Evidence map›Paper›PMID 32539733›Full record

ArticleBMC gastroenterology2020

Nomogram based on blood lipoprotein for estimation of mortality in patients with hepatitis B virus-related acute-on-chronic liver failure.

Cuicui Xiao, Jiao Gong, Shuguang Zhu, Zhiwei Zhang, Sujuan Xi, Yutian Chong, Yusheng Jie, Qi Zhang

Open access · goldAbstract readValidation Study
In one paragraph

Article in BMC gastroenterology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed
0.9field-weighted citation impact, top 26% of its field
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

6 citing papers in PubMed, 9 citations in OpenAlex.

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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

8 authors at 1 institution in 1 country.

Cuicui XiaoCell-gene Therapy Translational Medicine Research Center, Key Laboratory of Liver Disease of Guangdong Province, Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, People's Republic of China.
Jiao GongDepartment of Laboratory Medicine, Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, People's Republic of China.
Shuguang ZhuDepartment of Hepatic Surgery and Liver Transplantation Center, Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, People's Republic of China.
Zhiwei ZhangDepartment of Infectious Diseases, Key Laboratory of Liver Disease of Guangdong Province, Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, People's Republic of China.
Sujuan XiDepartment of Infectious Diseases, Key Laboratory of Liver Disease of Guangdong Province, Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, People's Republic of China.
Yutian ChongDepartment of Infectious Diseases, Key Laboratory of Liver Disease of Guangdong Province, Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, People's Republic of China.
Yusheng JieDepartment of Infectious Diseases, Key Laboratory of Liver Disease of Guangdong Province, Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, People's Republic of China. jieyusheng@163.com.ORCID http://orcid.org/0000-0003-3756-0653
Qi ZhangCell-gene Therapy Translational Medicine Research Center, Key Laboratory of Liver Disease of Guangdong Province, Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, People's Republic of China. kee_kee@126.com.
Sun Yat-sen University · CN

Funding

A2017118 A2017118National Natural Science Foundation of China 31401095National Natural Science Foundation of China 81670601Provincial Science and Technology Project of Guangdong province 2015B020226004the 5010 Project of Clinical Research in Sun Yat-sen University 2016009
6 · The paper itself

Abstract

backgroundAcute-on-chronic liver failure (ACLF) is a clinic syndrome with substantial high short-term mortality. It is very important to stratify patients according to prognosis to decide management strategy. This study aimed to formulate and validate a nomogram model based on blood lipoprotein for prediction of 3-month mortality in patients with hepatitis B virus (HBV)-related ACLF.

methodsData on 393 consecutive patients who were diagnosed as HBV-related ACLF at the Third Affiliated Hospital of Sun Yat-sen University between June 1, 2013, and February 1, 2015, were prospectively collected. Of these, 260 patients who were collected in an earlier period formed the training cohort for the development of nomogram, while 133 patients who were collected thereafter formed the validation cohort for confirming the performance of nomogram.

resultsMultivariate analysis showed that low density lipoprotein cholesterol (LDL-C), age, prothrombin time, and creatinine were independently associated with 3-month mortality of patients with HBV-related ACLF. Kaplan-Meier survival analysis revealed that the high LDL-C (LDL-C ≥ 1.0 mmol/L, cut-off value) was significantly associated with elevated overall survival (P < 0.001). All independent factors for survival were selected into the nomogram. The calibration plot for the probability of survival showed good agreement between prediction by nomogram and actual observation.

conclusionThis study highlighted that reduction of serum LDL-C level was an independent risk factor for the survival in patients with HBV-related ACLF, and the nomogram based on serum LDL-C was an accurate and practical model for predicting the 3-month mortality in patients with this disease.

Indexed as

Hepatitis B virusNomogramsAcute-On-Chronic Liver FailureAdultAge FactorsCholesterol, LDLCreatinineFemaleHumansKaplan-Meier EstimateMaleMiddle AgedPredictive Value of TestsProspective StudiesProthrombin TimeReproducibility of ResultsCholesterol, LDLCreatinineAcute-on-chronic liver failureHepatitis B virusLow-density lipoproteinNomogram

Identifiers

PMID32539733
PMCPMC7294630
OpenAlexW3034193906

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Registered trials

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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.