A Pilot, Predictive Surveillance Model in Pharmacovigilance Using Machine Learning Approaches

INTRODUCTION

The identification of a new adverse event (AE) caused by a drug product is one of the key activities in the pharmaceutical industry to ensure the safety profile of a drug product. Machine learning (ML) has the potential to assist with signal detection and supplement traditional pharmacovigilance (PV) surveillance methods. This pilot ML modeling study was designed to detect potential safety signals for two AbbVie products and test the model's capability of detecting safety signals earlier than humans.

METHODS

Drug X, a mature product with post-marketing data, and Drug Y, a recently approved drug in another therapeutic area, were selected. Gradient boosting-based ML approaches (e.g., XGBoost) were applied as the main modeling strategy.

RESULTS

For Drug X, eight true signals were present in the test set. Among 12 potential new signals generated, four were true signals with a 50.0% sensitivity rate and a 33.3% positive predictive value (PPV) rate. Among the remaining eight potential new signals, one was confirmed as a signal and detected six months earlier than humans. For Drug Y, nine true signals were present in the test set. Among 13 potential new signals generated, five were true signals with a 55.6% sensitivity rate and a 38.5% PPV rate. Among the remaining eight potential new signals, none were confirmed as true signals upon human review.

CONCLUSION

This model demonstrated acceptable accuracy for safety signal detection and potential for earlier detection when compared to humans. Expert judgment, flexibility, and critical thinking are essential human skills required for the final, accurate assessment of adverse event cases.

Knowledge Graphs in Pharmacovigilance: A Step-By-Step Guide

PURPOSE

This work aims to demystify Knowledge Graphs (KGs) in pharmacovigilance (PV). It complements the scoping review within this issue. By bridging knowledge gaps and stimulating interest, further engagement with this topic by pharmacovigilance professionals will be facilitated.

METHODS

We elucidate fundamental KGs concepts and terminology, followed by delineating a sequence of implementation steps: use case definition, data type selection, data sourcing, KG construction, KG embedding, and deriving actionable insights. Information technology options and limitations are also explored.

FINDINGS

KGs in pharmacovigilance is a multi-disciplinary field involving information technology, machine learning, biology, and PV. We were able to synthesize the relevant core concepts to create an intuitive exposition of KGs in PV.

IMPLICATIONS

This work demystifies KGs with a pharmacovigilance focus, preparing readers for the accompanying in-depth scoping review. that follows. It lays the groundwork for advancing PV research and practice by emphasizing the importance of engaging with vigilance experts. This approach enhances knowledge sharing and collaboration, contributing to more effective and informed pharmacovigilance efforts and optimal assessment and deployment of KGs in PV.

Greater target or lure variability? An exploration on the effects of stimulus types and memory paradigms

In recognition memory, the variance of the target distribution is almost universally found to be greater than that of the lure distribution. However, these estimates commonly come from long-term memory paradigms where words are used as stimuli. Two exceptions to this rule have found evidence for greater lure variability: a short-term memory task (Yotsumoto et al., Memory & Cognition, 36, 282-294 2008) and in an eyewitness memory paradigm (Wixted et al., Cognitive Psychology, 105, 81-114 2018). In the present work, we conducted a series of recognition memory experiments using different stimulus (faces vs. words) along with different paradigms (long-term vs. short-term paradigms) to evaluate whether either of these conditions would result in greater variability in lure items. Greater target variability was observed across stimulus types and memory paradigms. This suggests that factors other than stimuli and retention interval might be responsible for cases where variability is less for targets than lures.

Recognition memory: The probe, the returned signal, and the decision

In an attempt to better understand recognition memory we look at how three approaches (dual processing, signal detection, and global matching) have addressed the probe, the returned signal and the decision in four recognition paradigms. These are single-item recognition (including the remember/know paradigm), recognition in relational context, associative recognition, and source monitoring. The contrast, with regards to the double-miss rate (the probability of recognizing neither item in intact and rearranged pairs) and the effect of the oldness of the other member of the test pair, between identifying the old words in test pairs (the relational context paradigm) and first identifying the intact test pairs and then identifying the old words (adding associative recognition to the relational context paradigm) suggests that the retrieval of associative information in the relational context paradigm is unintentional, unlike the retrieval of associative information in associative recognition. It also seems possible that the information that is spontaneously retrieved in single-item recognition, possibly including the remember/know paradigm, is also unintentional, unlike the retrieval of information in source monitoring. Probable differences between intentional and unintentional retrieval, together with the pattern of effects with regards to the double-miss rate and the effect of the other member of the test pair, are used to evaluate the three approaches. Our conclusion is that all three approaches have something valid to say about recognition, but none is equally applicable across all four paradigms.

Observational study on the non-linear response of dolphins to the presence of vessels

With the large increase in human marine activity, our seas have become populated with vessels that can be overheard from distances of even 20 km. Prior investigations showed that such a dense presence of vessels impacts the behaviour of marine animals, and in particular dolphins. While previous explorations were based on a linear observation for changes in the features of dolphin whistles, in this work we examine non-linear responses of bottlenose dolphins (Tursiops Truncatus) to the presence of vessels. We explored the response of dolphins to vessels by continuously recording acoustic data using two long-term acoustic recorders deployed near a shipping lane and a dolphin habitat in Eilat, Israel. Using deep learning methods we detected a large number of 50,000 whistles, which were clustered to associate whistle traces and to characterize their features to discriminate vocalizations of dolphins: both structure and quantities. Using a non-linear classifier, the whistles were categorized into two classes representing the presence or absence of a nearby vessel. Although our database does not show linear observable change in the features of the whistles, we obtained true positive and true negative rates exceeding 90% accuracy on separate, left-out test sets. We argue that this success in classification serves as a statistical proof for a non-linear response of dolphins to the presence of vessels.

Monocular and binocular mechanisms detect modulations of dot density and dot contrast

Strong reciprocity has been demonstrated between (1) spatial modulations of dot density and modulations of dot luminance, and (2) modulations of dot density and modulations of dot contrast, in textures. The latter are much easier to detect when presented in phase with one another than when presented 180° out of phase, although out-of-phase modulations can also be detected given sufficient amplitude. This result supports the existence of two detection mechanisms: one that is excited by both density modulations and contrast modulations (quiescent when those modulations are presented 180° out of phase) and another that is relatively insensitive to either density modulations or contrast modulations (thus remaining stimulated regardless of phase angle). We investigate whether the mechanism responsible for detecting out-of-phase modulations depends on high-level computations (downstream from the confluence of monocular signals) or whether both mechanisms are situated at the monocular level of visual processing. Specifically, density-modulated and/or contrast-modulated stimuli were presented monocularly (i.e., to the same eye) or dichoptically (i.e., to opposite eyes). Out-of-phase modulations of density were much easier to detect when presented dichoptically. A dichoptic advantage was also found for out-of-phase density and contrast modulations. These dichoptic advantages imply conscious access to a mechanism at the monocular level of processing. When density modulations were presented dichoptically, 180° out of phase, detection thresholds were highest. Consequently, a mechanism with binocular input must also contribute to the detection of these modulations. We describe a minimal, image-based model for these results that contains one monocular computation and one binocular computation.

Root cause analysis of medication errors of the most frequently involved active substances in paediatric patients

BACKGROUND

Use of medicinal products in paediatric patients is identified as a risk factor for the occurrence of medication errors.

OBJECTIVES

To describe and identify root causes of medication errors in children and adolescents spontaneously reported to Agency for Medicinal Products and Medical Devices of Croatia (Agency).

METHOD

Agency's adverse drug reaction database was searched by using the Standardised MedDRA Query: medication errors (Broad) with data lock point set at 30th June 2022. Cases in which medication errors occurred in patients up to 18 years of age were analysed according to the patients' age group and gender, reporter's qualification, seriousness, reported preferred terms and active substances. For the first 30 most frequently reported active substances, an in-depth analysis was performed to identify the root cause of medication errors.

RESULTS

Altogether, 6254 reports were spontaneously reported to the Agency, out of which 1947 (31 %) contained at least one preferred term belonging to Standardised MedDRA Query medication errors. More than half of patients experiencing medication errors belonged to the age group 2-11 years (66 %) and male gender (53 %). The most frequently reported ME PTs included accidental exposure to product by a child (64 %) and accidental overdose (17 %). Medication error root causes for the first 30 most frequently involved active substances included misinterpretation of prescribed dosage due to a very small volume resulting in salbutamol overdose; replacing millilitre and milligram units resulting in paracetamol solution overdose; interchange between medicinal products due to primary package similarities resulting in cholecalciferol overdose and interchange between oral solution and syrup resulting in valproate overdose.

