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Identifying a targeted population at high risk for infections after liver transplantation in the MELD era
Author(s) -
Sun HsinYun,
Cacciarelli Thomas V.,
Singh Nina
Publication year - 2011
Publication title -
clinical transplantation
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.918
H-Index - 76
eISSN - 1399-0012
pISSN - 0902-0063
DOI - 10.1111/j.1399-0012.2010.01262.x
Subject(s) - medicine , liver transplantation , transplantation , population , risk factor , liver disease , logistic regression , proportional hazards model , gastroenterology , dialysis , model for end stage liver disease , surgery , environmental health
Sun H‐Y, Cacciarelli TV, Singh N. Identifying a targeted population at high risk for infections after liver transplantation in the MELD era.
Clin Transplant 2011: 25: 420–425. © 2010 John Wiley & Sons A/S. Abstract: Impact of model for end‐stage liver disease (MELD) scoring system on post‐transplant infections and associated risk factors are unknown. Infections <90 d post‐transplant were assessed in 277 consecutive liver transplant recipients from 1999 to 2008. “High‐risk” factors for infections were pre‐defined as MELD score >30, ICU stay >48 h prior to transplant, intraoperative transfusion ≥15 units, retransplantation, post‐transplant dialysis, or reoperation. Of the 240 recipients in the MELD era (2002–2008), 48.5% had any high‐risk factor. The OR for infection was 1.69, 2.00, 18.00, and 4.50 in recipients with any 1, 2, 3, and ≥4 high‐risk factors, respectively ( χ 2 for trend, p < 0.001). In logistic regression model, recipient age (OR 1.12, p < 0.05) and any high‐risk factor (OR 2.42, p < 0.05) were associated with infections. Compared with 37 pre‐MELD recipients, the overall infections and mortality at 12 months did not differ in the two eras. In Cox regression model, recipient age (OR 1.09, p < 0.05) and any high‐risk factor (OR 2.42, p < 0.05) remained associated with infections. The overall frequency of infections did not increase in the MELD era. Pre‐defined risk factors accurately predicted the risk of infections in these patients.