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Decision tree for adjuvant right ventricular support in patients receiving a left ventricular assist device

Elsevier Inc.
DOI: 10.1016/j.healun.2011.11.003
  • Left Ventricular Assist Device
  • Right Ventricular Assist Device
  • Right Ventricular Failure
  • Decision Tree
  • Machine Learning
  • Decision Support
  • Mathematics
  • Medicine


Background Right ventricular (RV) failure is a significant complication after implantation of a left ventricular assist device (LVAD). It is therefore important to identify patients at risk a priori. However, prognostic models derived from multivariate analyses have had limited predictive power. Methods This study retrospectively analyzed the records of 183 LVAD recipients between May 1996 and October 2009; of these, 27 later required a RVAD (RVAD+) and 156 remained on LVAD only (RVAD−) until transplant or death. A decision tree model was constructed to represent combinatorial non-linear relationships of the pre-operative data that are predictive of the need for RVAD support. Results An optimal set of 8 pre-operative variables were identified: transpulmonary gradient, age, right atrial pressure, international normalized ratio, heart rate, white blood cell count, alanine aminotransferase, and the number of inotropic agents. The resultant decision tree, which consisted of 28 branches and 15 leaves, identified RVAD+ patients with 85% sensitivity, RVAD− patients with 83% specificity, and exhibited an area under the receiver operating characteristic curve of 0.87. Conclusions The decision tree model developed in this study exhibited several advantages compared with existing risk scores. Quantitatively, it provided improved prognosis of RV support by encoding the non-linear, synergic interactions among pre-operative variables. Because of its intuitive structure, it more closely mimics clinical reasoning and therefore can be more readily interpreted. Further development with additional multicenter, longitudinal data may provide a valuable prognostic tool for triage of LVAD therapy and, potentially, improve outcomes.

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