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


Journal

BMC gastroenterology
ISSN: 1471-230X
Titre abrégé: BMC Gastroenterol
Pays: England
ID NLM: 100968547

Informations de publication

Date de publication:
15 Jun 2020
Historique:
received: 16 05 2019
accepted: 01 06 2020
entrez: 17 6 2020
pubmed: 17 6 2020
medline: 27 3 2021
Statut: epublish

Résumé

Acute-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. Data 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. Multivariate 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. This 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.

Sections du résumé

BACKGROUND BACKGROUND
Acute-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.
METHODS METHODS
Data 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.
RESULTS RESULTS
Multivariate 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.
CONCLUSION CONCLUSIONS
This 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.

Identifiants

pubmed: 32539733
doi: 10.1186/s12876-020-01324-w
pii: 10.1186/s12876-020-01324-w
pmc: PMC7294630
doi:

Substances chimiques

Cholesterol, LDL 0
Creatinine AYI8EX34EU

Types de publication

Journal Article Validation Study

Langues

eng

Sous-ensembles de citation

IM

Pagination

188

Subventions

Organisme : National Natural Science Foundation of China
ID : 31401095
Organisme : National Natural Science Foundation of China
ID : 81670601
Organisme : Provincial Science and Technology Project of Guangdong province
ID : 2015B020226004
Organisme : the 5010 Project of Clinical Research in Sun Yat-sen University
ID : 2016009
Organisme : A2017118
ID : A2017118

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Auteurs

Cuicui Xiao (C)

Cell-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 Gong (J)

Department of Laboratory Medicine, Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, People's Republic of China.

Shuguang Zhu (S)

Department of Hepatic Surgery and Liver Transplantation Center, Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, People's Republic of China.

Zhiwei Zhang (Z)

Department 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 Xi (S)

Department 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 Chong (Y)

Department 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 Jie (Y)

Department 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.

Qi Zhang (Q)

Cell-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.

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Classifications MeSH