- Email: [email protected]
Clinical Impact of Right Ventricular Diastolic Patterns in Idiopathic Pulmonary Arterial Hypertension by Speckle Traiking
Abstracts treatments for severe PH pre-CTx are needed to improve outcome in these high-risk patients. 1228 Severe Pulmonary Arterial Hypertension Treated with ABI-009, nabSirolimus, an mTOR Inhibitor M. Simon,1 M. Gomberg-Maitland,2 R.J. Oudiz,3 R. Machado,4 F. Rischard,5 J.M. Elinoff,6 B. Grigorian,7 A.N. Schmid,7 S. Hou,7 N. Desai,7 and M. Gladwin.1 1Pittsburgh Heart, Lung, Blood and Vascular Medicine Institute, University of Pittsburgh, Pittsburgh, PA; 2MSc. Inova Heart and Vascular Institute, VCU Medicine, Falls Church, VA; 3LA Biomedical Research Institute, Harbor-UCLA Medical Center, Los Angeles, CA; 4Indiana University, Indianapolis, IN; 5Department of Medicine, University of Arizona, Tucson, AZ; 6The NIH Clinical Center, Bethesda, MD; and the 7Aadi Bioscience, Pacific Palisades, CA. Purpose: mTOR signaling is markedly upregulated in cells from patients with pulmonary arterial hypertension (PAH). Importantly, sirolimus at high dose both prevented and reversed smooth muscle cell proliferation in a rat model of PAH (Houssaini 2013). ABI-009, albumin-bound sirolimus nanoparticles, has improved pharmacokinetics and biodistribution compared to oral sirolimus and is a promising novel treatment approach in patients with severe PAH. Methods: This ongoing phase 1 study investigates the safety and efficacy of ABI-009 given as a weekly IV infusion for 16 weeks at dose levels of 1, 2.5, 5, and 10 mg/m2. Patients with PAH and WHO functional class (FC) III symptoms despite treatment with ≥2 PAH-specific therapies are eligible for enrollment. Primary endpoints are dose-limiting toxicities (DLTs) and adverse events. Secondary endpoints are changes in WHO FC, 6-minute walk distance (6MWD), and hemodynamics from baseline. Exploratory endpoints include sirolimus trough levels and biomarkers. Up to 22 patients will be enrolled in the dose-finding portion, using a 3+3 design. Results: Six patients have received ABI-009 and enrollment is ongoing. Four patients were treated at the original starting dose of 10 mg/m2: 1 without any DLT, 2 patients required dose reductions to 5 mg/m2 due to a DLT (rash at week 5 and paresthesia at week 7, respectively), and 1 patient discontinued treatment at week 8 due to cellulitis. Due to the DLTs at 10 mg/m2, the next cohort of patients were enrolled at 1 mg/ m2 dose level. Thus far, 2 patients have completed 16 weeks of ABI009 at 1 mg/m2 without significant safety concerns. Descriptive functional measures and hemodynamics at baseline and end-of treatment (EOT) were available for 5 patients who completed the 16-week therapy. WHO FC decreased from III to II in 3/5 patients; 6MWD increased (median [range]) from baseline 318 [290 - 378] m to EOT 425 [299 - 490] m (3/5 pts increased by >130 m); PVR decreased 616 [443 - 1011] to 498 [351 - 664] dyn.sec/cm5 (2/5 pts decreased by >30%); cardiac index increased 2.7 [1.8 -3.6] to 3.0 [2.6 - 3.8] L/min/ m2 (3/5 pts increased by >40%); NTproBNP decreased 1041 [179 -2485] to 492 [117 - 2510] pg/ml (3/5 pts decreased by >30%). Conclusion: The safety and functional and hemodynamic outcomes in this preliminary analysis support the ongoing clinical investigation of ABI-009 in patients with severe PAH. NCT02587325 1229 Clinical Impact of Right Ventricular Diastolic Patterns in Idiopathic Pulmonary Arterial Hypertension by Speckle Traiking R. Badagliacca,1 B. Pezzuto,1 S. Papa,1 R. Poscia,1 G. Manzi,1 A. Pascaretta,1 R. Torre,1 G. Casu,2 S. Sciomer,1 F. Fedele,1 R. Naeije,3 and C. Vizza.1 1Dept. of Cardiovascular and Respiratory Science, Univ of Rome Sapienza, Rome, Italy; 2Dept. of Cardiology, Dept. of Cardiology, Nuoro, Italy; and the 3Dept. of Cardiology, Erasme University Hospital, Brussels, Belgium. Purpose: Aim of this study was to describe strain-derived right ventricular (RV) diastolic patterns by speckle-tracking echocardiography (STE) and its clinical impact in idiopathic pulmonary arterial hypertension (IPAH). STE of the RV has been extensively described in PAH. However, diastolic function has been yet underlooked (neglected) for no reason.
