Literature DB >> 32495139

Evaluation of the Iwate Model for Predicting the Difficulty of Laparoscopic Liver Resection: Does Tumor Size Matter?

Arpad Ivanecz1, Irena Plahuta2, Tomislav Magdalenić2, Bojan Ilijevec2, Matej Mencinger3,4,5, Iztok Peruš3,6, Stojan Potrč2.   

Abstract

BACKGROUND: This study aimed to externally validate the Iwate scoring model and its prognostic value for predicting the risks of intra- and postoperative complications of laparoscopic liver resection.
METHODS: Consecutive patients who underwent pure laparoscopic liver resection between 2008 and 2019 at a single tertiary center were included. The Iwate scores were calculated according to the original proposition (four difficulty levels based on six indices). Intra- and postoperative complications were compared across difficulty levels. Fitting the obtained data to the cumulative density function of the Weibull distribution and a linear function provided the mean risk curves for intra- and postoperative complications, respectively.
RESULTS: The difficulty levels of 142 laparoscopic liver resections were scored as low, intermediate, advanced, and expert level in 41 (28.9%), 53 (37.3%), 32 (22.5%), and 16 (11.3%) patients, respectively. Intraoperative complications were detected in 26 (18.3%) patients and its rates (2.4%, 7.5%, 34.3%, and 62.5%) increased gradually with statistically significant values among difficulty levels (P ˂ 0.001). Major postoperative complications occurred in 21 (14.8%) patients and its rates (4.8%, 5.6%, 28.1%, 43.7%; P ˂ 0.001) showed the same trend as for intraoperative complications. Then, the mean risk curves of both complications were obtained. Due to outliers, a new threshold for a tumor size index was proposed at 38 mm. The repeated analysis showed improved results.
CONCLUSIONS: The Iwate scoring model predicts the probability of complications across difficulty levels. Our proposed tumor size threshold (38 mm) improves the quality of the prediction. The model is upgraded by a probability of complications for every difficulty score.

Entities:  

Keywords:  Forecasting; Hepatectomy; Intraoperative complications; Laparoscopy; Postoperative complications

Year:  2020        PMID: 32495139     DOI: 10.1007/s11605-020-04657-9

Source DB:  PubMed          Journal:  J Gastrointest Surg        ISSN: 1091-255X            Impact factor:   3.452


  2 in total

1.  Recommendations for laparoscopic liver resection: a report from the second international consensus conference held in Morioka.

Authors:  Go Wakabayashi; Daniel Cherqui; David A Geller; Joseph F Buell; Hironori Kaneko; Ho Seong Han; Horacio Asbun; Nicholas OʼRourke; Minoru Tanabe; Alan J Koffron; Allan Tsung; Olivier Soubrane; Marcel Autran Machado; Brice Gayet; Roberto I Troisi; Patrick Pessaux; Ronald M Van Dam; Olivier Scatton; Mohammad Abu Hilal; Giulio Belli; Choon Hyuck David Kwon; Bjørn Edwin; Gi Hong Choi; Luca Antonio Aldrighetti; Xiujun Cai; Sean Cleary; Kuo-Hsin Chen; Michael R Schön; Atsushi Sugioka; Chung-Ngai Tang; Paulo Herman; Juan Pekolj; Xiao-Ping Chen; Ibrahim Dagher; William Jarnagin; Masakazu Yamamoto; Russell Strong; Palepu Jagannath; Chung-Mau Lo; Pierre-Alain Clavien; Norihiro Kokudo; Jeffrey Barkun; Steven M Strasberg
Journal:  Ann Surg       Date:  2015-04       Impact factor: 12.969

2.  Liver cirrhosis grading Child-Pugh class B: a Goliath to challenge in laparoscopic liver resection?-prior experience and matched comparisons.

Authors:  Xiujun Cai; Xiao Liang; Tunan Yu; Yuelong Liang; Renan Jing; Wenbing Jiang; Jianbo Li; Hanning Ying
Journal:  Hepatobiliary Surg Nutr       Date:  2015-12       Impact factor: 7.293

  2 in total
  2 in total

1.  A machine learning analysis of difficulty scoring systems for laparoscopic liver surgery.

Authors:  Andrea Ruzzenente; Fabio Bagante; Edoardo Poletto; Tommaso Campagnaro; Simone Conci; Mario De Bellis; Corrado Pedrazzani; Alfredo Guglielmi
Journal:  Surg Endosc       Date:  2022-05-23       Impact factor: 3.453

2.  The learning curve of laparoscopic liver resection utilising a difficulty score.

Authors:  Arpad Ivanecz; Irena Plahuta; Matej Mencinger; Iztok Perus; Tomislav Magdalenic; Spela Turk; Stojan Potrc
Journal:  Radiol Oncol       Date:  2021-09-06       Impact factor: 2.991

  2 in total

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