CONCLUSIONS

Healthcare professionals should counsel caregivers about the importance of keeping medicinal products out of children's reach and provide detailed instructions on how to appropriately use medicinal products.

Prescription Sequence Symmetry Analysis (PSSA) to assess prescribing cascades: a step-by-step guide

Prescribing cascades occur when patients are prescribed medication to treat the adverse drug reaction of previously prescribed medication. Prescription sequence symmetry analysis (PSSA) can be used to assess the association between two medications in prescription or dispensing databases and thus the potential occurrence of prescribing cascades. In this article, a step-by-step guide is presented for conducting PSSA to assess prescribing cascades. We describe considerations for medication data collection and setting time periods for relevant parameters, including washout window, exposure window, continued exposure interval and blackout period. With two examples, we illustrate the impact of changes in these parameters on the strengths of associations observed. Given the impact seen, we recommend that researchers clearly specify and explain all considerations regarding medication included and time windows set when studying prescribing cascades with PSSA, and conduct subgroup and sensitivity analyses.

A Novel Method for Deriving Adverse Event Prevalence in Randomized Controlled Trials: Potential for Improved Understanding of Benefit-Risk Ratio and Application to Drug Labels

INTRODUCTION

Adverse event (AE) data in randomized controlled trials (RCTs) allow quantification of a drug's safety risk relative to placebo and comparison across medications. The standard US label for Food and Drug Administration-approved drugs typically lists AEs by MedDRA Preferred Term that occur at ≥ 2% in drug and with greater incidence than in placebo. We suggest that the drug label can be more informative for both patients and physicians if it includes, in addition to AE incidence (percent of subjects who reported the AE out of the total subjects in treatment), the absolute prevalence (percent of subject-days spent with an AE out of the total subject-days spent in treatment) and expected duration (days required for AE incidence to be reduced by half). We also propose a new method to analyze AEs in RCTs using drug-placebo difference in AE prevalence to improve safety signal detection.

METHODS

AE data from six RCTs in schizophrenia were analyzed (five RCTs of the dopamine D2 receptor-based antipsychotic lurasidone and one RCT of the novel trace amine-associated receptor 1 [TAAR1] agonist ulotaront). We determined incidence, absolute prevalence, and expected duration of AEs for lurasidone and ulotaront vs respective placebo. We also calculated areas under the curve of drug-placebo difference in AE prevalence and mean percent contribution of each AE to this difference.

RESULTS

A number of AEs with the same incidence had different absolute prevalence and expected duration. When accounting for these two parameters, AEs that did not appear in the 2% incidence tables of the drug label turned out to contribute substantially to drug tolerability. The percent contribution of a drug-related AE to the overall side effect burden increased the drug-placebo difference in AE prevalence, whereas the percent contribution of a placebo-related AE decreased such difference, revealing a continuum of risk between drug and placebo. AE prevalence curves for drug were generally greater than those for placebo. Ulotaront exhibited a small drug-placebo difference in AE prevalence curves due to a relatively low incidence and short duration of AEs in the ulotaront treatment arm as well as the emergence of disease-related AEs in the placebo arm.

CONCLUSION

Reporting AE absolute prevalence and expected duration for each RCT and incorporating them in the drug label is possible, is clinically relevant, and allows standardized comparison of medications. Our new metric, the drug-placebo difference in AE prevalence, facilitates signal detection in RCTs. We piloted this metric in RCTs of several neuropsychiatric indications and drugs, offering a new way to compare AE burden and tolerability among treatments using existing clinical trial information.

Nationwide safety surveillance of COVID-19 mRNA vaccines following primary series and first booster vaccination in Singapore

Background

The real-world safety profile of COVID-19 mRNA vaccines remains incompletely elucidated.

Methods

We performed a nationwide post-market safety surveillance analysis in Singapore, on vacinees aged 5 years and older, through mid-September 2022. Observed-over-expected (O/E) analyses were performed to identify potential safety signals among eight shortlisted adverse events of special interest (AESIs): strokes, cerebral venous thrombosis (CVT), acute myocardial infarction, myocarditis/pericarditis, pulmonary embolism, immune thrombocytopenia, convulsions and appendicitis. Self-controlled case series analyses (SCCS) were performed to validate signals of concern, occurring within 42 days of vaccination.

Findings

Elevated risks were observed on O/E analyses for the following AESIs: myocarditis/pericarditis, [rate ratio (RR): 3.66, 95 % confidence interval (95 % CI): 2.71 to 4.94], appendicitis [RR: 1.14 (1.02 to 1.27)] and CVT [RR: 2.11 (1.18 to 3.77)]. SCCS analyses generated corroborative findings: myocarditis/pericarditis, [relative incidence (RI): 6.96 (3.95 to 12.27) at 1 to 7 days post-dose 2], CVT [RI: 4.30 (1.30 to 14.20) at 22 to 42 days post-dose 1] and appendicitis [RI: 1.31 (1.03 to 1.67) at 1 to 7 days post-dose 1]. Booster dose 1 continued to be associated with higher rates of myocarditis/pericarditis on O/E analysis [RR: 2.30, (1.39 to 3.80) and 1.69, (1.11 to 2.59)] at 21- and 42-days post-booster dose 1, respectively. Males aged 12 to 17 exhibited highest risks of both myocarditis/pericarditis [RI: 6.31 (1.36 to 29.3)] and appendicitis [RI: 2.01 (1.12 to 3.64)] after primary vaccination. Similarly, CVT was also predominantly observed in males aged above 50 (11 out of 16 cases), within 42-days of vaccination.

Interpretation

Our data suggest that myocarditis/pericarditis, appendicitis and CVT are associated with primary vaccination using COVID-19 mRNA vaccines. Males at specific ages exhibit higher risks for all three AEs identified. The risk of myocarditis/pericarditis continues to be elevated after booster dose 1.

Identification of Novel Signals Associated with US-FDA Approved Drugs (2013) Using Disproportionality Analysis

BACKGROUND

Drugs are related with various adverse drug reactions (ADRs), however, many unexpected ADRs of drugs are reported through post-marketing surveillance.

AIM

The current study's goal is to uncover potential signals connected with FDA-approved medications in the United States (2013).

METHODOLOGY

Open Vigil 2.1-MedDRA-v24 (data 20004Q1-2021Q3) was used as a tool to query the FAERS data. To find possible signals, disproportionality measures such as Proportional Reporting Ratio (PRR 2) with associated Chi-square value, Reporting Odds Ratio (ROR 2) with 95% confidence interval, and case count (3) were calculated.

RESULTS

A total of eight potential signals were identified with five drugs. Positive signals were found with pomalidomide, canagliflozin, dolutegravir sodium, macitentan and ibrutinib.

CONCLUSION

However, further causality assessment is required to confirm the association of these drugs with identified potential signals.

Increasing familiarity with the heartbeat counting task does not affect performance

BACKGROUND

Interoception is typically defined as the processing and perception of internal signals. A common evaluation of interoceptive abilities is the heartbeat counting task, during which participants count their heartbeats over a period of time. It has been argued recently, however, that performance in this task may reflect processes other than cardiac sensation.

OBJECTIVE

This study aimed to: 1) observe heartbeat counting task performance changes across multiple repetitions of the task; and 2) compare performance in the heartbeat counting task with that for a visual counting task to investigate generalised propensities for how uncertain stimuli are reported.

METHODS

Seventy-nine healthy participants performed seven blocks each of the heartbeat and visual counting tasks. Scores for each of the tasks were compared across blocks to identify any change in performance. Performance in the two tasks was then correlated. The study was preregistered at https://doi.org/10.17605/OSF.IO/GWAB7.

RESULTS

Heartbeat counting task performance did not change over blocks. In contrast, scores for the visual counting task reduced over time. A positive correlation was seen between performance in the two tasks (ρ(79) = .27).

CONCLUSION

Heartbeat counting task performance is stable across repetitions of the task, not changing with increasing familiarity. This suggests that non-interoceptive factors, such as beliefs, may be involved. The correlation between the heartbeat and visual counting tasks may point to a general propensity in counting uncertain stimuli across sensory domains. Together, these results raise questions about the interpretation of the heartbeat counting task as a measure of interoception.

