Alam, M M

Abstract Background Speckle tracking echocardiography (STE) has shown to be a good tool to foresee early myocardial dysfunction in lymphoma patients who receive anthracycline based chemotherapy. Conventional STE such as global longitudinal strain (GLS) is a good predictor of cardiotoxicity in these patients, however, a more in-depth characterization of conventional and comprehensive STE parameters to predict a hard end-point as chemotherapeutic related heart failure (HF) has not been evaluated. Purpose The aim of this prospective study was to evaluate predictability of cancer therapeutic-related clinical HF by conventional and comprehensive STE. Methods We enrolled 269 Hodgkin & non-Hodgkin lymphoma patients who underwent chemotherapy at Mayo Clinic from 2013 through 2021. All patients had an echocardiogram performed at baseline (T0), during chemotherapy (T1) and after (T2). HF was diagnosed by a cardiologist and defined as the first occurrence after the initiation of chemotherapy. Conventional (GLS) and comprehensive strain analyses that included: global circumferential strain (GCS), global radial strain (GRS), global longitudinal early diastolic strain rate (GLSRe), global longitudinal systolic strain rate (GLSRs), global circumferential early diastolic strain rate (GCSRe), global circumferential systolic strain rate (GCSRs), global radial early diastolic strain rate (GRSRe), and global radial systolic strain rate (GRSRs), were performed offline. Logistic regression analyses were used to evaluate the association of 2D and 3D STE measurements with the development of clinical HF. Results Overall, 215 (79.9%) patients had non-Hodgkin lymphoma while 54 (20.1%) had Hodgkin lymphoma. Mean age was 58.4±16.1 years and 64.7% of the patients were males. The most prevalent comorbidities were hypertension (101/37.5%), dyslipidemia (87/32.3%) and diabetes (28/10.4%). HF occurred in 21 (7.8%) patients, including 9 (3.3%) during chemotherapy and 12 (4.5%) after chemotherapy. The best predictors of HF were: i) GLSRe and GCSRs performed at baseline (T0) to predict HF at T1 with an AUC of 0.85 each and p values of 0.0006 and 0.0005 respectively (Table 1); ii) GCSRs and GCS at baseline (T0) to predict HF at T1 or T2 with AUCs of 0.82 (p, <0.0001) and 0.81 (p, 0.0004), respectively. Basic strain (GLS) was able to predict HF when measured at T0 but not when measured at T1. All the AUCs for GLS were below 0.75 (Figure 1). Conclusions To our knowledge this is the first study to evaluate the use of conventional and comprehensive STE to predict a hard end-point as heart failure in patients with lymphoma who received anthracyclines. Comprehensive STE measurements as GLSRs, GLSRe, GCS, GCSRs and GCSRe are better than GLS to predict HF in patients with lymphoma who received anthracycline based chemotherapy. These findings can be crucial for the management of these patients by guiding when to start cardioprotection and/or avoid interruptions of cancer treatment. Funding Acknowledgement Type of funding sources: Private hospital(s). Main funding source(s): Department of cardiovascular diseases, Mayo Clinic, Rochester, MN

