Numerical Simulation of Concussive-Generated Cortical Spreading Depolarization to Optimize DC-EEG Electrode Spacing for Noninvasive Visual Detection.

BACKGROUND: Cortical spreading depolarization (SD) is a propagating depolarization wave of neurons and glial cells in the cerebral gray matter. SD occurs in all forms of severe acute brain injury, as documented by using invasive detection methods. Based on many experimental studies of mechanical brain deformation and concussion, the occurrence of SDs in human concussion has often been hypothesized. However, this hypothesis cannot be confirmed in humans, as SDs can only be detected with invasive detection methods that would require either a craniotomy or a burr hole to be performed on athletes. Typical electroencephalography electrodes, placed on the scalp, can help detect the possible presence of SD but have not been able to accurately and reliably identify SDs. METHODS: To explore the possibility of a noninvasive method to resolve this hurdle, we developed a finite element numerical model that simulates scalp voltage changes that are induced by a brain surface SD. We then compared our simulation results with retrospectively evaluated data in patients with aneurysmal subarachnoid hemorrhage from Drenckhahn et al. (Brain 135:853, 2012). RESULTS: The ratio of peak scalp to simulated peak cortical voltage, Vscalp/Vcortex, was 0.0735, whereas the ratio from the retrospectively evaluated data was 0.0316 (0.0221, 0.0527) (median [1st quartile, 3rd quartile], n = 161, p < 0.001, one sample Wilcoxon signed-rank test). These differing values provide validation because their differences can be attributed to differences in shape between concussive SDs and aneurysmal subarachnoid hemorrhage SDs, as well as the inherent limitations in human study voltage measurements. This simulated scalp surface potential was used to design a virtual scalp detection array. Error analysis and visual reconstruction showed that 1 cm is the optimal electrode spacing to visually identify the propagating scalp voltage from a cortical SD. Electrode spacings of 2 cm and above produce distorted images and high errors in the reconstructed image. CONCLUSIONS: Our analysis suggests that concussive (and other) SDs can be detected from the scalp, which could confirm SD occurrence in human concussion, provide concussion diagnosis on the basis of an underlying physiological mechanism, and lead to noninvasive SD detection in the setting of severe acute brain injury.

Morphological Analysis of the Right Ventricular Endocardial Wall in Pulmonary Hypertension.

Pulmonary hypertension (PH) is a chronic progressive disease diagnosed when the pressure in the main pulmonary artery, assessed by right heart catheterization (RHC), is greater than 25 mmHg. Changes in the pulmonary vasculature due to the high pressure yield an increase in the right ventricle (RV) afterload. This starts a remodeling process during which the ventricle exhibits changes in shape and eventually fails. RV models were obtained from the segmentation of cardiac magnetic resonance images at baseline and 1-year follow-up for a pilot study that involved 12 PH and 7 control subjects. The models were used to create surface meshes of the geometry and to compute the principal, mean, and Gaussian curvatures. Ten global curvature indices were calculated for each of the RV endocardial wall reconstructions at the end-diastolic volume (EDV) and end-systolic volume (ESV) phases of the cardiac cycle. Statistical analysis of the data was performed to discern if there are significant differences in the curvature indices between controls and the PH group, as well as between the baseline and follow-up phases for the PH subjects. Six curvature indices, namely, the Gaussian curvature at ESV, the mean curvature at EDV and ESV, the L2-norm of the mean curvature at ESV, and the L2-norm of the major principal curvature at EDV and ESV, were found to be significantly different between controls and PH subjects (p < 0.05). We infer that these geometry measures could be used as indicators of RV endocardial wall morphology changes. Two global parameters, the Gaussian and mean curvatures at ESV, showed significant changes at the one-year follow-up for the PH subjects (p < 0.05). The aforementioned geometry measures to assess changes in RV shape could be used as part of a noninvasive computational tool to aid clinicians in PH diagnostic and progression assessment, and to evaluate the effectiveness of treatment.

Design, Development, and Temporal Evaluation of a Magnetic Resonance Imaging-Compatible In Vitro Circulation Model Using a Compliant Abdominal Aortic Aneurysm Phantom.

