by
Mohammad H. Al-Shaer;
Wael A. Alghamdi;
Abdullah Alsultan;
Guohua An;
Shahriar Ahmed;
Yosra Alkabab;
Sayera Banu;
Ketevan Barbakadze;
Eric Houpt;
Maia Kipiani;
Lali Mikiashvili;
J. Cegielski;
Russell Kempker;
Scott K. Heysell;
Charles A. Peloquin
Fluoroquinolones are group A drugs in tuberculosis guidelines. We aim to compare the culture conversion between new-generation (levofloxacin and moxifloxacin) and old-generation (ciprofloxacin and ofloxacin) fluoroquinolones, develop pharmacokinetic models, and calculate target attainment for levofloxacin and moxifloxacin.
We included three U.S. tuberculosis centers. Patients admitted between 1984 and 2015, infected with drug-resistant tuberculosis, and who had received fluoroquinolones for 28 days were included. Demographics, sputum cultures and susceptibility, treatment regimens, and serum concentrations were collected. A time-to-event analysis was conducted, and Cox proportional hazards model was used to compare the time to culture conversion. Using additional data from ongoing studies, pharmacokinetic modelling and Monte Carlo simulations were performed to assess target attainment for different doses. Overall, 124 patients received fluoroquinolones. The median age was 40 years, and the median weight was 60 kg. Fifty-six patients (45%) received old-generation fluoroquinolones.
New-generation fluoroquinolones showed a faster time to culture conversion (median 16 versus 40 weeks, P 0.012). After adjusting for isoniazid and clofazimine treatment, patients treated with new-generation fluoroquinolones were more likely to have culture conversion (adjusted hazards ratio, 2.16 [95% confidence interval, 1.28 to 3.64]). We included 178 patients in the pharmacokinetic models. Levofloxacin and moxifloxacin were best described by a one-compartment model with first-order absorption and elimination. At least 1,500 to 1,750 mg levofloxacin and 800 mg moxifloxacin may be needed for maximum kill at the current epidemiologic cutoff values.
In summary, new-generation fluoroquinolones showed faster time to culture conversion compared to the old generation. For optimal target attainment at the current MIC values, higher doses of levofloxacin and moxifloxacin may be needed.
Although it is possible to produce the same movement using an infinite number of different muscle activation patterns owing to musculoskeletal redundancy, the degree to which observed variations in muscle activity can deviate from optimal solutions computed from biomechanical models is not known. Here, we examined the range of biomechanically permitted activation levels in individual muscles during human walking using a detailed musculoskeletal model and experimentally-measured kinetics and kinematics. Feasible muscle activation ranges define the minimum and maximum possible level of each muscle's activation that satisfy inverse dynamics joint torques assuming that all other muscles can vary their activation as needed. During walking, 73% of the muscles had feasible muscle activation ranges that were greater than 95% of the total muscle activation range over more than 95% of the gait cycle, indicating that, individually, most muscles could be fully active or fully inactive while still satisfying inverse dynamics joint torques. Moreover, the shapes of the feasible muscle activation ranges did not resemble previously-reported muscle activation patterns nor optimal solutions, i.e. static optimization and computed muscle control, that are based on the same biomechanical constraints. Our results demonstrate that joint torque requirements from standard inverse dynamics calculations are insufficient to define the activation of individual muscles during walking in healthy individuals. Identifying feasible muscle activation ranges may be an effective way to evaluate the impact of additional biomechanical and/or neural constraints on possible versus actual muscle activity in both normal and impaired movements.
Functional mitral regurgitation (FMR) is a significant complication of left ventricular dysfunction and strongly associated with a poor prognosis. In this study, we developed a patient-specific finite element (FE) model of the mitral apparatus in a FMR patient which included: both leaflets with thickness, annulus, chordae tendineae, and chordae insertions on the leaflets and origins on the papillary muscles. The FE model incorporated human age- and gender-matched anisotropic hyperelastic material properties, and MV closure at systole was simulated. The model was validated by comparing the FE results from valve closure simulation with the in vivo geometry of the MV at systole. It was found that the FE model could not replicate the in vivo MV geometry without the application of tethering pre-tension force in the chordae at diastole. Upon applying the pre-tension force and performing model optimization by adjusting the chordal length, position, and leaflet length, a good agreement between the FE model and the in vivo model was established. Not only were the chordal forces high at both diastole and systole, but the tethering force on the anterior papillary muscle was higher than that of the posterior papillary muscle, which resulted in an asymmetrical gap with a larger orifice area at the anterolateral commissure resulting in MR. The analyses further show that high peak stress and strain were found at the chordal insertions where large chordal tethering forces were found. This study shows that the pre-tension tethering force plays an important role in accurately simulating the MV dynamics in this FMR patient, particularly in quantifying the degree of leaflet coaptation and stress distribution. Due to the complexity of the disease, the patient-specific computational modeling procedure of FMR patients presented should be further evaluated using a large patient cohort. However, this study provides useful insights into the MV biomechanics of a FMR patient, and could serve as a tool to assist in pre-operative planning for MV repair or replacement surgical or interventional procedures.
