Identifying Consensus and Open Questions around Assessing or Predicting the Quality and Success of Cartilage Repair: A Delphi Study

A range of surgical techniques have been developed for the repair or regeneration of lesioned cartilage in the human knee and a corresponding array of scoring systems have been created to assess their outcomes. The published literature displays a wide range of opinions regarding the factors that influence the success of surgical cartilage repair and which parameters are the most useful for measuring the quality of the repair at follow-up. Our objective was to provide some clarity to the field by collating items that were agreed upon by a panel of experts to be important in these areas. A modified, three-round Delphi consensus study was carried out consisting of one idea-generating focus-group and two subsequent, self-completed questionnaire rounds. In each round, items were assessed for their importance and level of consensus against pre-determined threshold levels. In total, 31 items reached consensus, including a hierarchy of tissues in the joint based on their importance in cartilage repair, markers of repair cartilage quality and the implications of environmental and patient-related factors. Items were stratified into those that can be employed for predicting the success of cartilage repair and those that could be used for assessing the structural quality of the resulting repair cartilage. Items that did not reach consensus represent areas where dissent remains and could, therefore, be used to guide future clinical and fundamental scientific research.</jats:p

The absence of detectable ADAMTS-4 (aggrecanase-1) activity in synovial fluid is a predictive indicator of autologous chondrocyte implantation success

Background: Autologous chondrocyte implantation (ACI) is used worldwide in the treatment of cartilage defects in the knee. Several demographic and injury-specific risk factors have been identified that can affect the success of ACI treatment. However, the discovery of predictive biomarkers in this field has thus far been overlooked. Purpose: To identify potential biomarkers in synovial fluid and plasma that can be used in the preoperative setting to help optimize patient selection for cell-based cartilage repair strategies. Study Design: Controlled laboratory study. Methods: Fifty-four ACI-treated patients were included. Cartilage oligomeric matrix protein (COMP), hyaluronan, soluble CD14 levels, and aggrecanase-1 (ADAMTS-4) activity in synovial fluid and COMP and hyaluronan in plasma were measured. Baseline and postoperative functional outcomes were determined using the patient-reported Lysholm score. To find predictors of postoperative function, linear and logistic regression analyses were performed. The dependent variables were the baseline and postoperative Lysholm score; the independent variables were patient age and body mass index, defect location, defect area, having a bone-on-bone defect, type of defect patch (periosteum or collagen), requirement of an extra procedure, and baseline biomarker levels. Results: The mean baseline Lysholm score was 47.4 ± 17.0, which improved to 64.6 ± 21.7 postoperatively. The activity of ADAMTS-4 in synovial fluid was identified as an independent predictor of the postoperative Lysholm score. Indeed, simply the presence or absence of ADAMTS-4 activity in synovial fluid appeared to be the most important predictive factor. As determined by contingency analysis, when ADAMTS-4 activity was detectable, the odds of being a responder were 3 times smaller than when ADAMTS-4 activity was not detectable. Other predictive factors were the baseline Lysholm score, age at ACI, and defect patch type used. Conclusion: The absence of ADAMTS-4 activity in the synovial fluid of joints with cartilage defects may be used in conjunction with known demographic risk factors in the development of an ACI treatment algorithm to help inform the preclinical decision

The Automation of the Born Inversion for Ultrasonic Flaw Sizing

The Born approximation has been widely employed as a basis for determining flaw sizes using individual pulse-echo waveforms together with the assumption of an ellipsoidal flaw geometry. A major difficulty in implementing such algorithms has been the determination of the time delay corresponding to the flaw centroid. However, both the time delay calculation and the flaw size determination itself can be performed in an optimal fashion using statistical estimation techniques with an appropriate error model. We will discuss the application of these techniques to an automated flaw-sizing algorithm requiring a minimum of operator input, and will compare the results obtained by this method with those obtained by previous operator-intensive methods

Which Algorithm Best Propagates the Meyer-Miller-Stock-Thoss Mapping Hamiltonian for Non-Adiabatic Dynamics?