Biomechanical characterization of abdominal aortic aneurysms (AAAs) has become commonplace in rupture risk assessment studies. However, its translation to the clinic has been greatly limited due to the complexity associated with its tools and their implementation. The unattainability of patient-specific tissue properties leads to the use of generalized population-averaged material models in finite element analyses, which adds a degree of uncertainty to the wall mechanics quantification. In addition, computational fluid dynamics modeling of AAA typically lacks the patient-specific inflow and outflow boundary conditions that should be obtained by nonstandard of care clinical imaging. An alternative approach for analyzing AAA flow and sac volume changes is to conduct in vitro experiments in a controlled laboratory environment. In this study, we designed, built, and characterized quantitatively a benchtop flow loop using a deformable AAA silicone phantom representative of a patient-specific geometry. The impedance modules, which are essential components of the flow loop, were fine-tuned to ensure typical intraluminal pressure conditions within the AAA sac. The phantom was imaged with a magnetic resonance imaging (MRI) scanner to acquire time-resolved images of the moving wall and the velocity field inside the sac. Temporal AAA sac volume changes lead to a corresponding variation in compliance throughout the cardiac cycle. The primary outcome of this work was the design optimization of the impedance elements, the quantitative characterization of the resistive and capacitive attributes of a compliant AAA phantom, and the exemplary use of MRI for flow visualization and quantification of the deformed AAA geometry.

Alk1 controls arterial endothelial cell migration in lumenized vessels.

Heterozygous loss of the arterial-specific TGFß type I receptor, activin receptor-like kinase 1 (ALK1; ACVRL1), causes hereditary hemorrhagic telangiectasia (HHT). HHT is characterized by development of fragile, direct connections between arteries and veins, or arteriovenous malformations (AVMs). However, how decreased ALK1 signaling leads to AVMs is unknown. To understand the cellular mis-steps that cause AVMs, we assessed endothelial cell behavior in alk1-deficient zebrafish embryos, which develop cranial AVMs. Our data demonstrate that alk1 loss has no effect on arterial endothelial cell proliferation but alters arterial endothelial cell migration within lumenized vessels. In wild-type embryos, alk1-positive cranial arterial endothelial cells generally migrate towards the heart, against the direction of blood flow, with some cells incorporating into endocardium. In alk1-deficient embryos, migration against flow is dampened and migration in the direction of flow is enhanced. Altered migration results in decreased endothelial cell number in arterial segments proximal to the heart and increased endothelial cell number in arterial segments distal to the heart. We speculate that the consequent increase in distal arterial caliber and hemodynamic load precipitates the flow-dependent development of downstream AVMs.

A novel toolkit to improve percutaneous subxiphoid needle access to the healthy pericardial sac.

INTRODUCTION: The current practice of percutaneous subxiphoid needle access to the "healthy" pericardial sac has significant limitations. We sought to examine the feasibility of a novel toolkit designed to improve this procedure. METHODS AND RESULTS: The toolkit included a pericardial access needle and a virtual imaging platform. The needle had a 0.036 inch outer diameter, abbreviated 25° bevel, and was electrically insulated except for two small surfaces recessed from the tip. Radiofrequency energy was delivered via these surfaces to facilitate pericardial perforation. The virtual imaging system demonstrated the needle in real time and in its entirety within the thoracic anatomy of the individual animal, which was reconstructed from computed tomographic images obtained preoperatively and registered to the operative field. In five large (40-60 kg) healthy pigs, percutaneous subxiphoid access to the sac using both anterior and posterior approaches was performed. Spatial inaccuracy was measured as the distance between the pericardial puncture site and the anterior or posterior descending coronary artery, the pericardium contiguous to which had been targeted by the needle. In each animal, pericardial access was gained at 4 discrete sites (2 anterior, 2 posterior). Inaccuracy was 4.2 ± 2.2 millimeters (range 0-8 millimeters) and did not differ significantly between anterior and posterior approaches. No damage to the epicardium or coronary arteries was observed. CONCLUSIONS: Percutaneous subxiphoid access to the pericardial sac utilizing this toolkit was feasible, including safety and reasonable accuracy.

CT-Electromagnetic Three-Dimensional Tracking for Renal Endovascular Sympathetic Ablation Catheter Positioning in an Animal Model.

