Biomarkers in solid organ transplantation: establishing personalized transplantation medicine.

Technological advances in molecular and in silico research have enabled significant progress towards personalized transplantation medicine. It is now possible to conduct comprehensive biomarker development studies of transplant organ pathologies, correlating genomic, transcriptomic and proteomic information from donor and recipient with clinical and histological phenotypes. Translation of these advances to the clinical setting will allow assessment of an individual patient's risk of allograft damage or accommodation. Transplantation biomarkers are needed for active monitoring of immunosuppression, to reduce patient morbidity, and to improve long-term allograft function and life expectancy. Here, we highlight recent pre- and post-transplantation biomarkers of acute and chronic allograft damage or adaptation, focusing on peripheral blood-based methodologies for non-invasive application. We then critically discuss current findings with respect to their future application in routine clinical transplantation medicine. Complement-system-associated SNPs present potential biomarkers that may be used to indicate the baseline risk for allograft damage prior to transplantation. The detection of antibodies against novel, non-HLA, MICA antigens, and the expression of cytokine genes and proteins and cytotoxicity-related genes have been correlated with allograft damage and are potential post-transplantation biomarkers indicating allograft damage at the molecular level, although these do not have clinical relevance yet. Several multi-gene expression-based biomarker panels have been identified that accurately predicted graft accommodation in liver transplant recipients and may be developed into a predictive biomarker assay

Diffusion-weighted magnetic resonance imaging in diagnosing graft dysfunction : a non-invasive alternative to renal biopsy.

The thesis is divided into three parts. The first part focuses on background information including how the kidney functions, diseases, and available kidney disease treatment strategies. In addition, the thesis provides information on imaging instruments and how they can be used to diagnose renal graft dysfunction. The second part focuses on elucidating the parameters linked with highly accurate diagnosis of rejection. Four parameters categories were tested: clinical biomarkers alone, individual mean apparent diffusion coefficient (ADC) at 11-different b- values, mean ADCs of certain groups of b-value, and fusion of clinical biomarkers and all b-values. The most accurate model was found to be when the b-value of b=100 s/mm2 and b=700 s/mm2 were fused. The third part of this thesis focuses on a study that uses Diffusion-Weighted MRI to diagnose and differentiate two types of renal rejection. The system was found to correctly differentiate the two types of rejection with a 98% accuracy. The last part of this thesis concludes the work that has been done and states the possible trends and future avenues

A non-invasive diagnostic system for early assessment of acute renal transplant rejection.

Early diagnosis of acute renal transplant rejection (ARTR) is of immense importance for appropriate therapeutic treatment administration. Although the current diagnostic technique is based on renal biopsy, it is not preferred due to its invasiveness, recovery time (1-2 weeks), and potential for complications, e.g., bleeding and/or infection. In this thesis, a computer-aided diagnostic (CAD) system for early detection of ARTR from 4D (3D + b-value) diffusion-weighted (DW) MRI data is developed. The CAD process starts from a 3D B-spline-based data alignment (to handle local deviations due to breathing and heart beat) and kidney tissue segmentation with an evolving geometric (level-set-based) deformable model. The latter is guided by a voxel-wise stochastic speed function, which follows from a joint kidney-background Markov-Gibbs random field model accounting for an adaptive kidney shape prior and for on-going visual kidney-background appearances. A cumulative empirical distribution of apparent diffusion coefficient (ADC) at different b-values of the segmented DW-MRI is considered a discriminatory transplant status feature. Finally, a classifier based on deep learning of a non-negative constrained stacked auto-encoder is employed to distinguish between rejected and non-rejected renal transplants. In the “leave-one-subject-out” experiments on 53 subjects, 98% of the subjects were correctly classified (namely, 36 out of 37 rejected transplants and 16 out of 16 nonrejected ones). Additionally, a four-fold cross-validation experiment was performed, and an average accuracy of 96% was obtained. These experimental results hold promise of the proposed CAD system as a reliable non-invasive diagnostic tool

Role of machine learning in early diagnosis of kidney diseases.