Pharmacovigilance studies without a priori hypothesis: systematic review highlights inappropriate multiple testing correction procedures

OBJECTIVES

The purpose of this study was to systematically review the statistical methods used in pharmacovigilance studies without a priori hypotheses.

STUDY DESIGN AND SETTING

A systematic review was performed on studies published in the MEDLINE database between 2012 and 2021. The included studies were analyzed for database name and type, statistical methods, anatomical therapeutic chemical class for the studied drug(s), and SOC MedDRA classification for the studied adverse drug reaction.

RESULTS

Ninety-two studies were included, with pharmacovigilance databases being the most used type. Disproportionality analysis using frequentist or Bayesian methods was the most common statistical method employed. The most studied drug classes were anti-infectives, nervous system drugs, and antineoplastics and immunomodulators. However, no common procedure was implemented to correct for multiple testing.

CONCLUSION

This review highlights the limited number of statistical methods employed for pharmacovigilance studies without a priori hypotheses, with no established consensus-based method and a lack of interest in multiple testing correction. The establishment of guidelines is recommended to improve the performance of such studies.

Simple manipulations of anthropomorphism fail to induce perceptions of humanness or improve trust in an automated agent

Although automation is employed as an aid to human performance, operators often interact with automated decision aids inefficiently. The current study investigated whether anthropomorphic automation would engender higher trust and use, subsequently improving human-automation team performance. Participants performed a multi-element probabilistic signal detection task in which they diagnosed a hypothetical nuclear reactor as in a state of safety or danger. The task was completed unassisted and assisted by a 93%-reliable agent varying in anthropomorphism. Results gave no evidence that participants' perceptions of anthropomorphism differed between conditions. Further, anthropomorphic automation failed to bolster trust and automation-aided performance. Findings suggest that the benefits of anthropomorphism may be limited in some contexts.

Monitoring the safety of drugs and COVID-19 vaccines by the French Pharmacovigilance Centers during the pandemic: A win-win bet with Health Authorities!

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV2) pandemic virus was a "health crisis" and a significant burden also for the French pharmacovigilance system. It took its toll in 2 phases, the first being in early 2020 when very little was known, and during which the missions of the 31 Regional Pharmacovigilance Centers (RPVCs) from university hospitals were to detect adverse reactions of drugs used in the context of the disease. Whether as a possible aggravating role on COVID-19, or displaying a different safety profile during its course, or to assess safety of curative treatment, this phase preceded that of the arrival of dedicated vaccines. Then the RPVCs' missions were to detect, as early as possible, any new serious adverse effect leading to a potential signal that would modify the benefit/risk ratio of a vaccine and require the implementation of health safety measures. During these two distinct periods, signal detection remained the core business of the RPVCs. Each RPVC had to organize itself to handle an unprecedented surge of declarations and requests for advice, from health care professionals and patients alike. "Leading" RPVCs, who were in charge of monitoring vaccines, had to deal with an extraordinary workload (still going on to this date), to generate in real-time and on a weekly basis, a summary of all the adverse drug reaction (ADR) reports as well as an extended analysis of the different safety signals. The organization put in place at the beginning of the health crisis, adapted to the context of the vaccines, allowed to meet the challenge of real-time pharmacovigilance monitoring, and to identify many safety signals. Efficient "short-circuits exchanges" with the French Regional Pharmacovigilance Centers Network (RPVCN) were paramount to the National Agency for the Safety of Medicines and Health Products (ANSM) to develop an optimal collaborative partnership. The French RPVCN has shown at this occasion both agility and flexibility, swiftly adapting to vaccine- and media-related unrest, and demonstrated its effectiveness in the early detection of safety signals. This crisis also confirmed the superiority of manual/human signal detection over automated ones, as the most effective and powerful tool to date to rapidly detect and validate a new ADR and enable to elaborate rapid risk reduction measures. To maintain the performance of French RPVCN in signal detection and to monitor all drugs as they should, and as expected by our fellow citizens, a new funding model should be considered.

[Monitoring the safety of drugs and COVID-19 vaccines by the French Pharmacovigilance Centers during the pandemic: a win-win bet with Health Authorities!]

The pandemic subsequent to the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) virus resulted, for the French institutional pharmacovigilance, in a "health crisis" in 2 phases: the coronavirus disease 2019 - "COVID-19" phase during which the missions of the Regional Pharmacovigilance Centres (RPVC) were to detect a possible impact of drugs on this disease, as whether existed a possible aggravating role of certain drugs, or the safety profile of drugs used for the management of COVID-19 could evolve. The second phase followed the availability of COVID-19 vaccines, during which the RPVCs' missions were to detect as early as possible any new serious adverse effect, source of a potential signal that would modify the benefit/risk ratio of a vaccine and require the implementation of health safety measures. During these two periods, signal detection remained the core business of the RPVCs. The RPVCs had to organize themselves to handle an historical surge of declarations and requests for advice, whereas the RPVCs in charge of monitoring vaccines had to deal with an extraordinary dense activity over a long period of time, in order to produce in real time and on a weekly basis, a summary of all the declarations and an analysis of safety signals. The national organization put in place made it possible to meet the challenge of real-time pharmacovigilance monitoring of 4 vaccines with conditional marketing authorizations. Short-circuit efficient exchanges with the French Regional Pharmacovigilance Centres Network was paramount for the French National Agency for medicines and health products (Agence nationale de sécurité du médicament et des produits de santé) to develop an optimal collaborative partnership. The RPVC network has shown agility and flexibility, has been able to adapt swiftly and demonstrated its effectiveness in the early detection of safety signals. This crisis confirmed the superiority of manual/human signal detection as the most effective and powerful tool to date, to rapidly detect a new adverse drug reaction and enable to elaborate rapid measures of risk reduction. In order to maintain the performance of French RPVCs in signal detection and to monitor all drugs as they should and as expected by our fellow citizens, a new funding model correcting the inadequacy of RPVCs' expertise resources in relation to the volume of reports should be considered.

d[Formula: see text]: Sensitivity at the optimal criterion location

Signal detection theory (SDT) was developed to provide a measure of the discriminability of a signal against background noise, independently of response bias. However, equal discriminability over a range of bias is only achieved by the traditional signal detection measure d[Formula: see text] under a narrow set of conditions - i.e., binormal noise and signal distributions of equal variance and base rates. In response to observed departures from these conditions, more robust alternative measures of d[Formula: see text] have been developed, including da and, more recently, d[Formula: see text]. Each of these alternatives addresses some, but not all, of the difficulties that arise when the assumptions of SDT are violated. Moreover, none of these measures directly follow from a central idea of discriminability by an observer that adopts a minimize error count (MEC) strategy. I propose a new d[Formula: see text] alternative, d[Formula: see text], that is robust to violations of the standard signal detection assumptions, remains consistent with varying bias, and is grounded in the principle of discriminability following a MEC strategy. Simulations illustrate how d[Formula: see text] is similar to the recently developed d[Formula: see text] when the observer optimizes their criterion placement to minimize the number of errors but, unlike d[Formula: see text], remains consistent irrespective of the observer's criterion placement Moreover, unlike da, d[Formula: see text] reflects changes in discriminability related to base rates of signal vs. noise presentations. The use of d[Formula: see text] also has implications for the interpretation of bias metrics, such as β and c, which are examined at the optimal criterion under a variety of conditions.

Prior experience modifies acquisition trajectories via response-strategy sampling

Few studies have considered how signal detection parameters evolve during acquisition periods. We addressed this gap by training mice with differential prior experience in a conditional discrimination, auditory signal detection task. Naïve mice, mice given separate experience with each of the later correct choice options (Correct Choice Response Transfer, CCRT), and mice experienced in conditional discriminations (Conditional Discrimination Transfer, CDT) were trained to detect the presence or absence of a tone in white noise. We analyzed data assuming a two-period model of acquisition: a pre-solution and solution period (Heinemann EG (1983) in The Presolution period and the detection of statistical associations. In: Quantitative analyses of behavior: discrimination processes, vol. 4, pp. 21-36). Ballinger. http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.536.1978andrep=rep1andtype=pdf ). The pre-solution period was characterized by a selective sampling of biased response strategies until adoption of a conditional responding strategy in the solution period. Correspondingly, discriminability remained low until the solution period; criterion took excursions reflecting response-strategy sampling. Prior experience affected the length and composition of the pre-solution period. Whereas CCRT and CDT mice had shorter pre-solution periods than naïve mice, CDT and Naïve mice developed substantial criterion biases and acquired asymptotic discriminability faster than CCRT mice. To explain these data, we propose a learning model in which mice selectively sample and test different response-strategies and corresponding task structures until they exit the pre-solution period. Upon exit, mice adopt the conditional responding strategy and task structure, with action values updated via inference and generalization from the other task structures. Simulations of representative mouse data illustrate the viability of this model.

