This study will use a non-invasive, precision medicine approach to study kidney complications in people with severe heart failure who need a mechanical heart pump. Kidney complications can occur before or after placement of the mechanical pump and are associated with worse outcomes. Currently, physicians must rely on only a few limited lab values to assess how patients’ kidneys are functioning. These lab values do not allow physicians to distinguish reversible kidney dysfunction that will get better after a mechanical pump is placed from irreversible damage due to kidney scarring, and do not help physicians to determine what is causing kidney dysfunction after the mechanical pumps are placed. Evidence is growing for a way to determine accurately what is happening in patients’ kidneys non-invasively. Kidney cells release small membrane-bound bodies into the urine (called exosomes) that can provide windows into the kidneys. We aim to study these exosomes in patients who have a mechanical heart pump surgically placed, measuring them both before the surgery and one week after. The levels of many proteins in these exosomes will be measured via LC/MS, and this information will then be used to assess how the patients’ kidneys are functioning before and after surgery and how the kidney cells have been affected. Students with a broad knowledge in bioinformatics analysis will be preferred. The results of this study will then be used to perform a larger study to assess whether these techniques are applicable to a broad array of people who have mechanical heart pumps placed, with the potential to improve physicians’ ability to assess how patients’ kidney function will respond to the treatment and help them make treatment decisions. Ultimately, the results may help in the development of targeted treatments to prevent or improve kidney complications in people who have mechanical heart pumps.
Infection is the most common complication in patients with heart failure (HF) who have undergone continuous-flow left ventricular assist device (CF-LVAD) implantation. In fact, HF patients have the highest risk of mortality and complications during the early stages (first 3 months) after CF-LVAD implantation. Therefore, it is crucial to identify prognostic biomarkers associated with infection in HF patients with CF-LVAD. However, analyzing high-dimensional biomarker information and obtaining accurate predictions with current biomedical analysis tools is challenging. With the availability of patient-related clinical data, machine learning (ML) technologies have shown great potential in promoting biomedical data analysis toward discovering biomarkers for accurate infection detection, early diagnosis, and prognosis in the management of CF-LVAD patients. We are currently enrolling CF-LVAD patients and collecting demographic, clinical, and surgical data. We are recording their post-surgical infection status and measuring potential blood biomarkers before and multiple time points after LVAD implantation. The aim of this proposal is to
- Utilize ML models to investigate infection-related biomarkers in HF patients before and after CF-LVAD implantation within the first three months
- Use principal component analysis (PCA) to describe the connection between different prognostic biomarkers
- Formulate the influence of patient characteristics on infection-related biomarkers based on statistical correlation analysis
In this proposal, we aim to develop accurate machine learning (ML) methods for Neurological dysfunction (ND) prediction in heart failure (HF) patients implanted with Mechanical circulatory support devices [also known as continuous-flow left ventricular assist device (CF-LVAD]. The ND, especially stroke, is found to be the top most significant clinical complication leading to the specific cause of death (19%) for CF-LVAD patients. Our hospital experiences 27% ND after CF-LVAD implantation. Further investigation is necessary to identify the potential blood markers that can predict the risk of stroke in these patients. CF-LVADs can serve as a “stopgap” measure for patients on heart transplant waiting lists, a costly and invasive procedure that does not benefit all patients equally. Thus, it can be hypothesized that the outcome and the optimal timing of the CF-LVAD implantation varies extensively from individual to individual. Thus, there is a need to be able to learn the individualized survival benefits of CF-LVADs for cardiac patients waiting for a heart. Thus, we plan on leveraging machine learning to estimate the effect of the CF-VLAD implantation on an individual using the static and time-series data of the biomarkers. Modern ML, especially Deep Learning (DL) models often allow timely and accurate detection of anomalous events in massive medical datasets. Surprisingly no efficient ML methods are known for ND prediction in CF-LVAD patients. We will employ classical ML techniques such as Support Vector Machines (SVM), non-linear dimension reduction and manifold learning such as tSNE and UMAP, k-means++ clustering and modern ML tools such as meta-learning and transfer learning. Our long-term approach will translate basic science research to clinical guidelines for managing patients prior to and after CF-LVAD implantation to avoid the risk of stroke.