Machine learning (ML) and deep learning (DL) approaches have been used as indispensable tools in modern artificial intelligence-based computer-aided diagnostic (AIbased CAD) systems that can provide non-invasive, early, and accurate diagnosis of a given medical condition. These AI-based CAD systems have proven themselves to be reproducible and have the generalization ability to diagnose new unseen cases with several diseases and medical conditions in different organs (e.g., kidneys, prostate, brain, liver, lung, breast, and bladder). In this dissertation, we will focus on the role of such AI-based CAD systems in early diagnosis of two kidney diseases, namely: acute rejection (AR) post kidney transplantation and renal cancer (RC). A new renal computer-assisted diagnostic (Renal-CAD) system was developed to precisely diagnose AR post kidney transplantation at an early stage. The developed Renal-CAD system perform the following main steps: (1) auto-segmentation of the renal allograft from surrounding tissues from diffusion weighted magnetic resonance imaging (DW-MRI) and blood oxygen level-dependent MRI (BOLD-MRI), (2) extraction of image markers, namely: voxel-wise apparent diffusion coefficients (ADCs) are calculated from DW-MRI scans at 11 different low and high b-values and then represented as cumulative distribution functions (CDFs) and extraction of the transverse relaxation rate (R2*) values from the segmented kidneys using BOLD-MRI scans at different echotimes, (3) integration of multimodal image markers with the associated clinical biomarkers, serum creatinine (SCr) and creatinine clearance (CrCl), and (4) diagnosing renal allograft status as nonrejection (NR) or AR by utilizing these integrated biomarkers and the developed deep learning classification model built on stacked auto-encoders (SAEs). Using a leaveone- subject-out cross-validation approach along with SAEs on a total of 30 patients with transplanted kidney (AR = 10 and NR = 20), the Renal-CAD system demonstrated 93.3% accuracy, 90.0% sensitivity, and 95.0% specificity in differentiating AR from NR. Robustness of the Renal-CAD system was also confirmed by the area under the curve value of 0.92. Using a stratified 10-fold cross-validation approach, the Renal-CAD system demonstrated its reproduciblity and robustness with a diagnostic accuracy of 86.7%, sensitivity of 80.0%, specificity of 90.0%, and AUC of 0.88. In addition, a new renal cancer CAD (RC-CAD) system for precise diagnosis of RC at an early stage was developed, which incorporates the following main steps: (1) estimating the morphological features by applying a new parametric spherical harmonic technique, (2) extracting appearance-based features, namely: first order textural features are calculated and second order textural features are extracted after constructing the graylevel co-occurrence matrix (GLCM), (3) estimating the functional features by constructing wash-in/wash-out slopes to quantify the enhancement variations across different contrast enhanced computed tomography (CE-CT) phases, (4) integrating all the aforementioned features and modeling a two-stage multilayer perceptron artificial neural network (MLPANN) classifier to classify the renal tumor as benign or malignant and identify the malignancy subtype. On a total of 140 RC patients (malignant = 70 patients (ccRCC = 40 and nccRCC = 30) and benign angiomyolipoma tumors = 70), the developed RC-CAD system was validated using a leave-one-subject-out cross-validation approach. The developed RC-CAD system achieved a sensitivity of 95.3% ± 2.0%, a specificity of 99.9% ± 0.4%, and Dice similarity coefficient of 0.98 ± 0.01 in differentiating malignant from benign renal tumors, as well as an overall accuracy of 89.6% ± 5.0% in the sub-typing of RCC. The diagnostic abilities of the developed RC-CAD system were further validated using a randomly stratified 10-fold cross-validation approach. The results obtained using the proposed MLP-ANN classification model outperformed other machine learning classifiers (e.g., support vector machine, random forests, and relational functional gradient boosting) as well as other different approaches from the literature. In summary, machine and deep learning approaches have shown potential abilities to be utilized to build AI-based CAD systems. This is evidenced by the promising diagnostic performance obtained by both Renal-CAD and RC-CAD systems. For the Renal- CAD, the integration of functional markers extracted from multimodal MRIs with clinical biomarkers using SAEs classification model, potentially improved the final diagnostic results evidenced by high accuracy, sensitivity, and specificity. The developed Renal-CAD demonstrated high feasibility and efficacy for early, accurate, and non-invasive identification of AR. For the RC-CAD, integrating morphological, textural, and functional features extracted from CE-CT images using a MLP-ANN classification model eventually enhanced the final results in terms of accuracy, sensitivity, and specificity, making the proposed RC-CAD a reliable noninvasive diagnostic tool for RC. The early and accurate diagnosis of AR or RC will help physicians to provide early intervention with the appropriate treatment plan to prolong the life span of the diseased kidney, increase the survival chance of the patient, and thus improve the healthcare outcome in the U.S. and worldwide

Analysis of contrast-enhanced medical images.

