Title |
Proteinuria can predict prognosis after liver transplantation
|
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Published in |
BMC Surgery, September 2016
|
DOI | 10.1186/s12893-016-0176-8 |
Pubmed ID | |
Authors |
Heng-Chih Pan, Ying-Jen Chen, Jhe-Ping Lin, Ming-Jung Tsai, Chang-Chyi Jenq, Wei-Chen Lee, Ming-Hung Tsai, Pei-Chun Fan, Chih-Hsiang Chang, Ming-Yang Chang, Ya-Chung Tian, Cheng-Chieh Hung, Ji-Tseng Fang, Chih-Wei Yang, Yung-Chang Chen |
Abstract |
Proteinuria is a manifestation of renal dysfunction and it has been demonstrated to be a significant prognostic factor in various clinical situations. The study was designed to analyze prognosis of patients receiving liver transplantation as well as to determine predictive performance of perioperative proteinuria. We retrospectively reviewed data of patients who had received a liver transplant in a medical center between 2002 and 2010. Demographic information and clinical characteristic parameters were recorded on the day of intensive care unit admission before operation and on postoperative days 1, 7, and 14. Among a total of 323 patients, in-hospital mortality and 90-day mortality rates were 13.0 % (42/323) and 14.2 % (46/323), respectively. Patients with proteinuria on admission had higher rates of acute kidney injury (26.8 % vs. 8.8 %, p < 0.001), severe infection episodes (48.8 % vs. 30.7 %, p = 0.023), hospital death (31.1 % vs. 10.1 %, p < 0.001), and 90-day mortality (37.7 % vs. 10.9 %, p < 0.001). Multivariate analysis showed that proteinuria on admission and Sequential Organ Failure Assessment (SOFA) score were independent predictors of in-hospital mortality. The discriminatory ability of proteinuria plus SOFA was even better than that of SOFA alone, especially on postoperative day 1. The presence of proteinuria before liver transplantation is supposed to be recognized as a negative predictor for in-hospital survival. Moreover, the presence of proteinuria after liver transplantation can assist in the early prediction of poor short-term prognosis for patients receiving liver transplantation. |
X Demographics
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Unknown | 2 | 100% |
Demographic breakdown
Type | Count | As % |
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Science communicators (journalists, bloggers, editors) | 1 | 50% |
Practitioners (doctors, other healthcare professionals) | 1 | 50% |
Mendeley readers
Geographical breakdown
Country | Count | As % |
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Unknown | 28 | 100% |
Demographic breakdown
Readers by professional status | Count | As % |
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Student > Ph. D. Student | 3 | 11% |
Student > Master | 3 | 11% |
Student > Bachelor | 2 | 7% |
Student > Doctoral Student | 2 | 7% |
Researcher | 2 | 7% |
Other | 7 | 25% |
Unknown | 9 | 32% |
Readers by discipline | Count | As % |
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Medicine and Dentistry | 12 | 43% |
Unspecified | 1 | 4% |
Nursing and Health Professions | 1 | 4% |
Biochemistry, Genetics and Molecular Biology | 1 | 4% |
Psychology | 1 | 4% |
Other | 1 | 4% |
Unknown | 11 | 39% |