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

JOURNAL OF HEART AND LUNG TRANSPLANTATION

卷:31

Article

Wang, Yajuan¹  Simon, Marc A.²  Bonde, Pramod²  Harris, Bronwyn U.³  Teuteberg, Jeffrey J.²  Kormos, Robert L.²  Antaki, James F.¹ 

[1] Carnegie Mellon Univ, Dept Biomed Engn, Pittsburgh, PA 15219 USA

[2] Univ Pittsburgh, Med Ctr, Inst Heart & Vasc, Pittsburgh, PA USA

[3] Stanford Med Ctr, Stanford, CA USA

关键词: left ventricular assist device;    right ventricular assist device;    right ventricular failure;    decision tree;    machine learning;    decision support;   

DOI  :  10.1016/j.healun.2011.11.003

来源: Elsevier

【 摘 要 】

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. J Heart Lung Transplant 2012;31:140-9 (C) 2012 International Society for Heart and Lung Transplantation. All rights reserved.

【 授权许可】

Free