Evidence map›Paper›PMID 34203396›Full record

ArticleJournal of clinical medicine2021

Tailored Prediction Model of Survival after Liver Transplantation for Hepatocellular Carcinoma.

Indah Jamtani, Kwang-Woong Lee, Yunhee Choi, YoungRok Choi, Jeong-Moo Lee, Eui-Soo Han, Kwangpyo Hong, Gyu-Seong Choi, Jong Man Kim, Nam-Joon Yi and 6 more

Abstract read
In one paragraph

Article in Journal of clinical medicine, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing 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

4 citing papers in PubMed.

  1. Article
  2. Review
  3. Long-Term Prognostic Value of AFP and PIVKA-II in HCC After Living Donor Liver Transplantation: A Single-Center Retrospective Study.Transplant international : official journal of the European Society for Organ Transplantation · 2025
    Article
  4. Article
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

16 authors.

Indah JamtaniDepartment of Surgery, Seoul National University College of Medicine, Seoul 03080, Korea.ORCID 0000-0002-4809-0063
Kwang-Woong LeeDepartment of Surgery, Seoul National University College of Medicine, Seoul 03080, Korea.ORCID 0000-0001-6412-1926
Yunhee ChoiDivision of Medical Statistics, Medical Research Collaborating Center, Seoul National University College of Medicine, Seoul 03080, Korea.
YoungRok ChoiDepartment of Surgery, Seoul National University College of Medicine, Seoul 03080, Korea.
Jeong-Moo LeeDepartment of Surgery, Seoul National University College of Medicine, Seoul 03080, Korea.ORCID 0000-0001-7806-8759
Eui-Soo HanDepartment of Surgery, Seoul National University College of Medicine, Seoul 03080, Korea.
Kwangpyo HongDepartment of Surgery, Seoul National University College of Medicine, Seoul 03080, Korea.
Gyu-Seong ChoiDepartment of Surgery, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul 06351, Korea.
Jong Man KimDepartment of Surgery, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul 06351, Korea.ORCID 0000-0002-1903-8354
Nam-Joon YiDepartment of Surgery, Seoul National University College of Medicine, Seoul 03080, Korea.ORCID 0000-0002-5467-425X
Suk Kyun HongDepartment of Surgery, Seoul National University College of Medicine, Seoul 03080, Korea.ORCID 0000-0002-0020-6215
Jeik ByunDepartment of Surgery, Seoul National University College of Medicine, Seoul 03080, Korea.
Su Young HongDepartment of Surgery, Seoul National University College of Medicine, Seoul 03080, Korea.
Sanggyeun SuhDepartment of Surgery, Seoul National University College of Medicine, Seoul 03080, Korea.
Jae-Won JohDepartment of Surgery, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul 06351, Korea.
Kyung-Suk SuhDepartment of Surgery, Seoul National University College of Medicine, Seoul 03080, Korea.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

This study aimed to create a tailored prediction model of hepatocellular carcinoma (HCC)-specific survival after transplantation based on pre-transplant parameters. Data collected from June 2006 to July 2018 were used as a derivation dataset and analyzed to create an HCC-specific survival prediction model by combining significant risk factors. Separate data were collected from January 2014 to June 2018 for validation. The prediction model was validated internally and externally. The data were divided into three groups based on risk scores derived from the hazard ratio. A combination of patient demographic, laboratory, radiological data, and tumor-specific characteristics that showed a good prediction of HCC-specific death at a specific time (t) were chosen. Internal and external validations with Uno's C-index were 0.79 and 0.75 (95% confidence interval (CI) 0.65-0.86), respectively. The predicted survival after liver transplantation for HCC (SALT) at a time "t" was calculated using the formula: [1 - (HCC-specific death(t'))] × 100. The 5-year HCC-specific death and recurrence rates in the low-risk group were 2% and 5%; the intermediate-risk group was 12% and 14%, and in the high-risk group were 71% and 82%. Our HCC-specific survival predictor named "SALT calculator" could provide accurate information about expected survival tailored for patients undergoing transplantation for HCC.

Indexed as

hepatocellular carcinomaliver transplantationrecurrencesurvival

Identifiers

PMID34203396
PMCPMC8268829

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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.