A common strategy to simulate mixed quantum-classical dynamics is by propagating classical trajectories with mapping variables, often using the Meyer-Miller-Stock-Thoss (MMST) Hamiltonian or the related spin-mapping approach. When mapping the quantum subsystem, the coupled dynamics reduce to a set of equations of motion to integrate. Several numerical algorithms have been proposed, but a thorough performance comparison appears to be lacking. Here, we compare three time-propagation algorithms for the MMST Hamiltonian: the Momentum Integral (MInt) (J. Chem. Phys., 2018, 148, 102326), the Split-Liouvillian (SL) (Chem. Phys., 2017, 482, 124-134), and the algorithm in J. Chem. Phys., 2012, 136, 084101 that we refer to as the Degenerate Eigenvalue (DE) algorithm due to the approximation required during derivation. We analyze the accuracy of individual trajectories, correlation functions, energy conservation, symplecticity, Liouville's theorem, and the computational cost. We find that the MInt algorithm is the only rigorously symplectic algorithm. However, comparable accuracy at a lower computational cost can be obtained with the SL algorithm. The approximation implicitly made within the DE algorithm conserves energy poorly, even for small timesteps, and thus leads to slightly different results. These results should guide future mapping-variable simulations

Development of a tool to predict outcome of Autologous Chondrocyte Implantation

Objective. The study had 2 objectives: first, to evaluate the success of autologous chondrocyte implantation (ACI) in terms of incidence of surgical re-intervention, including arthroplasty, and investigate predictors of successful treatment outcome. The second objective was to derive a tool predicting a patient’s arthroplasty risk following ACI. Design. In this Level II, prognostic study, 170 ACI-treated patients (110 males [aged 36.8 ± 9.4 years]; 60 females [aged 38.1 ± 10.2 years]) completed a questionnaire about further surgery on their knee treated with ACI 10.9 ± 3.5 years previously. Factors commonly assessed preoperatively (age, gender, defect location and number, previous surgery at this site, and the preoperative Lysholm score) were used as independent factors in regression analyses. Results. At final follow-up (maximum of 19 years post-ACI), 40 patients (23.5%) had undergone surgical re-intervention following ACI. Twenty-six patients (15.3%) underwent arthroplasty, more commonly females (25%) than males (10%; P = 0.001). Cox regression analyses identified 4 factors associated with re-intervention: age at ACI, multiple operations before ACI, patellar defects, and lower pretreatment Lysholm scores (Nagelkerke’s R2 = 0.20). Six predictive items associated with risk of arthroplasty following ACI (Nagelkerke’s R2 = 0.34) were used to develop the Oswestry Risk of Knee Arthroplasty index with internal crossvalidation. Conclusion. In a single-center study, we have identified 6 factors (age, gender, location and number of defects, number of previous operations, and Lysholm score before ACI) that appear to influence the likelihood of ACI patients progressing to arthroplasty. We have used this information to propose a formula or “tool” that could aid treatment decisions and improve patient selection for ACI

The Inverse Born Approximation: Exact Determination of Shape of Convex Voids

The Inverse Born Approximation (IBA) to the elastic wave inverse scattering problem is known to give highly accurate results for the shape of complex voids. In this paper we present an argument demonstrating that the IBA is, in fact, exact for determining the size, shape and orientation of a wide class of these scatterers given infinite bandwidth and unlimited aperture information. Essentially, our argument demonstrates how the IBA algorithm picks out the singular contribution to the impulse response function and correctly relates it to the shape of the scatterer. Some specific examples will be used to illustrate the more intuitive aspects of the discussion

Streaming Algorithm for Euler Characteristic Curves of Multidimensional Images

We present an efficient algorithm to compute Euler characteristic curves of gray scale images of arbitrary dimension. In various applications the Euler characteristic curve is used as a descriptor of an image. Our algorithm is the first streaming algorithm for Euler characteristic curves. The usage of streaming removes the necessity to store the entire image in RAM. Experiments show that our implementation handles terabyte scale images on commodity hardware. Due to lock-free parallelism, it scales well with the number of processor cores. Our software---CHUNKYEuler---is available as open source on Bitbucket. Additionally, we put the concept of the Euler characteristic curve in the wider context of computational topology. In particular, we explain the connection with persistence diagrams

Long and Intermediate Wavelength Flaw Reconstruction

An automated multiviewing ultrasonic transducer and a first generation signal processing program have been developed for the purpose of detecting and characterizing flaws in materials. The multiviewing transducer has been designed and developed to exploit advances in theoretical inverse elastic wave scattering in the long and intermediate wavelength regime made in recent years. The signal processing algorithm has been assembled as a first step in the development of a decision-tree algorithm for flaw characterization. First results that have been obtained in a completely automatic mode are given in this paper. It is concluded that this new long and intermediate wavelength, model-based reconstruction is feasible and potentially very useful in quantitative NDE applications on real systems