Approaches to characterizing oscillatory burst detection algorithms for electrophysiological recordings

BACKGROUND

Cognitive processes are associated with fast oscillations of the local field potential and electroencephalogram. There is a growing interest in targeting them because these are disrupted by aging and disease. This has proven challenging because they often occur as short-lasting bursts. Moreover, they are obscured by broad-band aperiodic activity reflecting other neural processes. These attributes have made it exceedingly difficult to develop analytical tools for estimating the reliability of detection methods.

NEW METHOD

To address this challenge, we developed an open-source toolkit with four processing steps, that can be tailored to specific brain states and individuals. First, the power spectrum is decomposed into periodic and aperiodic components, each of whose properties are estimated. Second, the properties of the transient oscillatory bursts that contribute to the periodic component are derived and optimized to account for contamination from the aperiodic component. Third, using the burst properties and aperiodic power spectrum, surrogate neural signals are synthesized that match the observed signal's spectrotemporal properties. Lastly, oscillatory burst detection algorithms run on the surrogate signals are subjected to a receiver operating characteristic analysis, providing insight into their performance.

RESULTS

The characterization algorithm extracted features of oscillatory bursts across multiple frequency bands and brain regions, allowing for recording-specific evaluation of detection performance. For our dataset, the optimal detection threshold for gamma bursts was found to be lower than the one commonly used.

COMPARISON WITH EXISTING METHODS

Existing methods characterize the power spectrum, while ours evaluates the detection of oscillatory bursts.

CONCLUSIONS

This pipeline facilitates the evaluation of thresholds for detection algorithms from individual recordings.

Important Considerations for Signal Detection and Evaluation

Safety clinicians have a wealth of resources describing how to perform signal detection. Nevertheless, there are some nuances concerning approaches taken by regulatory authorities and statistical considerations that should be appreciated. New approaches, such as the FDA Medical Queries, illustrate the value of considering medical concepts over individual adverse events. One area which would benefit from further clarity is how safety signals may be evaluated for evidence of a causal relationship to the drug of interest. Just as such safety signals can take many forms, the types of tools and methods required to interrogate these signals are equally as diverse. An understanding of the complexity of this process can aid the safety reviewer in successfully characterizing the emerging safety profile of a drug during the pre-marketing phase of development.

Signal detection and imaging methods for MEMS electron microscope

In the paper, new detection methods that can be used to detect signals in a miniature MEMS (Micro-Electro-Mechanical System) electron microscope were presented. The methods were designed to fit the structure of the developed MEMS microscope, equipped with an electron-optical microcolumn and a scanning system based on an octupole deflector. In the experiments carried out, imaging was performed using a system of three silicon detectors placed above, below, and at the sample level and integrated with the octupole deflection system. Measurements were carried out under different pressure conditions (vacuum and atmospheric pressure) and for different detector bias voltage and electron beam energy. Good quality images were obtained using all three detectors, both in vacuum and in air. The results presented indicate that in the final implementation of the MEMS electron microscope, all three detection systems can be successfully incorporated.

Pantoprazole associated dyspepsia hypocalcemia and hyponatremia: A disproportionality analysis in FDA adverse event reporting system (FAERS) database

BACKGROUND AND STUDY AIM

The study was designed to detect novel Adverse Events (AEs) of pantoprazole by disproportionality analysis in the FDA (Food and Drug Administration) database of Adverse Event Reporting System (FAERS) using Data Mining Algorithms (DMAs). Pantoprazole, the most commonly over-utilized Over The Counter (OTC) medication, was selected to assess any short-term or long-term AEs. The study aimed to analyze the novel adverse events of pantoprazole using the FAERS database.

MATERIALS AND METHODS

A retrospective case/non-case disproportionality analysis was performed in the FAERS database. This study was based on AEs reported to FAERS from 2006Q1-2021Q3. Openvigil 2.1 was used for data extraction. Reporting Odds Ratio (ROR), Proportional Reporting Ratio (PRR), and Information Component (IC) were applied to measure the disproportionality in reporting. A value of ROR-1.96SE > 1, PRR ≥ 2, and IC-2SD > 0 were considered as the threshold for a positive signal.

RESULTS

A total of 1050 reports of dyspepsia, 7248 reports of hypocalcemia and 995 reports of hyponatremia were identified. A potential positive signal for dyspepsia (ROR-1.96SE = 2.231, PRR = 2.359, IC-2SD = 1.13), hypocalcemia (4.961, 5.45, 2.23) and hyponatremia (3.948, 4.179, 1.92) were identified for pantoprazole.

CONCLUSION

Data mining in the FAERS database produced three potential signals associated with pantoprazole. As a result, further clinical surveillance is needed to quantify and validate potential hazards associated with pantoprazole-related adverse events.

Evaluation of lung adverse events with trastuzumab using the Japanese pharmacovigilance database

The present study aimed to determine the risk of trastuzumab-induced lung toxicity, time to onset, and post hoc outcomes using the Japanese Adverse Drug Event Report database. We analyzed data for the period between April 2004 and March 2021. Data on lung toxicities were extracted, and relative risk of adverse events (AEs) was estimated using the reporting odds ratio. We analyzed 1,772,494 reports and identified 4362 reports of AEs caused by trastuzumab. Of these, 693 lung toxicities were reportedly associated with trastuzumab. Signals were detected for seven lung toxicities: interstitial lung disease, pulmonary edema, pleural effusion, lung disorder, acute pulmonary edema, pulmonary fibrosis, and radiation pneumonitis. Among these, interstitial lung disease was the most frequently reported (61.8%). A histogram of times to onset showed occurrence from 1 to 105 days, but some cases of interstitial lung disease occurred even more than one year after the start of administration. The AEs showing the highest fatality rates were interstitial lung disease, pulmonary fibrosis, and radiation pneumonitis. This study focused on lung toxicities caused by trastuzumab as post-marketing AEs. Some cases could potentially involve serious outcomes; therefore, patients should be monitored for signs of the onset of these AEs not only at the start of administration, but also over an extended period, especially for interstitial lung disease.

Classification of patient characteristics associated with reported adverse drug events to neuraminidase inhibitors: an applicability study of latent class analysis in pharmacovigilance

BACKGROUND

Signal detection in reports of spontaneous adverse drug reactions is useful in pharmacovigilance, but does not adequately consider potential confounding factors such as patient background information contained in the report data. Multiple indicators should be considered when generating safety hypotheses.

AIM

The aim of this study was to evaluate whether latent class analysis (LCA) can complement conventional methods in pharmacovigilance.

METHOD

We conducted LCA of 2732 reports of adverse drug reactions involving four widely used anti-influenza neuraminidase inhibitors in the Japanese Adverse Drug Event Report (JADER) database covering April 2004 to June 2020. LCA classifies the target population into multiple clusters based on response probability. The same data was subjected to multivariate logistic regression using an adjusted reporting odds ratio.

RESULTS

LCA grouped the target population into three classes. Cluster 1 (46.4%) contained patients who developed adverse events other than neuropsychiatric events; these events were specific to adult females. Cluster 2 (28.7%) contained patients who developed abnormal behavior; these events were specific to underage males. Cluster 3 (24.8%) contained patients who developed adverse neuropsychiatric events other than abnormal behavior, such as hallucinations and convulsion; these events were specific to minors. Logistic regression of adverse events for which a signal was detected identified factors similar to those found in LCA.

CONCLUSION

LCA classified adverse events in JADER with similar incidence tendencies into the same cluster. The results included signals identified by conventional logistic regression, suggesting that LCA may be useful as a complementary tool for generating drug safety hypotheses.

