Automation of online particle measurement during the simulated use of catheters and stent systems

The assessment of the coating integrity of cardiovascular implants such as catheters and stent systems is of crucial importance for device approval. Released particles may represent a potential health risk for the patients. Thus, an analysis of the particles released at simulated in-vivo conditions depending on their size and number is required by international standards (ISO, ASTM) as well as national authorities. In this study, an automated test bench for online particle measurement is presented. For software controlled automation, sensor data transmission and solenoid valves were implemented. A user interface was created for setting test parameters and data recording. The setup was validated by investigating standard particles as well as those released during the simulated application of non-industrial coated balloons. The measurement data were compared with results generated using the previous manual test routine. The results show an improvement in the reproducibility of the measurements, which can be attributed to the simplified handling for the user.

Paradise™ Ultrasound Renal Denervation System for the treatment of hypertension

Catheter-based renal denervation is a novel treatment approach for patients with hypertension and initial unblinded trials have shown promising results. The Paradise™ Ultrasound Renal Denervation System (ReCor Medical, CA, USA) is an ultrasound-based catheter with a distal balloon that acts as a coolant to protect the renal arterial wall. This device received CE-mark in 2012. Randomized, sham-controlled trials and postmarket studies have shown promising efficacy and safety results. Currently, three additional ongoing randomized, sham-controlled trials are underway in the USA, Europe, Japan and Korea, and the results will be pivotal in device approval in some of these countries. These studies with larger numbers of patients and longer duration of follow-up are needed to further confirm the safety and efficacy of this device.

Key principles of clinical validation, device approval, and insurance coverage decisions of artificial intelligence

Artificial intelligence (AI) will likely affect various fields of medicine. This article aims to explain the fundamental principles of clinical validation, device approval, and insurance coverage decisions of AI algorithms for medical diagnosis and prediction. Discrimination accuracy of AI algorithms is often evaluated with the Dice similarity coefficient, sensitivity, specificity, and traditional or free-response receiver operating characteristic curves. Calibration accuracy should also be assessed, especially for algorithms that provide probabilities to users. As current AI algorithms have limited generalizability to real-world practice, clinical validation of AI should put it to proper external testing and assisting roles. External testing could adopt diagnostic case-control or diagnostic cohort designs. A diagnostic case-control study evaluates the technical validity/accuracy of AI while the latter tests the clinical validity/accuracy of AI in samples representing target patients in real-world clinical scenarios. Ultimate clinical validation of AI requires evaluations of its impact on patient outcomes, referred to as clinical utility, and for which randomized clinical trials are ideal. Device approval of AI is typically granted with proof of technical validity/accuracy and thus does not intend to directly indicate if AI is beneficial for patient care or if it improves patient outcomes. Neither can it categorically address the issue of limited generalizability of AI. After achieving device approval, it is up to medical professionals to determine if the approved AI algorithms are beneficial for real-world patient care. Insurance coverage decisions generally require a demonstration of clinical utility that the use of AI has improved patient outcomes.

Structured benefit–risk evaluation for medicinal products: review of quantitative benefit–risk assessment findings in the literature

A favorable benefit–risk profile remains an essential requirement for marketing authorization of medicinal drugs and devices. Furthermore, prior subjective, implicit and inconsistent ad hoc benefit–risk assessment methods have rightly evolved towards more systematic, explicit or “structured” approaches. Contemporary structured benefit–risk evaluation aims at providing an objective assessment of the benefit–risk profile of medicinal products and a higher transparency for decision making purposes. The use of a descriptive framework should be the preferred starting point for a structured benefit–risk assessment. In support of more precise assessments, quantitative and semi-quantitative methodologies have been developed and utilized to complement descriptive or qualitative frameworks in order to facilitate the structured evaluation of the benefit–risk profile of medicinal products. In addition, quantitative structured benefit–risk analysis allows integration of patient preference data. Collecting patient perspectives throughout the medical product development process has become increasingly important and key to the regulatory decision-making process. Both industry and regulatory authorities increasingly rely on descriptive structured benefit–risk evaluation and frameworks in drug, vaccine and device evaluation and comparison. Although varied qualitative methods are more commonplace, quantitative approaches have recently been emphasized. However, it is unclear how frequently these quantitative frameworks have been used by pharmaceutical companies to support submission dossiers for drug approvals or to respond to the health authorities’ requests. The objective of this study has been to identify and review, for the first time, currently available, published, structured, quantitative benefit–risk evaluations which may have informed health care professionals and/or payor as well as contributed to decision making purposes in the regulatory setting for drug, vaccine and/or device approval. Plain language summary Quantitative evaluation of the benefit–risk balance for medicinal products The review of the benefits and the risks associated with a medicinal product is called benefit–risk assessment. One of the conditions for a medicinal product to receive marketing authorization is to demonstrate a positive benefit–risk balance in which the benefits outweigh the risks. In order to enhance the transparency and consistency in the assessment of benefit–risk balance, frameworks and quantitative methods have been developed for decision making purposes and regulatory approvals of medicinal products. This article considers published quantitative benefit–risk evaluations which may have informed health care professionals and/or payor as well as contributed to decision making purposes in the regulatory setting for drug, vaccine and/or device approval.