Evidence map›Paper›PMID 36568745›Full record

ArticleFrontiers in public health2022

Development and validation of a risk prediction model for incident liver cancer.

Yingxin Liu, Jingyi Zhang, Weifeng Wang, Guowei Li

Abstract read
In one paragraph

Article in Frontiers in public health, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Article
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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

4 authors.

Yingxin LiuCenter for Clinical Epidemiology and Methodology, Guangdong Second Provincial General Hospital, Guangzhou, China.
Jingyi ZhangCenter for Clinical Epidemiology and Methodology, Guangdong Second Provincial General Hospital, Guangzhou, China.
Weifeng WangDepartment of Gastroenterology and Hepatology, Guangdong Second Provincial General Hospital, Guangzhou, China.
Guowei LiCenter for Clinical Epidemiology and Methodology, Guangdong Second Provincial General Hospital, Guangzhou, China.

Funding

Medical Research Council MC_PC_17228Medical Research Council MC_QA137853
6 · The paper itself

Abstract

Objective: We aimed to develop and validate a risk prediction model for liver cancer based on routinely available risk factors using the data from UK Biobank prospective cohort study. Methods: This analysis included 359,489 participants (2,894,807 person-years) without a previous diagnosis of cancer. We used the Fine-Gray regression model to predict the incident risk of liver cancer, accounting for the competing risk of all-cause death. Model discrimination and calibration were validated internally. Decision curve analysis was conducted to quantify the clinical utility of the model. Nomogram was built based on regression coefficients. Results: Good discrimination performance of the model was observed in both development and validation datasets, with an area under the curve (95% confidence interval) for 5-year risk of 0.782 (0.748-0.816) and 0.771 (0.702-0.840) respectively. The calibration showed fine agreement between observed and predicted risks. The model yielded higher positive net benefits in the decision curve analysis than considering either all participants as being at high or low risk, which indicated good clinical utility. Conclusion: A new risk prediction model for liver cancer composed of routinely available risk factors was developed. The model had good discrimination, calibration and clinical utility, which may help with the screening and management of liver cancer for general population in the public health field.

Indexed as

Liver NeoplasmsNomogramsHumansProspective StudiesRisk Factorsliver cancernomogramrisk managementrisk predictionscreening

Identifiers

PMID36568745
PMCPMC9768800

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