There is a disparity between low and middle-income countries (LMICs) and high-income countries (HICs) in translating medical device innovations to the market, affecting health care service delivery. ...Whereas medical technologies developed in HICs face substantial challenges in getting to the bedside, there are at least clear pathways in most of the major markets, such as the UK, the EU, and the USA. Much less is known about the challenges that innovators of medical technologies face in LMICs. The aim of this study was to map out current bottlenecks in medical device innovation in Uganda, a LMIC in Sub-Saharan East Africa.
Advances in the medical industry has become a major trend because of the new developments in information technologies. This research offers a novel approach for estimating the smart medical devices ...(SMDs) selection process in a group decision making (GDM) in a vague decision environment. The complexity of the selected decision criteria for the smart medical devices is a significant feature of this analysis. To simulate these processes, a methodology that combines neutrosophics using bipolar numbers with Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) under GDM is suggested. Neutrosophics with TOPSIS approach is applied in the decision making process to deal with the vagueness, incomplete data and the uncertainty, considering the decisions criteria in the data collected by the decision makers (DMs). In this research, the stress is placed upon the choosing of sugar analyzing smart medical devices for diabetics’ patients. The main objective is to present the complications of the problem, raising interest among specialists in the healthcare industry and assessing smart medical devices under different evaluation criteria. The problem is formulated as a multi criteria decision type with seven alternatives and seven criteria, and then edited as a multi criteria decision model with seven alternatives and seven criteria. The results of the neutrosophics with TOPSIS model are analyzed, showing that the competence of the acquired results and the rankings are sufficiently stable. The results of the suggested method are also compared with the neutrosophic extensions AHP and MOORA models in order to validate and prove the acquired results. In addition, we used the SPSS program to check the stability of the variations in the rankings by the Spearman coefficient of correlation. The selection methodology is applied on a numerical case, to prove the validity of the suggested approach.
The European Union Medical Device Regulations 2017/745 entered into force on May 2021 with changes related to strengthening the clinical evaluation requirements, particularly for high-risk devices. ...This study investigates how the increased requirements on medical device manufacturers in relation to how clinical evaluation will challenge manufacturers. A quantitative survey study was utilized with responses from 68 senior or functional area subject matter experts working in medical device manufacturing Regulatory or Quality roles. The findings from the study demonstrated that the highest source of reactive Post-Market Surveillance data was customer complaints and proactive data were Post-Market Clinical Follow-Up. In contrast, the top 3 sources for generating clinical evaluation data for legacy devices under the new Medical Device Regulations were Post-Market Surveillance data, Scientific literature reviews, and Post-Market Clinical Follow-Up studies. Manufacturers’ biggest challenge under the new Medical Device Regulations is determining the amount of data needed to generate sufficient clinical evidence, while over 60% of high-risk device manufacturers have outsourced the writing of their clinical evaluation reports. Manufacturers also reported a high investment in clinical evaluation training and highlighted inconsistencies in the requirements for clinical data by different notified bodies. These challenges may lead to a potential shortage of certain medical devices in the E.U. and a delay in access to new devices, negatively impacting patient quality of life (1). This study provides a unique insight into the challenges currently experienced by medical device manufacturers as they transition to the MDR clinical evaluation requirements and the subsequent impact on the continued availability of medical devices in the E.U.
The European Union Medical Device Regulation 2017/745 challenges key stakeholders to follow transparent and rigorous approaches to the clinical evaluation of medical devices. The purpose of this ...study is a systematic evaluation of published clinical evidence underlying selected high-risk cardiovascular medical devices before and after market access in the European Union (CE-marking) between 2000 and 2021.
Pre-specified strategies were applied to identify published studies of prospective design evaluating 71 high-risk cardiovascular devices in seven different classes (bioresorbable coronary scaffolds, left atrial appendage occlusion devices, transcatheter aortic valve implantation systems, transcatheter mitral valve repair/replacement systems, surgical aortic and mitral heart valves, leadless pacemakers, subcutaneous implantable cardioverter-defibrillator). The search time span covered 20 years (2000-21). Details of study design, patient population, intervention(s), and primary outcome(s) were summarized and assessed with respect to timing of the corresponding CE-mark approval.
At least one prospective clinical trial was identified for 70% (50/71) of the pre-specified devices. Overall, 473 reports of 308 prospectively designed studies (enrolling 97 886 individuals) were deemed eligible, including 81% (251/308) prospective non-randomized clinical trials (66 186 individuals) and 19% (57/308) randomized clinical trials (31 700 individuals). Pre-registration of the study protocol was available in 49% (150/308) studies, and 16% (48/308) had a peer-reviewed publicly available protocol. Device-related adverse events were evaluated in 82% (253/308) of studies. An outcome adjudication process was reported in 39% (120/308) of the studies. Sample size was larger for randomized in comparison to non-randomized trials (median of 304 vs. 100 individuals, P < .001). No randomized clinical trial published before CE-mark approval for any of the devices was identified. Non-randomized clinical trials were predominantly published after the corresponding CE-mark approval of the device under evaluation (89%, 224/251). Sample sizes were smaller for studies published before (median of 31 individuals) than after (median of 135 individuals) CE-mark approval (P < .001). Clinical trials with larger sample sizes (>50 individuals) and those with longer recruitment periods were more likely to be published after CE-mark approval, and were more frequent during the period 2016-21.