Early detection of human organ diseases is of great importance for the accurate diagnosis and institution of appropriate therapies. This can potentially prevent progression to end-stage disease by detecting precursors that evaluate organ functionality. In addition, it also assists the clinicians for therapy evaluation, tracking diseases progression, and surgery operations. Advances in functional and contrast-enhanced (CE) medical images enabled accurate noninvasive evaluation of organ functionality due to their ability to provide superior anatomical and functional information about the tissue-of-interest. The main objective of this dissertation is to develop a computer-aided diagnostic (CAD) system for analyzing complex data from CE magnetic resonance imaging (MRI). The developed CAD system has been tested in three case studies: (i) early detection of acute renal transplant rejection, (ii) evaluation of myocardial perfusion in patients with ischemic heart disease after heart attack; and (iii), early detection of prostate cancer. However, developing a noninvasive CAD system for the analysis of CE medical images is subject to multiple challenges, including, but are not limited to, image noise and inhomogeneity, nonlinear signal intensity changes of the images over the time course of data acquisition, appearances and shape changes (deformations) of the organ-of-interest during data acquisition, determination of the best features (indexes) that describe the perfusion of a contrast agent (CA) into the tissue. To address these challenges, this dissertation focuses on building new mathematical models and learning techniques that facilitate accurate analysis of CAs perfusion in living organs and include: (i) accurate mathematical models for the segmentation of the object-of-interest, which integrate object shape and appearance features in terms of pixel/voxel-wise image intensities and their spatial interactions; (ii) motion correction techniques that combine both global and local models, which exploit geometric features, rather than image intensities to avoid problems associated with nonlinear intensity variations of the CE images; (iii) fusion of multiple features using the genetic algorithm. The proposed techniques have been integrated into CAD systems that have been tested in, but not limited to, three clinical studies. First, a noninvasive CAD system is proposed for the early and accurate diagnosis of acute renal transplant rejection using dynamic contrast-enhanced MRI (DCE-MRI). Acute rejection–the immunological response of the human immune system to a foreign kidney–is the most sever cause of renal dysfunction among other diagnostic possibilities, including acute tubular necrosis and immune drug toxicity. In the U.S., approximately 17,736 renal transplants are performed annually, and given the limited number of donors, transplanted kidney salvage is an important medical concern. Thus far, biopsy remains the gold standard for the assessment of renal transplant dysfunction, but only as the last resort because of its invasive nature, high cost, and potential morbidity rates. The diagnostic results of the proposed CAD system, based on the analysis of 50 independent in-vivo cases were 96% with a 95% confidence interval. These results clearly demonstrate the promise of the proposed image-based diagnostic CAD system as a supplement to the current technologies, such as nuclear imaging and ultrasonography, to determine the type of kidney dysfunction. Second, a comprehensive CAD system is developed for the characterization of myocardial perfusion and clinical status in heart failure and novel myoregeneration therapy using cardiac first-pass MRI (FP-MRI). Heart failure is considered the most important cause of morbidity and mortality in cardiovascular disease, which affects approximately 6 million U.S. patients annually. Ischemic heart disease is considered the most common underlying cause of heart failure. Therefore, the detection of the heart failure in its earliest forms is essential to prevent its relentless progression to premature death. While current medical studies focus on detecting pathological tissue and assessing contractile function of the diseased heart, this dissertation address the key issue of the effects of the myoregeneration therapy on the associated blood nutrient supply. Quantitative and qualitative assessment in a cohort of 24 perfusion data sets demonstrated the ability of the proposed framework to reveal regional perfusion improvements with therapy, and transmural perfusion differences across the myocardial wall; thus, it can aid in follow-up on treatment for patients undergoing the myoregeneration therapy. Finally, an image-based CAD system for early detection of prostate cancer using DCE-MRI is introduced. Prostate cancer is the most frequently diagnosed malignancy among men and remains the second leading cause of cancer-related death in the USA with more than 238,000 new cases and a mortality rate of about 30,000 in 2013. Therefore, early diagnosis of prostate cancer can improve the effectiveness of treatment and increase the patient’s chance of survival. Currently, needle biopsy is the gold standard for the diagnosis of prostate cancer. However, it is an invasive procedure with high costs and potential morbidity rates. Additionally, it has a higher possibility of producing false positive diagnosis due to relatively small needle biopsy samples. Application of the proposed CAD yield promising results in a cohort of 30 patients that would, in the near future, represent a supplement of the current technologies to determine prostate cancer type. The developed techniques have been compared to the state-of-the-art methods and demonstrated higher accuracy as shown in this dissertation. The proposed models (higher-order spatial interaction models, shape models, motion correction models, and perfusion analysis models) can be used in many of today’s CAD applications for early detection of a variety of diseases and medical conditions, and are expected to notably amplify the accuracy of CAD decisions based on the automated analysis of CE images

