Evidence map›Paper›PMID 27694914›Full record

ArticleScientific reports2016

Prediction of postoperative liver regeneration from clinical information using a data-led mathematical model.

Kimiyo N Yamamoto, Masatsugu Ishii, Yoshihiro Inoue, Fumitoshi Hirokawa, Ben D MacArthur, Akira Nakamura, Hiroshi Haeno, Kazuhisa Uchiyama

Abstract read
In one paragraph

Article in Scientific reports, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.

0numbers the graph read from it
0cells of the map it votes in
13citing 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

13 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Article
  5. Review
  6. Article
  7. Article
  8. Article
  9. Review
  10. Volumetric and Functional Regeneration of Remnant Liver after Hepatectomy.Journal of gastrointestinal surgery : official journal of the Society for Surgery of the Alimentary Tract · 2019
    Article
  11. Article
  12. Article
  13. Computational Modeling in Liver Surgery.Frontiers in physiology · 2017
    Review
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

8 authors.

Kimiyo N YamamotoDepartments of General and Gastroenterological Surgery, Osaka Medical College Hospital, Osaka, Japan.
Masatsugu IshiiDepartments of General and Gastroenterological Surgery, Osaka Medical College Hospital, Osaka, Japan.
Yoshihiro InoueDepartments of General and Gastroenterological Surgery, Osaka Medical College Hospital, Osaka, Japan.
Fumitoshi HirokawaDepartments of General and Gastroenterological Surgery, Osaka Medical College Hospital, Osaka, Japan.
Ben D MacArthurMathematical Sciences, University of Southampton, SO17 1BJ, UK.
Akira NakamuraDepartment of Radiation Oncology, Massachusetts General Hospital, Boston, MA, USA.
Hiroshi HaenoMathematical Biology Laboratory, Department of Biology, Faculty of Sciences, Kyushu University, Fukuoka, Japan.
Kazuhisa UchiyamaDepartments of General and Gastroenterological Surgery, Osaka Medical College Hospital, Osaka, Japan.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Although the capacity of the liver to recover its size after resection has enabled extensive liver resection, post-hepatectomy liver failure remains one of the most lethal complications of liver resection. Therefore, it is clinically important to discover reliable predictive factors after resection. In this study, we established a novel mathematical framework which described post-hepatectomy liver regeneration in each patient by incorporating quantitative clinical data. Using the model fitting to the liver volumes in series of computed tomography of 123 patients, we estimated liver regeneration rates. From the estimation, we found patients were divided into two groups: i) patients restored the liver to its original size (Group 1, n = 99); and ii) patients experienced a significant reduction in size (Group 2, n = 24). From discriminant analysis in 103 patients with full clinical variables, the prognosis of patients in terms of liver recovery was successfully predicted in 85-90% of patients. We further validated the accuracy of our model prediction using a validation cohort (prediction = 84-87%, n = 39). Our interdisciplinary approach provides qualitative and quantitative insights into the dynamics of liver regeneration. A key strength is to provide better prediction in patients who had been judged as acceptable for resection by current pragmatic criteria.

Indexed as

Liver RegenerationModels, StatisticalPostoperative PeriodAdultAgedAged, 80 and overFemaleHumansMaleMiddle Aged

Identifiers

PMID27694914
PMCPMC5046126

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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.