I completed my medical training from the University College of Medical Sciences (Univ. of Delhi), New Delhi. I worked with the departments of Cardiothoracic Surgery at Mount Sinai, NY and Stanford University for my advanced elective rotations. During my medical school years, I worked on designing and developing novel mechanical heart valves making use of computational fluid dynamics methods and additive manufacturing techniques. I’m a postdoctoral fellow in the department of Cardiothoracic Surgery working in Hiesinger's Lab where we write code in an effort to understand cardiovascular disease. We build novel computer vision systems for cardiovascular imaging (echocardiography and cardiac MRI). We also study the underlying mechanisms of heart disease using transcriptomics and protein design, and design devices for patients with severe heart failure. My work is funded by the American Heart Association postdoctoral fellowship award. A list of my publications and patents are available on google scholar.

Honors & Awards

  • Postdoctoral Fellowship Award, American Heart Association (April 2021)
  • Vivien Thomas Early Career Investigator Award - Finalist, American Heart Association (November 2020)

Professional Education

  • MBBS, University College of Medical Sicences, (Medical Doctorate) (2019)


  • Rohan Arora. "United States Patent US20170202664A1 Suturing ring for prosthetic heart valves", Dec 11, 2018

All Publications

  • A design-based model of the aortic valve for fluid-structure interaction. Biomechanics and modeling in mechanobiology Kaiser, A. D., Shad, R., Hiesinger, W., Marsden, A. L. 2021


    This paper presents a new method for modeling the mechanics of the aortic valve and simulates its interaction with blood. As much as possible, the model construction is based on first principles, but such that the model is consistent with experimental observations. We require that tension in the leaflets must support a pressure, then derive a system of partial differential equations governing its mechanical equilibrium. The solution to these differential equations is referred to as the predicted loaded configuration; it includes the loaded leaflet geometry, fiber orientations and tensions needed to support the prescribed load. From this configuration, we derive a reference configuration and constitutive law. In fluid-structure interaction simulations with the immersed boundary method, the model seals reliably under physiological pressures and opens freely over multiple cardiac cycles. Further, model closure is robust to extreme hypo- and hypertensive pressures. Then, exploiting the unique features of this model construction, we conduct experiments on reference configurations, constitutive laws and gross morphology. These experiments suggest the following conclusions: (1) The loaded geometry, tensions and tangent moduli primarily determine model function. (2) Alterations to the reference configuration have little effect if the predicted loaded configuration is identical. (3) The leaflets must have sufficiently nonlinear material response to function over a variety of pressures. (4) Valve performance is highly sensitive to free edge length and leaflet height. These conclusions suggest appropriate gross morphology and material properties for the design of prosthetic aortic valves. In future studies, our aortic valve modeling framework can be used with patient-specific models of vascular or cardiac flow.

    View details for DOI 10.1007/s10237-021-01516-7

    View details for PubMedID 34549354

  • Patient-Specific Computational Fluid Dynamics Reveal Localized Flow Patterns Predictive of Post-Left Ventricular Assist Device Aortic Incompetence. Circulation. Heart failure Shad, R., Kaiser, A. D., Kong, S., Fong, R., Quach, N., Bowles, C., Kasinpila, P., Shudo, Y., Teuteberg, J., Woo, Y. J., Marsden, A. L., Hiesinger, W. 2021: CIRCHEARTFAILURE120008034


