Development of a Model to Predict Transplant-free Survival of Patients With Acute Liver Failure

David G. Koch, Holly Tillman, Valerie Durkalski, William M. Lee, Adrian Reuben

Research output: Contribution to journalArticle

25 Citations (Scopus)

Abstract

Background & Aims Patients with acute liver failure (ALF) have a high risk of death that can be substantially reduced with liver transplantation. It is a challenge to predict which patients with ALF will survive without liver transplant because available prognostic scoring systems are inadequate. We devised a mathematical model, using a large dataset collected by the Acute Liver Failure Study Group, which can predict transplant-free survival in patients with ALF. Methods We performed a retrospective analysis of data from 1974 subjects who met criteria for ALF (coagulopathy and hepatic encephalopathy within 26 weeks of the first symptoms, without pre-existing liver disease) enrolled in the Acute Liver Failure Study Group database from January 1, 1998 through June 11, 2013. We randomly assigned the subjects to development and validation cohorts. Data from the development cohort were analyzed to identify factors associated with transplant-free survival (alive without transplantation by 21 days after admission to the study). Statistically significant variables were used to create a multivariable logistic regression model. Results Most subjects were women (70%) and white (78%); acetaminophen overdose was the most common cause (48% of subjects). The rate of transplant-free survival was 50%. Admission values of hepatic encephalopathy grade, ALF etiology, vasopressor use, and log transformations of bilirubin and international normalized ratio were significantly associated with transplant-free survival, based on logistic regression analysis. In the validation cohort, the resulting model predicted transplant-free survival with a C statistic value of 0.84, 66.3% accuracy (95% confidence interval, 63.1%–69.4%), 37.1% sensitivity (95% confidence interval, 32.5%–41.8%), and 95.3% specificity (95% confidence interval, 92.9%–97.1%). Conclusions Using data from the Acute Liver Failure Study Group, we developed a model that predicts transplant-free survival of patients with ALF based on easily identifiable hospital admission data. External validation studies are required.

Original languageEnglish (US)
Pages (from-to)1199-1206.e2
JournalClinical Gastroenterology and Hepatology
Volume14
Issue number8
DOIs
StatePublished - Aug 1 2016

Fingerprint

Acute Liver Failure
Transplants
Survival
Hepatic Encephalopathy
Logistic Models
Confidence Intervals
Preexisting Condition Coverage
International Normalized Ratio
Validation Studies
Acetaminophen
Bilirubin
Liver Transplantation
Liver Diseases
Theoretical Models
Transplantation
Regression Analysis
Databases
Liver

Keywords

  • Acute Liver Failure
  • Mortality
  • Predictive Model
  • Prognosis

ASJC Scopus subject areas

  • Hepatology
  • Gastroenterology

Cite this

Development of a Model to Predict Transplant-free Survival of Patients With Acute Liver Failure. / Koch, David G.; Tillman, Holly; Durkalski, Valerie; Lee, William M.; Reuben, Adrian.

In: Clinical Gastroenterology and Hepatology, Vol. 14, No. 8, 01.08.2016, p. 1199-1206.e2.

Research output: Contribution to journalArticle

Koch, David G. ; Tillman, Holly ; Durkalski, Valerie ; Lee, William M. ; Reuben, Adrian. / Development of a Model to Predict Transplant-free Survival of Patients With Acute Liver Failure. In: Clinical Gastroenterology and Hepatology. 2016 ; Vol. 14, No. 8. pp. 1199-1206.e2.
@article{1fcfd43843844a5dba1591c0b8302cfa,
title = "Development of a Model to Predict Transplant-free Survival of Patients With Acute Liver Failure",
abstract = "Background & Aims Patients with acute liver failure (ALF) have a high risk of death that can be substantially reduced with liver transplantation. It is a challenge to predict which patients with ALF will survive without liver transplant because available prognostic scoring systems are inadequate. We devised a mathematical model, using a large dataset collected by the Acute Liver Failure Study Group, which can predict transplant-free survival in patients with ALF. Methods We performed a retrospective analysis of data from 1974 subjects who met criteria for ALF (coagulopathy and hepatic encephalopathy within 26 weeks of the first symptoms, without pre-existing liver disease) enrolled in the Acute Liver Failure Study Group database from January 1, 1998 through June 11, 2013. We randomly assigned the subjects to development and validation cohorts. Data from the development cohort were analyzed to identify factors associated with transplant-free survival (alive without transplantation by 21 days after admission to the study). Statistically significant variables were used to create a multivariable logistic regression model. Results Most subjects were women (70{\%}) and white (78{\%}); acetaminophen overdose was the most common cause (48{\%} of subjects). The rate of transplant-free survival was 50{\%}. Admission values of hepatic encephalopathy grade, ALF etiology, vasopressor use, and log transformations of bilirubin and international normalized ratio were significantly associated with transplant-free survival, based on logistic regression analysis. In the validation cohort, the resulting model predicted transplant-free survival with a C statistic value of 0.84, 66.3{\%} accuracy (95{\%} confidence interval, 63.1{\%}–69.4{\%}), 37.1{\%} sensitivity (95{\%} confidence interval, 32.5{\%}–41.8{\%}), and 95.3{\%} specificity (95{\%} confidence interval, 92.9{\%}–97.1{\%}). Conclusions Using data from the Acute Liver Failure Study Group, we developed a model that predicts transplant-free survival of patients with ALF based on easily identifiable hospital admission data. External validation studies are required.",
keywords = "Acute Liver Failure, Mortality, Predictive Model, Prognosis",
author = "Koch, {David G.} and Holly Tillman and Valerie Durkalski and Lee, {William M.} and Adrian Reuben",
year = "2016",
month = "8",
day = "1",
doi = "10.1016/j.cgh.2016.03.046",
language = "English (US)",
volume = "14",
pages = "1199--1206.e2",
journal = "Clinical Gastroenterology and Hepatology",
issn = "1542-3565",
publisher = "W.B. Saunders Ltd",
number = "8",