PURPOSE: To examine the feasibility of a computed tomography (CT)-guided renal endoarterial ablation procedure. MATERIALS AND METHODS: Five large (40-60 kg) pigs were studied. Abdominal CT images that were obtained preoperatively were registered to the operative field, and subsequent virtual navigation of a multielectrode ablation catheter within the CT environment was performed. The ablation electrodes were visualized using an impedance technique. Ablation lesions were applied within each artery. Histologic analysis was performed to judge accuracy of the catheter location as portrayed in the virtual image. RESULTS: The error between virtual and actual lesion locations along the longitudinal plane of the artery was 3.0 mm ± 2.5 (range, 0-6 mm) in the left renal artery and 3.4 mm ± 2.7 (range, 0-7 mm; P = .7) in the right renal artery. The accuracy of circumferential localization of the electrode was 92% in the right artery and 88% in the left artery. CONCLUSIONS: These data demonstrate that a CT-guided renal artery ablation procedure is feasible.

cMRI-BED: A novel informatics framework for cardiac MRI biomarker extraction and discovery applied to pediatric cardiomyopathy classification.

BACKGROUND: Pediatric cardiomyopathies are a rare, yet heterogeneous group of pathologies of the myocardium that are routinely examined clinically using Cardiovascular Magnetic Resonance Imaging (cMRI). This gold standard powerful non-invasive tool yields high resolution temporal images that characterize myocardial tissue. The complexities associated with the annotation of images and extraction of markers, necessitate the development of efficient workflows to acquire, manage and transform this data into actionable knowledge for patient care to reduce mortality and morbidity. METHODS: We develop and test a novel informatics framework called cMRI-BED for biomarker extraction and discovery from such complex pediatric cMRI data that includes the use of a suite of tools for image processing, marker extraction and predictive modeling. We applied our workflow to obtain and analyze a dataset of 83 de-identified cases and controls containing cMRI-derived biomarkers for classifying positive versus negative findings of cardiomyopathy in children. Bayesian rule learning (BRL) methods were applied to derive understandable models in the form of propositional rules with posterior probabilities pertaining to their validity. Popular machine learning methods in the WEKA data mining toolkit were applied using default parameters to assess cross-validation performance of this dataset using accuracy and percentage area under ROC curve (AUC) measures. RESULTS: The best 10-fold cross validation predictive performance obtained on this cMRI-derived biomarker dataset was 80.72% accuracy and 79.6% AUC by a BRL decision tree model, which is promising from this type of rare data. Moreover, we were able to verify that mycocardial delayed enhancement (MDE) status, which is known to be an important qualitative factor in the classification of cardiomyopathies, is picked up by our rule models as an important variable for prediction. CONCLUSIONS: Preliminary results show the feasibility of our framework for processing such data while also yielding actionable predictive classification rules that can augment knowledge conveyed in cardiac radiology outcome reports. Interactions between MDE status and other cMRI parameters that are depicted in our rules warrant further investigation and validation. Predictive rules learned from cMRI data to classify positive and negative findings of cardiomyopathy can enhance scientific understanding of the underlying interactions among imaging-derived parameters.

The Computational Fluid Dynamics Rupture Challenge 2013--Phase II: Variability of Hemodynamic Simulations in Two Intracranial Aneurysms.

