Browsing by Author "Sawchuk, Alan P."

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    A new noninvasive and patient-specific hemodynamic index for the severity of renal stenosis and outcome of interventional treatment
    (Wiley, 2022-07) Yu, Huidan; Khan, Monsurul; Wu, Hao; Du, Xiaoping; Chen, Rou; Rollins, Dave M.; Fang, Xin; Long, Jianyun; Xu, Chenke; Sawchuk, Alan P.; Surgery, School of Medicine
    Renal arterial stenosis (RAS) often causes renovascular hypertension, which may result in kidney failure and life-threatening consequences. Direct assessment of the hemodynamic severity of RAS has yet to be addressed. In this work, we present a computational concept to derive a new, noninvasive, and patient-specific index to assess the hemodynamic severity of RAS and predict the potential benefit to the patient from a stenting therapy. The hemodynamic index is derived from a functional relation between the translesional pressure indicator (TPI) and lumen volume reduction (S) through a parametric deterioration of the RAS. Our in-house computational platform, InVascular, for image-based computational hemodynamics is used to compute the TPI at given S. InVascular integrates unified computational modeling for both image processing and computational hemodynamics with graphic processing unit parallel computing technology. The TPI-S curve reveals a pair of thresholds of S indicating mild or severe RAS. The TPI at S = 0 represents the pressure improvement following a successful stenting therapy. Six patient cases with a total of 6 aortic and 12 renal arteries are studied. The computed blood pressure waveforms have good agreements with the in vivo measured ones and the systolic pressure is statistical equivalence to the in-vivo measurements with p < .001. Uncertainty quantification provides the reliability of the computed pressure through the corresponding 95% confidence interval. The severity assessments of RAS in four cases are consistent with the medical practice. The preliminary results inspire a more sophisticated investigation for real medical insights of the new index. This computational concept can be applied to other arterial stenoses such as iliac stenosis. Such a noninvasive and patient-specific hemodynamic index has the potential to aid in the clinical decision-making of interventional treatment with reduced medical cost and patient risks.
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    Aggressive Surveillance Is Needed to Detect Endoleaks and Junctional Separation between Device Components after Zenith Fenestrated Aortic Reconstruction
    (Elsevier, 2019) Wang, S. Keisin; Lemmon, Gary W.; Gupta, Alok K.; Dalsing, Michael C.; Sawchuk, Alan P.; Motaganahalli, Raghu L.; Murphy, Michael P.; Fajardo, Andres; Surgery, School of Medicine
    Background Junctional separation and resulting type IIIa endoleak is a well-known problem after EVAR (endovascular aneurysm repair). This complication results in sac pressurization, enlargement, and eventual rupture. In this manuscript, we review the incidence of this late finding in our experience with the Cook Zenith fenestrated endoprosthesis (ZFEN, Bloomington, IN). Methods A retrospective review was performed of a prospectively maintained institutional ZFEN fenestrated EVAR database capturing all ZFENs implanted at a large-volume, academic hospital system. Patients who experienced junctional separation between the fenestrated main body and distal bifurcated graft (with or without type IIIa endoleak) at any time after initial endoprosthesis implantation were subject to further evaluation of imaging and medical records to abstract clinical courses. Results In 110 ZFENs implanted from October 2012 to December 2017 followed for a mean of 1.5 years, we observed a 4.5% and 2.7% incidence of clinically significant junctional separation and type IIIa endoleak, respectively. Junctional separation was directly related to concurrent type Ib endoleak in all 5 patients. Three patients presented with sac enlargement. One patient did not demonstrate any evidence of clinically significant endoleak and had a decreasing sac size during follow-up imaging. The mean time to diagnosis of modular separation in these patients was 40 months. Junctional separation was captured in surveillance in 2 patients and reintervened upon before manifestation of endoleak. However, the remaining 3 patients completed modular separation resulting in rupture and emergent intervention in 2 and an aortic-related mortality in the other. Conclusions Junctional separation between the fenestrated main and distal bifurcated body with the potential for type IIIa endoleak is an established complication associated with the ZFEN platform. Therefore, we advocate for maximizing aortic overlap during the index procedure followed by aggressive surveillance and treatment of stent overlap loss captured on imaging.
