Prevalence and predictors of right ventricular dysfunction in cancer patients treated with cardiotoxic chemotherapy – a prospective cardiovascular magnetic resonance study

Background

Right ventricular (RV) function has an established incremental prognostic value in cardiomyopathy. Studies on cancer therapeutics-related cardiac dysfunction (CTRCD) primarily focused on the left ventricle (LV), with conflicting results from small studies dedicated to RV dysfunction.

Purpose

We sought to investigate the influence of chemotherapy on RV function relative to LV function using serial cardiac magnetic resonance (CMR).

Methods

Patients were enrolled as part of Cardiotoxicity Prevention Research Initiative (CAPRI) Registry aimed at evaluating CMR-based markers for surveillance of CTRCD. Patients underwent non-contrast CMR imaging prior to initiation of anthracyclines and/or trastuzumab and serially every 3 months during the first year, then annually thereafter. We included patients who had a baseline and ≥1 follow-up scan and excluded those with baseline LV ejection fraction (EF)<50%, providing 320 patients completing 1,453 CMR studies. Cine images were analysed to calculate chamber volumes indexed to body surface area and EF. We defined LV CTRCD using CMR modality specific criteria of a drop in LV EF ≥5% from baseline to <57%; RV CTRCD as a drop ≥5% to <49% in females and <47% in males. We used linear mixed models to study the changes in ventricular volumes and EF with time.

Results

The majority of patients were females (80%), had breast cancer (68%) or lymphoma (32%), with a mean age of 52.7±13 years. Figure 1 shows temporal changes in mean ventricular volumes and function over the first year. Mean changes in RV function followed those of the LV, with the nadir of EF and maximum of volumes occurring at 6 months. Respective values for mean decrease in LV and RV EF at this time point versus baseline were 4.1 and 2.9% (p<0.001). Concomitant mean increase in indexed RV end-diastolic (ED) and end-systolic (ES) volumes were 1.6 and 2.7 ml/m2 (p=0.2 and <0.001). There was significant interaction of chemotherapy regimen with time for RV volumes (p=0.001 and 0.003), but not RV EF (p=0.7), with worst changes occurring with combined anthracyclines and trastuzumab. In all, 70 (22%) and 28 (9%) patients met criteria for LV and RV CTRCD, respectively. Among those who developed RV CTRCD, 10 had persistently normal LV function. Figure 2 shows the results of logistic regression to predict RV CTRCD. Significant univariable predictors included combined chemotherapy regimen and baseline LV and RV volumes and LV EF. Adjusting for age, sex, and chemotherapy regimen, baseline RV ED volume remained associated with RV CTRCD (odds ratio 1.6; p=0.005).

Conclusion

In this large study, RV volumes and function were similarly influenced by chemotherapy versus comparable LV-based measures. Using similar threshold criteria, the incidence of RV CTRCD was lower than for LV CTRCD; however, one third of those who develop RV CTRCD showed normal LV function. Future studies are warranted to study the prognostic influence of RV injury in cancer patients.

Funding Acknowledgement

Type of funding sources: Other. Main funding source(s): Alberta InnovatesGenome Alberta Figure 1. Temporal changes in LV & RV functionFigure 2. Predictors of RV CTRCD

Identifying the value of RVEF for the prediction of major cardiovascular outcomes: a study of 7,131 patients undergoing cardiovascular magnetic resonance imaging

Background

Right ventricular (RV) function remains poorly recognized for its value in predicting cardiovascular events at a population level. Cardiovascular Magnetic Resonance (CMR) imaging is the gold standard for RV assessment.

Purpose

To define the independent prognostic value of RVEF for the prediction of major adverse cardiovascular events (MACE) as primary outcome in patients with known or suspected cardiovascular disease.

Methods

Data was obtained from the Cardiovascular Imaging Registry of Calgary (CIROC). Patients underwent standardized CMR imaging protocols and analysis. Clinical events were identified from administrative data.

Results

7,131 patients were included. 870 primary outcome events occurred over 2.5 years follow-up. RVEF provided equivalent predictive utility versus LVEF (Table 1). There was an increase in events with worsening severity of RVEF (Figure 1), with a significant “threshold-effect” at an RVEF of 40%.

Conclusions

RVEF is a strong and independent predictor of MACE at a population level.

Figure 1

Funding Acknowledgement

Type of funding source: None

Development and validation of a risk model for the prediction of cardiovascular hospital admission using CMR-based phenotype in patients with known or suspected cardiovascular disease

Background

Cardiovascular diseases remain the leading cause of morbidity worldwide and impose the highest economic burden among noncommunicable diseases. Much of these costs are related to hospitalizations for adverse cardiovascular events, which may be reduced by targeted management of high-risk patients. Cardiac markers derived from CMR imaging have been shown to be strong independent predictors of prognosis within specific cohorts. However, its capacity to broadly contribute to risk models aimed at predicting incident cardiac hospitalization has not been demonstrated.

Purpose

Using a large clinical outcomes registry of patients clinically referred for CMR, develop and validate a nomogram for prediction of cardiovascular hospital admission.

Methods

A total of 7127 consecutive patients were prospectively recruited between 02/2015 and 07/2019. All patients completed standardized health questionnaires and CMR imaging protocols. A nomogram was developed for prediction of cardiovascular hospitalization, inclusive of admission for heart failure, MI, cardiac arrest, heart transplant, LVAD implantation, or stroke. The risk model was derived from 80% (n=5702) of the cohort using Cox modelling that included CMR, medication, laboratory, and patient-reported health variables. Model validation was assessed by discrimination and calibration procedures applied to the remaining 20% of patients (n=1425). A minimum follow-up of six months was mandated.