Changes in metabolism have been implicated in renal ischemia/reperfusion injury (IRI). However, a global analysis of the metabolic changes in heart failure patients with continuous-flow Left Ventricular Assist Devices is lacking, and the association of the changes with post-LVAD ischemic kidney injury and subsequent recovery is unclear. An improved understanding of kidney health states in LVAD recipients before and after device implantation is essential to improving outcomes. However, in LVAD recipients, clinical tenuousness and anticoagulation prevent kidney biopsy and necessitate a non-invasive strategy. For non-invasive diagnosis, urine has proven especially useful, enabling metabolome investigation of kidney processes that have revealed pathophysiologic insights. Investigation of urine extracellular vesicles, derived mainly from kidney and urinary tract cells, has shown substantial value for diagnosis and investigation in several kidney disease phenotypes as well. Our central premise is that non-invasive characterization of kidney health states and trajectories in persons with advanced heart failure receiving LVAD implantation, by applying untargeted metabolomics strategy to urine and urine extracellular vesicles, will enable identification of mechanisms of kidney dysfunction, insights into longitudinal kidney health changes across a range of stressors, and characterization of kidney disease phenotypes.
Donor hearts are a precious resource, and heart transplantation is a procedure that can save the lives of many patients. Unfortunately, many potential donor hearts remain unused due to the strict limitations on warm ischemic time (WIT) imposed by current regulations. However, our basic science research is the first to explore the impact of a range of WIT on human hearts donated after circulatory death (DCD) in comparison to Brain death donor hearts (DBD), allowing us to push the boundaries of what is currently considered safe. By potentially expanding the pool of donor hearts beyond the current limit of 30 minutes, we can help more patients in need and give hope to those on the waitlist. In the face of this urgent need for available hearts, the goal of this project is to expand the number of organs available for transplant by demonstrating the safety and feasibility of using DCD hearts. Using functional, molecular, and biochemical analyses to evaluate and compare donor hearts, we will devise a novel, reliable protocol whereby we can acquire DCD hearts, preserve them in an innovative fashion, and transplant them with good results.
The study will delve into the impact of long-term mechanical circulatory support on kidney parenchymal health, and examine the relationship between longitudinal kidney parenchymal health, neurohormonal activation, and inflammation. This innovative research will employ a precise and thorough approach to gain comprehensive insights into the effects of durable mechanical circulatory support on global kidney health. The findings will prove instrumental in reducing the growing burden of cardiorenal disease, an urgent public health concern.
Adverse kidney outcomes in patients with left ventricular assist devices (LVADs) may be caused by inflammation, cellular apoptosis, and fibrotic development in the kidneys, which can be driven by ischemia-reperfusion injury, hyperfiltration injury, subclinical hemolysis, and systemic inflammation. MicroRNAs (miRNAs) have been found to have both protective and harmful effects on the development and progression of acute kidney injury (AKI) and chronic kidney disease (CKD) in various settings. Identifying changes in miRNA expression patterns is an area of growing interest for biomarker and mechanistic research, but it has not been studied in advanced heart failure or long-term mechanical circulatory support. Non-invasive assessment of exo-miRs (miRNAs from urinary extracellular vesicles or exosomes) is a promising area of investigation that has yielded several successes in kidney diseases. It has the potential to identify biomarkers and mechanisms related to adverse kidney outcomes in LVAD recipients, in whom access to kidney tissue is generally not feasible due to clinical tenuousness and anticoagulation. We hypothesize that analyzing exo-miRs before and after LVAD implantation will identify a signature of kidney ischemia-reperfusion injury, which can predict progression to CKD. In this study, the Mondal lab is investigating exo-miRs by next-generation sequencing (NGS) in the discovery cohort and selecting miRNA candidates from the dysregulated exo-miR profile, subsequently validating them by real-time quantitative polymerase chain reaction (RT-qPCR) in the confirmation cohort.
