Evidence map›Paper›PMID 30350105›Full record

SynthesisSurgical endoscopy2019

Critical appraisal of predictive tools to assess the difficulty of laparoscopic liver resection: a systematic review.

Julie Hallet, Patrick Pessaux, Kaitlyn A Beyfuss, Shiva Jayaraman, Pablo E Serrano, Guillaume Martel, Natalie G Coburn, Tullio Piardi, Alyson L Mahar

Abstract readSystematic Review
PubMed Publisher
In one paragraph

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.

0numbers the graph read from it
0cells of the map it votes in
8citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

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.

2 · The registry

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.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

8 citing papers in PubMed.

  1. Article
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  6. Review
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

9 authors.

Julie HalletDepartment of Surgery, Odette Cancer Centre - Sunnybrook Health Sciences Centre, 2075, Bayview Avenue, Toronto, ON, M4N 3M5, Canada. Julie.hallet@sunnybrook.ca.
Patrick PessauxDepartment of Digestive and Endocrine Surgery, Nouvel Hôpital Civil, Strasbourg, France.
Kaitlyn A BeyfussSunnybrook Research Institute, Toronto, ON, Canada.
Shiva JayaramanDepartment of Surgery, University of Toronto, Toronto, ON, Canada.
Pablo E SerranoDepartment of Surgery, McMaster University, Hamilton, ON, Canada.
Guillaume MartelDepartment of Surgery, The Ottawa Hospital, Ottawa, ON, Canada.
Natalie G CoburnDepartment of Surgery, Odette Cancer Centre - Sunnybrook Health Sciences Centre, 2075, Bayview Avenue, Toronto, ON, M4N 3M5, Canada.
Tullio PiardiDepartment of Surgery, Centre Hospitalier Universitaire de Reims, Reims, France.
Alyson L MaharDepartment of Community Health Sciences, University of Manitoba, Winnipeg, MB, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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.

Indexed as

Clinical Decision RulesLaparoscopyClinical Decision-MakingHepatectomyHumansIntraoperative ComplicationsOperative TimePostoperative ComplicationsHepatectomyLaparoscopyLiverPredictionScore

Identifiers

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

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.