Results

The derivation cohort was comprised of 38% females with a median age of 56 (IQR 44–65) years. During a median follow-up of 934 days, 514 (9.0%) events occurred. The validation cohort was similarly comprised of 37% females with a median age of 57 (IQR 44–66) years. During a median follow-up of 970 days, 142 (10.0%) events occurred. Numerous CMR parameters were significantly different between those experiencing versus not experiencing the primary composite outcome, including: LVEF (44% vs 59%, p<0.0001), RVEF (52% vs 55%, p<0.0001), LV mass (65g/m2 vs 56g/m2, p<0.0001), and LA volume (43mL/m2 vs 34mL/m2, p<0.0001). These and other CMR-derived characteristics were independently predictive of the composite outcome by univariate modelling (Figure 1A). An eight-variable nomogram (Figure 1B) was developed using a stepwise multivariate model that exhibited high discrimination in both the derivation and validation cohorts (C-index 0.81 and 0.83, respectively). Continuous model calibration curves indicated satisfactory external performance. The model was able to discriminate risk of hospitalization at 1-year with a dynamic range of 20–99%.

Conclusion

Using data available at time of CMR imaging, we derived and validated a Cox-based nomogram that offers robust prediction of future cardiovascular admissions. This tool may provide value for the identification of patients who may benefit from targeted surveillance and management strategies, and may offer a foundation for improved patient-specific cost modelling.

Figure 1

Funding Acknowledgement

Type of funding source: None

4941Machine learning based automated diagnosis of ischemic vs non-ischemic dilated cardiomyopathy using 3D myocardial deformation analysis

Background

Late Gadolinium Enhancement (LGE) imaging is a reference standard technique for the differentiation of ischemic cardiomyopathy (ICM) from non-ischemic dilated cardiomyopathy (NIDCM) in patients with heart failure and reduced ejection fraction (HFrEF). 3D myocardial deformation analysis (3D-MDA) offers highly reproducible phenotypic assessments of regional architecture and function that may provide value for artificial-intelligence-assisted cardiomyopathy diagnosis without need for LGE imaging.

Purpose

In this study, we trained and validated a machine-learning-based model to enable automated diagnosis of ischemic versus non-ischemic dilated cardiomyopathy exclusively using regional patterns of deformation among patients otherwise matched by age, sex and global contractile dysfunction.

Methods

100 ICM and 100 NIDCM patients matched for age, sex, and LVEF underwent standard cine SSFP and LGE imaging. Patient diagnoses were established using a combination of clinical and LGE-based criteria. 3D-MDA was performed using validated software (GIUSEPPE) to compute regional 3D strain measures at each cardiac phase in both conventional and principal strain directions. Principal Component Analysis (PCA) was performed on the composite 3D-MDA dataset. The first 20 components were chosen, accounting for approximately 65% of the population variance. Subsequently, a support-vector-machine-based algorithm was used with 10-fold cross-validation to discriminate ICM from NIDCM.

Results

Patients were 63±10 years (ICM: 63±10 years, NIDCM: 63±10 years, p=0.955), 74% male (ICM: 74%, NIDCM: 74%, p=1.000), and had a mean LVEF of 27±8% (ICM: 27±7%, NIDCM: 28±7%, p=0.688). Global time to peak strain was significantly shorter in ICM patients relative to NIDCM patients across all surfaces and in all directions (p<0.05). The highest single-variable Area Under the Curve (AUC) achieved for the classification of ICM versus NIDCM from global data was for minimum principal strain (ICM: 43.7±7.8, NIDCM: 48.3±7.5, p<0.001, AUC: 0.682) (Figure 1). However, a multi-feature machine-learning-based model exposed to all available regional 3D deformation data achieved an AUC of 0.903 (sensitivity 87.7%, specificity 75.5%).

Conclusions

Machine learning-based analyses of3D regionaldeformation patterns allows for robust discrimination of ICM versus NIDCM. Further expansion of the presented findings is planned on a wider, multi-centre cohort.

Acknowledgement/Funding

Dr. White was supported by an award from Heart and Stroke Foundation of Alberta. This study was funded in part by Calgary Health Trust.

Predictors of Ominous Outcome in Infants Undergone to Cardiac Surgery and Cardiopulmonary by-Pass: S100B Protein

S100B protein has been recently proposed as a consolidated marker of brain damage and death in adult, children and newborn patients. The present study evaluates whether the longitudinal measurement of S100B at different perioperative time-points may be a useful tool to identify the occurrence of perioperative early death in congenital heart disease (CHD) newborns. We conducted a case-control study in 88 CHD infants, without pre-existing neurological disorders or other co-morbidities, of whom 22 were complicated by perioperative death in the first week from surgery. Control group was composed by 66 uncomplicated CHD infants matched for age at surgical procedure. Blood samples were drawn at five predetermined time-points before during and after surgery. In all CHD children, S100B values showed a pattern characterized by a significant increase in protein's concentration from hospital admission up to 24-h after procedure reaching their maximum peak (P<0.01) during cardiopulmonary by-pass and at the end of the surgical procedure. Moreover, S100B concentrations in CHD death group were significantly higher (P<0.01) than controls at all monitoring time-points. The ROC curve analysis showed that S100B measured before surgical procedure was the best predictor of perioperative death, among a series of clinical and laboratory parameters, reaching at a cut-off of 0.1 μg/L a sensitivity of 100% and a specificity of 63.7%. The present data suggest that in CHD infants biochemical monitoring in the perioperative period is becoming possible and S100B can be include among a series of parameters for adverse outcome prediction.