The patient population that requires coronary artery bypass grafting (CABG) surgery is increasingly elderly and has a growing number of comorbidities. Currently, there are two surgical treatment options available for these patients. The first option is conventional CABG surgery, which requires making a large incision in the chest, stopping the heart, and using a bypass machine to keep the blood oxygenated and moving to the body. The second option is a newer approach using the assistance of a robotic machine, which allows the surgeon to use much smaller incisions without stopping the heart. However, this robotic-assisted technique can only be used in very select patients currently. Higher-risk patients and those with difficult anatomy are not able to receive the robotic-assisted technique. Dr. Liao at BSLMC has developed an innovative approach that uses elective peripheral extracorporeal membrane oxygenation (ECMO) as an alternative to the traditional bypass machine during Robotic CABG. This allows the heart to be stopped while the ECMO circuit provides blood and oxygenation to the body.
ECMO is thought to be less inflammatory than the traditional bypass machine, and using ECMO with robotic CABG will now allow the Robotic CABG technique to be used in those patients who could not previously get robotic CABG. To investigate and compare multiple inflammation pathways, The Mondal Lab focused on identifying the changes in those inflammation cascades in blood specimens at pre-, intra-, and post-operatively in ECMO-supported robotic CABG and the conventional CABG with bypass. Patients will be followed for 3-month post-operatively to identify clinical complications (if any). This study will examine the relationship between the magnitude of the inflammatory response, the post-operative complications, and outcomes following conventional and robotic CABG surgery. We expect that ECMO-supported robotic CABG patients may demonstrate lower inflammasome activation and better patient outcomes compared to conventional CABG patients. This will allow us to assess the utility of expanding ECMO’s role in robotic and conventional CABG procedures, as well as any procedures amenable to short-term ECMO use. Furthermore, it may provide evidence for targets of anti-inflammatory agents that could be utilized to optimize patients perioperatively.
Left ventricular assist device (LVAD) insertion is a crucial intervention for patients suffering from severe heart failure. However, the occurrence of LVAD-related neurological dysfunction remains a significant concern. At Baylor St. Luke's Medical Center, the Mondal Lab is conducting a study to investigate the potential imaging signatures associated with LVAD-related neurological dysfunction. To achieve this, they are utilizing brain CT scans along with advanced image analysis techniques such as CTSeg, synthseg, synthsr, and pyradiomics. The goal is to build and test a model in patients who underwent durable LVAD insertion that will predict neurological dysfunction using a generative model. This model will create synthetic MRI brain scans with isotropic 1-mm resolution which will be segmented using a hierarchical CNNs-based algorithm to obtain volumetric measures of cortical and subcortical structures. Finally, the white matter (WM) and subcortical gray matter segmentation will be used to extract features and analyze them using a radiomics approach, obtaining a model to predict neurological dysfunction. The study is investigating a variety of methods for image generation and enhancement using deep learning, including removing image artifacts, normalizing/harmonizing images, improving image quality, reducing radiation and contrast dose, and shortening the duration of imaging studies. The findings of this study may contribute to early detection, risk stratification, and personalized treatment strategies for LVAD patients. By integrating advanced segmentation techniques and radiomics analysis, this study aims to improve patient outcomes and enhance our understanding of the underlying mechanisms of LVAD-related neurological dysfunction.
