Literature DB >> 35278226

Accurate long-term prediction of death for patients with cirrhosis.

David Goldberg1,2, Alejandro Mantero2, David Kaplan3,4, Cindy Delgado1, Binu John1,5, Nadine Nuchovich1, Ezekiel Emanuel6, Peter P Reese7.   

Abstract

BACKGROUND AND AIMS: Cirrhosis is a major cause of death and is associated with extensive health care use. Patients with cirrhosis have complex treatment choices due to risks of morbidity and mortality. To optimally counsel and treat patients with cirrhosis requires tools to predict their longer-term liver-related survival. We sought to develop and validate a risk score to predict longer-term survival of patients with cirrhosis. APPROACH AND
RESULTS: We conducted a retrospective cohort study of adults with cirrhosis with no major life-limiting comorbidities. Adults with cirrhosis within the Veterans Health Administration were used for model training and internal validation, and external validation used the OneFlorida Clinical Research Consortium. We used four model-building approaches including variables predictive of cirrhosis-related mortality, focused on discrimination at key time points (1, 3, 5, and 10 years). Among 30,263 patients with cirrhosis ≤75 years old without major life-limiting comorbidities and complete laboratory data during the baseline period, the boosted survival tree models had the highest discrimination, with 1-year, 3-year, 5-year, and 10-year survival rates of 0.77, 0.81, 0.84, and 0.88, respectively. The 1-year, 3-year, and 5-year discrimination was nearly identical in external validation. Secondary analyses with imputation of missing data and subgroups by etiology of liver disease had similar results to the primary model.
CONCLUSIONS: We developed and validated (internally and externally) a risk score to predict longer-term survival of patients with cirrhosis. This score would transform management of patients with cirrhosis in terms of referral to specialty care and treatment decision-making for non-liver-related care.
© 2022 American Association for the Study of Liver Diseases.

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Year:  2022        PMID: 35278226      PMCID: PMC9378359          DOI: 10.1002/hep.32457

Source DB:  PubMed          Journal:  Hepatology        ISSN: 0270-9139            Impact factor:   17.298


  56 in total

1.  Patients With Persistently Low MELD-Na Scores Continue to Be at Risk of Liver-related Death.

Authors:  Nikhilesh R Mazumder; Kofi Atiemo; Amna Daud; Abel Kho; Michael Abecassis; Josh Levitsky; Daniela P Ladner
Journal:  Transplantation       Date:  2020-07       Impact factor: 4.939

2.  Definition and classification of chronic kidney disease: a position statement from Kidney Disease: Improving Global Outcomes (KDIGO).

Authors:  Andrew S Levey; Kai-Uwe Eckardt; Yusuke Tsukamoto; Adeera Levin; Josef Coresh; Jerome Rossert; Dick De Zeeuw; Thomas H Hostetter; Norbert Lameire; Garabed Eknoyan
Journal:  Kidney Int       Date:  2005-06       Impact factor: 10.612

3.  Black Patients Have Unequal Access to Listing for Liver Transplantation in the United States.

Authors:  Russell Rosenblatt; Nabeel Wahid; Karim J Halazun; Alyson Kaplan; Arun Jesudian; Catherine Lucero; Jihui Lee; Lorna Dove; Alyson Fox; Elizabeth Verna; Benjamin Samstein; Brett E Fortune; Robert S Brown
Journal:  Hepatology       Date:  2021-09       Impact factor: 17.425

Review 4.  Applying Machine Learning in Liver Disease and Transplantation: A Comprehensive Review.

Authors:  Ashley Spann; Angeline Yasodhara; Justin Kang; Kymberly Watt; Bo Wang; Anna Goldenberg; Mamatha Bhat
Journal:  Hepatology       Date:  2020-03-06       Impact factor: 17.425

5.  Association of distance from a transplant center with access to waitlist placement, receipt of liver transplantation, and survival among US veterans.

Authors:  David S Goldberg; Benjamin French; Kimberly A Forde; Peter W Groeneveld; Therese Bittermann; Lisa Backus; Scott D Halpern; David E Kaplan
Journal:  JAMA       Date:  2014-03-26       Impact factor: 56.272

6.  HCV testing: Order and completion rates among baby boomers obtaining care from seven health systems in Florida, 2015-2017.

Authors:  Susan T Vadaparampil; Lindsay N Fuzzell; Julie Rathwell; Richard R Reich; Elizabeth Shenkman; David R Nelson; Erin Kobetz; Patricia D Jones; Richard Roetzheim; Anna R Giuliano
Journal:  Prev Med       Date:  2020-07-25       Impact factor: 4.018

7.  Predicting survival after liver transplantation in patients with hepatocellular carcinoma using the LiTES-HCC score.

Authors:  David Goldberg; Alejandro Mantero; Craig Newcomb; Cindy Delgado; Kimberly A Forde; David E Kaplan; Binu John; Nadine Nuchovich; Barbara Dominguez; Ezekiel Emanuel; Peter P Reese
Journal:  J Hepatol       Date:  2021-01-13       Impact factor: 30.083

8.  Machine learning for fast identification of bacteraemia in SIRS patients treated on standard care wards: a cohort study.

Authors:  Franz Ratzinger; Helmuth Haslacher; Thomas Perkmann; Matilde Pinzan; Philip Anner; Athanasios Makristathis; Heinz Burgmann; Georg Heinze; Georg Dorffner
Journal:  Sci Rep       Date:  2018-08-15       Impact factor: 4.379

9.  Mortality due to cirrhosis and liver cancer in the United States, 1999-2016: observational study.

Authors:  Elliot B Tapper; Neehar D Parikh
Journal:  BMJ       Date:  2018-07-18

10.  Derivation and Validation of Novel Phenotypes of Multiple Organ Dysfunction Syndrome in Critically Ill Children.

Authors:  L Nelson Sanchez-Pinto; Emily K Stroup; Tricia Pendergrast; Neethi Pinto; Yuan Luo
Journal:  JAMA Netw Open       Date:  2020-08-03
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