Abstract Background Assessing cardiac performance of patients receiving chemotherapy is a cornerstone for adequate cardiovascular care. Mitral annular plane systolic excursion (MAPSE) has been considered as a surrogate for Ejection Fraction (EF). However, little is known about its role in predicting Cardiotoxicity or Heart Failure in Lymphoma patients, as its relationship with Global Longitudinal Strain (GLS) and EF. Purpose Our aims were: i) to evaluate if MAPSE and GLS can predict the development of CT and/or HF in lymphoma patients treated with anthracyclines and ii) to evaluate its correlation with GLS and EF. Methods For this prospective observational study, 325 Hodgkin (HL) & non-Hodgkin (NHL) lymphoma patients (n=325) treated with anthracyclines were recruited from 2013 to 2021 and followed for 1 year. MAPSE by M-mode and GLS by Speckle-Tracking (ST) were measured at baseline (T0), during treatment (T1), and up to 1 year after chemotherapy completion (T2). CT was defined as a decrease in EF by >10% to a value <50% and HF by a cardiologist as the first occurrence after the initiation of anthracyclines. Logistic regression analyses with Receiving operator characteristics (ROC) and Area under the curve (AUC) were performed. Pearson's correlation coefficient was also calculated. A p-value <0.05 was considered statistically significant. Results Two hundred sixty-four patients (81.2%) had NHL and 61 (18.8%) HL. Of these, fifteen (4.6%) and 21 individuals (6.4%) developed CT at T1 and T2, respectively. Nine subjects (2.8%) developed HF at T1 and 14 (4.3%) at T2. MAPSE at T0 had the highest AUC to predict both HF at T1 (AUC=0.865, cut-off 14.9, sensitivity 100%, specificity 63%, p=0.008) and at T2 (AUC=0.757, cut-off 10.9, sensitivity 67%, specificity 93%, p=0.045). This same variable at T1 predicted HF at T2 with an AUC of 0.752 (cut-off 11.4, sensitivity 67%, specificity 94%, p=0.004). For CT prediction at T2, MAPSE at T1 had an AUC of 0.738 (cut-off 12.5, sensitivity 56%, specificity 85%, p<0.0001). GLS at T0 predicted CT at T1 (AUC=0.657, cut-off −19, sensitivity 67%, specificity 63%, p=0.012) and when obtained at T1, it predicted CT at T2 (AUC=0.776, cut-off −17, sensitivity 74%, specificity 75%, p-value <0.0001) (Table 1). Pearson's correlation between MAPSE and GLS at T0 (coefficient −0.25, p=0.023) at T1 (coefficient −0.38, p<0.0001) at T2 (coefficient −0.037, p<0.0001) and MAPSE with EF at T0 (coefficient 0.33, p=0.0002) at T1 (coefficient 0.28, p<0.0001) and T2 (coefficient 0.29, p<0.001). Conclusions To our best knowledge, this is the first time that MAPSE and GLS were compared to predict CT and HF in lymphoma patients receiving anthracycline-based chemotherapy; we have demonstrated that MAPSE measured at T0 was a very good predictor of HF at T1. Either MAPSE or GLS assessment at T0 and T1 were able to predict CT or HF. Future studies could explore the combination of these two variables to predict either CT or HF. Funding Acknowledgement Type of funding sources: Private hospital(s). Main funding source(s): Department of Cardiovascular Medicine. Mayo Clinic, Rochester-MN

Abstract Background The non-invasive calculation of right ventricular hemodynamics such as the pulmonary artery capacitance (PAC) has proven to be feasible and easy to perform. The simplified pulmonary embolism severity index (sPESI) and pulmonary vascular resistance (PVR) are other prognostic tools that have been used in the past. Purpose To evaluate if baseline PAC can outperform PVR and sPESI to predict all-cause short-term mortality in patients with acute pulmonary embolism (PE). Methods 373 patients with acute PE and a transthoracic echocardiogram performed within one day of diagnosis from March 2013 through June 2020 were followed prospectively. PAC was calculated as: Left ventricular stroke volume/ (Pulmonary artery systolic pressure – Pulmonary artery diastolic pressure). PVR was calculated as: Tricuspid regurgitant velocity / Right ventricular outflow tract velocity time integral x 10 + 0.16. The sPESI was used to evaluate prognosis by categorizing patients into low and high-risk categories. Multiple logistic regression analyses and receiver-operating characteristic curves were used to evaluate the ability of PAC, PVR, and sPESI to predict all-cause mortality up to six months. The Youden index was used to determine the cut-off value with the highest sensitivity and specificity. Kaplan–Meier curves were performed to determine event-free survival rate, differences between groups were assessed using the Wilcoxon Test. Results Mean age was 64±15 years (58% male, 28% cancer). PAC was associated with mortality at thirty days (p 0.0003), three months (p 0.003), and six months (p 0.011) (Table). PVR was associated with mortality at thirty days, three months, and six months (p 0.045, 0.012, and 0.031 respectively). The sPESI score ≥1 (high risk) was associated with all-cause mortality at three months and six months (p 0.0136 and <0.0001 respectively). PAC was the strongest predictor of all-cause mortality with an AUC of 0.95 (CI 0.82- 0.99) at thirty days, 0.84 (CI 0.65–0.99) at three months, and 0.77 (CI 0.57–0.96) for six months. Corresponding Kaplan-Meier survival curve of PAC and thirty days all-cause mortality is presented in the Figure. PVR had an AUC of 0.75 (CI 0.48–0.95) at thirty days, 0.79 (CI 0.58–0.94) at three months, and 0.72 (CI 0.50–0.90) at six months. The sPESI had an AUC of 0.62 (CI 0.54–0.66) at three months and 0.64 (CI 0.59–0.67) at six months. Conclusion In patients with acute PE, PAC outperforms sPESI and PVR as a predictor of short-term all-cause mortality at thirty days, three months, and six months. These three variables should be explored head to head prospectively for their utility to predict short term all-cause mortality. Funding Acknowledgement Type of funding sources: None.