Combined Coronary CT-Angiography and TAVI-Planning: A Contrast-Neutral Routine Approach for Ruling-Out Significant Coronary Artery Disease

Background: Significant coronary artery disease (CAD) is a common finding in patients undergoing transcatheter aortic valve implantation (TAVI). Assessment of CAD prior to TAVI is recommended by current guidelines and is mainly performed via invasive coronary angiography (ICA). In this study we analyzed the ability of coronary CT-angiography (cCTA) to rule out significant CAD (stenosis ≥ 50%) during routine pre-TAVI evaluation in patients with high pre-test probability for CAD. Methods: In total, 460 consecutive patients undergoing pre-TAVI CT (mean age 79.6 ± 7.4 years) were included. All patients were examined with a retrospectively ECG-gated CT-scan of the heart, followed by a high-pitch-scan of the vascular access route utilizing a single intravenous bolus of 70 mL iodinated contrast medium. Images were evaluated for image quality, calcifications, and significant CAD; CT-examinations in which CAD could not be ruled out were defined as positive (CAD+). Routinely, patients received ICA (388/460; 84.3%; Group A), which was omitted if renal function was impaired and CAD was ruled out on cCTA (Group B). Following TAVI, clinical events were documented during the hospital stay. Results: cCTA was negative for CAD in 40.2% (188/460). Sensitivity, specificity, PPV, and NPV in Group A were 97.8%, 45.2%, 49.6%, and 97.4%, respectively. Median coronary artery calcium score (CAC) was higher in CAD+-patients but did not have predictive value for correct classification of patients with cCTA. There were no significant differences in clinical events between Group A and B. Conclusion: cCTA can be incorporated into pre-TAVI CT-evaluation with no need for additional contrast medium. cCTA may exclude significant CAD in a relatively high percentage of these high-risk patients. Thereby, cCTA may have the potential to reduce the need for ICA and total amount of contrast medium applied, possibly making pre-procedural evaluation for TAVI safer and faster

Biomedical Image Processing and Classification

Biomedical image processing is an interdisciplinary field involving a variety of disciplines, e.g., electronics, computer science, physics, mathematics, physiology, and medicine. Several imaging techniques have been developed, providing many approaches to the study of the human body. Biomedical image processing is finding an increasing number of important applications in, for example, the study of the internal structure or function of an organ and the diagnosis or treatment of a disease. If associated with classification methods, it can support the development of computer-aided diagnosis (CAD) systems, which could help medical doctors in refining their clinical picture

A novel MRA-based framework for the detection of changes in cerebrovascular blood pressure.

Background: High blood pressure (HBP) affects 75 million adults and is the primary or contributing cause of mortality in 410,000 adults each year in the United States. Chronic HBP leads to cerebrovascular changes and is a significant contributor for strokes, dementia, and cognitive impairment. Non-invasive measurement of changes in cerebral vasculature and blood pressure (BP) may enable physicians to optimally treat HBP patients. This manuscript describes a method to non-invasively quantify changes in cerebral vasculature and BP using Magnetic Resonance Angiography (MRA) imaging. Methods: MRA images and BP measurements were obtained from patients (n=15, M=8, F=7, Age= 49.2 ± 7.3 years) over a span of 700 days. A novel segmentation algorithm was developed to identify brain vasculature from surrounding tissue. The data was processed to calculate the vascular probability distribution function (PDF); a measure of the vascular diameters in the brain. The initial (day 0) PDF and final (day 700) PDF were used to correlate the changes in cerebral vasculature and BP. Correlation was determined by a mixed effects linear model analysis. Results: The segmentation algorithm had a 99.9% specificity and 99.7% sensitivity in identifying and delineating cerebral vasculature. The PDFs had a statistically significant correlation to BP changes below the circle of Willis (p-value = 0.0007), but not significant (p-value = 0.53) above the circle of Willis, due to smaller blood vessels. Conclusion: Changes in cerebral vasculature and pressure can be non-invasively obtained through MRA image analysis, which may be a useful tool for clinicians to optimize medical management of HBP

CAD system for early diagnosis of diabetic retinopathy based on 3D extracted imaging markers.

