SynthesisSurgical endoscopy2019
Critical appraisal of predictive tools to assess the difficulty of laparoscopic liver resection: a systematic review.
Synthesis in Surgical endoscopy, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
8 citing papers in PubMed.
- From IDEAL learning curve to team maturation: a decade of experience reducing conversion in laparoscopic liver resection.Surgical endoscopy · 2026Article
- A machine learning analysis of difficulty scoring systems for laparoscopic liver surgery.Surgical endoscopy · 2022Article
- The learning curve of laparoscopic liver resection utilising a difficulty score.Radiology and oncology · 2021Article
- Impact of liver cirrhosis on the difficulty of minimally-invasive liver resections: a 1:1 coarsened exact-matched controlled study.Surgical endoscopy · 2021Article
- Comparison and validation of three difficulty scoring systems in laparoscopic liver surgery: a retrospective analysis on 300 cases.Surgical endoscopy · 2020Article
- Laparoscopic liver resection: indications, limitations, and economic aspects.Langenbeck's archives of surgery · 2020Review
- The external validation of a difficulty scoring system for predicting the risk of intraoperative complications during laparoscopic liver resection.BMC surgery · 2019Article
- Ultrasound liver map technique for laparoscopic liver resections: perioperative outcomes are not impaired by technical complexity.Updates in surgery · 2019Article
Corrections and comments
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Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundObjective 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.
methodsWe 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.
resultsFrom 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.
conclusionExisting 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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.