Developing and Validating a Model of Humeral Stem Primary Stability, Intended for In Silico Clinical Trials.

In silico clinical trials (ISCT) can contribute to demonstrating a device's performance via credible computational models applied on virtual cohorts. Our purpose was to establish the credibility of a model for assessing the risk of humeral stem loosening in total shoulder arthroplasty, based on a twofold validation scheme involving both benchtop and clinical validation activities, for ISCT applications. A finite element model computing bone-implant micromotion (benchtop model) was quantitatively compared to a bone foam micromotion test (benchtop comparator) to ensure that the physics of the system was captured correctly. The model was expanded to a population-based approach (clinical model) and qualitatively evaluated based on its ability to replicate findings from a published clinical study (clinical comparator), namely that grit-blasted stems are at a significantly higher risk of loosening than porous-coated stems, to ensure that clinical performance of the stem can be predicted appropriately. Model form sensitivities pertaining to surgical variation and implant design were evaluated. The model replicated benchtop micromotion measurements (52.1 ± 4.3 µm), without a significant impact of the press-fit ("Press-fit": 54.0 ± 8.5 µm, "No press-fit": 56.0 ± 12.0 µm). Applied to a virtual population, the grit-blasted stems (227 ± 78µm) experienced significantly larger micromotions than porous-coated stems (162 ± 69µm), in accordance with the findings of the clinical comparator. This work provides a concrete example for evaluating the credibility of an ISCT study. By validating the modeling approach against both benchtop and clinical data, model credibility is established for an ISCT application aiming to enrich clinical data in a regulatory submission.

Quantification of the passive behavior of the glenohumeral joint: A biomechanical study.

Shoulder stabilization and arthroplasty procedures aim to restore the complex motion innate to the glenohumeral joint relying on proper tensioning of the surrounding soft-tissues at the time of surgery. Joint instability remains a leading cause for revisions of these procedures necessitating a deeper understanding of the passive constraint of the intact glenohumeral joint. The current literature lacks comprehensive analysis of the passive glenohumeral joint in all degrees-of-freedom (DOF). The objective of the present study is to better understand this complex joint by quantifying the passive laxity of the glenohumeral joint in multiple DOFs over a range of motion. Sixteen fresh-frozen cadaveric shoulders were tested in the intact state using a robotic simulator capable of six-DOF motion. The limits of range of motion was quantified in separate laxity tests applying a ± 2 Nm internal-external (IE) torque, ±20 N anterior-posterior (AP) force, ±20 N superior-inferior (SI) force and a 44 N distraction force at six levels of glenohumeral abduction. Overall, glenohumeral joint laxity was greatest between 15° and 45° of abduction except for SI translation which increased with abduction. IE rotation and AP translation were dominated by external rotation and anterior translation, respectively. Although early abduction and late abduction produced similar laxities, the increase in laxity in the mid abduction range indicates it is important to assess the shoulder joint throughout the range of motion and not just at these two end points. The presented laxity data establishes a baseline for intact shoulder laxity over a range of motion in multiple DOFs under known loading conditions.

Identifying the patient harms to include in an in silico clinical trial.

BACKGROUND AND OBJECTIVE: Clinical trials represent a crucial step in the development and approval of medical devices. These trials involve evaluating the safety and efficacy of the device in a controlled setting with human subjects. However, traditional clinical trials can be expensive, time-consuming, and ethically challenging. Augmenting clinical trials with data from computer simulations, so called in silico clinical trials (ISCT), has the potential to address these challenges while satisfying regulatory requirements. However, determination of the patient harms in scope of an ISCT is necessary to ensure all harms are sufficiently addressed while maximizing the utility of the ISCT. This topic is currently lacking guidance. The objective of this work is to propose a general method to determine which patient harms should be included in an ISCT for a regulatory submission. METHODS: The proposed method considers the risk associated with the harm, the impact of the device on the likelihood of occurrence of the harm and the technical feasibility of evaluating the harm via ISCT. Consideration of the risk associated with the harm provides maximum clinical impact of the ISCT, in terms of focusing on those failure modes which are most relevant to the patient population. Consideration of the impact of the device on a particular harm, and the technical feasibility of modeling a particular harm supports that the technical effort is devoted to a problem that (1) is relevant to the device in question, and (2) can be solved with contemporary modeling techniques. RESULTS AND CONCLUSIONS: As a case study, the proposed method is applied to a total shoulder replacement humeral system. With this framework, it is hoped that a consistent approach to scoping an ISCT can be adopted, supporting investment in ISCT by the industry, enabling consistent review of the ISCT approach across device disciplines by regulators, and providing maximum impact of modeling technologies in support of devices to improve patient outcomes.

A Critical Comparison of Comparators Used to Demonstrate Credibility of Physics-Based Numerical Spine Models.

The ability of new medical devices and technology to demonstrate safety and effectiveness, and consequently acquire regulatory approval, has been dependent on benchtop, in vitro, and in vivo evidence and experimentation. Regulatory agencies have recently begun accepting computational models and simulations as credible evidence for virtual clinical trials and medical device development. However, it is crucial that any computational model undergo rigorous verification and validation activities to attain credibility for its context of use before it can be accepted for regulatory submission. Several recently published numerical models of the human spine were considered for their implementation of various comparators as a means of model validation. The comparators used in each published model were examined and classified as either an engineering or natural comparator. Further, a method of scoring the comparators was developed based on guidelines from ASME V&V40 and the draft guidance from the US FDA, and used to evaluate the pertinence of each comparator in model validation. Thus, this review article aimed to score the various comparators used to validate numerical models of the spine in order to examine the comparator's ability to lend credibility towards computational models of the spine for specific contexts of use.

In silico trials: Verification, validation and uncertainty quantification of predictive models used in the regulatory evaluation of biomedical products.

Historically, the evidences of safety and efficacy that companies provide to regulatory agencies as support to the request for marketing authorization of a new medical product have been produced experimentally, either in vitro or in vivo. More recently, regulatory agencies started receiving and accepting evidences obtained in silico, i.e. through modelling and simulation. However, before any method (experimental or computational) can be acceptable for regulatory submission, the method itself must be considered "qualified" by the regulatory agency. This involves the assessment of the overall "credibility" that such a method has in providing specific evidence for a given regulatory procedure. In this paper, we describe a methodological framework for the credibility assessment of computational models built using mechanistic knowledge of physical and chemical phenomena, in addition to available biological and physiological knowledge; these are sometimes referred to as "biophysical" models. Using guiding examples, we explore the definition of the context of use, the risk analysis for the definition of the acceptability thresholds, and the various steps of a comprehensive verification, validation and uncertainty quantification process, to conclude with considerations on the credibility of a prediction for a specific context of use. While this paper does not provide a guideline for the formal qualification process, which only the regulatory agencies can provide, we expect it to help researchers to better appreciate the extent of scrutiny required, which should be considered early on in the development/use of any (new) in silico evidence.