This dissertation makes significant contributions to the field of ophthalmology, addressing the segmentation of retinal layers and the diagnosis of diabetic retinopathy (DR). The first contribution is a novel 3D segmentation approach that leverages the patientspecific anatomy of retinal layers. This approach demonstrates superior accuracy in segmenting all retinal layers from a 3D retinal image compared to current state-of-the-art methods. It also offers enhanced speed, enabling potential clinical applications. The proposed segmentation approach holds great potential for supporting surgical planning and guidance in retinal procedures such as retinal detachment repair or macular hole closure. Surgeons can benefit from the accurate delineation of retinal layers, enabling better understanding of the anatomical structure and more effective surgical interventions. Moreover, real-time guidance systems can be developed to assist surgeons during procedures, improving overall patient outcomes. The second contribution of this dissertation is the introduction of a novel computeraided diagnosis (CAD) system for precise identification of diabetic retinopathy. The CAD system utilizes 3D-OCT imaging and employs an innovative approach that extracts two distinct features: first-order reflectivity and 3D thickness. These features are then fused and used to train and test a neural network classifier. The proposed CAD system exhibits promising results, surpassing other machine learning and deep learning algorithms commonly employed in DR detection. This demonstrates the effectiveness of the comprehensive analysis approach employed by the CAD system, which considers both low-level and high-level data from the 3D retinal layers. The CAD system presents a groundbreaking contribution to the field, as it goes beyond conventional methods, optimizing backpropagated neural networks to integrate multiple levels of information effectively. By achieving superior performance, the proposed CAD system showcases its potential in accurately diagnosing DR and aiding in the prevention of vision loss. In conclusion, this dissertation presents novel approaches for the segmentation of retinal layers and the diagnosis of diabetic retinopathy. The proposed methods exhibit significant improvements in accuracy, speed, and performance compared to existing techniques, opening new avenues for clinical applications and advancements in the field of ophthalmology. By addressing future research directions, such as testing on larger datasets, exploring alternative algorithms, and incorporating user feedback, the proposed methods can be further refined and developed into robust, accurate, and clinically valuable tools for diagnosing and monitoring retinal diseases