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    Computational methods to automate the initial interpretation of lower extremity arterial Doppler and duplex carotid ultrasound studies
    (Elsevier, 2021) Luo, Xiao; Ara, Lena; Ding, Haoran; Rollins, David; Motaganahalli, Raghu; Sawchuk, Alan P.; Surgery, School of Medicine
    Background: Lower extremity arterial Doppler (LEAD) and duplex carotid ultrasound studies are used for the initial evaluation of peripheral arterial disease and carotid stenosis. However, intra- and inter-laboratory variability exists between interpreters, and other interpreter responsibilities can delay the timeliness of the report. To address these deficits, we examined whether machine learning algorithms could be used to classify these Doppler ultrasound studies. Methods: We developed a hierarchical deep learning model to classify aortoiliac, femoropopliteal, and trifurcation disease in LEAD ultrasound studies and a random forest machine learning algorithm to classify the amount of carotid stenosis from duplex carotid ultrasound studies using experienced physician interpretation in an active, credentialed vascular laboratory as the reference standard. Waveforms, pressures, flow velocities, and the presence of plaque were input into a hierarchal neural network. Artificial intelligence was developed to automate the interpretation of these LEAD and carotid duplex ultrasound studies. Statistical analysis was performed using the confusion matrix. Results: We extracted 5761 LEAD ultrasound studies from 2015 to 2017 and 18,650 duplex carotid ultrasound studies from 2016 to 2018 from the Indiana University Health system. The results showed the ability of artificial intelligence algorithms and method, with 97.0% accuracy for predicting normal cases, 88.2% accuracy for aortoiliac disease, 90.1% accuracy for femoropopliteal disease, and 90.5% accuracy for trifurcation disease. For internal carotid artery stenosis, the accuracy was 99.2% for predicting 0% to 49% stenosis, 100% for predicting 50% to 69% stenosis, 100% for predicting >70% stenosis, and 100% for predicting occlusion. For common carotid artery stenosis, the accuracy was 99.9% for predicting 0% to 49% stenosis, 100% for predicting 50% to 99% stenosis, and 100% for predicting occlusion. Conclusions: The machine learning models using LEAD data, with the collected blood pressure and waveform data, and duplex carotid ultrasound data with the flow velocities and the presence of plaque, showed that novel machine learning models are reliable in differentiating normal from diseased arterial systems and accurate in classifying the extent of vascular disease.
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    Inlet and Outlet Boundary Conditions and Uncertainty Quantification in Volumetric Lattice Boltzmann Method for Image-Based Computational Hemodynamics
    (MDPI, 2022-01-10) Yu, Huidan; Khan, Monsurul; Wu, Hao; Zhang, Chunze; Du, Xiaoping; Chen, Rou; Fang, Xin; Long, Jianyun; Sawchuk, Alan P.; Surgery, School of Medicine
    Inlet and outlet boundary conditions (BCs) play an important role in newly emerged image-based computational hemodynamics for blood flows in human arteries anatomically extracted from medical images. We developed physiological inlet and outlet BCs based on patients’ medical data and integrated them into the volumetric lattice Boltzmann method. The inlet BC is a pulsatile paraboloidal velocity profile, which fits the real arterial shape, constructed from the Doppler velocity waveform. The BC of each outlet is a pulsatile pressure calculated from the three-element Windkessel model, in which three physiological parameters are tuned by the corresponding Doppler velocity waveform. Both velocity and pressure BCs are introduced into the lattice Boltzmann equations through Guo’s non-equilibrium extrapolation scheme. Meanwhile, we performed uncertainty quantification for the impact of uncertainties on the computation results. An application study was conducted for six human aortorenal arterial systems. The computed pressure waveforms have good agreement with the medical measurement data. A systematic uncertainty quantification analysis demonstrates the reliability of the computed pressure with associated uncertainties in the Windkessel model. With the developed physiological BCs, the image-based computation hemodynamics is expected to provide a computation potential for the noninvasive evaluation of hemodynamic abnormalities in diseased human vessels.
