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Transforming Clinical Trials with Artificial Intelligence
Published in Sandeep Reddy, Artificial Intelligence, 2020
Stefanie Lip, Shyam Visweswaran, Sandosh Padmanabhan
“Synthetic controls” comes from methods developed for website analytics and economics research (Brodersen, Gallusser, Koehler, Remy, & Scott, 2015) where a number of time series that are unaffected by the intervention are optimally weighted according to their fit to the outcome of interest in the period before the intervention, then combined into a composite time series (Abadie, Diamond, & Hainmueller, 2010). Application of synthetic controls method to nationwide administrative databases in Brazil, Chile, Ecuador, Mexico and the United States to evaluate changes in the burden of hospitalizations for all-cause pneumonia associated with the introduction of pneumococcal conjugate vaccines (PCVs) did not detect a decline in all-cause pneumonia in older adults in any country (Bruhn et al., 2017) which had implications on healthcare policies on more widespread use of the vaccine. Another potential use of synthetic data is in generating synthetic controls or placebo groups for trials. Although a placebo control arm is crucial in clinical trials to determine treatment effects, participants generally do not like the possibility of being placed in the placebo group. One option being explored is the use of synthetic control arms, which are in-silico placebo arms modelled using information that has previously been collected including historical control data, real-world data or the generation of a companion data set from other sources to serve as a comparator. Indeed, the FDA recognizes clinical trials that use this form of hybrid design where real-world data can be used as a basis for external controls.
The Role of Law in Protecting Medical Data in India
Published in Ahmed Elngar, Ambika Pawar, Prathamesh Churi, Data Protection and Privacy in Healthcare, 2021
Shambhu Prasad Chakrabarty, S. Mukherjee, A. Rodricks
Medical data is an emerging market for economic dominance. Personalized healthcare systems will slowly and steadily bridge the gap between clinical and real-world data. Controlling the health conditions of every individual and application of AI is going to be the order of the day and big data (or relevant data) will play a very significant role. As this transformation takes place, the possibilities for the misuse of data become a reality. The vulnerability of the state in regulating big data has been exposed and highlighted by the apex court in many cases. Where laws pertaining to medical data are going to be a reality, anticipating what is around the corner would eventually put the state in an advantageous position.
Pharmaceuticals: Some General Aspects
Published in Peter Grunwald, Pharmaceutical Biocatalysis, 2019
FDA uses RWD and RWE to monitor post-market safety and adverse events and to make regulatory decisions; the health care community is using these data to support coverage decisions and to develop guidelines and decision support tools for use in clinical practice, and medical product developers are using RWD and RWE to support clinical trial designs (e.g., large simple trials, pragmatic clinical trials) and observational studies to generate innovative, new treatment approaches (FDA, 2018b; 2017d). Both RWD and RWE should contribute to an acceleration concerning the development of scientific evidence for medical products; the 21st Century Cures Act places additional focus on the use of these types of data to support regulatory decision making (NIH, 2016). The FDA defines real-world evidence as “the clinical evidence regarding the usage and potential benefits or risks of a medical product derived from analysis of RWD.” It is of importance to evaluate treatment effectiveness in daily settings to guide clinical decision-making and answer scientific questions. Real-world data come (apart from those derived from RCTs and phase IV RCTs) from sources such as electronic health records (EHRs), insurance claims and billing activities, product and disease registries, patient-related activities in out-patient or in-home use settings, or health-monitoring devices and include data derived from RCTs and phase IV RCTs. An assessment and analysis of these large amounts of data has been eased due to advances in data management. According to this rather new trend the FDA recently released a document entitled “Use of Real-World Evidence to Support Regulatory Decision-Making for Medical Devices”; it clarifies how to “evaluate real-world data to determine whether it may be sufficiently relevant and reliable to generate the types of real-world evidence that can be used in FDA regulatory decision-making for medical devices” (FDA). For the many benefits RWE provides, including improved health outcomes, reduction of medical costs, better identification of medication side-effects and contraindications, increased personalization of medical care, or discover new indications for existing treatments, see Jadhav (2017). Zuidgeesta et al. (2017) provided an introduction to the topic “Pragmatic Trials and Real World Evidence. Concerning the EMA’s contributions to address these developments, see, e.g., Cave and Cerreta (2017), and EMA (2017, 2018a).