With the increased availability of computational resources, the past decade has seen a rise in the use of computational fluid dynamics (CFD) for medical applications. There has been an increase in the application of CFD to attempt to predict the rupture of intracranial aneurysms, however, while many hemodynamic parameters can be obtained from these computations, to date, no consistent methodology for the prediction of the rupture has been identified. One particular challenge to CFD is that many factors contribute to its accuracy; the mesh resolution and spatial/temporal discretization can alone contribute to a variation in accuracy. This failure to identify the importance of these factors and identify a methodology for the prediction of ruptures has limited the acceptance of CFD among physicians for rupture prediction. The International CFD Rupture Challenge 2013 seeks to comment on the sensitivity of these various CFD assumptions to predict the rupture by undertaking a comparison of the rupture and blood-flow predictions from a wide range of independent participants utilizing a range of CFD approaches. Twenty-six groups from 15 countries took part in the challenge. Participants were provided with surface models of two intracranial aneurysms and asked to carry out the corresponding hemodynamics simulations, free to choose their own mesh, solver, and temporal discretization. They were requested to submit velocity and pressure predictions along the centerline and on specified planes. The first phase of the challenge, described in a separate paper, was aimed at predicting which of the two aneurysms had previously ruptured and where the rupture site was located. The second phase, described in this paper, aims to assess the variability of the solutions and the sensitivity to the modeling assumptions. Participants were free to choose boundary conditions in the first phase, whereas they were prescribed in the second phase but all other CFD modeling parameters were not prescribed. In order to compare the computational results of one representative group with experimental results, steady-flow measurements using particle image velocimetry (PIV) were carried out in a silicone model of one of the provided aneurysms. Approximately 80% of the participating groups generated similar results. Both velocity and pressure computations were in good agreement with each other for cycle-averaged and peak-systolic predictions. Most apparent "outliers" (results that stand out of the collective) were observed to have underestimated velocity levels compared to the majority of solutions, but nevertheless identified comparable flow structures. In only two cases, the results deviate by over 35% from the mean solution of all the participants. Results of steady CFD simulations of the representative group and PIV experiments were in good agreement. The study demonstrated that while a range of numerical schemes, mesh resolution, and solvers was used, similar flow predictions were observed in the majority of cases. To further validate the computational results, it is suggested that time-dependent measurements should be conducted in the future. However, it is recognized that this study does not include the biological aspects of the aneurysm, which needs to be considered to be able to more precisely identify the specific rupture risk of an intracranial aneurysm.

Left atrial ligation alters intracardiac flow patterns and the biomechanical landscape in the chick embryo.

BACKGROUND: Hypoplastic left heart syndrome (HLHS) is a major human congenital heart defect that results in single ventricle physiology and high mortality. Clinical data indicate that intracardiac blood flow patterns during cardiac morphogenesis are a significant etiology. We used the left atrial ligation (LAL) model in the chick embryo to test the hypothesis that LAL immediately alters intracardiac flow streams and the biomechanical environment, preceding morphologic and structural defects observed in HLHS. RESULTS: Using fluorescent dye injections, we found that intracardiac flow patterns from the right common cardinal vein, right vitelline vein, and left vitelline vein were altered immediately following LAL. Furthermore, we quantified a significant ventral shift of the right common cardinal and right vitelline vein flow streams. We developed an in silico model of LAL, which revealed that wall shear stress was reduced at the left atrioventricular canal and left side of the common ventricle. CONCLUSIONS: Our results demonstrate that intracardiac flow patterns change immediately following LAL, supporting the role of hemodynamics in the progression of HLHS. Sites of reduced WSS revealed by computational modeling are commonly affected in HLHS, suggesting that changes in the biomechanical environment may lead to abnormal growth and remodeling of left heart structures.

Computed tomographic analysis of left ventricular volumes and function after implantation of the Parachute® endoventricular partitioning device.

BACKGROUND: Preliminary clinical experience with a percutaneous endoventricular partitioning device (Parachute®, CardioKinetix Inc., Menlo Park, CA, USA) suggests that it ameliorates global LV dysfunction and heart failure symptoms in selected patients who have suffered previous anterior myocardial infarction. Less is known of its effect on regional LV function. OBJECTIVE: To gain insight into device effect on regional LV function by analysis of cardiac computed tomographic (CT) images obtained before and after device implantation. METHODS: Comparative analysis of pre and 6 months post-implantation contrast-enhanced CT images from 6 subjects enrolled in the phase 1 Parachute clinical trials, including regional LV volume and systolic excursion, as well as device motion. RESULTS: After implantation, a significant reduction in volume of the "dynamic" LV compartment (that which was not excluded by the device) was accompanied by a significant reduction in dykinetic motion and a trend toward an improved ejection fraction. Penetration of contrast into the excluded compartment was still present at 6 months, however the apical motion was significantly less dyskinetic in 3 subjects and unchanged in the other 3. Overall device surface motion was inward in systole, a significant improvement relative to the overall dyskinetic LV apex pre-implantation. Device motion was spatially heterogeneous, which appeared to be dependent on the motility of the myocardium that anchored its individual splines. CONCLUSIONS: Our data suggest that the Parachute device acts as a functional impediment to flow and stretch, effectively depressurizing the apical segment.

Single-photon emission computed tomographic-multidetector computed tomographic fusion image integration: a potential aid to left ventricular substrate ablation.

