肝病
肝移植
终末期肝病模型
医学
阶段(地层学)
内科学
移植
地质学
古生物学
作者
W. Ray Kim,Ajitha Mannalithara,Julie K. Heimbach,Patrick S. Kamath,Sumeet K. Asrani,Scott W. Biggins,Nicholas L. Wood,Sommer E. Gentry,Allison J. Kwong
出处
期刊:Gastroenterology
[Elsevier]
日期:2021-09-03
卷期号:161 (6): 1887-1895.e4
被引量:298
标识
DOI:10.1053/j.gastro.2021.08.050
摘要
Background & Aims
The Model for End-Stage Liver Disease (MELD) has been established as a reliable indicator of short-term survival in patients with end-stage liver disease. The current version (MELDNa), consisting of the international normalized ratio and serum bilirubin, creatinine, and sodium, has been used to determine organ allocation priorities for liver transplantation in the United States. The objective was to optimize MELD further by taking into account additional variables and updating coefficients with contemporary data. Methods
All candidates registered on the liver transplant wait list in the US national registry from January 2016 through December 2018 were included. Uni- and multivariable Cox models were developed to predict survival up to 90 days after wait list registration. Model fit was tested using the concordance statistic (C-statistic) and reclassification, and the Liver Simulated Allocation Model was used to estimate the impact of replacing MELDNa with the new model. Results
The final multivariable model was characterized by (1) additional variables of female sex and serum albumin, (2) interactions between bilirubin and sodium and between albumin and creatinine, and (3) an upper bound for creatinine at 3.0 mg/dL. The final model (MELD 3.0) had better discrimination than MELDNa (C-statistic, 0.869 vs 0.862; P < .01). Importantly, MELD 3.0 correctly reclassified a net of 8.8% of decedents to a higher MELD tier, affording them a meaningfully higher chance of transplantation, particularly in women. In the Liver Simulated Allocation Model analysis, MELD 3.0 resulted in fewer wait list deaths compared to MELDNa (7788 vs 7850; P = .02). Conclusion
MELD 3.0 affords more accurate mortality prediction in general than MELDNa and addresses determinants of wait list outcomes, including the sex disparity.
科研通智能强力驱动
Strongly Powered by AbleSci AI