233 Contemporary Continuous Flow Devices: How Much Does It Cost To Keep a Patient on Support for One Year?

Goldstein, Dalit; Sileo, A.; Baker, Laura L.; Vandevoort, K.; Cotter, Paul

doi:10.1016/j.healun.2012.01.240

Cited by 3 publications

(2 citation statements)

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“…Recently, Goldstein et al 39 simply calculated all direct and indirect costs through 1 year in 20 recipients. For ease of analysis, LVAD costs were segmented by phase of care: initial admission (preimplant, implant, postimplant) and postdischarge, including readmissions.…”

Section: Continuous Flow Lvads As Destination Therapymentioning

confidence: 99%

Cost of Ventricular Assist Devices

2013

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Section: Continuous Flow Lvads As Destination Therapymentioning

confidence: 99%

Cost of Ventricular Assist Devices

2013

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“…education level) further variables were eliminated that would cause over-fitting the model to the data. In the present study, this was measured by information gain (24), which is based on the same principle as decision trees. This process provides ranking of variables based on their influence on outcome.…”

Section: Methodsmentioning

confidence: 99%

A Bayesian Model to Predict Right Ventricular Failure Following Left Ventricular Assist Device Therapy

Loghmanpour

Kormos

Kanwar

et al. 2016

JACC: Heart Failure

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Background Right ventricular failure (RVF) continues to be a major adverse event following left ventricular assist device (LVAD) implantation. This study investigates the use of a Bayesian statistical model to address the limited predictive capacity of existing risk scores derived from multivariate analyses. This is based on the hypothesis that it is necessary to consider the inter-relationships and conditional probabilities amongst independent variables to achieve sufficient statistical accuracy. Methods The data used for this study was derived from 10,909 adult patients from INTERMACS who had a primary LVAD from December 2006 – March 2014. An initial set of 176 pre-implant variables were considered. RVF post-implant was categorized as acute (<48 hours), early (48 hours–14 days) and late (>14 days) in onset. For each of these endpoints, a separate tree-augmented Naïve Bayes model was constructed using the most predictive variables using an open source Bayesian inference engine (SMILE.) Results The acute RVF model consisted of 33 variables, including: systolic pulmonary artery pressure (PAP), white blood cell count, left ventricular ejection fraction, cardiac index, sodium levels, and lymphocyte percentage. The early RVF model consisted of 34 variables, including systolic PAP, pre-albumin, LDH, INTERMACS profile, right ventricular ejection fraction, pro-B-type natriuretic peptide, age, heart rate, tricuspid regurgitation and BMI. The late RVF model included 33 variables and was mostly predicted by peripheral vascular resistance, MELD score, albumin, lymphocyte percentage, mean PAP and diastolic PAP. The accuracies of all the Bayesian models were between 91–97%, AUC between 0.83–0.90 sensitivity of 90% and specificity between 98–99%, significantly outperforming previously published risk scores. Conclusion A Bayesian prognostic model of RVF, based on the large, multi-center INTERMACS registry provided highly accurate predictions of acute, early, and late RVF based on preoperative variables. These models may facilitate clinical decision-making while screening candidates for LVAD therapy.

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The Economics of Long-Term Ventricular Assist Device Therapy for Patients with End-Stage Heart Failure

Bostic

2013

Ventricular Assist Devices in Advanced-Stage Heart Failure

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233 Contemporary Continuous Flow Devices: How Much Does It Cost To Keep a Patient on Support for One Year?

Cited by 3 publications

References 0 publications

Cost of Ventricular Assist Devices

Cost of Ventricular Assist Devices

A Bayesian Model to Predict Right Ventricular Failure Following Left Ventricular Assist Device Therapy

The Economics of Long-Term Ventricular Assist Device Therapy for Patients with End-Stage Heart Failure

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