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The medical device industry is characterized by a high level of innovation and the development of a continuous stream of devices that promise to enhance the life expectancy and quality of life of patients. With changes in US Food and Drug Administration (FDA) regulatory policies and increased demand for more rigorous clinical evidence by payors, physicians, and patients alike, there has been more emphasis on randomized trials to evaluate new devices as they move from bench to bedside.

A majority of patients with angina related to coronary artery disease respond to medical management, percutaneous coronary intervention (PCI), or coronary artery bypass grafting (CABG). There is, however, a growing number of patients who have medically refractory angina caused by diffuse coronary artery disease who are not eligible for PCI or CABG owing to diffuse, distal disease. 1 Treatment options are limited for this subset of patients.

Postoperative adhesions are scar tissues, which develop at the site of tissue injury after surgery. 1 Their potential significance is immense because of the frequency with which they form, as well as their consequences of potential morbidity and mortality. 2 Yet, many surgeons minimize their significance because of the general inability to identify adhesions without performing a repeat surgery, as there are not good serologic markers or accurate imaging modalities (e.g., ultrasound, computed tomography, or magnetic resonance imaging scans) and the perceived inability to do anything about them.

Missing data are a common problem with data sets in most clinical trials, including those dealing with devices. Imputation, or filling in the missing values, is an intuitive and flexible way to handle the incomplete data sets that arise because of such missing data. Here we present several imputation strategies and their theoretical background, as well as some current examples and advice on computation. Our focus is on multiple imputation, which is a statistically valid strategy for handling missing data. The analysis of a multiply imputed data set is now relatively standard, for example in SAS and in Stata. The creation of multiply imputed data sets is more challenging but still straightforward relative to other valid methods of handling missing data. Singly imputed data sets almost always lead to invalid inferences and should be eschewed.

As providers, beneficiaries, device manufacturers, and other stakeholders strive to more fully understand the working parameters of the Medicare local coverage process, there is considerable value in presenting a more global, integrated approach. There are three major defining forces, which provide such a framework, and can be further exemplified by selected recent coverage case studies. These three forces are (1) specific regulatory mandates of the Medicare program, (2) the creation of stakeholder partnerships, and (3) the need to properly use medical evidence. Most coverage policies represent a combination of these forces. In fact, there is only the occasional local coverage scenario, which is characterized by the pure expression of any solitary element.

Establishing reimbursement early in product development is essential to the success of a medical device company. Although reimbursement is comprised of coverage, coding, and payment procedures, coverage is the essential first step that drives subsequent coding and payment procedures. After all, if a product is not covered by insurance plans, there can be no reimbursement. Coverage occurs when a product is deemed a “reasonable and necessary” medical treatment. It is widely reported that Medicare takes an average of 2 to 5 years to create coverage for a new product. Given that a new device’s product life cycle may be only 10 years, the earlier the process is initiated to obtain coverage, the sooner reimbursement will be established. Perhaps most importantly, this 2- to 5-years anticipated timeframe for coverage can be reduced if a reimbursement plan is implemented early in product development. When developing a reimbursement plan, companies should address the following questions, preferably while their product is still in the development phase: 1. Where will this product fit in the larger health care arena? 2. How will this product meet the Food and Drug Administration (FDA) “safe and effective” standards, as well as payors’ “reasonable and necessary” requirements? 3. How can the reimbursement strategy support the company’s overall objectives?

The term clinical study can mean many things. Premarket clinical studies are necessary to verify the safety and efficacy of a new device, but they may not be able to detect low-level or long-term complications. No matter how much premarket work one does and no matter how sophisticated the protocols, the only valid proof of long-term reliability is performance in the field through postmarketing surveillance. Even studies on the long-term performance of marketed products may be misleading if not done appropriately. Clinically based postmarket surveillance can reveal the true actuarial survival of a device and the clinical mode of failure. Analysis of returned products may be required to understand the details of failure mechanisms per se .

Surgical replacement of diseased valves with functional substitutes is the dominant therapeutic modality in patients with symptomatic valvular heart disease and generally improves survival and enhances quality of life. 1 , 2 Nevertheless, problems associated with the valve replacement devices remain a major impediment to the long-term success of this procedure. Despite considerable improvement in the technology of heart valve prostheses since their first successful use approximately 45 years ago, both mechanical and tissue heart valve substitutes (illustrated in Fig. 1 ) remain imperfect, and prosthesis-associated complications have considerable impact on the long-term outlook for those who have had valve replacement surgery.

One of the most important aspects of study design is the selection of outcome measures. The choice of the most appropriate primary and secondary outcome measures can be a complicated decision and represents one of the most complex issues in the design of a clinical trial. Appropriate outcome measures will determine both safety and clinical effectiveness, as well as potential reimbursement, based on the application of the rigorous evidence-based approach taken by the Centers for Medicare and Medicaid Services (CMS) and other payor/providers. 1 Therefore, companies should devote considerable time to making certain the outcome measures chosen will be viewed as appropriate by reviewers and clinicians.