Pediatric Buried Bumper Syndrome: Diagnostic Validity of Transabdominal Ultrasound and Artificial Intelligence

Abstract

Purpose Buried bumper syndrome (BBS) is a severe complication of percutaneous endoscopic gastrostomy (PEG) resulting from overgrowth of gastric mucosa and penetration of the inner holding plate into the gastric wall. The aim of this study was to evaluate the diagnostic value of transabdominal ultrasound (US) in comparison to an artificial intelligence (AI) model for the diagnosis of BBS in children.

Materials and Methods In this monocentric retrospective study, pediatric US data concerning BBS from a ten-year period (2009–2019) were analyzed. US findings were compared to a clinical multiparameter-based AI model and reference standard endoscopy. Clinical risk factors for the occurrence of pediatric BBS were determined.

Results In n = 121 independent examinations of n = 82 patients, the placement of the inner holding plate of the PEG was assessed by US. In n = 18 cases BBS was confirmed. Recall and precision rates were 100 % for US and 88 % for the AI-based assessment. Risk factors for the occurrence of BBS were mobilization problems of the PEG (r_s = 0.66, p < 0.001), secretion/exudation (r_s = 0.29, p = 0.002), time between 1^st PEG placement and US (r_s = 0.38, p < 0.001), and elevated leukocyte count (r_s = 0.24, p = 0.016).

Conclusion Transabdominal US enables correct, rapid, and noninvasive diagnosis of BBS in pediatric patients. Preceding AI models could aid during diagnostic workup. To avoid unnecessary invasive procedures, US could be considered as a primary diagnostic procedure in suspected BBS. 

Zusammenfassung

Ziel Das Buried-Bumper-Syndrom (BBS) ist eine schwerwiegende Komplikation der perkutanen endoskopischen Gastrostomie (PEG) und beschreibt eine Schleimhautüberwucherung der inneren PEG-Halteplatte. Ziel dieser Studie war es, die Wertigkeit des transabdominalen Ultraschalls (US) mit einem Modell der künstlichen Intelligenz (KI) zur Diagnose eines BBS zu vergleichen.

Material und Methode In dieser monozentrischen retrospektiven Studie wurden US-Daten mit Bezug zu BBS aus einem Jahrzehnt (2009–2019) untersucht. Die US-Ergebnisse wurden mit der Endoskopie und mit einem eigens entwickelten KI-Modell verglichen. Darüber hinaus wurden Risikofaktoren für das Auftreten von BBS bewertet.

Ergebnisse Die Position der inneren PEG-Halteplatte wurde bei n = 82 pädiatrischen Patienten in n = 121 unabhängigen US-Untersuchungen bewertet. Alle n = 18 Fälle mit BBS wurden mittels US korrekt diagnostiziert (Sensitivität/positiver Prädiktionswert von 100 %). Das KI-basierte Modell zeigte eine Sensitivität/einen positiven Prädiktionswert von 88 %. Risikofaktoren für das Auftreten eines BBS waren Mobilisierungsprobleme der PEG (r_s = 0,66; p < 0,001), Sekretion/Exsudation (r_s = 0,29; p = 0,002), die Zeit zwischen erster PEG-Anlage und US (r_s = 0,38; p < 0,001) und erhöhte Leukozyten (r_s = 0,24; p = 0,016).

Schlussfolgerungen Transabdominaler US ermöglicht die korrekte, schnelle und nichtinvasive Diagnosestellung eines BBS bei Kindern. Vorangehende KI-Modelle könnten die diagnostische Abklärung unterstützen. Bei Verdacht auf ein BBS sollte primär ein US eingesetzt werden, um unnötige invasive Eingriffe zu vermeiden.

Key words

buried bumper syndrome - percutaneous endoscopic gastrostomy - ultrasound - artificial intelligence

^* Geteilte Erstautorenschaft.

^# Geteilte Letztautorenschaft.