    BACKGROUND: Progressive aortic valve disease has remained a persistent cause of concern in patients with left ventricular assist devices. Aortic incompetence (AI) is a known predictor of both mortality and readmissions in this patient population and remains a challenging clinical problem.METHODS: Ten left ventricular assist device patients with de novo aortic regurgitation and 19 control left ventricular assist device patients were identified. Three-dimensional models of patients' aortas were created from their computed tomography scans, following which large-scale patient-specific computational fluid dynamics simulations were performed with physiologically accurate boundary conditions using the SimVascular flow solver.RESULTS: The spatial distributions of time-averaged wall shear stress and oscillatory shear index show no significant differences in the aortic root in patients with and without AI (mean difference, 0.67 dyne/cm2 [95% CI, -0.51 to 1.85]; P=0.23). Oscillatory shear index was also not significantly different between both groups of patients (mean difference, 0.03 [95% CI, -0.07 to 0.019]; P=0.22). The localized wall shear stress on the leaflet tips was significantly higher in the AI group than the non-AI group (1.62 versus 1.35 dyne/cm2; mean difference [95% CI, 0.15-0.39]; P<0.001), whereas oscillatory shear index was not significantly different between both groups (95% CI, -0.009 to 0.001; P=0.17).CONCLUSIONS: Computational fluid dynamics serves a unique role in studying the hemodynamic features in left ventricular assist device patients where 4-dimensional magnetic resonance imaging remains unfeasible. Contrary to the widely accepted notions of highly disturbed flow, in this study, we demonstrate that the aortic root is a region of relatively stagnant flow. We further identified localized hemodynamic features in the aortic root that challenge our understanding of how AI develops in this patient population.

    View details for DOI 10.1161/CIRCHEARTFAILURE.120.008034

    View details for PubMedID 34139862

  • Computational fluid dynamics simulations to predict false lumen enlargement after surgical repair of Type-A aortic dissection. Seminars in thoracic and cardiovascular surgery Shad, R., Kong, S., Fong, R., Quach, N., Kasinpila, P., Bowles, C., Lee, A., Hiesinger, W. 2021


    We aim to use computational fluid dynamics to investigate the hemodynamic conditions that may predispose to false lumen enlargement in this patient population. Nine patients who received surgical repairs of their type-A aortic dissections between 2017-2018 were retrospectively identified. Multiple contrast-enhanced post-operative CT scans were used to construct 3D models of aortic geometries. Computational fluid dynamics simulations of the models were run on a high-performance computing cluster using SimVascular - an open source simulation package. Physiological pulsatile flow conditions (4.9 L/min) were used at the aortic true lumen inlet, and physiological vascular resistances were applied at the distal vascular ends. Exploratory analyses showed no correlation between rate of false lumen growth and blood pressure, immediate post-op aortic diameter, or the number of fenestrations (p = 0.2). 1-year post-operative CT scans showed a median (IQR) false lumen growth rate of 4.31 (3.66, 14.67) mm/year Median (Interquartile range) peak systolic, mid-diastolic, and late diastolic velocity magnitudes were 0.90 (1.40); 0.10 (0.16); and 0.06 (0.06) cm/s respectively. Spearman's ranked correlations between fenestration velocity and 1-year false lumen growth rates were found to be statistically significant: Velocity magnitude at peak systolic (p = 0.025; rho = 0.75), mid diastolic (p = 0.025; rho = 0.75) and late diastolic phases of the cardiac cycle (p = 0.006; rho = 0.85). We have shown that false lumen growth is strongly correlated to fenestration flow velocity, which has potential implications for post-operative surveillance and risk stratification.

    View details for DOI 10.1053/j.semtcvs.2021.05.012

    View details for PubMedID 34091015

  • Long-term survival in patients with post-LVAD right ventricular failure: multi-state modelling with competing outcomes of heart transplant. The Journal of heart and lung transplantation : the official publication of the International Society for Heart Transplantation Shad, R., Fong, R., Quach, N., Bowles, C., Kasinpila, P., Li, M., Callon, K., Castro, M., Guha, A., Suarez, E. E., Lee, S., Jovinge, S., Boeve, T., Shudo, Y., Langlotz, C. P., Teuteberg, J., Hiesinger, W. 2021


