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The learning curve of laparoscopic liver resection utilising a difficulty score.
Radiology and Oncology ( IF 2.4 ) Pub Date : 2021-09-06 , DOI: 10.2478/raon-2021-0035
Arpad Ivanecz 1, 2 , Irena Plahuta 1 , Matej Mencinger 3, 4, 5 , Iztok Perus 2, 6 , Tomislav Magdalenic 1 , Spela Turk 1 , Stojan Potrc 1, 2
Affiliation  

BACKGROUND This study aimed to quantitatively evaluate the learning curve of laparoscopic liver resection (LLR) of a single surgeon. PATIENTS AND METHODS A retrospective review of a prospectively maintained database of liver resections was conducted. 171 patients undergoing pure LLRs between April 2008 and April 2021 were analysed. The Halls difficulty score (HDS) for theoretical predictions of intraoperative complications (IOC) during LLR was applied. IOC was defined as blood loss over 775 mL, unintentional damage to the surrounding structures, and conversion to an open approach. Theoretical association between HDS and the predicted probability of IOC was utilised to objectify the shape of the learning curve. RESULTS The obtained learning curve has resulted from thirteen years of surgical effort of a single surgeon. It consists of an absolute and a relative part in the mathematical description of the additive function described by the logarithmic function (absolute complexity) and fifth-degree regression curve (relative complexity). The obtained learning curve determines the functional dependency of the learning outcome versus time and indicates several local extreme values (peaks and valleys) in the learning process until proficiency is achieved. CONCLUSIONS This learning curve indicates an ongoing learning process for LLR. The proposed mathematical model can be applied for any surgical procedure with an existing difficulty score and a known theoretically predicted association between the difficulty score and given outcome (for example, IOC).

中文翻译:

利用难度评分的腹腔镜肝切除的学习曲线。

背景 本研究旨在定量评估单个外科医生的腹腔镜肝切除术(LLR)的学习曲线。患者和方法 对前瞻性维护的肝切除数据库进行了回顾性审查。分析了 2008 年 4 月至 2021 年 4 月期间接受纯 LLR 的 171 名患者。应用了用于 LLR 期间术中并发症 (IOC) 的理论预测的 Halls 难度评分 (HDS)。IOC 被定义为失血超过 775 mL、对周围结构的意外损坏以及转换为开放式方法。HDS与IOC预测概率之间的理论关联被用来客观化学习曲线的形状。结果 获得的学习曲线是由一位外科医生 13 年的手术努力得出的。它由对数函数(绝对复杂度)和五次回归曲线(相对复杂度)描述的加法函数的数学描述中的绝对和相对部分组成。获得的学习曲线确定了学习结果与时间的函数依赖性,并指示了学习过程中的几个局部极值(峰值和谷值),直到达到熟练程度。结论 该学习曲线表明 LLR 是一个持续的学习过程。所提出的数学模型可以应用于任何具有现有难度评分和难度评分与给定结果(例如,IOC)之间的已知理论预测关联的外科手术。
更新日期:2021-09-06
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