}

TY - JOUR

T1 - Development of a Model to Predict Transplant-free Survival of Patients With Acute Liver Failure

AU - Koch, David G.

AU - Tillman, Holly

AU - Durkalski, Valerie

AU - Lee, William M.

AU - Reuben, Adrian

PY - 2016/8/1

Y1 - 2016/8/1

N2 - Background & Aims Patients with acute liver failure (ALF) have a high risk of death that can be substantially reduced with liver transplantation. It is a challenge to predict which patients with ALF will survive without liver transplant because available prognostic scoring systems are inadequate. We devised a mathematical model, using a large dataset collected by the Acute Liver Failure Study Group, which can predict transplant-free survival in patients with ALF. Methods We performed a retrospective analysis of data from 1974 subjects who met criteria for ALF (coagulopathy and hepatic encephalopathy within 26 weeks of the first symptoms, without pre-existing liver disease) enrolled in the Acute Liver Failure Study Group database from January 1, 1998 through June 11, 2013. We randomly assigned the subjects to development and validation cohorts. Data from the development cohort were analyzed to identify factors associated with transplant-free survival (alive without transplantation by 21 days after admission to the study). Statistically significant variables were used to create a multivariable logistic regression model. Results Most subjects were women (70%) and white (78%); acetaminophen overdose was the most common cause (48% of subjects). The rate of transplant-free survival was 50%. Admission values of hepatic encephalopathy grade, ALF etiology, vasopressor use, and log transformations of bilirubin and international normalized ratio were significantly associated with transplant-free survival, based on logistic regression analysis. In the validation cohort, the resulting model predicted transplant-free survival with a C statistic value of 0.84, 66.3% accuracy (95% confidence interval, 63.1%–69.4%), 37.1% sensitivity (95% confidence interval, 32.5%–41.8%), and 95.3% specificity (95% confidence interval, 92.9%–97.1%). Conclusions Using data from the Acute Liver Failure Study Group, we developed a model that predicts transplant-free survival of patients with ALF based on easily identifiable hospital admission data. External validation studies are required.

AB - Background & Aims Patients with acute liver failure (ALF) have a high risk of death that can be substantially reduced with liver transplantation. It is a challenge to predict which patients with ALF will survive without liver transplant because available prognostic scoring systems are inadequate. We devised a mathematical model, using a large dataset collected by the Acute Liver Failure Study Group, which can predict transplant-free survival in patients with ALF. Methods We performed a retrospective analysis of data from 1974 subjects who met criteria for ALF (coagulopathy and hepatic encephalopathy within 26 weeks of the first symptoms, without pre-existing liver disease) enrolled in the Acute Liver Failure Study Group database from January 1, 1998 through June 11, 2013. We randomly assigned the subjects to development and validation cohorts. Data from the development cohort were analyzed to identify factors associated with transplant-free survival (alive without transplantation by 21 days after admission to the study). Statistically significant variables were used to create a multivariable logistic regression model. Results Most subjects were women (70%) and white (78%); acetaminophen overdose was the most common cause (48% of subjects). The rate of transplant-free survival was 50%. Admission values of hepatic encephalopathy grade, ALF etiology, vasopressor use, and log transformations of bilirubin and international normalized ratio were significantly associated with transplant-free survival, based on logistic regression analysis. In the validation cohort, the resulting model predicted transplant-free survival with a C statistic value of 0.84, 66.3% accuracy (95% confidence interval, 63.1%–69.4%), 37.1% sensitivity (95% confidence interval, 32.5%–41.8%), and 95.3% specificity (95% confidence interval, 92.9%–97.1%). Conclusions Using data from the Acute Liver Failure Study Group, we developed a model that predicts transplant-free survival of patients with ALF based on easily identifiable hospital admission data. External validation studies are required.

KW - Acute Liver Failure

KW - Mortality

KW - Predictive Model

KW - Prognosis

UR - http://www.scopus.com/inward/record.url?scp=84994092913&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84994092913&partnerID=8YFLogxK

U2 - 10.1016/j.cgh.2016.03.046

DO - 10.1016/j.cgh.2016.03.046

M3 - Article

C2 - 27085756

AN - SCOPUS:84994092913

VL - 14

SP - 1199-1206.e2

JO - Clinical Gastroenterology and Hepatology

JF - Clinical Gastroenterology and Hepatology

SN - 1542-3565

IS - 8

ER -