The frequency of head computed tomography (CT) imaging for mild head trauma patients has raised safety and cost concerns. Validated clinical decision rules exist in the published literature and on-line sources to guide medical image ordering but are often not used by emergency department (ED) clinicians. Using simulation, we explored whether the presentation of a clinical decision rule (i.e. Canadian CT Head Rule - CCHR), findings from malpractice cases related to clinicians not ordering CT imaging in mild head trauma cases, and estimated patient out-of-pocket cost might influence clinician brain CT ordering. Understanding what type and how information may influence clinical decision making in the ordering advanced medical imaging is important in shaping the optimal design and implementation of related clinical decision support systems. Methods: Multi-center, double-blinded simulation-based randomized controlled trial. Following standardized clinical vignette presentation, clinicians made an initial imaging decision for the patient. This was followed by additional information on decision support rules, malpractice outcome review, and patient cost; each with opportunity to modify their initial order. The malpractice and cost information differed by assigned group to test the any temporal relationship. The simulation closed with a second vignette and an imaging decision. Results: One hundred sixteen of the 167 participants (66.9%) initially ordered a brain CT scan. After CCHR presentation, the number of clinicians ordering a CT dropped to 76 (45.8%), representing a 21.1% reduction in CT ordering (P = 0.002). This reduction in CT ordering was maintained, in comparison to initial imaging orders, when presented with malpractice review information (p = 0.002) and patient cost information (p = 0.002). About 57% of clinicians changed their order during study, while 43% never modified their imaging order. Conclusion: This study suggests that ED clinician brain CT imaging decisions may be influenced by clinical decision support rules, patient out-of-pocket cost information and findings from malpractice case review. Trial registration: NCT03449862, February 27, 2018, Retrospectively registered.
Computational fluid dynamics (CFD) tools have been extensively applied to study the hemodynamics in the total cavopulmonary connection (TCPC) in patients with only a single functioning ventricle. Without the contraction of a sub-pulmonary ventricle, pulsatility of flow through this connection is low and variable across patients, which is usually neglected in most numerical modeling studies. Recent studies suggest that such pulsatility can be non-negligible and can be important in hemodynamic predictions. The goal of this work is to compare the results of an in-house numerical methodology for simulating pulsatile TCPC flow with experimental results. Digital particle image velocimetry (DPIV) was acquired on TCPC in vitro models to evaluate the capability of the CFD tool in predicting pulsatile TCPC flow fields. In vitro hemodynamic measurements were used to compare the numerical prediction of power loss across the connection. The results demonstrated the complexity of the pulsatile TCPC flow fields and the validity of the numerical approach in simulating pulsatile TCPC flow dynamics in both idealized and complex patient specific models.
Simulating realistic musculoskeletal dynamics is critical to understanding neural control of muscle activity evoked in sensorimotor feedback responses that have inherent neural transmission delays. Thus, the initial mechanical response of muscles to perturbations in the absence of any change in muscle activity determines which corrective neural responses are required to stabilize body posture. Muscle short-range stiffness, a history-dependent property of muscle that causes a rapid and transient rise in muscle force upon stretch, likely affects musculoskeletal dynamics in the initial mechanical response to perturbations. Here we identified the contributions of short-range stiffness to joint torques and angles in the initial mechanical response to support surface translations using dynamic simulation. We developed a dynamic model of muscle short-range stiffness to augment a Hill-type muscle model. Our simulations show that short-range stiffness can provide stability against external perturbations during the neuromechanical response delay. Assuming constant muscle activation during the initial mechanical response, including muscle short-range stiffness was necessary to account for the rapid rise in experimental sagittal plane knee and hip joint torques that occurs simultaneously with very small changes in joint angles and reduced root mean square errors between simulated and experimental torques by 56% and 47%, respectively. Moreover, forward simulations lacking short-range stiffness produced unreasonably large joint angle changes during the initial response. Using muscle models accounting for short-range stiffness along with other aspects of history-dependent muscle dynamics may be important to advance our ability to simulate inherently unstable human movements based on principles of neural control and biomechanics.