An accelerated access pathway for innovative high-risk medical devices under the new European Union Medical Devices and health technology assessment regulations? Analysis and recommendations
Published in Expert Review of Medical Devices, 2023
Rosanna Tarricone, Helen Banks, Oriana Ciani, Werner Brouwer, Michael F Drummond, Reiner Leidl, Nicolas Martelli, Laura Sampietro-Colom, Rod S. Taylor
An implication of increased decision uncertainty in pre-market space is the expectation of the need for post-market confirmatory trials once (accelerated) conditional approval has been obtained [20,41]. As post-market evaluations are likely to be based on a larger patient population, weak or delayed signals of harm may be detected more easily and if significant, may lead to the withdrawal of the technology [58,63]. In those cases where RCTs are feasible, post-market trials could address larger patient populations. In other cases, real world data and real world evidence are indicated for post-marketing surveillance, but are also of potential use in clinical study designs and adjustments in earlier phases [64,65]. Registry studies can be particularly important for high-risk implantable MDs, given the expected impact of ancillary technologies, surgical technique and experience development over time, as well as any design modifications or failures related to design that may develop as the device is used on a larger scale in real world settings [3,65,66]. An important requisite is the willingness and ability to remove a product from the market should its early promise in terms of clinical efficacy and safety, not be confirmed by evidence generated during the post-market phase. However, unequivocal requirements for post-market data collection are not common [20,26,67]. Therefore, the use of registry results in regulatory assessment may benefit from implementation guidance, e.g. on data collection and quality assurance, registry governance, and planning of benefit-risk assessments [68].
Closed-loop insulin delivery: update on the state of the field and emerging technologies
Published in Expert Review of Medical Devices, 2022
Loop is an open-source iOS app developed by the Do-It-Yourself (DIY) community, incorporating an MPC algorithm that modulates insulin delivery continuously based on sensor glucose levels by altering pre-set basal rates. It is an inter-operable algorithm, which can pair with any alternate controller-enabled insulin pump and inter-operable CGM. There have been no randomized controlled studies of the Loop algorithm. There are real-world data from a 6-month prospective observational study of 558 children (age 1 year+) and adults [89]. Within the study, Medtronic and Insulet pumps were used, with a RileyLink bridging the iPhone’s Bluetooth and the sub-gigahertz radio frequency used by these pumps. Both Dexcom and Medtronic CGMs were used. Closed-loop was used a median 83% of the time, and glycemic outcomes were promising with time in range of 73% and HbA1c of 6.5%, although participants had fairly tight glycemic control at baseline. The not-for-profit company Tidepool (Palo Alto, CA, USA) are developing a commercial version of Loop (Tidepool Loop) and FDA approval is awaited.
The current status of breakthrough devices designation in the United States and innovative medical devices designation in Korea for digital health software
Published in Expert Review of Medical Devices, 2022
Jae Hyun Woo, Eun Cheol Kim, Sung Min Kim
AI -based SaMD can clearly identify clinical improvements using big data in the clinical environment. DTx, which is being developed by the group, is also expected to increase the clinical outcomes. The fundamental implications for resolving the limitations of the initial experience of these programs are the application of an economic premium policy [89–91] and stricter risk-benefit assessment [91–93] for rare incurable diseases or medical technologies for which no clear alternative exists and the mandatory requirements for disclosure among currently designated items. Systematic information sharing and dissemination are necessary for the total product life cycle management to create real-world data(RWD) and real-world evidence(RWE) of approved items and approaches to ensure clinical safety and efficacy [3,94–98]. This approach is thought to contribute to better and innovative medical technology by providing the best value to clinical sites and patients easily and rapidly.