Association between hospital liver transplantation volume and mortality after liver re-transplantation

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Abstract

Background

The relationship between institutional liver transplantation (LT) case volume and clinical outcomes after liver re-transplantation is yet to be determined.

Methods

Patients who underwent liver re-transplantation between 2007 and 2016 were selected from the Korean National Healthcare Insurance Service database. Liver transplant centers were categorized to either high-volume centers (≥ 64 LTs/year) or low-volume centers (< 64 LTs/year) according to the annual LT case volume. In-hospital and long-term mortality after liver re-transplantation were compared.

Results

A total of 258 liver re-transplantations were performed during the study period: 175 liver re-transplantations were performed in 3 high-volume centers and 83 were performed in 21 low-volume centers. In-hospital mortality after liver re-transplantation in high and low-volume centers were 25% and 36% ( P = 0.069), respectively. Adjusted in-hospital mortality was not different between low and high-volume centers. Adjusted 1-year mortality was significantly higher in low-volume centers (OR 2.14, 95% CI 1.05–4.37, P = 0.037) compared to high-volume centers. Long-term survival for up to 9 years was also superior in high-volume centers ( P = 0.005). Other risk factors of in-hospital mortality and 1-year mortality included female sex and higher Elixhauser comorbidity index.

Conclusion

Centers with higher case volume (≥ 64 LTs/year) showed lower in-hospital and overall mortality after liver re-transplantation compared to low-volume centers.

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Most cited references 41

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Coding algorithms for defining comorbidities in ICD-9-CM and ICD-10 administrative data.

Hude Quan, Vijaya Sundararajan, Patricia Halfon … (2005)

Implementation of the International Statistical Classification of Disease and Related Health Problems, 10th Revision (ICD-10) coding system presents challenges for using administrative data. Recognizing this, we conducted a multistep process to develop ICD-10 coding algorithms to define Charlson and Elixhauser comorbidities in administrative data and assess the performance of the resulting algorithms. ICD-10 coding algorithms were developed by "translation" of the ICD-9-CM codes constituting Deyo's (for Charlson comorbidities) and Elixhauser's coding algorithms and by physicians' assessment of the face-validity of selected ICD-10 codes. The process of carefully developing ICD-10 algorithms also produced modified and enhanced ICD-9-CM coding algorithms for the Charlson and Elixhauser comorbidities. We then used data on in-patients aged 18 years and older in ICD-9-CM and ICD-10 administrative hospital discharge data from a Canadian health region to assess the comorbidity frequencies and mortality prediction achieved by the original ICD-9-CM algorithms, the enhanced ICD-9-CM algorithms, and the new ICD-10 coding algorithms. Among 56,585 patients in the ICD-9-CM data and 58,805 patients in the ICD-10 data, frequencies of the 17 Charlson comorbidities and the 30 Elixhauser comorbidities remained generally similar across algorithms. The new ICD-10 and enhanced ICD-9-CM coding algorithms either matched or outperformed the original Deyo and Elixhauser ICD-9-CM coding algorithms in predicting in-hospital mortality. The C-statistic was 0.842 for Deyo's ICD-9-CM coding algorithm, 0.860 for the ICD-10 coding algorithm, and 0.859 for the enhanced ICD-9-CM coding algorithm, 0.868 for the original Elixhauser ICD-9-CM coding algorithm, 0.870 for the ICD-10 coding algorithm and 0.878 for the enhanced ICD-9-CM coding algorithm. These newly developed ICD-10 and ICD-9-CM comorbidity coding algorithms produce similar estimates of comorbidity prevalence in administrative data, and may outperform existing ICD-9-CM coding algorithms.

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A modification of the Elixhauser comorbidity measures into a point system for hospital death using administrative data.

