positive US finding with sensitivity and specificity of 92.59% and 44.44%, respectively, abdominal tenderness with sensitivity and specificity of 81.48% and 87.58%, respectively, hematuria with sensitivity and specificity of 62.96% and 50.33%, respectively, and high WBC level with sensitivity and specificity of 85.19% and 76.47%, had a significant diagnostic value in detecting the presence of IAI (P value < 0.001). A cutoff point ≥ 2 from the sum of the scores of these four criteria can predict the presence of IAI with a sensitivity of 81.48% and a specificity of 94.12% (AUC = 0.94; P value < 0.001).

CONCLUSION

This study shows that a scoring system based on positive US findings, abdominal tenderness, hematuria, and high WBC levels effectively diagnoses IAI in BAT children. A score of 2 or more strongly indicates the presence of IAI, improving decision-making for further imaging and treatment. Implementing this system can reduce unnecessary CT scans and radiation exposure, enhancing pediatric trauma care.

Total intestinal obstruction due to traumatic submucosal jejunal hematoma: A case report

INTRODUCTION AND IMPORTANCE

Blunt abdominal trauma can result in various injuries, including intestinal hematomas causing obstruction. Traumatic submucosal jejunal hematomas are rare, especially in children, with bicycle handlebar injuries being a potential cause. Prompt recognition and appropriate imaging are crucial for accurate diagnosis and management.

CASE PRESENTATION

A 10-year-old boy presented with delayed abdominal pain, vomiting, and absence of bowel movements following blunt abdominal trauma from a bicycle handlebar injury. Abdominal CT scan confirmed a submucosal jejunal hematoma, leading to exploratory laparotomy and duodenojejunostomy.

CLINICAL DISCUSSION

Bicycle handlebar injuries, although seemingly minor, can cause significant internal damage, especially in children. Delayed symptoms pose a diagnostic challenge, necessitating a high index of suspicion and imaging modalities like CT scans for timely intervention.

CONCLUSION

This case underscores the importance of considering traumatic submucosal jejunal hematomas in children with blunt abdominal trauma, particularly from bicycle handlebar injuries. Surgical intervention may be necessary in cases of complete intestinal obstruction, emphasizing the need for prompt diagnosis and treatment.

Echinococcal Cyst Rupture in a 3-Year-Old Boy With a Minor Abdominal Trauma Identified by a Point-of-Care Ultrasound E-Fast Examination: A Case Report and Literature Review

ABSTRACT

Point-of-care ultrasound is now widely used in adult and pediatric emergency departments as part of the rapid evaluation and treatment of patients in urgent care settings. It has been shown to be a powerful tool to aid in decision making in both trauma and acute medical situations. We describe a case of a 3-year-old boy who presented with abdominal pain and fever after a minor abdominal trauma, highlighting the use of point-of-care ultrasound to arrive at the diagnosis of a ruptured echinococcal cyst.

Using Deep Learning to Detect the Presence and Location of Hemoperitoneum on the Focused Assessment with Sonography in Trauma (FAST) Examination in Adults

Abdominal ultrasonography has become an integral component of the evaluation of trauma patients. Internal hemorrhage can be rapidly diagnosed by finding free fluid with point-of-care ultrasound (POCUS) and expedite decisions to perform lifesaving interventions. However, the widespread clinical application of ultrasound is limited by the expertise required for image interpretation. This study aimed to develop a deep learning algorithm to identify the presence and location of hemoperitoneum on POCUS to assist novice clinicians in accurate interpretation of the Focused Assessment with Sonography in Trauma (FAST) exam. We analyzed right upper quadrant (RUQ) FAST exams obtained from 94 adult patients (44 confirmed hemoperitoneum) using the YoloV3 object detection algorithm. Exams were partitioned via fivefold stratified sampling for training, validation, and hold-out testing. We assessed each exam image-by-image using YoloV3 and determined hemoperitoneum presence for the exam using the detection with highest confidence score. We determined the detection threshold as the score that maximizes the geometric mean of sensitivity and specificity over the validation set. The algorithm had 95% sensitivity, 94% specificity, 95% accuracy, and 97% AUC over the test set, significantly outperforming three recent methods. The algorithm also exhibited strength in localization, while the detected box sizes varied with a 56% IOU averaged over positive cases. Image processing demonstrated only 57-ms latency, which is adequate for real-time use at the bedside. These results suggest that a deep learning algorithm can rapidly and accurately identify the presence and location of free fluid in the RUQ of the FAST exam in adult patients with hemoperitoneum.