Complexity-Adjusted Learning Curves for Robotic and Laparoscopic Liver Resection

Felix Krenzien, MD; Christian Benzing, MD; Linda Feldbrügge, MD; Santiago Andres Ortiz Galindo, MD; Karl Hillebrandt, MD; Nathanael Raschzok, MD; Nora Nevermann, MD; Philipp Haber, MD; Thomas Malinka, MD; Wenzel Schöning, MD; Johann Pratschke, MD; Moritz Schmelzle, MD

doi:10.1097/AS9.0000000000000131

Annals of Surgery Open (Mar 2022)

Complexity-Adjusted Learning Curves for Robotic and Laparoscopic Liver Resection

Felix Krenzien, MD,
Christian Benzing, MD,
Linda Feldbrügge, MD,
Santiago Andres Ortiz Galindo, MD,
Karl Hillebrandt, MD,
Nathanael Raschzok, MD,
Nora Nevermann, MD,
Philipp Haber, MD,
Thomas Malinka, MD,
Wenzel Schöning, MD,
Johann Pratschke, MD,
Moritz Schmelzle, MD

Affiliations

Felix Krenzien, MD: From the * Department of Surgery, Campus Charité Mitte and Campus Virchow-Klinikum, Charité—Universitätsmedizin, Freie Universität Berlin, Humboldt-Universität zu Berlin, Berlin Institute of Health, Berlin, Germany
Christian Benzing, MD: From the * Department of Surgery, Campus Charité Mitte and Campus Virchow-Klinikum, Charité—Universitätsmedizin, Freie Universität Berlin, Humboldt-Universität zu Berlin, Berlin Institute of Health, Berlin, Germany
Linda Feldbrügge, MD: † Berlin Institute of Health (BIH), Berlin, Germany.
Santiago Andres Ortiz Galindo, MD: From the * Department of Surgery, Campus Charité Mitte and Campus Virchow-Klinikum, Charité—Universitätsmedizin, Freie Universität Berlin, Humboldt-Universität zu Berlin, Berlin Institute of Health, Berlin, Germany
Karl Hillebrandt, MD: From the * Department of Surgery, Campus Charité Mitte and Campus Virchow-Klinikum, Charité—Universitätsmedizin, Freie Universität Berlin, Humboldt-Universität zu Berlin, Berlin Institute of Health, Berlin, Germany
Nathanael Raschzok, MD: From the * Department of Surgery, Campus Charité Mitte and Campus Virchow-Klinikum, Charité—Universitätsmedizin, Freie Universität Berlin, Humboldt-Universität zu Berlin, Berlin Institute of Health, Berlin, Germany
Nora Nevermann, MD: From the * Department of Surgery, Campus Charité Mitte and Campus Virchow-Klinikum, Charité—Universitätsmedizin, Freie Universität Berlin, Humboldt-Universität zu Berlin, Berlin Institute of Health, Berlin, Germany
Philipp Haber, MD: From the * Department of Surgery, Campus Charité Mitte and Campus Virchow-Klinikum, Charité—Universitätsmedizin, Freie Universität Berlin, Humboldt-Universität zu Berlin, Berlin Institute of Health, Berlin, Germany
Thomas Malinka, MD: From the * Department of Surgery, Campus Charité Mitte and Campus Virchow-Klinikum, Charité—Universitätsmedizin, Freie Universität Berlin, Humboldt-Universität zu Berlin, Berlin Institute of Health, Berlin, Germany
Wenzel Schöning, MD: From the * Department of Surgery, Campus Charité Mitte and Campus Virchow-Klinikum, Charité—Universitätsmedizin, Freie Universität Berlin, Humboldt-Universität zu Berlin, Berlin Institute of Health, Berlin, Germany
Johann Pratschke, MD: From the * Department of Surgery, Campus Charité Mitte and Campus Virchow-Klinikum, Charité—Universitätsmedizin, Freie Universität Berlin, Humboldt-Universität zu Berlin, Berlin Institute of Health, Berlin, Germany
Moritz Schmelzle, MD: From the * Department of Surgery, Campus Charité Mitte and Campus Virchow-Klinikum, Charité—Universitätsmedizin, Freie Universität Berlin, Humboldt-Universität zu Berlin, Berlin Institute of Health, Berlin, Germany

DOI: https://doi.org/10.1097/AS9.0000000000000131
Journal volume & issue: Vol. 3, no. 1
p. e131

Abstract

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Background:. Minimally invasive liver surgery (MILS) has a high variance in the type of resection and complexity, which has been underestimated in learning curve studies in the past. The aim of this work was to evaluate complexity-adjusted learning curves over time for laparoscopic liver resection (LLR) and robotic liver resection (RLR). Methods:. Cumulative sum analysis (CUSUM) and complexity adjustment were performed using the Iwate score for LLR and RLR (n = 647). Lowest point of smoothed data was used to capture the cutoff of the increase in complexity. Data were collected retrospectively at the Department of Surgery of the Charité-Universitätsmedizin Berlin. Results:. A total of 132 RLR and 514 LLR were performed. According to the complexity-adjusted CUSUM analysis, the initial learning phase was reached after 117 for LLR and 93 procedures for RLR, respectively. With increasing experience, the rate of (extended) right hemihepatectomy multiplied from 8.4% to 18.9% for LLR (P = 0.031) and from 21.6% to 58.3% for RLR (P 0.05). The complexity-adjusted CUSUM analysis demonstrated for blood transfusion, conversion, and operative time an increase during the learning phase (T1), while a steady state was reached in the following (T2). Conclusions:. The learning phase for MILS after adjusting for complexity is about 4 times longer than assumed in previous studies, which should urge caution.

Published in Annals of Surgery Open

ISSN: 2691-3593 (Online)
Publisher: Wolters Kluwer Health
Country of publisher: United States
LCC subjects: Medicine: Surgery
Website: https://journals.lww.com/aosopen/pages/default.aspx

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