    BACKGROUND: Multicenter data on long term survival following LVAD implantation that make use of contemporary definitions of RV failure are limited. Furthermore, traditional survival analyses censor patients who receive a bridge to heart transplant. Here we compare the outcomes of LVAD patients who develop post-operative RV failure accounting for the transitional probability of receiving an interim heart transplantation.METHODS: We use a retrospective cohort of LVAD patients sourced from multiple high-volume centers based in the United States. Five- and ten-year survival accounting for transition probabilities of receiving a heart transplant were calculated using a multi-state Aalen Johansen survival model.RESULTS: Of the 897 patients included in the study, 238 (26.5%) developed post-operative RV failure at index hospitalization. At 10 years the probability of death with post-op RV failure was 79.28% vs 61.70% in patients without (HR 2.10; 95% CI 1.72 - 2.57; p=< .001). Though not significant, patients with RV failure were less likely to be bridged to a heart transplant (HR 0.87, p=.4). Once transplanted the risk of death between both patient groups remained equivalent; the probability of death after a heart transplant was 3.97% in those with post-operative RV failure shortly after index LVAD implant, as compared to 14.71% in those without.CONCLUSIONS AND RELEVANCE: Long-term durable mechanical circulatory support is associated with significantly higher mortality in patients who develop post-operative RV failure. Improving outcomes may necessitate expeditious bridge to heart transplant wherever appropriate, along with critical reassessment of organ allocation policies.

    View details for DOI 10.1016/j.healun.2021.05.002

    View details for PubMedID 34167863

  • Predicting post-operative right ventricular failure using video-based deep learning. Nature communications Shad, R., Quach, N., Fong, R., Kasinpila, P., Bowles, C., Castro, M., Guha, A., Suarez, E. E., Jovinge, S., Lee, S., Boeve, T., Amsallem, M., Tang, X., Haddad, F., Shudo, Y., Woo, Y. J., Teuteberg, J., Cunningham, J. P., Langlotz, C. P., Hiesinger, W. 2021; 12 (1): 5192


    Despite progressive improvements over the decades, the rich temporally resolved data in an echocardiogram remain underutilized. Human assessments reduce the complex patterns of cardiac wall motion, to a small list of measurements of heart function. All modern echocardiography artificial intelligence (AI) systems are similarly limited by design - automating measurements of the same reductionist metrics rather than utilizing the embedded wealth of data. This underutilization is most evident where clinical decision making is guided by subjective assessments of disease acuity. Predicting the likelihood of developing post-operative right ventricular failure (RV failure) in the setting of mechanical circulatory support is one such example. Here we describe a video AI system trained to predict post-operative RV failure using the full spatiotemporal density of information in pre-operative echocardiography. We achieve an AUC of 0.729, and show that this ML system significantly outperforms a team of human experts at the same task on independent evaluation.

    View details for DOI 10.1038/s41467-021-25503-9

    View details for PubMedID 34465780

  • Single-Cell Transcriptomic Profiling of Vascular Smooth Muscle Cell Phenotype Modulation in Marfan Syndrome Aortic Aneurysm. Arteriosclerosis, thrombosis, and vascular biology Pedroza, A. J., Tashima, Y., Shad, R., Cheng, P., Wirka, R., Churovich, S., Nakamura, K., Yokoyama, N., Cui, J. Z., Iosef, C., Hiesinger, W., Quertermous, T., Fischbein, M. P. 2020: ATVBAHA120314670


