Dynamic prediction of left ventricular assist device pump thrombosis based on lactate dehydrogenase trends. (18th July 2019)

• Record Type: Journal Article
• Authors: Hurst, Thomas E.; Xanthopoulos, Andrew; Ehrlinger, John; Rajeswaran, Jeevanantham; Pande, Amol; Thuita, Lucy; Smedira, Nicholas G.; Moazami, Nader; Blackstone, Eugene H.; Starling, Randall C.
• Is Part Of: ESC heart failure. Volume 6:Number 5(2019)
• Page Start: 1005
• Page End: 1014
• Publication Date: 2019-07-18
• Subjects: Pump thrombosis -- Left ventricular assist device -- Lactate dehydrogenase -- Biomarker; Heart failure -- Periodicals; 616.129005
• DOI: 10.1002/ehf2.12473
• Languages: English
• ISSNs: 2055-5822

Abstract

Abstract: Aims: The risk of HeartMate II (HMII) left ventricular assist device (LVAD) thrombosis has been reported, and serum lactate dehydrogenase (LDH), a biomarker of haemolysis, increases secondary to LVAD thrombosis. This study evaluated longitudinal measurements of LDH post‐LVAD implantation, hypothesizing that LDH trends could timely predict future LVAD thrombosis. Methods and results: From October 2004 to October 2014, 350 HMIIs were implanted in 323 patients at Cleveland Clinic. Of these, patients on 339 HMIIs had at least one post‐implant LDH value (7996 total measurements). A two‐step joint model combining longitudinal biomarker data and pump thrombosis events was generated to assess the effect of changing LDH on thrombosis risk. Device‐specific LDH trends were first smoothed using multivariate boosted trees, and then used as a time‐varying covariate function in a multiphase hazard model to analyse time to thrombosis. Pre‐implant variables associated with time‐varying LDH values post‐implant using boostmtree were also investigated. Standardized variable importance for each variable was estimated as the difference between model‐based prediction error of LDH when the variable was randomly permuted and prediction error without permuting the values. The larger this difference, the more important a variable is for predicting the trajectory of post‐implant LDH. Thirty‐five HMIIs (10%) had either confirmed (18) or suspected (17) thrombosis, with 15 (43%) occurring within 3 months of implant. LDH was associated with thrombosis occurring both early and late after implant ( P < 0.0001 for both hazard phases). The model demonstrated increased probability of HMII thrombosis as LDH trended upward, with steep changes in LDH trajectory paralleling trajectories in probability of pump thrombosis. The most important baseline variables predictive of the longitudinal pattern of LDH were higher bilirubin, higher pre‐implant LDH, and older age. The effect of some pre‐implant variables such as sodium on the post‐implant LDH longitudinal pattern differed across time. Conclusions: Longitudinal trends in surveillance LDH for patients on HMII support are useful for dynamic prediction of pump thrombosis, both early after implant and late. Incorporating upward and downward trends in LDH that dynamically update a model of LVAD thrombosis risk provides a useful tool for clinical management and decisions.