Total cavopulmonary connection is the result of a series of palliative surgical repairs performed on patients with single ventricle heart defects. The resulting anatomy has complex and unsteady hemodynamics characterized by flow mixing and flow separation. Although varying degrees of flow pulsatility have been observed in vivo, non-pulsatile (time-averaged) boundary conditions have traditionally been assumed in hemodynamic modeling, and only recently have pulsatile conditions been incorporated without completely characterizing their effect or importance. In this study, 3D numerical simulations with both pulsatile and non-pulsatile boundary conditions were performed for 24 patients with different anatomies and flow boundary conditions from Georgia Tech database. Flow structures, energy dissipation rates and pressure drops were compared under rest and simulated exercise conditions. It was found that flow pulsatility is the primary factor in determining the appropriate choice of boundary conditions, whereas the anatomic configuration and cardiac output had secondary effects. Results show that the hemodynamics can be strongly influenced by the presence of pulsatile flow. However, there was a minimum pulsatility threshold, identified by defining a weighted pulsatility index (wPI), above which the influence was significant. It was shown that when wPI < 30%, the relative error in hemodynamic predictions using time-averaged boundary conditions was less than 10% compared to pulsatile simulations. In addition, when wPI < 50, the relative error was less than 20%. A correlation was introduced to relate wPI to the relative error in predicting the flow metrics with non-pulsatile flow conditions.
Background: The development of clinically applicable fluid-structure interaction (FSI) models of the left heart is inherently challenging when using in vivo cardiovascular magnetic resonance (CMR) data for validation, due to the lack of a well-controlled system where detailed measurements of the ventricular wall motion and flow field are available a priori. The purpose of this study was to (a) develop a clinically relevant, CMR-compatible left heart physical model; and (b) compare the left ventricular (LV) volume reconstructions and hemodynamic data obtained using CMR to laboratory-based experimental modalities. Methods: The LV was constructed from optically clear flexible silicone rubber. The geometry was based off a healthy patient's LV geometry during peak systole. The LV phantom was attached to a left heart simulator consisting of an aorta, atrium, and systemic resistance and compliance elements. Experiments were conducted for heart rate of 70 bpm. Wall motion measurements were obtained using high speed stereo-photogrammetry (SP) and cine-CMR, while flow field measurements were obtained using digital particle image velocimetry (DPIV) and phase-contrast magnetic resonance (PC-CMR). Results: The model reproduced physiologically accurate hemodynamics (aortic pressure = 120/80 mmHg; cardiac output = 3.5 L/min). DPIV and PC-CMR results of the center plane flow within the ventricle matched, both qualitatively and quantitatively, with flow from the atrium into the LV having a velocity of about 1.15 m/s for both modalities. The normalized LV volume through the cardiac cycle computed from CMR data matched closely to that from SP. The mean difference between CMR and SP was 5.5 ± 3.7 %. Conclusions: The model presented here can thus be used for the purposes of: (a) acquiring CMR data for validation of FSI simulations, (b) determining accuracy of cine-CMR reconstruction methods, and (c) conducting investigations of the effects of altering anatomical variables on LV function under normal and disease conditions.
To conduct a patient-specific computational modeling of the aortic valve, 3-D aortic valve anatomic geometries of an individual patient need to be reconstructed from clinical 3-D cardiac images. Currently, most of computational studies involve manual heart valve geometry reconstruction and manual finite element (FE) model generation, which is both time-consuming and prone to human errors. A seamless computational modeling framework, which can automate this process based on machine learning algorithms, is desirable, as it can not only eliminate human errors and ensure the consistency of the modeling results but also allow fast feedback to clinicians and permits a future population-based probabilistic analysis of large patient cohorts. In this study, we developed a novel computational modeling method to automatically reconstruct the 3-D geometries of the aortic valve from computed tomographic images. The reconstructed valve geometries have built-in mesh correspondence, which bridges harmonically for the consequent FE modeling. The proposed method was evaluated by comparing the reconstructed geometries from 10 patients with those manually created by human experts, and a mean discrepancy of 0.69 mm was obtained. Based on these reconstructed geometries, FE models of valve leaflets were developed, and aortic valve closure from end systole to middiastole was simulated for 7 patients and validated by comparing the deformed geometries with those manually created by human experts, and a mean discrepancy of 1.57 mm was obtained. The proposed method offers great potential to streamline the computational modeling process and enables the development of a preoperative planning system for aortic valve disease diagnosis and treatment.
Neuromusculoskeletal models solve the basic problem of determining how the body moves under the influence of external and internal forces. Existing biomechanical modeling programs often emphasize dynamics with the goal of finding a feed-forward neural program to replicate experimental data or of estimating force contributions or individual muscles. The computation of rigid-body dynamics, muscle forces, and activation of the muscles are often performed separately. We have developed an intrinsically forward computational platform (Neuromechanic, www.neuromechanic.com) that explicitly represents the interdependencies among rigid body dynamics, frictional contact, muscle mechanics, and neural control modules. This formulation has significant advantages for optimization and forward simulation, particularly with application to neural controllers with feedback or regulatory features. Explicit inclusion of all state dependencies allows calculation of system derivatives with respect to kinematic states and muscle and neural control states, thus affording a wealth of analytical tools, including linearization, stability analyses and calculation of initial conditions for forward simulations. In this review, we describe our algorithm for generating state equations and explain how they may be used in integration, linearization, and stability analysis tools to provide structural insights into the neural control of movement.