Secondary aims were to identify differences between predicted and observed 90-days mortality using MELD and MELDNa scores at the time of listing. Among 126 patients included in this study, waiting list mortality was 35.0%. Ninety-day mortality was 21.1%, which was significantly
greater than the mortality estimated by the MELD (9.1%, 95% CI: 6.6-11.5) and MELDNa (9.3%, 95%CI: 6.0-12.5). Despite this underestimation, AUC for MELD and MELDNa was 0.80 and 0.78 respectively. In our study, independent predictors of waiting list mortality were age, diagnosis of cholestatic disease and residence over 500 km from our transplant centre. MELD and MELDNa underestimated the 90-day mortality MAPK inhibitor in patients with liver failure living in rural areas. Validation of these models should be performed in other transplant centres serving patients with limited access to specialized services.”
“Background: An increasing amount of studies report mapping algorithms which predict EQ-5 D utility values using disease specific non-preference-based measures. Selleckchem HKI 272 Yet many mapping algorithms have been found to systematically overpredict EQ-5 D utility values
for patients in poor health. Currently there are no guidelines on how to deal with this problem. This paper is concerned with the question of why overestimation of EQ-5 D utility values occurs for patients in poor health, and explores possible solutions.
Method: Three existing datasets are used to estimate mapping algorithms and assess existing mapping Selleckchem MK 1775 algorithms from the literature mapping the cancer-specific EORTC-QLQ C-30 and the arthritis-specific Health Assessment Questionnaire
(HAQ) onto the EQ-5 D. Separate mapping algorithms are estimated for poor health states. Poor health states are defined using a cut-off point for QLQ-C30 and HAQ, which is determined using association with EQ-5 D values.
Results: All mapping algorithms suffer from overprediction of utility values for patients in poor health. The large decrement of reporting ‘extreme problems’ in the EQ-5 D tariff, few observations with the most severe level in any EQ-5 D dimension and many observations at the least severe level in any EQ-5 D dimension led to a bimodal distribution of EQ-5 D index values, which is related to the overprediction of utility values for patients in poor health. Separate algorithms are here proposed to predict utility values for patients in poor health, where these are selected using cut-off points for HAQ-DI (> 2.0) and QLQ C-30 (< 45 average of QLQ C-30 functioning scales). The QLQ-C30 separate algorithm performed better than existing mapping algorithms for predicting utility values for patients in poor health, but still did not accurately predict mean utility values. A HAQ separate algorithm could not be estimated due to data restrictions.
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