S487 Methods: In 108 consecutive IPAH patients we identified three distinct strain-derived patterns from the mid-basal RV free wall segments. Each patient underwent baseline clinical, hemodynamic and complete echocardiographic evaluation and followed-up for clinical worsening occurrence. Results: The three strain-derived diastolic patterns were characterized by high reproducibility (Cohen’s k=0.64, p=0.0001). Pattern 1 was associated with mild pulmonary hypertension and preserved clinical and RV function (preserved RV phenotype). This pattern was repetitively found in a cohort of 30 healthy subjects. Pattern 2 was associated with moderate to severe pulmonary hypertension, WHO functional class II and III, still preserved RV function (RV adaptive phenotype). Pattern 3 was associated with advanced stage of IPAH, characterized by high right atrial pressure, low cardiac index and severe RV remodeling (RV maladaptive phenotype). Multivariable models for clinical worsening (CW) prediction demonstrated that the addition of RV diastolic patterns to clinical and hemodynamic variables significantly increased the prognostic power of the model (0.79 vs 0.66; p<0.001). Freedom from CW rates at 1 and 2 years from baseline were, respectively, 100% and 93% for Pattern 1; 80% and 55% for Pattern 2; 60% and 33% for Pattern 3. Conclusion: The results of the present study suggest that using speckle tracking echocardiography we can identify three phenotypically distinct, reproducible and clinically meaningful RV strain-derived diastolic patterns. 1230 Derivation of a Bayesian Network Model from an Existing Risk Score Calculator for Pulmonary Arterial Hypertension J. Kraisangka,1 L.C. Lohmueller,2 M.K. Kanwar,3 C. Zhao,4 M.J. Druzdzel,1 J.F. Antaki,5 M.A. Simon,6 and R.L. Benza.3 1School of Computing and Information, University of Pittsburgh, Pittsburgh, PA; 2 Language Technologies Institute, Carnegie Mellon University, Pittsburgh, PA; 3Cardiovascular Institute, Allegheny General Hospital, Pittsburgh, PA; 4Actelion Pharmaceuticals US, Inc., South San Francisco, CA; 5Meinig School of Biomedical Engineering, Cornell University, Ithaca, NY; and the 6Department of Medicine, University of Pittsburgh, Pittsburgh, PA. Purpose: We propose an alternative approach to the extensively validated REVEAL risk score calculator using Bayesian network (BN) modeling. We derived a BN model with the same variables and discretization cut points as the REVEAL risk score calculator and data from the REVEAL registry. This study compared the performance and relative impact of the variables in the two PAH risk assessment tools. Methods: 2,456 adult patients from the REVEAL registry were used to develop a Bayesian network model to predict 1-year survival using the Tree Augmented Naive Bayes (TAN) algorithm. We used 10-fold cross validation to measure the BN performance, reported as the area under the Receiver Operating Characteristic curve (AUC). We compared hazard ratios of the variables in the REVEAL calculator to cross entropy between risk factors and the outcome variable in the BN model. Cross-entropy measures the expected change in entropy of the probability distribution of the outcome variable as the risk becomes known and is generally a measure of expected information gain from knowing a risk. Cross-entropy is a dynamic measure and changes as other variables are observed. Results: The BN model demonstrated an AUC of 0.77 for predicting one-year survival. This was an improvement to the existing AUC of 0.71 for the original REVEAL calculator. There is a high correlation (r = 0.786) for the hazard ratio and the cross entropy, when all risk factors are absent (Figure 1a). In the same context of variables, the cross-entropy and the hazard ratio capture the similar influences to the outcome. However, when the risk factors are partially known for a given patient (Figure 1b), the cross-entropy changes based on the context of which risk factor are observed. Conclusion: BN demonstrated a modest improvement in performance over the REVEAL 1.0 model. Moreover, it improved the understandability of the relationship between risk factors, the dynamic influences
