Artificial intelligence-based skeletal muscle estimates and outcomes of EUS-guided treatment of pancreatic fluid collections

基于人工智能的骨骼肌评估和超声内镜引导下胰腺积液治疗的结果

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Abstract

BACKGROUND AND AIMS: Skeletal muscle status may affect clinical outcomes of a variety of pancreatic diseases. Skeletal muscle quality and quantity have not been fully examined in relation to the outcomes of EUS-guided treatment of pancreatic fluid collections (PFCs). METHODS: Using a multi-institutional cohort of 372 patients receiving EUS-guided treatment of PFCs in 2010 to 2020, we examined the association of skeletal muscle status with adverse outcomes, including clinical treatment failure and in-hospital mortality. We used an in-house deep learning-based platform for preprocedural CT images, and skeletal muscle density (SMD) and skeletal muscle index (SMI; height-adjusted muscle area) were calculated as surrogates for muscular quality and quantity, respectively. Multivariable logistic regression analysis was conducted to calculate odds ratios (ORs) for adverse outcomes. RESULTS: Lower-level SMD was associated with higher risks of clinical failure and in-hospital mortality (P (trend) < .001). The adjusted OR for clinical failure comparing the extreme quartiles was 3.64 (95% confidence interval, 1.52-8.72). Compared with patients in the top 2 quartiles, patients in the lowest quartile had an adjusted OR for in-hospital mortality of 12.4 (95% confidence interval, 3.43-44.8). No effect modification according to the PFC types on the SMD-outcome relationship (P (interaction) > .16) was observed. SMD was not associated with the risk of procedure-related adverse events or PFC recurrence. SMI was not associated with adverse outcomes (P (trend) > .39). CONCLUSIONS: In patients with endoscopically managed PFCs, SMD (but not SMI) was associated with the risks of clinical failure and in-hospital mortality, supporting the prognostic role of skeletal muscle quality.

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