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    Inlet and Outlet Boundary Conditions and Uncertainty Quantification in Volumetric Lattice Boltzmann Method for Image-Based Computational Hemodynamics
    (MDPI, 2022) Yu, Huidan; Khan, Monsurul; Wu, Hao; Zhang, Chunze; Du, Xiaoping; Chen, Rou; Fang, Xin; Long, Jianyun; Sawchuk, Alan P.; Mechanical and Energy Engineering, School of Engineering and Technology
    Inlet and outlet boundary conditions (BCs) play an important role in newly emerged image-based computational hemodynamics for blood flows in human arteries anatomically extracted from medical images. We developed physiological inlet and outlet BCs based on patients’ medical data and integrated them into the volumetric lattice Boltzmann method. The inlet BC is a pulsatile paraboloidal velocity profile, which fits the real arterial shape, constructed from the Doppler velocity waveform. The BC of each outlet is a pulsatile pressure calculated from the three-element Windkessel model, in which three physiological parameters are tuned by the corresponding Doppler velocity waveform. Both velocity and pressure BCs are introduced into the lattice Boltzmann equations through Guo’s non-equilibrium extrapolation scheme. Meanwhile, we performed uncertainty quantification for the impact of uncertainties on the computation results. An application study was conducted for six human aortorenal arterial systems. The computed pressure waveforms have good agreement with the medical measurement data. A systematic uncertainty quantification analysis demonstrates the reliability of the computed pressure with associated uncertainties in the Windkessel model. With the developed physiological BCs, the image-based computation hemodynamics is expected to provide a computation potential for the noninvasive evaluation of hemodynamic abnormalities in diseased human vessels.
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    A new noninvasive and patient-specific hemodynamic index for the severity of renal stenosis and outcome of interventional treatment
    (Wiley, 2022) Yu, Huidan; Khan, Monsurul; Wu, Hao; Du, Xiaoping; Chen, Rou; Rollins, Dave M.; Fang, Xin; Long, Jianyun; Xu, Chenke; Sawchuk, Alan P.; Mechanical and Energy Engineering, School of Engineering and Technology
    Renal arterial stenosis (RAS) often causes renovascular hypertension, which may result in kidney failure and life-threatening consequences. Direct assessment of the hemodynamic severity of RAS has yet to be addressed. In this work, we present a computational concept to derive a new, noninvasive, and patient-specific index to assess the hemodynamic severity of RAS and predict the potential benefit to the patient from a stenting therapy. The hemodynamic index is derived from a functional relation between the translesional pressure indicator (TPI) and lumen volume reduction (S) through a parametric deterioration of the RAS. Our in-house computational platform, InVascular, for image-based computational hemodynamics is used to compute the TPI at given S. InVascular integrates unified computational modeling for both image processing and computational hemodynamics with GPU parallel computing technology. The TPI-S curve reveals a pair of thresholds of S indicating mild or severe RAS. The TPI at S=0 represents the pressure improvement following a successful stenting therapy. Six patient cases with a total of 6 aortic and 12 renal arteries are studied. The computed blood pressure waveforms have good agreements with the in-vivo measured ones and the systolic pressure is statistical equivalence to the in-vivo measurements with p<0.001. Uncertainty quantification provides the reliability of the computed pressure through the corresponding 95% confidence interval. The severity assessments of RAS in four cases are consistent with the medical practice. The preliminary results inspire a more sophisticated investigation for real medical insights of the new index. This computational concept can be applied to other arterial stenoses such as iliac stenosis. Such a noninvasive and patient-specific hemodynamic index has the potential to aid in the clinical decision-making of interventional treatment with reduced medical cost and patient risks.