AIMS: To illustrate the feasibility of ventricular tachycardia (VT) ablation assisted by single photon emission computed tomography (SPECT)-multidetector (MDCT) computed tomography 'fusion' image guidance. METHODS AND RESULTS: A patient with ischaemic cardiomyopathy and recurrent VT underwent catheter ablation. Prior to the procedure, SPECT and MDCT had been obtained. A combined ('fusion') image was created, and this image was registered to the operative field using a commercial catheter navigation system. There was a close anatomic and electrophysiological correspondence between the left ventricular electroanatomic map obtained in the operating theatre and the fusion image. CONCLUSION: If accuracy of this technique can be confirmed, fusion image guidance may offer a significant value during catheter ablation of VT, including improved substrate detail and procedure abbreviation.

Presurgical evaluation of Fontan connection options for patients with apicocaval juxtaposition using computational fluid dynamics.

Apicocaval juxtaposition (ACJ) is a rare congenital heart defect associated with single ventricle physiology where optimal positioning of the Fontan conduit for completion of total cavopulmonary connection (TCPC) is still controversial. In ACJ, the cardiac apex is ipsilateral with the inferior vena cava (IVC), risking kinking and collapse of the Fontan conduit at the apex of the heart. The purpose of this study is to evaluate two viable routes for Fontan conduit connection in patients with ACJ, using computational fluid dynamics. Internal energy loss evaluations were used to determine contribution of conduit curvature to the energy efficiency of each cavopulmonary anastomosis configuration. This percentage of energy loss contribution was found to be greater in the case of a curved extracardiac conduit connection (44%, 4.1 mW) traveling behind the ventricular apex, connecting the IVC to the left pulmonary artery, than the straighter lateral tunnel conduit (6%, 1.4 mW) installed through the ventricular apex. In contrast, net energy loss across the anastomosis was significantly lower with extracardiac TCPC (9.3 mW) in comparison with lateral tunnel TCPC (23.2 mW), highlighting that a curved Fontan conduit is favorable provided that it is traded off for a superior cavopulmonary connection efficiency. Therefore, a relatively longer and curved Fontan conduit has been demonstrated to be a suitable connection option independent of anatomical situations.

Novel fenestration designs for controlled venous flow shunting in failing Fontans with systemic venous hypertension.

The Fontan procedure is employed as the final-stage palliation in single-ventricle congenital heart patients and results in diversion of venous blood directly to the pulmonary arteries. Fontan patients have been known to suffer from postoperative systemic venous hypertension, which in turn is associated with pleural effusions and protein losing enteropathy, leading to a decreased duration and quality of life. Despite the ongoing debate on its benefits, a circular fenestration hole (typically 4 mm) establishing a venous shunt to the common atrium is traditionally employed to relieve venous pressure in the Fontan conduit and improve early postoperative Fontan hemodynamics. However, these improvements come at the cost of reduced oxygen saturation due to excessive right-to-left shunting if the fenestration is permanent. The ideal "selective" fenestration would therefore limit or eliminate shunt flow at tolerable systemic venous pressures and allow increased flow at high pressures. The objective of this study is to introduce new fenestration designs that exhibit these desirable pressure-flow characteristics. Novel plus-shaped and S-shaped fenestration designs with leaflets are introduced as alternatives to the traditional circular fenestration, each having identical effective orifice areas at the fully open states. In vitro steady leakage flow tests were performed for physiological flow-driving pressures in order to obtain pressure-drop versus flow-rate characteristics. In addition, the leaflet opening kinematics of the plus-shaped fenestration was investigated computationally using finite element simulation. Fluid-structure interaction analysis was performed to determine leaflet displacements and pressure-flow characteristics at low pressures. Further, a lumped parameter model of the single-ventricle circuit was created to simulate pulsatile flow conditions For the plus-shaped fenestration, the flow rate was found to increase nonlinearly with increased driving systemic venous pressures at high physiological-pressure drops which did not cause the leaflets to fully open, and linearly for low driving pressures. These results indicate that leaflets of the plus-shaped fenestration design activated passively after a critical systemic venous pressure threshold. This feature is ideal for minimizing undesirable excessive venous shunting. A large variability in shunting flow rate may be obtained by changing the shape, thickness, size, and material of the fenestration to suit requirements of the patient, which can further limit shunt flow in a controlled manner.

