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

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