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Robotic Hepatectomy in Australia: First Case Series Following Introduction of Robotic Liver Surgery
Poster
Talk Description

Institution: Austin Health - Victoria, Australia

Purpose: Robotic liver resection (RLR) is a modern minimally invasive technique offering enhanced precision, dexterity and surgeon ergonomics in comparison with traditional open and laparoscopic techniques. In particularly challenging patient cohorts such as those with cirrhosis, super obesity or technically demanding liver resections, RLR may offer an advantage. This study details the initial experience and outcomes of introduction of RLR at metropolitan centres in Australia. Methodology: This is a retrospective review of 26 RLR cases over a three-year period. Data were collected prospectively. Analysis of patient demographics, procedural details, complications and outcomes was conducted. Continuous variables are expressed as mean or median with interquartile ranges (IQR) and categorical data as frequencies and percentages. Results: A total of 26 patients were considered for the analysis, of which 58% were male with a median age of 70. A pre-operative diagnosis of cirrhosis was recorded in 23% of patients. The median Charlson Comorbidity Index was seven. A major liver resection (three segmentectomy or more) was performed in 50% of cases. A median operative time of 220 minutes, blood loss of 200ml and planned ICU admission rate of 92% was observed. In 16 patients (62%) an IWATE score of seven or above was recorded indicating either an advanced or expert level case. Console and operative time were correlated with IWATE score, with R2 values of 0.49 and 0.65 respectively. Three patients (11.5%) required conversion to open surgery. There were no mortalities observed in the 90-day follow-up period. Conclusion: The robotic platform offers a safe and effective means for liver resection, including for more complex resections and patient cohorts. Future applications, including adoption within tertiary centres in Australia, could open further domains for RLR enhancing technical proficiency and patient outcomes.
Speakers
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Dr James Tai - , Dr Ruelan Furtado - , Dr Julian Choi - , Dr Jurstine Daruwalla - , Prof Mehrdad Nikfarjam - , Dr Osamu Yoshino -

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