Variability of computational fluid dynamics solutions for pressure and flow in a giant aneurysm: the ASME 2012 Summer Bioengineering Conference CFD Challenge.

Stimulated by a recent controversy regarding pressure drops predicted in a giant aneurysm with a proximal stenosis, the present study sought to assess variability in the prediction of pressures and flow by a wide variety of research groups. In phase I, lumen geometry, flow rates, and fluid properties were specified, leaving each research group to choose their solver, discretization, and solution strategies. Variability was assessed by having each group interpolate their results onto a standardized mesh and centerline. For phase II, a physical model of the geometry was constructed, from which pressure and flow rates were measured. Groups repeated their simulations using a geometry reconstructed from a micro-computed tomography (CT) scan of the physical model with the measured flow rates and fluid properties. Phase I results from 25 groups demonstrated remarkable consistency in the pressure patterns, with the majority predicting peak systolic pressure drops within 8% of each other. Aneurysm sac flow patterns were more variable with only a few groups reporting peak systolic flow instabilities owing to their use of high temporal resolutions. Variability for phase II was comparable, and the median predicted pressure drops were within a few millimeters of mercury of the measured values but only after accounting for submillimeter errors in the reconstruction of the life-sized flow model from micro-CT. In summary, pressure can be predicted with consistency by CFD across a wide range of solvers and solution strategies, but this may not hold true for specific flow patterns or derived quantities. Future challenges are needed and should focus on hemodynamic quantities thought to be of clinical interest.

Automatic Segmentation of the Left Atrium from Computed Tomography Angiography Images.

The left atrial appendage (LAA) causes 91% of thrombi in atrial fibrillation patients, a potential harbinger of stroke. Leveraging computed tomography angiography (CTA) images, radiologists interpret the left atrium (LA) and LAA geometries to stratify stroke risk. Nevertheless, accurate LA segmentation remains a time-consuming task with high inter-observer variability. Binary masks of the LA and their corresponding CTA images were used to train and test a 3D U-Net to automate LA segmentation. One model was trained using the entire unified-image-volume while a second model was trained on regional patch-volumes which were run for inference and then assimilated back into the full volume. The unified-image-volume U-Net achieved median DSCs of 0.92 and 0.88 for the train and test sets, respectively; the patch-volume U-Net achieved median DSCs of 0.90 and 0.89 for the train and test sets, respectively. This indicates that the unified-image-volume and patch-volume U-Net models captured up to 88 and 89% of the LA/LAA boundary's regional complexity, respectively. Additionally, the results indicate that the LA/LAA were fully captured in most of the predicted segmentations. By automating the segmentation process, our deep learning model can expedite LA/LAA shape, informing stratification of stroke risk.

Subject-specific factors affecting particle residence time distribution of left atrial appendage in atrial fibrillation: A computational model-based study.

Background: Atrial fibrillation (AF) is a prevalent arrhythmia, that causes thrombus formation, ordinarily in the left atrial appendage (LAA). The conventional metric of stroke risk stratification, CHA2DS2-VASc score, does not account for LAA morphology or hemodynamics. We showed in our previous study that residence time distribution (RTD) of blood-borne particles in the LAA and its associated calculated variables (i.e., mean residence time, tm , and asymptotic concentration, C ∞) have the potential to improve CHA2DS2-VASc score. The purpose of this research was to investigate the effects of the following potential confounding factors on LAA tm and C ∞: (1) pulmonary vein flow waveform pulsatility, (2) non-Newtonian blood rheology and hematocrit level, and (3) length of the simulation. Methods: Subject-Specific data including left atrial (LA) and LAA cardiac computed tomography, cardiac output (CO), heart rate, and hematocrit level were gathered from 25 AF subjects. We calculated LAA tm and C ∞ based on series of computational fluid dynamics (CFD) analyses. Results: Both LAA tm and C ∞ are significantly affected by the CO, but not by temporal pattern of the inlet flow. Both LAA tm and C ∞ increase with increasing hematocrit level and both calculated indices are higher for non-Newtonian blood rheology for a given hematocrit level. Further, at least 20,000 s of CFD simulation is needed to calculate LAA tm and C ∞ values reliably. Conclusions: Subject-specific LA and LAA geometries, CO, and hematocrit level are essential to quantify the subject-specific proclivity of blood cell tarrying inside LAA in terms of the RTD function.