S487 Methods: In 108 consecutive IPAH patients we identified three distinct strain-derived patterns from the mid-basal RV free wall segments. Each patient underwent baseline clinical, hemodynamic and complete echocardiographic evaluation and followed-up for clinical worsening occurrence. Results: The three strain-derived diastolic patterns were characterized by high reproducibility (Cohen’s k=0.64, p=0.0001). Pattern 1 was associated with mild pulmonary hypertension and preserved clinical and RV function (preserved RV phenotype). This pattern was repetitively found in a cohort of 30 healthy subjects. Pattern 2 was associated with moderate to severe pulmonary hypertension, WHO functional class II and III, still preserved RV function (RV adaptive phenotype). Pattern 3 was associated with advanced stage of IPAH, characterized by high right atrial pressure, low cardiac index and severe RV remodeling (RV maladaptive phenotype). Multivariable models for clinical worsening (CW) prediction demonstrated that the addition of RV diastolic patterns to clinical and hemodynamic variables significantly increased the prognostic power of the model (0.79 vs 0.66; p<0.001). Freedom from CW rates at 1 and 2 years from baseline were, respectively, 100% and 93% for Pattern 1; 80% and 55% for Pattern 2; 60% and 33% for Pattern 3. Conclusion: The results of the present study suggest that using speckle tracking echocardiography we can identify three phenotypically distinct, reproducible and clinically meaningful RV strain-derived diastolic patterns. 1230 Derivation of a Bayesian Network Model from an Existing Risk Score Calculator for Pulmonary Arterial Hypertension J. Kraisangka,1 L.C. Lohmueller,2 M.K. Kanwar,3 C. Zhao,4 M.J. Druzdzel,1 J.F. Antaki,5 M.A. Simon,6 and R.L. Benza.3 1School of Computing and Information, University of Pittsburgh, Pittsburgh, PA; 2 Language Technologies Institute, Carnegie Mellon University, Pittsburgh, PA; 3Cardiovascular Institute, Allegheny General Hospital, Pittsburgh, PA; 4Actelion Pharmaceuticals US, Inc., South San Francisco, CA; 5Meinig School of Biomedical Engineering, Cornell University, Ithaca, NY; and the 6Department of Medicine, University of Pittsburgh, Pittsburgh, PA. Purpose: We propose an alternative approach to the extensively validated REVEAL risk score calculator using Bayesian network (BN) modeling. We derived a BN model with the same variables and discretization cut points as the REVEAL risk score calculator and data from the REVEAL registry. This study compared the performance and relative impact of the variables in the two PAH risk assessment tools. Methods: 2,456 adult patients from the REVEAL registry were used to develop a Bayesian network model to predict 1-year survival using the Tree Augmented Naive Bayes (TAN) algorithm. We used 10-fold cross validation to measure the BN performance, reported as the area under the Receiver Operating Characteristic curve (AUC). We compared hazard ratios of the variables in the REVEAL calculator to cross entropy between risk factors and the outcome variable in the BN model. Cross-entropy measures the expected change in entropy of the probability distribution of the outcome variable as the risk becomes known and is generally a measure of expected information gain from knowing a risk. Cross-entropy is a dynamic measure and changes as other variables are observed. Results: The BN model demonstrated an AUC of 0.77 for predicting one-year survival. This was an improvement to the existing AUC of 0.71 for the original REVEAL calculator. There is a high correlation (r = 0.786) for the hazard ratio and the cross entropy, when all risk factors are absent (Figure 1a). In the same context of variables, the cross-entropy and the hazard ratio capture the similar influences to the outcome. However, when the risk factors are partially known for a given patient (Figure 1b), the cross-entropy changes based on the context of which risk factor are observed. Conclusion: BN demonstrated a modest improvement in performance over the REVEAL 1.0 model. Moreover, it improved the understandability of the relationship between risk factors, the dynamic influences