Dataset Information

Dynamic Assessment of Pulmonary Artery Pulsatility Index Provides Incremental Risk Assessment for Early Right Ventricular Failure After Left Ventricular Assist Device.

ABSTRACT:

Background

The pulmonary artery pulsatility index (PAPi) has been studied to predict right ventricular failure (RVF) after left ventricular assist device (LVAD) implantation, but only as a single time point before LVAD implantation. Multiple clinical factors and therapies impact RV function in pre-LVAD patients. Thus, we hypothesized that serial PAPi measurements during cardiac intensive care unit (CICU) optimization before LVAD implantation would provide incremental risk stratification for early RVF after LVAD implantation.

Methods and results

Consecutive patients who underwent sequential pulmonary artery catherization with cardiac intensive care optimization before durable LVAD implantation were included. Serial hemodynamics were reviewed retrospectively across the optimization period. The optimal PAPi was defined by the initial PAPi + the PAPi at optimized hemodynamics. RVF was defined as need for a right ventricular assist device or prolonged inotrope use (>14 days postoperatively). Patients with early RVF had significantly lower mean optimal PAPi (3.5 vs 7.5, P < .001) compared with those who did not develop RVF. After adjusting for established risk factors of early RVF after LVAD implantation, the optimal PAPi was independently and incrementally associated with early RVF after LVAD implantation (odds ratio 0.64, 95% confidence interval 0.532-0.765, P < .0001).

Conclusions

Optimal PAPi achieved during medical optimization before LVAD implantation provides independent and incremental risk stratification for early RVF, likely identifying dynamic RV reserve.

SUBMITTER: Gonzalez MH

PROVIDER: S-EPMC10257976 | biostudies-literature | 2021 Jul

REPOSITORIES: biostudies-literature

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Publications

Dynamic Assessment of Pulmonary Artery Pulsatility Index Provides Incremental Risk Assessment for Early Right Ventricular Failure After Left Ventricular Assist Device.

Gonzalez Matthew H MH Wang Qiuqing Q Yaranov Dmitry M DM Albert Chony C Wolski Kathy K Wagener John J Aggarwal Appas A Menon Venu V Jacob Miriam M Tang Wilson W Hsich Eileen E Taylor David D Tong Michael M Soltesz Edward E Estep Jerry D JD Starling Randall C RC Joyce Emer E

Journal of cardiac failure 20210225 7

<h4>Background</h4>The pulmonary artery pulsatility index (PAPi) has been studied to predict right ventricular failure (RVF) after left ventricular assist device (LVAD) implantation, but only as a single time point before LVAD implantation. Multiple clinical factors and therapies impact RV function in pre-LVAD patients. Thus, we hypothesized that serial PAPi measurements during cardiac intensive care unit (CICU) optimization before LVAD implantation would provide incremental risk stratification ...[more]

PMID: 33640481

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Project description:ObjectivesThis 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 interrelationships and conditional probabilities among independent variables to achieve sufficient statistical accuracy.BackgroundRight ventricular failure (RVF) continues to be a major adverse event following left ventricular assist device (LVAD) implantation.MethodsData used for this study were derived from 10,909 adult patients from the Inter-Agency Registry for Mechanically Assisted Circulatory Support (INTERMACS) who had a primary LVAD implanted between December 2006 and March 2014. An initial set of 176 pre-implantation variables were considered. RVF post-implant was categorized as acute (<48 h), early (48 h to 14 daysays), 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 employing an open source Bayesian inference engine.ResultsThe 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, lactate dehydrogenase level, INTERMACS profile, right ventricular ejection fraction, pro-B-type natriuretic peptide, age, heart rate, tricuspid regurgitation, and body mass index. The late RVF model included 33 variables and was predicted mostly by peripheral vascular resistance, model for end-stage liver disease score, albumin level, lymphocyte percentage, and mean and diastolic PAP. The accuracy of all Bayesian models was between 91% and 97%, with an area under the receiver operator characteristics curve between 0.83 and 0.90, sensitivity of 90%, and specificity between 98% and 99%, significantly outperforming previously published risk scores.ConclusionsA Bayesian prognostic model of RVF based on the large, multicenter INTERMACS registry provided highly accurate predictions of acute, early, and late RVF on the basis of pre-operative variables. These models may facilitate clinical decision making while screening candidates for LVAD therapy.

Dataset Information

Dynamic Assessment of Pulmonary Artery Pulsatility Index Provides Incremental Risk Assessment for Early Right Ventricular Failure After Left Ventricular Assist Device.

Background

Methods and results

Conclusions

Publications

Dynamic Assessment of Pulmonary Artery Pulsatility Index Provides Incremental Risk Assessment for Early Right Ventricular Failure After Left Ventricular Assist Device.

Similar Datasets

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