P. Austin, Alison Jennings, Hude Quan … (2009)

Comorbidity measures are necessary to describe patient populations and adjust for confounding. In direct comparisons, studies have found the Elixhauser comorbidity system to be statistically slightly superior to the Charlson comorbidity system at adjusting for comorbidity. However, the Elixhauser classification system requires 30 binary variables, making its use for reporting and analysis of comorbidity cumbersome. Modify the Elixhauser classification system into a single numeric score for administrative data. For all hospitalizations at the Ottawa Hospital, Canada, between 1996 and 2008, we determined if International Classification of Disease codes for chronic diagnoses were in any of the 30 Elixhauser comorbidity groups. We then used backward stepwise multivariate logistic regression to determine the independent association of each comorbidity group with death in hospital. Regression coefficients were modified into a scoring system that reflected the strength of each comorbidity group's independent association with hospital death. Hospitalizations that were included were 345,795 (derivation: 228,565; validation 117,230). Twenty-one of the 30 groups were independently associated with hospital mortality. The resulting comorbidity score had an equivalent discrimination in the derivation and validation groups (overall c-statistic 0.763, 95% CI: 0.759-0.766). This was similar to models having all Elixhauser groups (0.760, 95% CI: 0.756-0.764) or significant groups only (0.759, 95% CI: 0.754-0.762), but significantly exceeded discrimination when comorbidity was expressed using the Charlson score (0.745, 95% CI: 0.742-0.749). When analyzing administrative data, the Elixhauser comorbidity system can be condensed to a single numeric score that summarizes disease burden and is adequately discriminative for death in hospital.

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Author and article information

Contributors

Seung-Young Oh: Role: ConceptualizationRole: InvestigationRole: MethodologyRole: Writing – original draftRole: Writing – review & editing

Eun Jin Jang: Role: Data curationRole: Formal analysisRole: Writing – original draftRole: Writing – review & editing

Ga Hee Kim: Role: Data curationRole: Formal analysisRole: Investigation

Hannah Lee: Role: ConceptualizationRole: InvestigationRole: Writing – review & editing

Nam-Joon Yi: Role: Writing – review & editing

Seokha Yoo:

ORCID: https://orcid.org/0000-0003-4679-6027

Role: Formal analysisRole: Writing – original draft

Bo Rim Kim: Role: MethodologyRole: Writing – review & editing

Ho Geol Ryu:

ORCID: https://orcid.org/0000-0001-8952-6049

Role: ConceptualizationRole: InvestigationRole: MethodologyRole: SupervisionRole: Writing – review & editing

Frank JMF Dor: Role: Editor

Journal

Journal ID (nlm-ta): PLoS One

Journal ID (iso-abbrev): PLoS One

Journal ID (publisher-id): plos

Title: PLoS ONE

Publisher: Public Library of Science (San Francisco, CA USA )

ISSN (Electronic): 1932-6203

Publication date (Electronic): 5 August 2021

Publication date Collection: 2021

Volume: 16

Issue: 8

Electronic Location Identifier: e0255655

Affiliations

[1 ] Critical Care Center, Seoul National University College of Medicine, Seoul National University Hospital, Seoul, Korea

[2 ] Department of Surgery, Seoul National University College of Medicine, Seoul National University Hospital, Seoul, Korea

[3 ] Department of Information Statistics, Andong National University, Gyeongsangbuk-do, Korea

[4 ] Department of Statistics, Kyungpook National University, Daegu, Korea

[5 ] Department of Anesthesiology, Seoul National University College of Medicine, Seoul National University Hospital, Seoul, Korea

Imperial College Healthcare NHS Trust, UNITED KINGDOM

Author notes

Competing Interests: The authors have declared that no competing interests exist.

* E-mail: hogeol@ 123456gmail.com

Author information

Seokha Yoo https://orcid.org/0000-0003-4679-6027

Ho Geol Ryu https://orcid.org/0000-0001-8952-6049

Article

Publisher ID: PONE-D-20-31343

DOI: 10.1371/journal.pone.0255655

PMC ID: 8341477

PubMed ID: 34351979

SO-VID: 0bb29c08-df57-4cf5-adee-dcef9a24aeeb

License:

This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

History

Date received : 5 October 2020

Date accepted : 21 July 2021

Page count

Figures: 3, Tables: 5, Pages: 15

Funding

The author(s) received no specific funding for this work.

Custom metadata

Data Availability The data used in this study are third party data from the National Health Insurance Service ( https://nhiss.nhis.or.kr/bd/ay/bdaya001iv.do) and can be accessed following the protocol outlined in the Methods section.

Association between hospital liver transplantation volume and mortality after liver re-transplantation

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Most cited references 41

Coding algorithms for defining comorbidities in ICD-9-CM and ICD-10 administrative data.

Comorbidity Measures for Use with Administrative Data

A modification of the Elixhauser comorbidity measures into a point system for hospital death using administrative data.

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