Vascular Function in Chronic Kidney Disease and in Renovascular Disease

Cardiovascular mortality is 15 to 30 times higher in patients with chronic kidney disease than in the age-adjusted general population. Even minor renal dysfunction predicts cardiovascular events and death in the general population. In patients with atherosclerotic renovascular disease the annual cardiovascular event and death rate is even higher. The abnormalities in coronary and peripheral artery function in the different stages of chronic kidney disease and in renovascular disease are still poorly understood, nor have the cardiac effects of renal artery revascularization been well characterized, although considered to be beneficial. This study was conducted to characterize myocardial perfusion and peripheral endothelial function in patients with chronic kidney disease and in patients with atherosclerotic renovascular disease. Myocardial perfusion was measured with positron emission tomography (PET) and peripheral endothelial function with brachial artery flow-mediated dilatation. It has been suggested that the poor renal outcomes after the renal artery revascularization could be due to damage in the stenotic kidney parenchyma; especially the reduction in the microvascular density, changes mainly evident at the cortical level which controls almost 80% of the total renal blood flow. This study was also performed to measure the effect of renal artery stenosis revascularization on renal perfusion in patients with renovascular disease. In order to do that a PET-based method for quantification of renal perfusion was developed. The coronary flow reserve of patients with chronic kidney disease was similar to the coronary flow reserve of healthy controls. In renovascular disease the coronary flow reserve was, however, markedly reduced. Flow-mediated dilatation of brachial artery was decreased in patients with chronic kidney disease compared to healthy controls, and even more so in patients with renovascular disease. After renal artery stenosis revascularization, coronary vascular function and renal perfusion did not improve in patients with atherosclerotic renovascular disease, but in patients with bilateral renal artery stenosis, flow-mediated dilatation improved. Chronic kidney disease does not significantly affect coronary vascular function. On the contrary, coronary vascular function was severely deteriorated in patients with atherosclerotic renovascular disease, possibly because of diffuse coronary artery disease and/or diffuse microvascular disease. The peripheral endothelial function was disturbed in patients with chronic kidney disease and even more so in patient with atherosclerotic renovascular disease. Renal artery stenosis dilatation does not seem to offer any benefits over medical treatment in patients with renovascular disease, since revascularization does not improve coronary vascular function or renal perfusion.Vaskulaarifunktio kroonisessa munuaisten vajaatoiminnassa ja renovaskulaaritaudissa Kuolleisuus sydän- ja verisuonisairauksiin kroonista munuaisten vajaatoimintaa sairastavilla potilailla on 15 – 30 -kertainen taustaväestöön verrattuna. On osoitettu, että jo lieväkin munuaisten vajaatoiminta ennustaa sydän- ja verisuonitauti tapahtumia ja kuolleisuutta väestössä. Ateroskleroottista renovaskulaaritautia sairastavilla vuotuinen kuolleisuus sydän- ja verisuonisairauksiin on vielä suurempi. Sepelvaltimoiden ja ääreisverenkierron toiminnasta on vain vähän tutkimustietoa niin munuaisten vajaatoimintaa kuin renovaskulaaritautiakin sairastavilla. Myöskään munuaisvaltimoahtauman laajentamisen aiheuttamia muutoksia sydämen ja ääreisverenkierron toimintaan ei ole juuri tutkittu. Tämän tutkimuksen tarkoituksena oli ensisijaisesti selvittää sydämen verenvirtausta, sepelvaltimoiden toiminnallista kapasiteettia eli sepelvaltimoiden virtausreserviä ja ääreisverenkierron toiminnan muutoksia niin kroonista munuaisten vajaatoimintaa sairastavilla kuin ateroskleroottista renovaskulaaritautia sairastavilla potilailla. Tutkimusmenetelminä käytettiin positroniemissiotomografiaa (PET) sydämen verenvirtauksen määrittämiseksi sekä ultraäänitutkimusta perifeerisen ääreisverenkierron toiminnan tutkimiseksi. Viime vuosina on arveltu, että kliinisten tutkimusten huonot tulokset munuaisvaltimoahtauman laajennuksen jälkeisesti liittyisivät munuaisten pienten verisuonten toiminnallisiin ja rakenteellisiin muutoksiin munuaisvaltimoahtauman vaikutuksesta. Muutoksia on havaittu erityisesti munuaisten kuorikerroksessa, joka käyttää noin 80 % munuaiseen ohjautuvasta verenvirtauksesta. Tutkimuksen toisena tavoitteena oli mitata mahdollisia muutoksia munuaisten verenvirtauksessa munuaisvaltimoahtauman laajentamisen jälkeisesti. Tätä varten kehitimme PET -metodin munuaisten verenvirtauksen määrittämiseksi. Sepelvaltimoiden virtausreservi kroonista munuaisten vajaatoimintaa sairastavilla ei eronnut tilastollisesti merkitsevästi terveiden verrokeiden virtausreservistä. Sen sijaan renovaskulaaritautipotilaiden virtausreservi oli vaikea-asteisesti huonontunut. Olkavarsivaltimon laajenemiskapasitetti oli huonontunut munuaisten vajaatoimintaa sairastavilla terveisiin verrattuna ja renovaskulaaritautipotilailla vielä enemmän. Munuaisvaltimoahtauman laajentamisen jälkeisesti renovaskulaaritautipotilaiden sepelvaltimoiden funktiossa tai munuaisten verenvirtauksessa ei havaittu parantumista. Kuitenkin molemminpuolisesta munuaisvaltimoahtaumasta kärsivillä ääreisverenkierron toiminta parantui tilastollisesti merkitsevästi toimenpiteen jälkeen. Johtopäätöksinä voidaan todeta, että munuaisten vajaatoiminta sinällään ei näyttäisi merkitsevästi huonontavan sepelvaltimoiden virtausreserviä. Sen sijaan renovaskulaaritautia sairastavilla virtausreservi on huono, todennäköisesti joko subkliinisen sepelvaltimotaudin ja/tai mikrovaskulaaritaudin aiheuttamana. Ääreisverenkierron toiminta sen sijaan oli huonontunut molemmissa potilasryhmissä, renovaskulaaritautipotilailla vielä enemmän. Munuaisvaltimoahtauman laajentaminen ei näytä tarjoavan hyötyä lääkehoitoon verrattuna, sillä toimenpiteen jälkeisesti ei havaittu muutoksia sydämen virtausreservissä sen paremmin kuin munuaisten verenvirtauksessakaan.Siirretty Doriast