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Quick Literature Updates Ep 24

Video Published 2025-11-12 Updated 2025-11-13

Timestops (7)

Introduction to Pediatrics Updates

Intro to the series by Em Gohri

Pectus Bar Dislocation Study

Observational study on stabilization techniques in Italy from 2013-2022

Bridge Fixation vs Single Bar Fixation

Findings show bridge fixation is more stable than single bar fixation

Machine Learning for Appendicitis Detection

Researchers developed a machine learning model to accurately identify appendicitis cases

Pediatric Appendectomies Reduced

Model achieved 99.7% sensitivity and potentially reduced negative appendectomies by up to 17%

Common-Vile Duct Exploration Study

Multi-center study comparing trans-cistic laparoscopic vs ERCP for pediatric patients with chalactic lithiasis

Trans-Cistic Laparoscopic Benefits

Patients had significantly less complication rates and lower hospital stay lengths with the new procedure

Topic Overview

Episode reviews three recent pediatric surgery studies: bridge fixation prevents pectus bar dislocation better than single-bar technique; machine learning model identifies appendicitis with 99.7% sensitivity using clinical data alone; trans-cystic laparoscopic common bile duct exploration shows lower complications than ERCP-first approach for choledocholithiasis.

Key Takeaways

Bridge fixation prevents pectus bar dislocation better than single-bar technique (0% vs higher dislocation rates in 733 bars placed).
Machine learning model achieved 99.7% sensitivity for appendicitis using clinical/lab data alone, potentially reducing negative appendectomies by 17%.
Trans-cystic laparoscopic CBD exploration had fewer complications and shorter hospital stays than ERCP-first approach in pediatric choledocholithiasis.
The ML appendicitis model differentiates complicated from uncomplicated cases without advanced imaging, using readily available clinical data.
Multi-center US data (2018-2023) supports laparoscopic CBD exploration as first-line for pediatric bile duct stones over endoscopic approaches.

Keywords

Pectus Excavatum Appendicitis Diagnosis Machine Learning Choledocholithiasis Laparoscopic Surgery Pediatric Surgery Literature Review

Hashtags

#PediatricSurgery #PectusExcavatum #MachineLearning #Choledocholithiasis #LaparoscopicSurgery

Transcript

Hello, Pediatrics, your Drew family. I'm Em Gohri from Cincinnati Children's Hospital Medical Center. And today our team is going to deliver the articles that you should know about. We have three papers today but don't have much time. So let's start. Our first paper titled, Pectus Bar Dislocation, comparison between three different stabilization techniques adopted in a single center by Donati et al. This paper is summarized by Lizzie Lee, a physician associate by profession and a member of our team here at Cincinnati Children's, dedicated to creating content for pediatric surgery. This was an observational perspective study in Italy from 2013 to 2022 on 468 patients around 15 years old. The surgeons placed 733 bars total and they wanted to figure out the best technique for preventing the bar from dislocating. Dislocation is when a bar rotates more than 30 degrees out of place. So what do they find? Bridge fixation was significantly more stable than single bar fixation. None of the patients with bridge fixation had dislocated bars compared to those who underwent the single bar technique. This means that bridge fixation is the best for preventing bar dislocation in tectus excavatum patients. Our second paper is, applying an explainable machine learning model, might reduce the number of negative appendectomies in pediatric patients with a high probability of acute appendicitis by males et al. And this paper is summarized by Carlos Columga. He is a pediatric surgeon from Mexico and collaborates with us to produce these article reviews. Researchers from the University of Split have developed a machine learning model with the goal of accurately identifying appendicitis cases while minimizing unnecessary surgery. They did this by garnering data from 551 pediatric patients who underwent appendectomy and using their clinical laboratory and anthropometric information to train their machine learning model. Their best performing model achieved an impressive 99.7% sensitivity in identifying appendicitis cases, with a specificity that could potentially help reduce up to 17% of the negative appendectomies in high risk patients. And once they're up to a task, it can also differentiate between complicated and uncomplicated appendicitis with a high degree of accuracy. And most importantly, it pulls this off by using readily available clinical unlab data without using any advanced imaging. Let's move to the last paper of the day. A trans-cistic, laparoscopic common-vile duct exploration for pediatric patients with chaletic lathiasis, a multi-center study by Rao et al. This paper is summarized by Cecilia Hihanna. She's one of the previous research fellows at Cincinnati Children's Hospital. This is a multi-center research-spective study that in the US from 2018 to 2023. Under aim was to compare patients with suspected clinical unlab biases that underwent a trans-cistic laparoscopic common-vile duct exploration versus those who underwent ERCP first. They got 252 patients. 156 in group 1, the trans-cistic laparoscopic common-vile duct exploration, and 96 in group 2 patients who underwent ERCP first. And what they found is that patients with group 1 had significantly less complication rates and lower length of state. So it seems that a tempting trans-cistic laparoscopic common-vile duct exploration may benefit our patients.

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