The quantity and quality of publicly available data from prospective clinical investigations across selected categories of cardiovascular devices, before and after CE approval during the period 2000-21, were deemed insufficient. The majority of studies was non-randomized, with increased risk of bias, and performed in small populations without provision of power calculations, and none of the reviewed devices had randomized trial results published prior to CE-mark certification.
Growing demand for customized pharmaceutics and medical devices makes the impact of additive manufacturing increased rapidly in recent years. The 3D printing has become one of the most revolutionary ...and powerful tool serving as a technology of precise manufacturing of individually developed dosage forms, tissue engineering and disease modeling. The current achievements include multifunctional drug delivery systems with accelerated release characteristic, adjustable and personalized dosage forms, implants and phantoms corresponding to specific patient anatomy as well as cell-based materials for regenerative medicine. This review summarizes the newest achievements and challenges of additive manufacturing in the field of pharmaceutical and biomedical research that have been published since 2015. Currently developed techniques of 3D printing are briefly described while comprehensive analysis of extrusion-based methods as the most intensively investigated is provided. The issue of printlets attributes, i.e. shape and size is described with regard to personalized dosage forms and medical devices manufacturing. The undeniable benefits of 3D printing are highlighted, however a critical view resulting from the limitations and challenges of the additive manufacturing is also included. The regulatory issue is pointed as well.
Modern surgical departments are characterized by a high degree of automation supporting complex procedures. It recently became apparent that integrated operating rooms can improve the quality of ...care, simplify clinical workflows, and mitigate equipment-related incidents and human errors. Particularly using computer assistance based on data from integrated surgical devices is a promising opportunity. However, the lack of manufacturer-independent interoperability often prevents the deployment of collaborative assistive systems. The German flagship project OR.NET has therefore developed, implemented, validated, and standardized concepts for open medical device interoperability. This paper describes the universal OR.NET interoperability concept enabling a safe and dynamic manufacturer-independent interconnection of point-of-care (PoC) medical devices in the operating room and the whole clinic. It is based on a protocol specifically addressing the requirements of device-to-device communication, yet also provides solutions for connecting the clinical information technology (IT) infrastructure. We present the concept of a service-oriented medical device architecture (SOMDA) as well as an introduction to the technical specification implementing the SOMDA paradigm, currently being standardized within the IEEE 11073 service-oriented device connectivity (SDC) series. In addition, the Session concept is introduced as a key enabler for safe device interconnection in highly dynamic ensembles of networked medical devices; and finally, some security aspects of a SOMDA are discussed.
In recent years, the use of Artificial Intelligence (AI) in the medical field has attracted increased attention. Due to their impressive advantages, AI systems offer excellent prospects for medical ...device manufacturers using these systems to upgrade their products. Such AI-based medical devices are already subject to partial regulation within the lines of Medical device regulation 745/2017. However, following the proposal for a regulation on artificial intelligence published by the European Commission, the regulatory landscape for these devices has partially changed. This article aims to clarify the influences that this regulatory intervention by the European Commission brings to the path towards the use and marketing of AI-based medical devices.
Peripherally inserted central catheters (PICC-lines) used in neonatology are made of thermoplastic polyurethane (TPU) or silicone. These materials usually contain substances that may leach into drug ...vehicles or blood. In this extractables study, we determined the optimal extraction conditions using TPU films containing defined amounts of butylhydroxytoluene (BHT) and then applied them on unused and explanted PICC-lines. Maceration and sonication tests were carried out with hexane, acetone and water as the extraction solvents. The analyses were performed using gas and liquid chromatography coupled with mass spectrometry detectors, as well as inductive coupled plasma optical emission spectroscopy to detect a wide range of extractables. We selected a limited list of substances to be sought from the usual adjuvants and monomers, related to their carcinogenic, mutagenic or reprotoxic properties and/or existence in endocrine disruptors lists. The TPU-film experiments showed that acetone was slightly better than hexane, and maceration better than sonication. When applied to PICC-lines, the extraction methods were almost similar but acetone was clearly better than hexane for TPU. From the 48 peaks initially observed in GC-MS, we ended up with 37 peaks to follow in TPU PICC-lines, among which were those of BHT and 4,4′-Methylenebis(cyclohexyl isocyanate) isomers. For silicone PICC-lines, out of 41 peaks initially observed in GC-MS, we followed 20 peaks, most of them being identified as cyclosiloxanes. Barium was the main inorganic element extracted for both PICC-lines. For TPU PICC-lines, the inter-batch variability was higher than for intra-batch, but in silicone devices both were similar. When compared to new PICC-lines, explanted TPU PICC-lines extracted peaks had a lower area under the curve (AUC), while the AUCs of the peaks were higher for the majority of silicone PICC-lines extract compounds. No identified substances were detected above their toxicological threshold, but isocyanates and cyclosiloxanes toxicity was mostly studied for other exposition routes than intravenous. The methods defined in this study were efficient in producing extractable profiles from both PICC-lines.
