Quantification of muscle recovery in post-ICU patients admitted for acute pancreatitis: a longitudinal single-center study

Abstract

Abstract Objectives Critically ill patients with severe pancreatitis exhibit substantial muscle wasting, which limits in-hospital and post-hospital outcomes. Survivors of critical illness undergo extensive recovery processes. Previous studies have explored pancreatic function, quality of life, and costs post-hospitalization for AP patients, but none have comprehensively quantified muscle loss and recovery post-discharge. By applying an AI-based automated segmentation tool, we aimed to quantify muscle mass recovery in ICU patients after discharge. Materials Muscle segmentation was performed on 22 patients, with a minimum of three measurements taken during hospitalization and one clinically indicated examination after hospital discharge. Changes in psoas muscle area (PMA) between admission, discharge and follow up were calculated. T-Test was performed to identify significant differences between patients able and not able to recover their muscle mass. Results Monitoring PMA shows muscle loss during and gain after hospitalization: The mean PMA at the first scan before or at ICU admission (TP1) was 17.08 cm², at the last scan before discharge (TP2), mean PMA was 9.61 cm². The percentage change in PMA between TP1 and TP2 ranged from − 85.42% to -2.89%, with a mean change of -40.18%. The maximum muscle decay observed during the stay was − 50.61%. After a mean follow-up period of 438.73 days most patients (81%) were able to increase their muscle mass. Compared to muscle status at TP1, only 27% of patients exhibited full recovery, with the majority still presenting a deficit of 31.96%. Conclusion Muscle recovery in ICU patients suffering from severe AP is highly variable, with only about one third of patients recovering to their initial physical status. Opportunistic screening of post-ICU patient recovery using clinically indicated imaging and AI-based segmentation tools enables precise quantification of patients’ muscle status and can be employed to identify individuals who fail to recover and would benefit from secondary rehabilitation. Understanding the dynamics of muscle atrophy may improve prognosis and support personalized patient care.

Keywords

• Critical care
• Acute pancreatitis
• Muscle wasting
• Artificial intelligence
• Computed tomography