Falletta, Calogero

Background: Identifying candidates for left ventricular assist device surgery at risk of right ventricular failure remains difficult. The aim was to identify the most accurate predictors of right ventricular failure among clinical, biological, and imaging markers, assessed by agreement of different supervised machine learning algorithms. Methods: Seventy-four patients, referred to HeartWare left ventricular assist device since 2010 in two Italian centers, were recruited. Biomarkers, right ventricular standard, and strain echocardiography, as well as cath-lab measures, were compared among patients who did not develop right ventricular failure (N = 56), those with acute–right ventricular failure (N = 8, 11%) or chronic–right ventricular failure (N = 10, 14%). Logistic regression, penalized logistic regression, linear support vector machines, and naïve Bayes algorithms with leave-one-out validation were used to evaluate the efficiency of any combination of three collected variables in an “all-subsets” approach. Results: Michigan risk score combined with central venous pressure assessed invasively and apical longitudinal systolic strain of the right ventricular–free wall were the most significant predictors of acute–right ventricular failure (maximum receiver operating characteristic–area under the curve = 0.95, 95% confidence interval = 0.91–1.00, by the naïve Bayes), while the right ventricular–free wall systolic strain of the middle segment, right atrial strain (QRS-synced), and tricuspid annular plane systolic excursion were the most significant predictors of Chronic-RVF (receiver operating characteristic–area under the curve = 0.97, 95% confidence interval = 0.91–1.00, according to naïve Bayes). Conclusion: Apical right ventricular strain as well as right atrial strain provides complementary information, both critical to predict acute–right ventricular failure and chronic–right ventricular failure, respectively.

Abstract Right ventricular failure (RVF) after left ventricular assist device (LVAD) implantation is associated with increased morbidity and mortality, but the identification of LVAD candidates at risk for RVF remains challenging. We undertook a systematic review and meta-analysis of observational studies of risk factors associated with RVF after LVAD implant. Thirty-six studies published between 1 January 1995 and 30 April 2015, comprising 995 RVF patients out of a pooled final population of 4428 patients, were identified. Meta-analysed prevalence of post-LVAD RVF was 35%. A need for mechanical ventilation [odds ratio (OR) 2.99], or continuous renal replacement therapy (CRRT; OR 4.61, area under the curve 0.78, specificity 0.91) were the clinical variables with the highest effect size (ES) in predicting RVF. International normalized ratio [INR; standardized mean difference (SMD) 0.49] and N-terminal pro-brain natriuretic peptide (NT-proBNP) (SMD 0.52) were the biochemical markers that best discriminated between RVF and No-RVF populations, though NT-proBNP was highly heterogeneous. Right ventricular stroke work index (RVSWI) and central venous pressure (CVP) (SMD −0.58 and 0.47, respectively) were the haemodynamic measures with the highest ES in identifying patients at risk of post-LVAD RVF; CVP was particularly useful in risk stratifying patients undergoing continuous-flow LVAD implant (SMD 0.59, P < 0.001, I2 = 20.9%). Finally, pre-implant moderate to severe right ventricular (RV) dysfunction, as assessed qualitatively (OR 2.82), or a greater RV/LV diameter ratio (SMD 0.51) were the standard echocardiographic measurements with the highest ES in comparing RVF with No-RVF patients. Longitudinal systolic strain of the RV free wall had the highest ES (SMD 0.73) but also the greatest heterogeneity (I2 = 74%) and was thus only marginally significant (P = 0.05). Patients on ventilatory support or CRRT are at high risk for post-LVAD RVF, similarly to patients with slightly increased INR, high NT-proBNP or leukocytosis. High CVP, low RVSWI, an enlarged right ventricle with concomitant low RV strain also identify patients at higher risk.