BACKGROUND & AIMS: A slowed electroencephalogram (EEG) is indicative of the presence of hepatic encephalopathy (HE). Since HE is not reflected in the MELD score and is an important prognostic parameter, we assess the prognostic benefit of the addition of an EEG-based HE index to the MELD. METHODS: Three hundred and ninety-two patients with cirrhosis underwent EEG and automated determination of its mean dominant frequency (MDF). MELD was calculated at the time of EEG recording. Patients were monitored for up to 18 months in relation to the occurrence of death/transplantation. The prognostic value of the stand-alone/combined MELD and MDF was calculated using standard survival analysis techniques. Patients transplanted for hepatic decompensation were considered dead on the day of transplantation, those transplanted for hepatocellular carcinoma were censored. The findings were validated using a split-sample technique (reference group: n = 256; test group: n = 136). During the follow-up period, 107 patients died/were transplanted for hepatic decompensation. RESULTS: Both MELD and MDF predicted mortality on Kaplan-Meier analysis, and both were independent predictors of mortality on a Cox model. Based on Cox regression parameters, a novel prognostic index was devised, as follows: MELD-EEG = 0.087*MELD-0.306*MDF. On ROC curve analysis, MELD-EEG had higher prognostic accuracy in predicting 12- and 18-month mortality compared to MELD (P = 0.016 and P = 0.018, respectively). In addition, it had better sensitivity and reduced the misclassification rate for given levels of specificity. On validation, no significant differences were observed between the reference/test groups. CONCLUSIONS: The addition of an automatically obtained EEG-based index improves the prognostic accuracy of the MELD score.
BACKGROUND & AIMS: A slowed electroencephalogram (EEG) is indicative of the presence of hepatic encephalopathy (HE). Since HE is not reflected in the MELD score and is an important prognostic parameter, we assess the prognostic benefit of the addition of an EEG-based HE index to the MELD. METHODS: Three hundred and ninety-two patients with cirrhosis underwent EEG and automated determination of its mean dominant frequency (MDF). MELD was calculated at the time of EEG recording. Patients were monitored for up to 18 months in relation to the occurrence of death/transplantation. The prognostic value of the stand-alone/combined MELD and MDF was calculated using standard survival analysis techniques. Patients transplanted for hepatic decompensation were considered dead on the day of transplantation, those transplanted for hepatocellular carcinoma were censored. The findings were validated using a split-sample technique (reference group: n = 256; test group: n = 136). During the follow-up period, 107 patients died/were transplanted for hepatic decompensation. RESULTS: Both MELD and MDF predicted mortality on Kaplan-Meier analysis, and both were independent predictors of mortality on a Cox model. Based on Cox regression parameters, a novel prognostic index was devised, as follows: MELD-EEG = 0.087*MELD-0.306*MDF. On ROC curve analysis, MELD-EEG had higher prognostic accuracy in predicting 12- and 18-month mortality compared to MELD (P = 0.016 and P = 0.018, respectively). In addition, it had better sensitivity and reduced the misclassification rate for given levels of specificity. On validation, no significant differences were observed between the reference/test groups. CONCLUSIONS: The addition of an automatically obtained EEG-based index improves the prognostic accuracy of the MELD score.
Authors: Kavish R Patidar; Leroy R Thacker; James B Wade; Richard K Sterling; Arun J Sanyal; Mohammad S Siddiqui; Scott C Matherly; R Todd Stravitz; Puneet Puri; Velimir A Luketic; Michael Fuchs; Melanie B White; Nicole A Noble; Ariel B Unser; HoChong Gilles; Douglas M Heuman; Jasmohan S Bajaj Journal: Am J Gastroenterol Date: 2014-09-02 Impact factor: 10.864
Authors: Reem Satti; Noor-Ul-Hoda Abid; Matteo Bottaro; Michele De Rui; Maria Garrido; Mohammad R Raoufy; Sara Montagnese; Ali R Mani Journal: Front Physiol Date: 2019-02-19 Impact factor: 4.566