Event-related potential correlates of affective response inhibition in bipolar I disorder: Comparison with schizophrenia

BACKGROUND

Individuals with bipolar I disorder (BD) have difficulty inhibiting context-inappropriate responses. The neural mechanisms contributing to these difficulties, especially in emotional contexts, are little understood. This study aimed to inform mechanisms of impaired impulsivity control in response to emotion in BD, and whether response inhibition indices are altered to a similar degree in schizophrenia spectrum disorders (SZ). We examined alterations to behavioral performance and event-related potentials (ERPs) during inhibition to affective stimuli in BD, relative to healthy control participants (HC) and SZ.

METHODS

Sixty-six participants with BD, 32 participants with SZ, and 48 HC completed a Go/No-Go task with emotional face stimuli while electroencephalography was recorded. Behavioral signal detection metrics (perceptual sensitivity, response bias) and ERPs (N200, P300) were compared across groups.

RESULTS

Relative to HC, participants with BD showed reduced (1) discrimination of Go vs. No-Go stimuli (i.e., emotional vs. neutral faces), and (2) P300 amplitudes elicited by emotional faces. Results similarly extended to SZ: BD and SZ groups did not differ on behavioral performance nor ERP amplitudes.

LIMITATIONS

Aspects of the Go/No-Go task design may have limited findings, and medication effects on ERP amplitudes in patient samples cannot be fully ruled out.

CONCLUSIONS

Findings suggest the difficulty participants with BD and SZ experienced on the current affective response inhibition task lied largely in discriminating between facial expressions. Difficulties with discriminating emotional from neutral expressions may contribute to difficulties with appropriate behavioral responding in social-affective contexts for individuals with BD and SZ.

Target-rate effect in continuous visual search

From infrared body temperature surveillance to lifeguarding, real-life visual search is usually continuous and comes with rare targets. Previous research has examined realistic search tasks involving separate slides (such as baggage screening and radiography), but search tasks that require continuous monitoring have generally received less attention. In this study, we investigated whether continuous visual search would display a target-rate effect similar to the low-prevalence effect (LPE) in regular visual search. We designed a continuous detection task for a target feature (e.g., a green color) among items of continuously and gradually changing features (e.g., other colors). In four experiments, we demonstrated target-rate effects in terms of slower hit response times (RTs) and higher miss rates when targets were rare. Similar to regular search, target-rate effects were also observed for relative frequencies across two target features. Taken together, these results suggest a target-rate effect in continuous visual search, and its behavioral characteristics are generally similar to those of the LPE in regular visual search.

Design and Implementation of a Computerized Management System for Adverse Drug Reactions at the Sourô SANOU University Hospital (CHUSS) in Bobo-Dioulasso, Burkina Faso

Pharmacovigilance is the science and activities related to the detection, evaluation, understanding and prevention of adverse drug reactions or any other possible drug-related problems. In our tropics, this discipline is in an embryonic state. The availability of a management system capable of responding to pharmacovigilance activities is the main objective of our study. The coding was done on the DJANGO Framework. Signal detection was done using the ROR method. We designed three modules which are the notification module, the analysis module and the statistics module. This study has allowed us to launch the basis for a computerization of the pharmacovigilance information system and partly meets our objective. However, it could lead to the integration of the dictionary of adverse effects such as MedDRA as well as the International Classification of Medicines (ATC, EphMRA).

The channel for detecting contrast modulation also responds to density modulation (or vice versa)

In textures composed of black and white dots, we modulated dot density and/or dot contrast in one direction of visual space. Just as Mulligan and MacLeod (Vision Research 28 (1988) 503-519) found a strong reciprocity between density and luminance for dots viewed against a darker background, we found a strong reciprocity between density and contrast: detection thresholds for in-phase modulations of density and contrast were 30% - 55% lower than detection thresholds for density and contrast modulations that were 180° out of phase. These findings support the existence of at least one psychophysical channel that is excited by both density modulations and contrast modulations. A good, quantitative fit to our data can be obtained with a two-channel model.

TEDAR: Temporal dynamic signal detection of adverse reactions

Computational approaches to detect the signals of adverse drug reactions are powerful tools to monitor the unattended effects that users experience and report, also preventing death and serious injury. They apply statistical indices to affirm the validity of adverse reactions reported by users. The methodologies that scan fixed duration intervals in the lifetime of drugs are among the most used. Here we present a method, called TEDAR, in which ranges of varying length are taken into account. TEDAR has the advantage to detect a greater number of true signals without significantly increasing the number of false positives, which are a major concern for this type of tools. Furthermore, early detection of signals is a key feature of methods to prevent the safety of the population. The results show that TEDAR detects adverse reactions many months earlier than methodologies based on a fixed interval length.

Item-specific overlap between hallucinatory experiences and cognition in the general population: A three-step multivariate analysis of international multi-site data

Hallucinatory experiences (HEs) can be pronounced in psychosis, but similar experiences also occur in nonclinical populations. Cognitive mechanisms hypothesized to underpin HEs include dysfunctional source monitoring, heightened signal detection, and impaired attentional processes. Using data from an international multisite study on non-clinical participants (N = 419), we described the overlap between two sets of variables - one measuring cognition and the other HEs - at the level of individual items. We used a three-step method to extract and examine item-specific signal, which is typically obscured when summary scores are analyzed using traditional methodologies. The three-step method involved: (1) constraining variance in cognition variables to that which is predictable from HE variables, followed by dimension reduction, (2) determining reliable HE items using split-halves and permutation tests, and (3) selecting cognition items for interpretation using a leave-one-out procedure followed by repetition of Steps 1 and 2. The results showed that the overlap between HEs and cognition variables can be conceptualized as bi-dimensional, with two distinct mechanisms emerging as candidates for separate pathways to the development of HEs: HEs involving perceptual distortions on one hand (including voices), underpinned by a low threshold for signal detection in cognition, and HEs involving sensory overload on the other hand, underpinned by reduced laterality in cognition. We propose that these two dimensions of HEs involving distortions/liberal signal detection, and sensation overload/reduced laterality may map onto psychosis-spectrum and dissociation-spectrum anomalous experiences, respectively.

The conditional approach to evaluating detection performance

In many applied single-point Yes/No signal-detection studies, the main interest is to evaluate the observer’s sensitivity, based on the observed rates of hits and false alarms. For example, Kostopoulou, Nurek, Cantarella et al. (2019, Medical Decision Making, 39, 21–31) presented general practitioners (GPs) with clinical vignettes of patients showing various cancer-related symptoms, and asked them to decide if urgent referral was required; the standard discrimination index d′ was calculated for each GP. An alternative conditional approach to statistical inference emphasizes explicitly the conditional nature of the inferences drawn, and argues on the basis of the response marginal (the number of “yes” responses) that was actually observed. It is closely related to, for example, Fisher’s exact test or the Rasch model in item response theory which have long been valuable and prominent in psychology. The conditional framework applied to single-point Yes/No detection studies is based on the noncentral hypergeometric sampling distribution and permits, for samples of any size, exact inference because it eliminates nuisance (i.e., bias) parameters by conditioning. We describe in detail how the conditional approach leads to conditional maximum likelihood sample estimates of sensitivity, and to exact confidence intervals for the underlying (log) odds ratio. We relate the conditional approach to classical (logistic) detection models also leading to analyses of the odds ratio, compare its statistical power to that of the unconditional approach, and conclude by discussing some of its pros and cons.

Nonhuman primates learn adjacent dependencies but fail to learn nonadjacent dependencies in a statistical learning task with a salient cue

There is ample evidence that humans and nonhuman animals can learn complex statistical regularities presented within various types of input. However, humans outperform their nonhuman primate counterparts when it comes to recognizing relationships that exist across one or several intervening stimuli (nonadjacent dependencies). This is especially true when the two elements in the dependency do not share any perceptual similarity (arbitrary associations). In the present study, we investigated whether manipulating the saliency of the predictive stimulus would enhance nonadjacent dependency learning in nonhuman primates. Rhesus macaques and tufted capuchins engaged in a computerized signal detection task that included sequences that were random in nature, included an adjacent dependency, or included a nonadjacent dependency. We manipulated the saliency of the predictive stimulus, such that the predictor jittered in place on the screen in some grammar blocks, as well as the transitional probability (the likelihood of the stimulus preceding the target to accurately predict the target's appearance) from block to block. Some monkeys evidenced learning of adjacent dependencies by faster response times to targets that followed a predictive stimulus compared to targets that were not preceded by a predictor. However, consistent with the body of evidence that indicates that nonhuman animals' statistical learning mechanisms are not at the same level of sophistication as humans', there was no evidence that monkeys learned nonadjacent dependencies of arbitrary associations, even when the salient cue was present.

