Objective: We aim to detect over-time variations in mortality of liver transplant recipients stratified by the period of transplant. Since this is a retrospective investigation, bias reduction caused by possible confo...Objective: We aim to detect over-time variations in mortality of liver transplant recipients stratified by the period of transplant. Since this is a retrospective investigation, bias reduction caused by possible confounding effects can be achieved by using propensity score weighting in a multivariate logistic regression model. Methods: Medical charts of all adult liver transplant recipients (n = 250) who were transplanted in three periods 2005-2009, 2010-2014 and 2015-2019 were retrospectively reviewed. The following recipient factors were analyzed: recipients and donors’ ages, sex, renal impairment, body mass index (BMI), presence of bacterial or viral infections, MELD (Model for end-stage diseases). Multivariate logistic model adjusted by Propensity Scores (PS) was used to identify the effect of the risk factors on mortality, and death within five years, in the targeted time frame. Patient outcomes are recorded as;(patient status = 1 if dead, or patient status = 0 if alive). Results: Meld score, recipient age, and renal impairments were shown to be predictors of mortality in transplanted patients. Multivariate regression model was used to identify the significance of the specified risk factors, followed by pairwise comparisons between periods. Pairwise comparisons between periods using logistic regression weighted by the inverse propensity score, correcting for the possible confounding effect of measured covariates showed that the death rate is significantly reduced in subsequent periods as compared to the initial period. Conclusions: The clinical implications of these findings are the ability to stratify patients at high risk of posttransplant death by planning more intensive and accurate management for them.展开更多
文摘Objective: We aim to detect over-time variations in mortality of liver transplant recipients stratified by the period of transplant. Since this is a retrospective investigation, bias reduction caused by possible confounding effects can be achieved by using propensity score weighting in a multivariate logistic regression model. Methods: Medical charts of all adult liver transplant recipients (n = 250) who were transplanted in three periods 2005-2009, 2010-2014 and 2015-2019 were retrospectively reviewed. The following recipient factors were analyzed: recipients and donors’ ages, sex, renal impairment, body mass index (BMI), presence of bacterial or viral infections, MELD (Model for end-stage diseases). Multivariate logistic model adjusted by Propensity Scores (PS) was used to identify the effect of the risk factors on mortality, and death within five years, in the targeted time frame. Patient outcomes are recorded as;(patient status = 1 if dead, or patient status = 0 if alive). Results: Meld score, recipient age, and renal impairments were shown to be predictors of mortality in transplanted patients. Multivariate regression model was used to identify the significance of the specified risk factors, followed by pairwise comparisons between periods. Pairwise comparisons between periods using logistic regression weighted by the inverse propensity score, correcting for the possible confounding effect of measured covariates showed that the death rate is significantly reduced in subsequent periods as compared to the initial period. Conclusions: The clinical implications of these findings are the ability to stratify patients at high risk of posttransplant death by planning more intensive and accurate management for them.
