Search

Intermittent hemodialysis (IH) is an important therapy in the context of kidney dysfunction in dogs. However, its impact on pro-inflammatory cytokines is unclear. This study assessed IL-1β, IL-6, and TNF-α serum concentrations in dogs with chronic kidney disease (CKD) undergoing one session of IH without bypass (IH group, n = 4) and with bypass (IH + bypass group, n = 4). The control group (CG) included four healthy dogs. Cytokine levels were measured before, during, and after the first IH session. Comparative analyses of each cytokine within each group and time point were performed, along with a global comparison between groups. No significant changes were observed in cytokines across evaluation times in the IH groups. IL-1β was significantly higher post-session in the IH and IH + bypass groups compared to CG. Globally, IL-1β and TNF-α concentrations were significantly higher in the IH (11.41 pg/mL (10–16.17) and 2 pg/mL (2–88.54), respectively) and IH + bypass groups (10 pg/mL (10–10) and 2 pg/mL (2–215.5), respectively) compared to CG (0.96 pg/mL (0–3.56) and 0 pg/mL (0–0.003), respectively). The IH group also showed elevated IL-6 concentration (0.1 ng/mL (0.1–0.5)) compared to CG (0 ng/mL (0–0.1)). Higher IL-1β and IL-6 concentrations were observed in the IH group (11.41 pg/mL (10–16.17) and 0.1 ng/mL (0.1–0.1), respectively) compared to the IH + bypass group (10 pg/mL (10–10) and 0.1 ng/mL (0.1–0.5), respectively). In conclusion, a single IH session, with or without bypass, did not increase pro-inflammatory cytokines in CKD dogs with uremic syndrome but maintained the inflammatory state. Dogs undergoing IH without bypass may receive a stronger stimulus for cytokine release.

BackgroundThe reporting of adverse events (AEs) relating to medical devices is a long-standing area of concern, with suboptimal reporting due to a range of factors including a failure to recognize the association of AEs with medical devices, lack of knowledge of how to report AEs, and a general culture of nonreporting. The introduction of artificial intelligence as a medical device (AIaMD) requires a robust safety monitoring environment that recognizes both generic risks of a medical device and some of the increasingly recognized risks of AIaMD (such as algorithmic bias). There is an urgent need to understand the limitations of current AE reporting systems and explore potential mechanisms for how AEs could be detected, attributed, and reported with a view to improving the early detection of safety signals. ObjectiveThe systematic review outlined in this protocol aims to yield insights into the frequency and severity of AEs while characterizing the events using existing regulatory guidance. MethodsPublicly accessible AE databases will be searched to identify AE reports for AIaMD. Scoping searches have identified 3 regulatory territories for which public access to AE reports is provided: the United States, the United Kingdom, and Australia. AEs will be included for analysis if an artificial intelligence (AI) medical device is involved. Software as a medical device without AI is not within the scope of this review. Data extraction will be conducted using a data extraction tool designed for this review and will be done independently by AUK and a second reviewer. Descriptive analysis will be conducted to identify the types of AEs being reported, and their frequency, for different types of AIaMD. AEs will be analyzed and characterized according to existing regulatory guidance. ResultsScoping searches are being conducted with screening to begin in April 2024. Data extraction and synthesis will commence in May 2024, with planned completion by August 2024. The review will highlight the types of AEs being reported for different types of AI medical devices and where the gaps are. It is anticipated that there will be particularly low rates of reporting for indirect harms associated with AIaMD. ConclusionsTo our knowledge, this will be the first systematic review of 3 different regulatory sources reporting AEs associated with AIaMD. The review will focus on real-world evidence, which brings certain limitations, compounded by the opacity of regulatory databases generally. The review will outline the characteristics and frequency of AEs reported for AIaMD and help regulators and policy makers to continue developing robust safety monitoring processes. International Registered Report Identifier (IRRID)PRR1-10.2196/48156

BackgroundArtificial intelligence (AI) medical devices have the potential to transform existing clinical workflows and ultimately improve patient outcomes. AI medical devices have shown potential for a range of clinical tasks such as diagnostics, prognostics, and therapeutic decision-making such as drug dosing. There is, however, an urgent need to ensure that these technologies remain safe for all populations. Recent literature demonstrates the need for rigorous performance error analysis to identify issues such as algorithmic encoding of spurious correlations (eg, protected characteristics) or specific failure modes that may lead to patient harm. Guidelines for reporting on studies that evaluate AI medical devices require the mention of performance error analysis; however, there is still a lack of understanding around how performance errors should be analyzed in clinical studies, and what harms authors should aim to detect and report. ObjectiveThis systematic review will assess the frequency and severity of AI errors and adverse events (AEs) in randomized controlled trials (RCTs) investigating AI medical devices as interventions in clinical settings. The review will also explore how performance errors are analyzed including whether the analysis includes the investigation of subgroup-level outcomes. MethodsThis systematic review will identify and select RCTs assessing AI medical devices. Search strategies will be deployed in MEDLINE (Ovid), Embase (Ovid), Cochrane CENTRAL, and clinical trial registries to identify relevant papers. RCTs identified in bibliographic databases will be cross-referenced with clinical trial registries. The primary outcomes of interest are the frequency and severity of AI errors, patient harms, and reported AEs. Quality assessment of RCTs will be based on version 2 of the Cochrane risk-of-bias tool (RoB2). Data analysis will include a comparison of error rates and patient harms between study arms, and a meta-analysis of the rates of patient harm in control versus intervention arms will be conducted if appropriate. ResultsThe project was registered on PROSPERO in February 2023. Preliminary searches have been completed and the search strategy has been designed in consultation with an information specialist and methodologist. Title and abstract screening started in September 2023. Full-text screening is ongoing and data collection and analysis began in April 2024. ConclusionsEvaluations of AI medical devices have shown promising results; however, reporting of studies has been variable. Detection, analysis, and reporting of performance errors and patient harms is vital to robustly assess the safety of AI medical devices in RCTs. Scoping searches have illustrated that the reporting of harms is variable, often with no mention of AEs. The findings of this systematic review will identify the frequency and severity of AI performance errors and patient harms and generate insights into how errors should be analyzed to account for both overall and subgroup performance. Trial RegistrationPROSPERO CRD42023387747; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=387747 International Registered Report Identifier (IRRID)PRR1-10.2196/51614