Cardiac surgery is a major systemic stress that is often associated with bleeding and thromboembolic complications leading to multiorgan dysfunctions. Kidney dysfunction and injury before and after cardiovascular surgery is a major complicating factor and one of the most important factors for adverse outcomes. Post-surgery uraemic toxins accumulation can lead to chronic cerebrovascular disease that may predispose these patients to stroke, as well as early cognitive impairment. The kidney dysfunction and injury coexist with acute inflammation, hemodynamic changes, volume status changes, and other organ system dysfunctions. Improved understanding of characteristic dynamics of kidney dysfunction with cardiac surgery and the relationships with dysfunction of other organ systems is important to early identification of adverse outcome risk and to identification of ways to intervene, including with medication modification, fluid and hemodynamic adjustments. Current, widely used approaches, focusing on undifferentiated syndromes of acute kidney injury (AKI) and chronic kidney disease (CKD) do not take advantage of the multisystem and dynamic nature of physiology (reflected in the broad spectrum of commonly available clinical variables measured in multiple times per day in the cardiovascular surgery patients and present in the electronic health record). In other areas of critical illness, analysis using a coregulatory dynamic framework has enabled the identification of an acute inflammatory recovery pattern, which is conserved across a wide range of critical illnesses. A subset of cardiac surgery, left ventricular assist device (LVAD), implantation is of particular interest for investigation using a co-regulatory dynamic framework, given the complex interactions of hemodynamics, cardiac surgery, kidney health/function, and other factors. Identification of the coregulatory dynamics of kidney function, inflammation, hemodynamics, and hematologic system function following cardiac surgery (in the pre-operative, peri-operative, post-operative, and chronic periods) may enable early identification of risk of adverse outcomes and pathophysiologic insights into characteristic disease patterns, potentially enabling study of targeted interventions to improve outcomes.
The Mondal lab has dedicated years of effort toward creating an innovative blood-shearing portable device that should have the potential to test any biomedical devices, including pediatric or adult mechanical circulatory support devices or blood pumps, that are currently available, under investigation, or in their early stage of development. Blood cell damage is mainly caused by mechanical shear stress and exposure time. In the past, device-induced hemolysis was a significant problem. However, with the current understanding of how shear stress and exposure time contribute to hemolysis, most blood-contacting devices have been designed to mitigate this problem. Unfortunately, Shear-Induced Hemostatic Dysfunction (SIHD) is a complex and multifaceted issue. Dr. Mondal, in collaboration with Dr. Chan at Texas Heart Institute, working towards developing a novel instrument that can evaluate shear-induced hemostatic dysfunction using only a small portion of blood samples collected from patients. This small instrument can identify blood cell dysfunction caused by a wide range of shear stress and exposure times. Scientists can use this instrument to design mechanistic studies to evaluate any blood-contacting biomedical devices and identify patient-specific signaling pathways influenced by the non-physiological shear stress equivalent to particular biomedical devices. After thorough development, this instrument will undoubtedly hold significant commercial value owing to its high demand in the global research community. We have a functional prototype of the SIHD Mini Analyzer and are currently in an experimental stage. We are confident that this scientifically proven novel analyzer will attract the attention of scientists, physicians, and biomedical engineers who are actively working on developing new blood-contacting biomedical devices. Furthermore, this small instrument will create new avenues for mechanistic research opportunities for scientists. The idea had been presented at the Department of Surgery INSTINCT SHARK TANK this year to obtain initial funding.
Mondal Lab is committed to improving the outcomes of cardiac surgical patients. By enrolling patients and developing biorepositories of blood samples, the lab has made some important discoveries. Patients who undergo aortic surgery with prolonged cardiopulmonary bypass time are at a significantly higher risk for adverse postoperative complications. These complications can be life-threatening, including acute kidney injury, mortality, and vasogenic shock. The Mondal Lab is working to investigate the effects of prolonged cardiopulmonary bypass on endothelial function in patients undergoing aortic valve surgical procedures compared to patients without prolonged cardiopulmonary bypass. The endothelial glycocalyx plays a crucial role in maintaining endothelial integrity and vascular homeostasis. Glypican 1, a core protein in the glycocalyx family, is highly sensitive to shear stress and can be released into circulation through MMP9-mediated shedding. The hypothesis is that prolonged cardiopulmonary bypass leads to glypican 1 shedding via inflammation, resulting in worse postoperative outcomes for patients undergoing aortic valve surgical procedures. This study helps better understand the effects of prolonged cardiopulmonary bypass in the setting of aortic valve surgical procedures.