Length of Stay in the Neonatal ICU is Predictable using Heart Rate: An Opportunity for Optimizing Managed Care.

We explore the use of classification and regression models for predicting the length of stay (LoS) of neonatal patients in the intensive care unit (ICU), using heart rate (HR) time-series data of 7,758 patients from the MIMIC-IH database. We find that aggregated features of HR on the first full-day of in-patient stay after admission (i.e. the first day with a full 24-hour record for each patient) can be leveraged to classify LoS in excess of 10 days with 89% sensitivity and 59% specificity. As such, LoS as a continuous variable was also found to be statistically significantly correlated to aggregate HR data corresponding to the first full-day after admission.

Convolutional Neural Network based Segmentation of Abdominal Aortic Aneurysms.

Abdominal aortic aneurysms (AAAs) are balloonlike dilations in the descending aorta associated with high mortality rates. Between 2009 and 2019, reported ruptured AAAs resulted in ~28,000 deaths while reported unruptured AAAs led to ~15,000 deaths. Automating identification of the presence, 3D geometric structure, and precise location of AAAs can inform clinical risk of AAA rupture and timely interventions. We investigate the feasibility of automatic segmentation of AAAs, inclusive of the aorta, aneurysm sac, intra-luminal thrombus, and surrounding calcifications, using 30 patient-specific computed tomography angiograms (CTAs). Binary masks of the AAA and their corresponding CTA images were used to train and test a 3D U-Net - a convolutional neural network (CNN) - model to automate AAA detection. We also studied model-specific convergence and overall segmentation accuracy via a loss-function developed based on the Dice Similarity Coefficient (DSC) for overlap between the predicted and actual segmentation masks. Further, we determined optimum probability thresholds (OPTs) for voxel-level probability outputs of a given model to optimize the DSC in our training set, and utilized 3D volume rendering with the visualization tool kit (VTK) to validate the same and inform the parameter optimization exercise. We examined model-specific consistency with regard to improving accuracy by training the CNN with incrementally increasing training samples and examining trends in DSC and corresponding OPTs that determine AAA segmentations. Our final trained models consistently produced automatic segmentations that were visually accurate with train and test set losses in inference converging as our training sample size increased. Transfer learning led to improvements in DSC loss in inference, with the median OPT of both the training segmentations and testing segmentations approaching 0.5, as more training samples were utilized.

Intelligent patient monitoring for proactive alerting of key personnel in intensive care: A single-center study.

A code blue event is an emergency code to indicate when a patient goes into cardiac arrest and needs resuscitation. In this paper, we model the binary response of a intensive care unit (ICU) patients experiencing a code-blue event, starting with vital time-series data of patients in 12 ICU beds. Our study introduces day-of and day-ahead risk scoring models trained against ground truth information on per-patient-per-day code-blue events, starting with multi-variate vital-time-series-sequences of varying durations with a plurality of engineered features capturing temporal variations of these signals. Actionable events, including code-blue events, aggregated by patient by day were predicted on the day-of or day-ahead with an overall accuracy of over 80% in our best models. Such models have potential to improve healthcare delivery by providing just-in-time alerting, enabling proactive and preventative clinical interventions, through continuous patient monitoring.

Modeling Cases and Deaths per Million using Daily-Aggregated Facebook COVID-19 Symptom Survey Data.

We develop a novel analytic approach to modeling future COVID-19 risk using COVID-19 Symptom Survey data aggregated daily by US state, joined with daily time-series data on confirmed cases and deaths. Specifically, we model N-day forward-looking estimates for per-US-state-per-day change in deaths per million (DPM) and cases per million (CPM) using a multivariate regression model to below baseline error (65% and 38% mean absolute percentage error for DPM/CPM, respectively). Additionally, we model future changes in the curvature of CPM/DPM as "increasing" or "decreasing" using a random forest classifier to above 72% accuracy. In sum, we develop and characterize models to establish a relationship between behaviors and beliefs of individuals captured via the Facebook COVID-19 Symptom Surveys and the trajectory of COVID-19 outbreaks evidenced in terms of CPM and DPM. Such information can be helpful in assessing collective risks of infection and death during a pandemic as well as in determining the effectiveness of appropriate risk mitigation strategies based on behaviors evidenced through survey responses.