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•Method allowed extractables and leachables study from PICC-lines.•Maceration in acetone produced the most extractables, especially for TPU PICC-lines.•At least 48 (TPU PICC-lines) and 41 (silicone PICC-lines) substances were extracted.•The main extractables observed from both PICC-lines were monomers and oligomers.•PICC-line implantation significantly impacted the extractables profile.
Most regulated medical devices enter the US market via the 510(k) regulatory submission pathway, wherein manufacturers demonstrate that applicant devices are "substantially equivalent" to 1 or more ..."predicate" devices (legally marketed medical devices with similar intended use). Most recalled medical devices are 510(k) devices.
To examine the association between characteristics of predicate medical devices and recall probability for 510(k) devices.
In this exploratory cross-sectional analysis of medical devices cleared by the US Food and Drug Administration (FDA) between 2003 and 2018 via the 510(k) regulatory submission pathway, linear probability models were used to examine associations between a 510(k) device's recall status and characteristics of its predicate medical devices. Public documents for the 510(k) medical devices were collected using FDA databases. A text extraction algorithm was applied to identify predicate medical devices cited in 510(k) regulatory submissions. Algorithm-derived metadata were combined with 2003-2020 FDA recall data.
Citation of predicate medical devices with certain characteristics in 510(k) regulatory submissions, including the total number of predicate medical devices cited by the applicant device, the age of the predicate medical devices, the lack of similarity of the predicate medical devices to the applicant device, and the recall status of the predicate medical devices.
Class I or class II recall of a 510(k) medical device between its FDA regulatory clearance date and December 31, 2020.
The sample included 35 176 medical devices, of which 4007 (11.4%) were recalled. The applicant devices cited a mean of 2.6 predicate medical devices, with mean ages of 3.6 years and 7.4 years for the newest and oldest, respectively, predicate medical devices. Of the applicant devices, 93.9% cited predicate medical devices with no ongoing recalls, 4.3% cited predicate medical devices with 1 ongoing class I or class II recall, 1.0% cited predicate medical devices with 2 ongoing recalls, and 0.8% cited predicate medical devices with 3 or more ongoing recalls. Applicant devices citing predicate medical devices with 3 or more ongoing recalls were significantly associated with a 9.31-percentage-point increase (95% CI, 2.84-15.77 percentage points) in recall probability compared with devices without ongoing recalls of predicate medical devices, or an 81.2% increase in recall probability relative to the mean recall probability. A 1-SD increase in the total number of predicate medical devices cited by the applicant device was significantly associated with a 1.25-percentage-point increase (95% CI, 0.62-1.87 percentage points) in recall probability, or an 11.0% increase in recall probability relative to the mean recall probability. A 1-SD increase in the newest age of a predicate medical device was significantly associated with a 0.78-percentage-point decrease (95% CI, 1.29-0.30 percentage points) in recall probability, or a 6.8% decrease in recall probability relative to the mean recall probability.
This exploratory cross-sectional study of 510(k) medical devices cleared by the FDA between 2003 and 2018 demonstrated significant associations between 510(k) submission characteristics and recalls of medical devices. Further research is needed to understand the implications of these associations.
In the US, nearly all medical devices progress to market under the 510(k) pathway, which uses previously authorized devices (predicates) to support new authorizations. Current regulations permit ...manufacturers to use devices subject to a Class I recall-the FDA's most serious designation indicating a high probability of adverse health consequences or death-as predicates for new devices. The consequences for patient safety are not known.
To determine the risk of a future Class I recall associated with using a recalled device as a predicate device in the 510(k) pathway.
In this cross-sectional study, all 510(k) devices subject to Class I recalls from January 2017 through December 2021 (index devices) were identified from the FDA's annual recall listings. Information about predicate devices was extracted from the Devices@FDA database. Devices authorized using index devices as predicates (descendants) were identified using a regulatory intelligence platform. A matched cohort of predicates was constructed to assess the future recall risk from using a predicate device with a Class I recall.
Devices were characterized by their regulatory history and recall history. Risk ratios (RRs) were calculated to compare the risk of future Class I recalls between devices descended from predicates with matched controls.
Of 156 index devices subject to Class I recall from 2017 through 2021, 44 (28.2%) had prior Class I recalls. Predicates were identified for 127 index devices, with 56 (44.1%) using predicates with a Class I recall. One hundred four index devices were also used as predicates to support the authorization of 265 descendant devices, with 50 index devices (48.1%) authorizing a descendant with a Class I recall. Compared with matched controls, devices authorized using predicates with Class I recalls had a higher risk of subsequent Class I recall (6.40 95% CI, 3.59-11.40; P<.001).
Many 510(k) devices subjected to Class I recalls in the US use predicates with a known history of Class I recalls. These devices have substantially higher risk of a subsequent Class I recall. Safeguards for the 510(k) pathway are needed to prevent problematic predicate selection and ensure patient safety.
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