High prevalence of spin was found in pharmacovigilance studies using disproportionality analyses to detect safety signals: a meta-epidemiological study

OBJECTIVE

To systematically review and appraise misinterpretation of pharmacovigilance disproportionality analysis results in published studies.

STUDY DESIGN AND SETTING

We randomly selected 100 studies that performed disproportionality analyses and indexed in Medline identified during a systematic literature search. Titles, abstracts and main texts (results, discussion and conclusion) were evaluated for spin independently by two reviewers. Spin in pharmacovigilance studies was classified according to three main categories: inappropriate interpretation, inappropriate extrapolations and misleading reporting.

RESULTS

Of the 100 studies evaluated, we found that 63%, 56% and 51% had at least one type of spin in their abstract, main text or conclusion respectively, and 40% used causal language to interpret their results in the abstract or conclusion. Spin in titles and results were exclusively represented by inappropriate interpretations of findings (12% and 21% respectively), with terms such as "risk of" or "risks associated with" or results erroneously presented as regular Odds Ratios. Spin in discussion sections mostly concerned inappropriate interpretations (38%)and misleading reporting (12%). Misleading reporting, notably failing to acknowledge the limitations of disproportionality analyses, was the most frequent type of spin in abstracts (55%) and conclusion sections (37%).

CONCLUSION

We found that spin is frequent in publications of pharmacovigilance disproportionality analyses, notably in abstracts. This consisted notably in an over-interpretation of the results suggesting a proven causative link between a drug use and the risk of an event.

Neuroleptic malignant syndrome associated with long-acting injectable versus oral second-generation antipsychotics: Analyses based on a spontaneous reporting system database in Japan

Long-acting injectable antipsychotics (LAI-APs) remain underutilized. One reason is the concern that LAI-APs might cause serious adverse events such as neuroleptic malignant syndrome (NMS) and lead to prolonged symptoms compared with oral treatment. Because the risk of NMS associated with LAI second-generation antipsychotics (LAI-SGAs) remains unclear, we compared reporting frequency, time to onset, and mortality of NMS between LAI- and oral SGAs using data from a Japanese spontaneous adverse event reporting database between April 2004 and September 2019. Of 5791 patients reporting adverse events due to LAI-SGAs or the equivalent oral SGAs, 768 (13%) developed NMS. LAI aripiprazole and LAI paliperidone were associated with a significantly lower reporting frequency of NMS than the equivalent oral SGAs (adjusted reporting odds ratio [95% confidence interval]: 0.35 [0.19-0.63] and 0.40 [0.27-0.59], respectively). Between 42% and 62% of the NMS associated with LAI- and oral SGAs other than LAI risperidone occurred within 30 days after initiation. The proportion of mortality due to NMS associated with oral aripiprazole was 13.1% and no deaths occurred in patients with NMS associated with LAI aripiprazole. The proportions of mortality due to NMS associated with oral risperidone/paliperidone, LAI risperidone, and LAI paliperidone were 8.8%, 4.2%, and 3.4%, respectively. Our findings showed that LAI-SGAs were not associated with a higher reporting frequency and mortality of NMS compared with oral SGAs, although clinicians need to closely monitor the occurrence of NMS not only during oral SGA treatment, but also, and in particular, in the early stage of LAI-SGA treatment.

Plausible drug interaction between cyclophosphamide and voriconazole via inhibition of CYP2B6

The inhibitory activities of eight cytochrome P450 (CYP) isoenzymes for representative or suspected inhibitors of CYPs, including pesticides, were evaluated simultaneously using an in vitro cocktail incubation method to demonstrate the importance of systematic evaluation of CYP inhibitory risks in drug interaction (DI). Potent inhibition of CYP2B6 was noticeable for some azoles, including voriconazole. When voriconazole and cyclophosphamide were co-administered in mice, cyclophosphamide-induced alopecia and leukopenia were significantly suppressed by approximately 50% with increased blood concentrations of cyclophosphamide. The formation of an active metabolite of cyclophosphamide was suppressed effectively by voriconazole in the mouse liver microsomes. Surveys of adverse event reporting databases in Japan (JADER) and the U.S. (FAERS) showed that the proportional reporting ratios of neutropenia, hemorrhagic cystitis, and alopecia for cyclophosphamide, which is principally activated by CYP2B6 in humans, were mostly reduced, or tended to be reduced when azoles, including voriconazole, were prescribed in combination. It is highly likely that DIs between cyclophosphamide and azoles occur in the clinical setting. This study also suggests that more proper consideration of CYP2B6-mediated DIs is warranted. The combination of the in vitro cocktail method and a survey of adverse event reporting databases was a useful method to comprehensively detect pharmacokinetic DIs.

Drug safety in multiple sclerosis: From reporting to signal detection and benefit-risk management

BACKGROUND

Pharmacovigilance (PV) rules emerged in the late 60s-early 70s. Since that time, the World Health Organization Center for International Drug Monitoring carries out the corresponding tasks. Multiple sclerosis (MS) is a chronic inflammatory demyelinating disease of the central nervous system that generally starts in young adults between 20 and 40 years of age. Over the last 25 years, MS patients have benefited from the development of a plethora of disease modifying drugs (DMD). These changes in the therapeutic armamentarium have been associated with some serious adverse reactions challenging health authorities and neurologists involved in treatment and care for MS patients.

METHODS

The present review aims to describe, for MS DMDs, how adverse drug reactions are reported during clinical trials and the post-marketing period and how important signal detection and benefit-risk management have been in this disease until now. Several examples are reported to illustrate the different steps of PV processes.

CONCLUSION

Improvement of the PV system procedures has led to significant progress in the detection of signals, allowing better assessment of the benefit-risk balance and the implementation of risk management plans for MS treatments. The involvement of neurologists is essential to improve knowledge on the benefit-risk balance of these drugs. In addition, adverse drug reactions reporting by persons with MS should be encouraged.

A Polarity Alignment Method for Group-Averaging of Event-Related Neural Signals at Source Level in MEG Beamforming Applications

Brain waveforms reconstructed at source level, like in beamforming, suffer polarity indeterminacy, which precludes direct group averaging of associated waveforms. We describe a polarity alignment method as an alternative of averaging rectified (i.e. absolute value) waveforms. Using MEG from an auditory localisation task, we compare the ability of the two approaches to enable signal detection in the primary auditory cortex over increasing sample size. The two methods are comparable in signal detection sensitivity, but the alignment method preserves group-average polarity alternation.

Combining two optimized and affordable methods to assign chemoreceptors to a specific signal

Chemotaxis allows bacteria to detect specific compounds and move accordingly. This pathway involves signal detection by chemoreceptors (MCPs). Attributing a chemoreceptor to a ligand is difficult because there is a lot of redundancy in the MCPs that recognize a single ligand. We propose a methodology to define which chemoreceptors bind a given ligand. First, an MCP is overproduced to increase sensitivity to the ligand(s) it recognizes, thus promoting accumulation of cells around an agarose plug containing a low attractant concentration. Second, the ligand-binding domain (LBD) of the chemoreceptor is fused to maltose-binding protein (MBP), which facilitates purification and provides a control for a thermal shift assay (TSA). An increase in the melting temperature of the LBD in the presence of the ligand indicates that the chemoreceptor directly binds it. We showed that overexpression of two Shewanella oneidensis chemoreceptors (SO_0987 and SO_1056) promoted swimming toward an agarose plug containing a low concentration of chromate. The LBD of each of the two chemoreceptors was fused to MBP. A TSA revealed that only the LBD from SO_1056 had its melting temperature increased by chromate. In conclusion, we describe an efficient approach to define chemoreceptor-ligand pairs before undertaking more-sophisticated biochemical and structural studies.

Construction and analysis of a database for medication errors in a pharmacovigilance centre-the Moroccan experience

PURPOSE

The study aimed to describe the epidemiological profile of medication errors (MEs) reported to the Moroccan Pharmacovigilance Center (MPVC), to determine factors associated with serious MEs, and to describe signals related to them.

