Validation of days alive and out of hospital as a new patient-centered outcome to quantify life impact after heart transplantation

Abstract

The number of patients waiting for heart transplantation (HTX) is increasing. Thus, identification of outcome-relevant factors is crucial. This study aimed to identify perioperative factors associated with days alive and out of hospital (DAOH)-a patient-centered outcome to quantify life impact-after HTX. This retrospective cohort study screened 187 patients who underwent HTX at university hospital Duesseldorf, Germany from September 2010 to December 2020. The primary endpoint was DAOH at 1 year. Risk factors for mortality after HTX were assessed in univariate analysis. Variables with significant association were entered into multivariable quantile regression. In total, 175 patients were included into analysis. Median DAOH at 1 year was 295 (223-322) days. In univariate analysis the following variables were associated with reduced DAOH: recipient or donor diabetes pre-HTX, renal replacement therapy (RRT), VA-ECMO therapy, recipient body mass index, recipient estimated glomerular filtration rate (eGFR) and postoperative duration of mechanical ventilation. After adjustment, mechanical ventilation, RRT, eGFR and recipient diabetes showed significant independent association with DAOH. This study identified risk factors associated with reduced DAOH at 1-year after HTX. These findings might complement existing data for outcome of patients undergoing HTX.

Figure 1

Influence of categorical variables on DAOH. Univariate analysis for the association of 13 categorical variables with days alive and out of hospital (DAOH) at 1 year after heart transplantation (HTX).

Figure 2

Influence of continuous variables on DAOH. Univariate analysis for the association of 6 continuous variables with days alive and out of hospital (DAOH) at 1 year after heart transplantation (HTX). To visualize distribution of DAOH, all continuous variables were stratified by quartiles.

Figure 3

Association of variables with DAOH in multivariable quantile regression model. The figure shows the influence of selected variables on DAOH in a multivariable quantile regression model. Y-axis shows DAOH estimates while X-axis shows different quantiles. The black line represents parameter estimates at different regression quantiles. The Confidence intervals of quantile regression are presented in blue. Red lines represent parameter estimates and confidence interval of an ordinary linear regression with same variables. Duration of mechanical ventilation, renal replacement therapy (RRT), estimated glomerular filtration rate (eGFR), recipient diabetes mellitus (DM) were significantly associated with lower DAOH in the 10th and 20th percentile in this quantile regression model.