    OBJECTIVE: To delineate temporal and spatial dynamics of vascular smooth muscle cell (SMC) transcriptomic changes during aortic aneurysm development in Marfan syndrome (MFS). Approach and Results: We performed single-cell RNA sequencing to study aortic root/ascending aneurysm tissue from Fbn1C1041G/+ (MFS) mice and healthy controls, identifying all aortic cell types. A distinct cluster of transcriptomically modulated SMCs (modSMCs) was identified in adult Fbn1C1041G/+ mouse aortic aneurysm tissue only. Comparison with atherosclerotic aortic data (ApoE-/- mice) revealed similar patterns of SMC modulation but identified an MFS-specific gene signature, including plasminogen activator inhibitor-1 (Serpine1) and Kruppel-like factor 4 (Klf4). We identified 481 differentially expressed genes between modSMC and SMC subsets; functional annotation highlighted extracellular matrix modulation, collagen synthesis, adhesion, and proliferation. Pseudotime trajectory analysis of Fbn1C1041G/+ SMC/modSMC transcriptomes identified genes activated differentially throughout the course of phenotype modulation. While modSMCs were not present in young Fbn1C1041G/+ mouse aortas despite small aortic aneurysm, multiple early modSMCs marker genes were enriched, suggesting activation of phenotype modulation. modSMCs were not found in nondilated adult Fbn1C1041G/+ descending thoracic aortas. Single-cell RNA sequencing from human MFS aortic root aneurysm tissue confirmed analogous SMC modulation in clinical disease. Enhanced expression of TGF (transforming growth factor)-beta-responsive genes correlated with SMC modulation in mouse and human data sets.CONCLUSIONS: Dynamic SMC phenotype modulation promotes extracellular matrix substrate modulation and aortic aneurysm progression in MFS. We characterize the disease-specific signature of modSMCs and provide temporal, transcriptomic context to the current understanding of the role TGF-beta plays in MFS aortopathy. Collectively, single-cell RNA sequencing implicates TGF-beta signaling and Klf4 overexpression as potential upstream drivers of SMC modulation.

    View details for DOI 10.1161/ATVBAHA.120.314670

    View details for PubMedID 32698686

  • A Design-Based Model of the Aortic Valve for Fluid-Structure Interaction Kaiser, A. D., Shad, R., Hiesinger, W., Marsden, A. L. arXiv preprint. 2020
  • Use of patient-specific computational models for optimization of aortic insufficiency after implantation of left ventricular assist device. The Journal of thoracic and cardiovascular surgery Kasinpila, P. n., Kong, S. n., Fong, R. n., Shad, R. n., Kaiser, A. D., Marsden, A. L., Woo, Y. J., Hiesinger, W. n. 2020


    Aortic incompetence (AI) is observed to be accelerated in the continuous-flow left ventricular assist device (LVAD) population and is related to increased mortality. Using computational fluid dynamics (CFD), we investigated the hemodynamic conditions related to the orientation of the LVAD outflow in these patients.We identified 10 patients with new aortic regurgitation, and 20 who did not, after LVAD implantation between 2009 and 2018. Three-dimensional models of patients' aortas were created from their computed tomography scans. The geometry of the LVAD outflow graft in relation to the aorta was quantified using azimuth angles (AA), polar angles (PAs), and distance from aortic root. The models were used to run CFD simulations, which calculated the pressures and wall shear stress (rWSS) exerted on the aortic root.The AA and PA were found to be similar. However, for combinations of high values of AA and low values of PA, there were no patients with AI. The distance from aortic root to the outflow graft was also smaller in patients who developed AI (3.39 ± 0.7 vs 4.07 ± 0.77 cm, P = .04). There was no significant difference in aortic root pressures in the 2 groups. The rWSS was greater in AI patients (4.60 ± 5.70 vs 2.37 ± 1.20 dyne/cm2, P < .001). Qualitatively, we observed a trend of greater perturbations, regions of high rWSS, and flow eddies in the AI group.Using CFD simulations, we demonstrated that patients who developed de novo AI have greater rWSS at the aortic root, and their outflow grafts were placed closer to the aortic roots than those patients without de novo AI.

    View details for DOI 10.1016/j.jtcvs.2020.04.164

    View details for PubMedID 32653292

  • MDCT-based lung volumetry as a prognostic tool-miles to go before we sleep INDIAN JOURNAL OF THORACIC AND CARDIOVASCULAR SURGERY Mahajan, H., Shad, R. 2017; 33 (3): 195–96