METHODS

We carried out a retrospective descriptive analysis of MEs reported to the MPVC from 2006 to 2016 and a secondary analysis of the seriousness of MEs with adverse drug reactions (ADRs). The reports were sorted by demographic profile and by ME and ADR characteristics. For signal detection, a quantitative approach was adopted, and the root cause analysis was completed. Epi info 7 software was used to perform descriptive and analytical statistics. The statistical significance level was set at p < 0.05.

RESULTS

A total of 1618 ME reports were retrieved. The proportion of MEs associated with serious ADRs was 23.9%. The factors statistically associated with serious MEs were as follows: (i) the age group 16 years old and less (p < 0.001), (ii) the gender (p = 0.01), (iii) the administration and the prescription stages (p < 0.001), and (iv) the ME types related to inappropriate schedule of drug administration, drug prescribing error (p < 0.001), and incorrect drug administration dosage form (p = 0.04). Fourteen signals related to MEs were detected, for which risk minimization actions were implemented.

CONCLUSION

The establishment of a ME unit within the MPVC was an opportunity to further improve the pharmacovigilance centre performance and consequently its contribution to medication safety. The lessons learned from MEs should be shared through pharmacovigilance networks and with institutions involved in medication safety for synergistic results to achieve patient safety worldwide.

Immune checkpoint inhibitors-related myocarditis in patients with cancer: an analysis of international spontaneous reporting systems

BACKGROUND

Immune checkpoint inhibitors-induced myocarditis presents unique clinical challenges. Here, we assessed post-marketing safety of cytotoxic T-lymphocyte-associated protein-4 (CTLA-4), programmed cell death-1 (PD-1), and programmed death-ligand 1 (PD-L1) inhibitors by mining the real-world data reported in two international pharmacovigilance databases.

METHODS

We analyzed immune checkpoint inhibitors (ICIs)-associated fatal adverse drug events (ADEs) reports from the US Food and Drug Administration (FDA) Adverse Event Reporting System (FAERS) collected from July 1, 2014 to December 31, 2019 and data from EudraVigilance (EV) database accessed on February 29, 2020. Three different data mining approaches were used to detect the signal of fatal myocarditis caused by ICIs.

RESULTS

Based on 7613 ICIs-related ADEs reported to the EV database and 5786 ICIs-associated ADEs submitted to the FAERS database, the most frequently reported ADE was ipilimumab-related colitis. For myocarditis, nivolumab-associated myocarditis was the most common. Among the five fatal toxic effects associated with ICIs, the lethality rate of myocarditis was the highest. Therefore, we further analyzed ICI-associated myocarditis and found that elderly patients and male patients were more likely to develop ICIs-related myocarditis. The results of signal detection showed that the risk signal of avelumab-related myocarditis detected by reporting odds ratio (ROR) method and proportional reporting ratios (PRR) method was the highest, whereas the signal strength of ipilimumab-related myocarditis detected by Bayesian confidence propagation neural networks (BCPNN) method was the strongest.

CONCLUSION

The findings of this study indicated the potential safety issues of developing myocarditis when using ICIs, which were consistent with the results of previous clinical trials and could provide a reference for clinical workers when using ICIs.

Learning to detect the onset of slow activity after a generalized tonic-clonic seizure

BACKGROUND

Sudden death in epilepsy (SUDEP) is a rare disease in US, however, they account for 8-17% of deaths in people with epilepsy. This disease involves complicated physiological patterns and it is still not clear what are the physio-/bio-makers that can be used as an indicator to predict SUDEP so that care providers can intervene and treat patients in a timely manner. For this sake, UTHealth School of Biomedical Informatics (SBMI) organized a machine learning Hackathon to call for advanced solutions https://sbmi.uth.edu/hackathon/archive/sept19.htm .

METHODS

In recent years, deep learning has become state of the art for many domains with large amounts data. Although healthcare has accumulated a lot of data, they are often not abundant enough for subpopulation studies where deep learning could be beneficial. Taking these limitations into account, we present a framework to apply deep learning to the detection of the onset of slow activity after a generalized tonic-clonic seizure, as well as other EEG signal detection problems exhibiting data paucity.

RESULTS

We conducted ten training runs for our full method and seven model variants, statistically demonstrating the impact of each technique used in our framework with a high degree of confidence.

CONCLUSIONS

Our findings point toward deep learning being a viable method for detection of the onset of slow activity provided approperiate regularization is performed.

Neural divergence and convergence for attention to and detection of interoceptive and somatosensory stimuli

Body awareness is constructed by signals originating from within and outside the body. How do these apparently divergent signals converge? We developed a signal detection task to study the neural convergence and divergence of interoceptive and somatosensory signals. Participants focused on either cardiac or tactile events and reported their presence or absence. Beyond some evidence of divergence, we observed a robust overlap in the pattern of activation evoked across both conditions in frontal areas including the insular cortex, as well as parietal and occipital areas, and for both attention and detection of these signals. Psycho-physiological interaction analysis revealed that right insular cortex connectivity was modulated by the conscious detection of cardiac compared to somatosensory sensations, with greater connectivity to occipito-parietal regions when attending to cardiac signals. Our findings speak in favour of the inherent convergence of bodily-related signals and move beyond the apparent antagonism between exteroception and interoception.

Statistical methods for the analysis of adverse event data in randomised controlled trials: a scoping review and taxonomy

BACKGROUND

Statistical methods for the analysis of harm outcomes in randomised controlled trials (RCTs) are rarely used, and there is a reliance on simple approaches to display information such as in frequency tables. We aimed to identify whether any statistical methods had been specifically developed to analyse prespecified secondary harm outcomes and non-specific emerging adverse events (AEs).

METHODS

A scoping review was undertaken to identify articles that proposed original methods or the original application of existing methods for the analysis of AEs that aimed to detect potential adverse drug reactions (ADRs) in phase II-IV parallel controlled group trials. Methods where harm outcomes were the (co)-primary outcome were excluded. Information was extracted on methodological characteristics such as: whether the method required the event to be prespecified or could be used to screen emerging events; and whether it was applied to individual events or the overall AE profile. Each statistical method was appraised and a taxonomy was developed for classification.

RESULTS

Forty-four eligible articles proposing 73 individual methods were included. A taxonomy was developed and articles were categorised as: visual summary methods (8 articles proposing 20 methods); hypothesis testing methods (11 articles proposing 16 methods); estimation methods (15 articles proposing 24 methods); or methods that provide decision-making probabilities (10 articles proposing 13 methods). Methods were further classified according to whether they required a prespecified event (9 articles proposing 12 methods), or could be applied to emerging events (35 articles proposing 61 methods); and if they were (group) sequential methods (10 articles proposing 12 methods) or methods to perform final/one analyses (34 articles proposing 61 methods).

CONCLUSIONS

This review highlighted that a broad range of methods exist for AE analysis. Immediate implementation of some of these could lead to improved inference for AE data in RCTs. For example, a well-designed graphic can be an effective means to communicate complex AE data and methods appropriate for counts, time-to-event data and that avoid dichotomising continuous outcomes can improve efficiencies in analysis. Previous research has shown that adoption of such methods in the scientific press is limited and that strategies to support change are needed.

TRIAL REGISTRATION

PROSPERO registration: https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=97442.

Simultaneous Detection of Signal Regions Using Quadratic Scan Statistics With Applications to Whole Genome Association Studies

We consider in this paper detection of signal regions associated with disease outcomes in whole genome association studies. Gene- or region-based methods have become increasingly popular in whole genome association analysis as a complementary approach to traditional individual variant analysis. However, these methods test for the association between an outcome and the genetic variants in a pre-specified region, e.g., a gene. In view of massive intergenic regions in whole genome sequencing (WGS) studies, we propose a computationally efficient quadratic scan (Q-SCAN) statistic based method to detect the existence and the locations of signal regions by scanning the genome continuously. The proposed method accounts for the correlation (linkage disequilibrium) among genetic variants, and allows for signal regions to have both causal and neutral variants, and the effects of signal variants to be in different directions. We study the asymptotic properties of the proposed Q-SCAN statistics. We derive an empirical threshold that controls for the family-wise error rate, and show that under regularity conditions the proposed method consistently selects the true signal regions. We perform simulation studies to evaluate the finite sample performance of the proposed method. Our simulation results show that the proposed procedure outperforms the existing methods, especially when signal regions have causal variants whose effects are in different directions, or are contaminated with neutral variants. We illustrate Q-SCAN by analyzing the WGS data from the Atherosclerosis Risk in Communities study.

