<abstract xmlns="http://www.w3.org/1999/xhtml">

<sec id="j_raon-2021-0035_s_005">
<h3>Background</h3>
<p>This study aimed to quantitatively evaluate the learning curve of laparoscopic liver resection (LLR) of a single surgeon.</p></sec>
<sec id="j_raon-2021-0035_s_006">
<h3>Patients and methods</h3>
<p>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.</p></sec>
<sec id="j_raon-2021-0035_s_007">
<h3>Results</h3>
<p>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.</p></sec>
<sec id="j_raon-2021-0035_s_008">
<h3>Conclusions</h3>
<p>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).</p></sec>
</abstract>

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).

The learning curve of laparoscopic liver resection utilising a difficulty score

Radiology and Oncology

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

{"article-title":"The learning curve of laparoscopic liver resection utilising a difficulty score"}

Background
This study aimed to quantitatively evaluate the learning curve of laparoscopic liver resection (LLR) of a single surgeon.

Patients and methods
A...

The learning curve of laparoscopic liver resection utilising a difficulty score

Article Category: Research Article

Pubblicato online: 06 set 2021

Pagine: 111 - 118

Ricevuto: 02 giu 2021

Accettato: 16 lug 2021

DOI: https://doi.org/10.2478/raon-2021-0035

© 2022 Arpad Ivanecz, Irena Plahuta, Matej Mencinger, Iztok Perus, Tomislav Magdalenic, Spela Turk, Stojan Potrc, published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.