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    Predicting future occlusion or stenosis of lower extremity bypass grafts using artificial intelligence to simultaneously analyze all flow velocities collected in current and previous ultrasound examinations
    (Elsevier, 2024-02-05) Luo, Xiao; Tahabi, Fattah Muhammad; Rollins, Dave M.; Sawchuk, Alan P.; Biostatistics and Health Data Science, Richard M. Fairbanks School of Public Health
    Objective: Routine surveillance with duplex ultrasound (DUS) examination is recommended after femoral-popliteal and femoral-tibial-pedal vein bypass grafts with various intervals postoperatively. The presently used methodology to analyze bypass graft DUS examination does not use all the available data and has been shown to have a significant rate for missing impending bypass graft failure. The objective of this research is to investigate recurrent neural networks (RNNs) to predict future bypass graft occlusion or stenosis. Methods: This study includes DUS examinations of 663 patients who had bypass graft operations done between January 2009 and June 2022. Only examinations without missing values were included. We developed two RNNs (a bidirectional long short-term memory unit and a bidirectional gated recurrent unit) to predict bypass graft occlusion and stenosis based on peak systolic velocities collected in the 2 to 5 previous DUS examinations. We excluded the examinations with missing values and split our data into training and test sets. Then, we applied 10-fold cross-validation on training to optimize the hyperparameters and compared models using the test data. Results: The bidirectional long short-term memory unit model can gain an overall sensitivity of 0.939, specificity of 0.963, and area under the curve of 0.950 on the prediction of bypass graft occlusion, and an overall sensitivity of 0.915, specificity of 0.909, and area under the curve of 0.912 predicting the development of a future critical stenosis. The results on different bypass types show that the system performs differently on different types. The results on subcohorts based on gender, smoking status, and comorbidities show that the performance on current smokers is lower than the never smoker. Conclusions: We found that RNNs can gain good sensitivity, specificity, and accuracy for the detection of impending bypass graft occlusion or the future development of a critical bypass graft stenosis using all the available peak systolic velocity data in the present and previous bypass graft DUS examinations. Integrating clinical data, including demographics, social determinants, medication, and other risk factors, together with the DUS examination may result in further improvements. Clinical relevance: Detecting bypass graft failure before it occurs is important clinically to prevent amputations, salvage limbs, and save lives. Current methods evaluating screening duplex ultrasound examinations have a significant failure rate for detecting a bypass graft at risk for failure. Artificial intelligence using recurrent neural networks has the potential to improve the detection of at-risk bypass graft before they fail. Additionally, artificial intelligence is in the news and is being applied to many fields. Vascular surgeons need to know its potential to improve vascular outcomes.
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    Treatment of a traumatic aortic bifurcation injury with an iliac branch endoprosthesis
    (Elsevier, 2020-04-23) Wang, S. Keisin; Motaganahalli, Raghu L.; Maijub, John G.; Sawchuk, Alan P.; Surgery, School of Medicine
    We present the case of a 62-year-old man who sustained a traumatic distal aortic injury associated with an adjacent lumbar vertebral body fracture resulting from a 20-ft fall. Given the site of injury, an iliac artery branched endograft was deployed off-label to preserve the aortic bifurcation and cover a limited amount of healthy aorta to preserve the collaterals. The procedure was successful, with no intraoperative complications or evidence of an endoleak. The aortic bifurcation and distal iliac arteries remained widely patent by computed tomography angiography at the follow-up examination without evidence of sequelae.
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    An unusual arteriovenous malformation involving the cervical vessels treated with endovascular repair
    (Elsevier, 2019-04-28) Miladore, Julia N.; Sawchuk, Alan P.; Surgery, School of Medicine
    We present an unusual and complex arteriovenous malformation involving the vertebral artery, subclavian artery, and internal jugular vein in a 31-year-old man with no history of trauma or catheterization. The repair was done using endovascular techniques to minimize complications from nerve or vascular injury. The massively dilated jugular vein has remained diminished in size and the patient has remained asymptomatic at 8 months. We discuss the occurrence of this rare malformation as well as treatment options along with their risks and benefits.