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A MELD-Based Model to Determine Risk of Mortality Among Patients With Acute Variceal Bleeding
Ist Teil von
Gastroenterology (New York, N.Y. 1943), 2014-02, Vol.146 (2), p.412-419.e3
Ort / Verlag
United States: Elsevier Inc
Erscheinungsjahr
2014
Quelle
MEDLINE
Beschreibungen/Notizen
Background & Aims Patients with cirrhosis with acute variceal bleeding (AVB) have high mortality rates (15%–20%). Previously described models are seldom used to determine prognoses of these patients, partially because they have not been validated externally and because they include subjective variables, such as bleeding during endoscopy and Child–Pugh score, which are evaluated inconsistently. We aimed to improve determination of risk for patients with AVB. Methods We analyzed data collected from 178 patients with cirrhosis (Child–Pugh scores of A, B, and C: 15%, 57%, and 28%, respectively) and esophageal AVB who received standard therapy from 2007 through 2010. We tested the performance (discrimination and calibration) of previously described models, including the model for end-stage liver disease (MELD), and developed a new MELD calibration to predict the mortality of patients within 6 weeks of presentation with AVB. MELD-based predictions were validated in cohorts of patients from Canada (n = 240) and Spain (n = 221). Results Among study subjects, the 6-week mortality rate was 16%. MELD was the best model in terms of discrimination; it was recalibrated to predict the 6-week mortality rate with logistic regression (logit, -5.312 + 0.207 • MELD; bootstrapped R2 , 0.3295). MELD values of 19 or greater predicted 20% or greater mortality, whereas MELD scores less than 11 predicted less than 5% mortality. The model performed well for patients from Canada at all risk levels. In the Spanish validation set, in which all patients were treated with banding ligation, MELD predictions were accurate up to the 20% risk threshold. Conclusions We developed a MELD-based model that accurately predicts mortality among patients with AVB, based on objective variables available at admission. This model could be useful to evaluate the efficacy of new therapies and stratify patients in randomized trials.