Measuring attention in rats with a visual signal detection task: Signal intensity vs. signal duration

Measurement of attentional performance in animal behavioral research allows us to investigate neural mechanisms underlying attentional processes and translate results to better understand human attentional function, dysfunction and drug treatments to reverse dysfunction. One useful method to measure attention in experimental animal studies is to use an operant visual signal detection paradigm, consisting of two levers and the rapid flashing of a cue lamp to signal a reward. In this study, we tested the relative sensitivity of this task when using different variants of the stimulus signal, varying brightness or duration of the light cue. To investigate roles of different neural systems underlying attentional processes, we assessed the sensitivity of attentional performance with these two different cue variations with blockade of muscarinic acetylcholine and NMDA glutamate receptors with scopolamine and MK-801 (dizocilpine). Operant signal detection was tested using a signal light that varied in intensity (0.027, 0.269, 1.22 lx) of the signal light or in a paradigm which varied the duration (0.5 s, 1 s, 2 s) of the signal light. Both methods of assessing attention showed construct validity for producing gradients of accuracy for signal detection; the dimmest cue led to less accurate responding compared to the brighter cues, and the shortest duration led to less accuracy compared to the longer durations. However, the tests differed in their sensitivity to pharmacological disruption. With the duration test, the high dose of MK-801 along with co-exposure of scopolamine and MK-801 caused a significant reduction of hit and rejection accuracy. Conversely, the intensity variation test did not show significant differences as a function of drug exposures. These data suggest that changes in signal duration, rather than signal intensity, during operant signal detection may have higher sensitivity to detecting drug effects and be a more useful technique for examining pharmacological interventions on attentional behavior and performance.

S-EQUOL: a neuroprotective therapeutic for chronic neurocognitive impairments in pediatric HIV

Chronic neurocognitive impairments, commonly associated with pediatric human immunodeficiency virus type 1 (PHIV), are a detrimental consequence of early exposure to HIV-1 viral proteins. Strong evidence supports S-Equol (SE) as an efficacious adjunctive neuroprotective and/or neurorestorative therapeutic for neurocognitive impairments in adult ovariectomized female HIV-1 transgenic (Tg) rats. There remains, however, a critical need to assess the therapeutic efficacy of SE when treatment occurs at an earlier age (i.e., resembling a therapeutic for children with PHIV) and across the factor of biological sex. Utilization of a series of signal detection operant tasks revealed prominent, sex-dependent neurocognitive deficits in the HIV-1 Tg rat, characterized by alterations in stimulus-reinforcement learning, the response profile, and temporal processing. Early (i.e., postnatal day 28) initiation of SE treatment precluded the development of chronic neurocognitive impairments in all (i.e., 100%) HIV-1 Tg animals, albeit not for all neurocognitive domains. Most notably, the therapeutic effects of SE are generalized across the factor of biological sex, despite the presence of endogenous hormones. Results support, therefore, the efficacy of SE as a neuroprotective therapeutic for chronic neurocognitive impairments in the post-cART era; an adjunctive therapeutic that demonstrates high efficacy in both males and females. Optimizing treatment conditions by evaluating multiple factors (i.e., age, neurocognitive domains, and biological sex) associated with PHIV and HIV-1 associated neurocognitive disorders (HAND) affords a key opportunity to improve the therapeutic efficacy of SE.

Signals of adverse drug reactions communicated by pharmacovigilance stakeholders: protocol for a scoping review of the global literature

BACKGROUND

Signals of adverse drug reactions (ADRs) form the basis of some regulatory risk-minimization actions in pharmacovigilance. Reviews of limited scope have highlighted that such signals are mostly supported by reports of ADRs or multiple types of evidence. The time that elapses between a report of a suspected ADR and the communication of a signal has not been systematically characterized. Neither has the features of reports of suspected ADRs that authors used to support putative causal relationships, although difficulties with establishing causal relationships between medicinal products and adverse events have been highlighted. The objectives of this study will be to describe the evidence underpinning signals in pharmacovigilance, the features of reports of ADRs supporting signals, and the time that it takes to communicate a signal.

METHODS

We shall retrieve records from PubMed, EMBASE, Web of Science, and PsycINFO (from inception onwards), without language/design restrictions, and apply backward citation screening. We shall hand-search the websites of 35 regulatory agencies/authorities, restricted publications from the Uppsala Monitoring Centre, and drug bulletins. Signals will be requested from the competent stakeholder, if absent from websites. We shall use VigiBase, the World Health Organization's Global Individual Case Safety Report database, to determine the dates on which ADRs were reported. We shall manage records using EndNote (v. 8.2); one reviewer will screen titles/abstracts and full texts, a second will cross-validate the findings, and a third will arbitrate disagreements. Data will be charted via the Systematic Reviews Data Repository, following the same procedures as for data retrieval. Evidence will be categorized according to the Oxford Centre for Evidence-Based Medicine Levels of Evidence. Features of reports of ADRs will be coded. Tables will display frequencies of types of evidence and features of reports of ADRs. We shall use plots or pictograms (if appropriate) to represent the time from the first report of a suspected ADR to a signal.

DISCUSSION

We expect the findings from this review will allow a better understanding of global patterns of similarities or differences in terms of supporting evidence and timing of communications and identify relevant research questions for future systematic reviews. SYSTEMATIC REVIEW REGISTRATION: osf.io/a4xns.

sdtlu: An R package for the signal detection analysis of eyewitness lineup data

In a standard eyewitness lineup scenario, a witness observes a culprit commit a crime and is later asked to identify the culprit from a set of faces, the lineup. Signal detection theory (SDT), a powerful modeling framework for analyzing data, has recently become a common way to analyze lineup data. The goal of this paper is to introduce a new R package, sdtlu (Signal Detection Theory - LineUp), that streamlines and automates the SDT analysis of lineup data. sdtlu provides functions to process lineup data, determine the best-fitting SDT parameters, compute model-based performance measures such as area under the curve (AUC) and diagnosticity, use bootstrapping to determine uncertainty intervals around these parameters and measures, and compare parameters across two different data sets. The package incorporates closed-form solutions for both simultaneous and sequential lineups that allow for model-based analyses without Monte Carlo simulation. Show-ups are also supported. The package can estimate the base-rate of lineups that include a guilty suspect when the guilt or innocence of each suspect in the data set is unknown, as in "real-world" lineups. The package can also produce a full set of graphs, including data and model-based ROC curves and the underlying SDT model.

Space Lower Bounds for the Signal Detection Problem

Many shared memory algorithms have to deal with the problem of determining whether the value of a shared object has changed in between two successive accesses of that object by a process when the responses from both are the same. Motivated by this problem, we define the signal detection problem, which can be studied on a purely combinatorial level. Consider a system with n + 1 processes consisting of n readers and one signaller. The processes communicate through a shared blackboard that can store a value from a domain of size m. Processes are scheduled by an adversary. When scheduled, a process reads the blackboard, modifies its contents arbitrarily, and, provided it is a reader, returns a Boolean value. A reader must return true if the signaller has taken a step since the reader's preceding step; otherwise it must return false. Intuitively, in a system with n processes, signal detection should require at least n bits of shared information, i.e., m ≥ 2 ⁿ . But a proof of this conjecture remains elusive. For the general case, we prove a lower bound of m ≥ n ². For restricted versions of the problem, where the processes are oblivious or where the signaller must write a fixed sequence of values, we prove a tight lower bound of m ≥ 2 ⁿ . We also consider a version of the problem where each reader takes at most two steps. In this case, we prove that m = n + 1 blackboard values are necessary and sufficient.

Email phishing and signal detection: How persuasion principles and personality influence response patterns and accuracy

Phishing is a social engineering tactic where a malicious actor impersonates a trustworthy third party with the intention of tricking the user into divulging sensitive information. Previous social engineering research in a real-world setting has shown an interaction between the personality of the target and the persuasion principle used. This study investigated whether this interaction is present in the realm of email phishing. Additionally, a signal detection theory framework was used to evaluate how the various persuasion principles influence accuracy, sensitivity (d'), and response criterion placement. A personality inventory and an email identification task (phishing or legitimate) were used. These data support previous findings that high extroversion is predictive of increased susceptibility to phishing attacks. The various persuasions principles elicited diverse response criterions and sensitivities, though all investigated persuasion principles resulted in a liberal decision criterion, except one. These findings are interpreted and discussed.