Julie Hallet1,2,3, Patrick Pessaux4,5, Kaitlyn A Beyfuss6, Shiva Jayaraman7,8, Pablo E Serrano9, Guillaume Martel10, Natalie G Coburn11,7,6, Tullio Piardi12, Alyson L Mahar13. 1. Department of Surgery, Odette Cancer Centre - Sunnybrook Health Sciences Centre, 2075, Bayview Avenue, Toronto, ON, M4N 3M5, Canada. Julie.hallet@sunnybrook.ca. 2. Department of Surgery, University of Toronto, Toronto, ON, Canada. Julie.hallet@sunnybrook.ca. 3. Sunnybrook Research Institute, Toronto, ON, Canada. Julie.hallet@sunnybrook.ca. 4. Department of Digestive and Endocrine Surgery, Nouvel Hôpital Civil, Strasbourg, France. 5. Institut Hospitalier Universitaire de Strasbourg, Strasbourg, France. 6. Sunnybrook Research Institute, Toronto, ON, Canada. 7. Department of Surgery, University of Toronto, Toronto, ON, Canada. 8. St-Joseph Health Centre, Toronto, ON, Canada. 9. Department of Surgery, McMaster University, Hamilton, ON, Canada. 10. Department of Surgery, The Ottawa Hospital, Ottawa, ON, Canada. 11. Department of Surgery, Odette Cancer Centre - Sunnybrook Health Sciences Centre, 2075, Bayview Avenue, Toronto, ON, M4N 3M5, Canada. 12. Department of Surgery, Centre Hospitalier Universitaire de Reims, Reims, France. 13. Department of Community Health Sciences, University of Manitoba, Winnipeg, MB, USA.
Abstract
BACKGROUND: Objective assessment of the difficulty of laparoscopic liver resection (LLR) preoperatively is key in improving its uptake. Difficulty scores are proposed but are not used routinely in practice. We identified and appraised predictive models to estimate LLR difficulty. METHODS: We systematically searched the literature for tools predicting LLR difficulty. Two independent reviewers selected studies, abstracted data and assessed methodology. We evaluated tools' quality and clinical relevance using the Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modelling Studies (CHARMS) guidelines. RESULTS: From 1037 citations, we included 8 studies reporting on 4 predictive tools using data from 1995 to 2016 in Asia and Europe. In 4 development studies, tools were designed to predict difficulty as assigned by experts using a 10-level difficulty index, operative time, post-operative morbidity or intra-operative complications. Internal validation and performance metrics were reported in one development study. One tool was subjected to external validations in 4 studies (1 independent and geographic). Validations compared post-operative outcomes (operative time, blood loss, transfusion, major morbidity and conversion) between the risk categories. One study validated discrimination (AUROC 0.53). Calibration was not assessed. CONCLUSION: Existing tools cannot be used confidently to predict LLR difficulty. Consistent objective clinical outcomes to predict to define LLR difficulty should be established, and better-quality tools developed and validated in a wide array of populations and clinical settings, following best practices for predictive tools development and validation. This will improve risk stratification for future trials and uptake of LLR.
BACKGROUND: Objective assessment of the difficulty of laparoscopic liver resection (LLR) preoperatively is key in improving its uptake. Difficulty scores are proposed but are not used routinely in practice. We identified and appraised predictive models to estimate LLR difficulty. METHODS: We systematically searched the literature for tools predicting LLR difficulty. Two independent reviewers selected studies, abstracted data and assessed methodology. We evaluated tools' quality and clinical relevance using the Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modelling Studies (CHARMS) guidelines. RESULTS: From 1037 citations, we included 8 studies reporting on 4 predictive tools using data from 1995 to 2016 in Asia and Europe. In 4 development studies, tools were designed to predict difficulty as assigned by experts using a 10-level difficulty index, operative time, post-operative morbidity or intra-operative complications. Internal validation and performance metrics were reported in one development study. One tool was subjected to external validations in 4 studies (1 independent and geographic). Validations compared post-operative outcomes (operative time, blood loss, transfusion, major morbidity and conversion) between the risk categories. One study validated discrimination (AUROC 0.53). Calibration was not assessed. CONCLUSION: Existing tools cannot be used confidently to predict LLR difficulty. Consistent objective clinical outcomes to predict to define LLR difficulty should be established, and better-quality tools developed and validated in a wide array of populations and clinical settings, following best practices for predictive tools development and validation. This will improve risk stratification for future trials and uptake of LLR.
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
Authors: John T Mullen; Dario Ribero; Srinevas K Reddy; Matteo Donadon; Daria Zorzi; Shiva Gautam; Eddie K Abdalla; Steven A Curley; Lorenzo Capussotti; Bryan M Clary; Jean-Nicolas Vauthey Journal: J Am Coll Surg Date: 2007-02-15 Impact factor: 6.113
Authors: Karel G M Moons; Douglas G Altman; Johannes B Reitsma; John P A Ioannidis; Petra Macaskill; Ewout W Steyerberg; Andrew J Vickers; David F Ransohoff; Gary S Collins Journal: Ann Intern Med Date: 2015-01-06 Impact factor: 25.391