Purpose Right heart failure (RHF) continues to be a major adverse event post LVAD implantation. The risk of developing RHF post implant is multifactorial and difficult to predict. We have previously published Bayesian models to predict early and late RHF. In this analysis, we focused on pre-operative variables that predicted severe acute RHF as defined in INTERMACS as 'need for RVAD following LVAD or death during the VAD implants hospitalization with RHF as the primary cause'. Methods We used INTERMACS data from continuous flow LVADs from 2012-2016 (n=2,661). The model using pre-implant variables was made using Tree Augmented Naïve Bayes Analysis (GeNie) and predictors ranked by their diagnostic value. The impact of these variables on each other as well as the final outcome (RHF) were noted. Results Post LVAD implantation, severe acute RHF was noted in 126 patients (4.7%). Vast majority of patients in INTERMACS were males (80%) with over 50% patients implanted as destination therapy. 34 pre-implant variables, including both modifiable and non-modifiable, were found to impact the risk of post-implant acute severe RHF. (Figure 1) Key factors included INTERMACS Profile, major clinical event pre-implant (ECMO, IABP, mechanical ventilation), inotropes, platelets, albumin and AST. The performance assessment using areas under the curve ROC was 0.79 for the derived model. Conclusion By using a Bayesian approach, we explored pre-implant factors that were predictive of post-operative acute severe RHF. This would allow further risk stratification for patients undergoing LVAD implantation. Right heart failure (RHF) continues to be a major adverse event post LVAD implantation. The risk of developing RHF post implant is multifactorial and difficult to predict. We have previously published Bayesian models to predict early and late RHF. In this analysis, we focused on pre-operative variables that predicted severe acute RHF as defined in INTERMACS as 'need for RVAD following LVAD or death during the VAD implants hospitalization with RHF as the primary cause'. We used INTERMACS data from continuous flow LVADs from 2012-2016 (n=2,661). The model using pre-implant variables was made using Tree Augmented Naïve Bayes Analysis (GeNie) and predictors ranked by their diagnostic value. The impact of these variables on each other as well as the final outcome (RHF) were noted. Post LVAD implantation, severe acute RHF was noted in 126 patients (4.7%). Vast majority of patients in INTERMACS were males (80%) with over 50% patients implanted as destination therapy. 34 pre-implant variables, including both modifiable and non-modifiable, were found to impact the risk of post-implant acute severe RHF. (Figure 1) Key factors included INTERMACS Profile, major clinical event pre-implant (ECMO, IABP, mechanical ventilation), inotropes, platelets, albumin and AST. The performance assessment using areas under the curve ROC was 0.79 for the derived model. By using a Bayesian approach, we explored pre-implant factors that were predictive of post-operative acute severe RHF. This would allow further risk stratification for patients undergoing LVAD implantation.
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