Publication History

Received: 19 December 2020

Accepted: 22 March 2021

Article published online:
25 May 2021

• References

• 1 Köhler H, Lang T, Behrens R. Buried bumper syndrome after percutaneous endoscopic gastrostomy in children and adolescents. Endoscopy 2008; 40 (Suppl. 02) E85-E86
• 2 Behrens R, Lang T, Muschweck H. et al. Percutaneous endoscopic gastrostomy in children and adolescents. J Pediatr Gastroenterol Nutr 1997; 25: 487-491
• 3 Avitsland TL, Kristensen C, Emblem R. et al. Percutaneous endoscopic gastrostomy in children: a safe technique with major symptom relief and high parental satisfaction. J Pediatr Gastroenterol Nutr 2006; 43: 624-628
• 4 Blumenstein I, Shastri YM, Stein J. Gastroenteric tube feeding: techniques, problems and solutions. World J Gastroenterol 2014; 20: 8505-8524
• 5 Klein S, Heare BR, Soloway RD. The “buried bumper syndrome”: a complication of percutaneous endoscopic gastrostomy. Am J Gastroenterol 1990; 85: 448-451
• 6 Braden B, Brandstaetter M, Caspary WF. et al. Buried bumper syndrome: treatment guided by catheter probe US. Gastrointest Endosc 2003; 57: 747-751
• 7 Mueller-Gerbes D, Hartmann B, Lima JP. et al. Comparison of removal techniques in the management of buried bumper syndrome: a retrospective cohort study of 82 patients. Endosc Int Open 2017; 5: E603-E607
• 8 Stewart CE, Mutalib M, Pradhan A. et al. Short article: Buried bumper syndrome in children: incidence and risk factors. Eur J Gastroenterol Hepatol 2017; 29: 181-184
• 9 Anagnostopoulos GK, Kostopoulos P, Arvanitidis DM. Buried bumper syndrome with a fatal outcome, presenting early as gastrointestinal bleeding after percutaneous endoscopic gastrostomy placement. J Postgrad Med 2003; 49: 325-327
• 10 Cyrany J, Rejchrt S, Kopacova M. et al. Buried bumper syndrome: A complication of percutaneous endoscopic gastrostomy. World J Gastroenterol 2016; 22: 618-627
• 11 Heuschkel RB, Gottrand F, Devarajan K. et al. ESPGHAN position paper on management of percutaneous endoscopic gastrostomy in children and adolescents. J Pediatr Gastroenterol Nutr 2015; 60: 131-141
• 12 Chang WK, Huang WC, Yu CY. et al. Long-term percutaneous endoscopic gastrostomy: characteristic computed tomographic findings. Abdom Imaging 2011; 36: 684-688
• 13 Krull CM, Dennison AC. A Case Series: The Identification of Buried Bumper Syndrome With Abdominal Computed Tomography Scan in Two Severely Brain Injured Rehabilitation Patients. PM R 2016; 8: 913-916
• 14 Tanaka Y, Akahoshi K, Motomura Y. et al. Pretherapeutic evaluation of buried bumper syndrome by endoscopic ultrasonography. Endoscopy 2012; 44 UCTN (Suppl. 02) E162
• 15 Tringali A, Thomson M, Dumonceau JM. et al. Pediatric gastrointestinal endoscopy: European Society of Gastrointestinal Endoscopy (ESGE) and European Society for Paediatric Gastroenterology Hepatology and Nutrition (ESPGHAN) Guideline Executive summary. Endoscopy 2017; 49: 83-91
• 16 Bizzarri B, Nervi G, Ghiselli A. et al. Endoscopic ultrasound in pediatric population: a comprehensive review of the literature. Acta Biomed 2018; 89: 33-39
• 17 Strobel D, Goertz RS, Bernatik T. Diagnostics in inflammatory bowel disease: ultrasound. World journal of gastroenterology: WJG 2011; 17: 3192-3197
• 18 Gilja OH. Ultrasound of the stomach--the EUROSON lecture 2006. Ultraschall in Med 2007; 28: 32-39
• 19 Lu C, Merrill C, Medellin A. et al. Bowel Ultrasound State of the Art: Grayscale and Doppler Ultrasound, Contrast Enhancement, and Elastography in Crohn Disease. J Ultrasound Med 2019; 38: 271-288
• 20 Elliott CL, Maclachlan J, Beal I. Paediatric bowel ultrasound in inflammatory bowel disease. Eur J Radiol 2018; 108: 21-27
• 21 Regensburger AP, Knieling F, Feldkamp A. et al. Time Tracking of Standard Ultrasound Examinations in Pediatric Hospitals and Pediatric Medical Practices – A Multicenter Study by the Pediatric Section of the German Society of Ultrasound in Medicine (DEGUM). Ultraschall in Med 2019; DOI: 10.1055/a-1023-4024.
• 22 Le Berre C, Sandborn WJ, Aridhi S. et al. Application of Artificial Intelligence to Gastroenterology and Hepatology. Gastroenterology 2020; 158: 76-94, e72
• 23 Yang YJ, Bang CS. Application of artificial intelligence in gastroenterology. World J Gastroenterol 2019; 25: 1666-1683
• 24 Godeke J, Muensterer O, Rohleder S. [Artificial intelligence in pediatric surgery: Present and future]. Chirurg 2020; 91: 222-228
• 25 Oelen D, Kaiser P, Baumann T. et al. Accuracy of Trained Physicians is Inferior to Deep Learning-Based Algorithm for Determining Angles in Ultrasound of the Newborn Hip. Ultraschall in Med 2020; DOI: 10.1055/a-1177-0480.
• 26 Richter-Schrag HJ, Fischer A. [Buried bumper syndrome: A new classification and therapy algorithm]. Chirurg 2015; 86: 963-969
• 27 Loftus TJ, Tighe PJ, Filiberto AC. et al. Artificial Intelligence and Surgical Decision-making. JAMA Surg 2020; 155: 148-158
• 28 Ahmed Z, Mohamed K, Zeeshan S. et al. Artificial intelligence with multi-functional machine learning platform development for better healthcare and precision medicine. Database (Oxford) 2020; 2020: 1-35
• 29 Reismann J, Romualdi A, Kiss N. et al. Diagnosis and classification of pediatric acute appendicitis by artificial intelligence methods: An investigator-independent approach. PLoS One 2019; 14: e0222030
• 30 Brun PM, Chenaitia H, Lablanche C. et al. 2-point ultrasonography to confirm correct position of the gastric tube in prehospital setting. Mil Med 2014; 179: 959-963
• 31 Kim HM, So BH, Jeong WJ. et al. The effectiveness of ultrasonography in verifying the placement of a nasogastric tube in patients with low consciousness at an emergency center. Scand J Trauma Resusc Emerg Med 2012; 20: 38