Safety Surveillance Research Articles

Integrating electronic health records with other data sources for postmarket drug safety signal identification: a review

Electronic health records (EHRs) have emerged as resources for both the identification of adverse drug events (ADEs) and general population health surveillance, however questions remain around how best to utilize EHR data for drug safety signal identification. While the majority of signal identification research has utilized spontaneous reports and health insurance claims, these data also have limitations. Pharmacovigilance approaches combining EHR data with other data sources have the potential to address many of the shortcomings of individual sources. This mini-review seeks to provide an overview of some of the recent research leveraging EHR data in combination with spontaneous reports, claims data, and other pharmacovigilance data sources for drug safety signal identification. Studies have shown that combining EHR data with these and other sources is often beneficial compared to the use of a single source alone, however the synergism or friction introduced is insufficiently explored in current literature. Our review explores how EHR data benefits signal identification when used in combination with other sources, what methods have been applied, and what considerations have previously been noted. Finally, we identify gaps in current research and highlight important considerations for future work using multiple real world data sources for drug safety surveillance.

Crowdsourcing Adverse Events Associated With Monoclonal Antibodies Targeting Calcitonin Gene-Related Peptide Signaling for Migraine Prevention: Natural Language Processing Analysis of Social Media.

Clinical trials demonstrate the efficacy and tolerability of medications targeting calcitonin gene-related peptide (CGRP) signaling for migraine prevention. However, these trials may not accurately reflect the real-world experiences of more diverse and heterogeneous patient populations, who often have higher disease burden and more comorbidities. Therefore, postmarketing safety surveillance is warranted. Regulatory organizations encourage marketing authorization holders to screen digital media for suspected adverse reactions, applying the same requirements as for spontaneous reports. Real-world data from social media platforms constitute a potential venue to capture diverse patient experiences and help detect treatment-related adverse events. However, while social media holds promise for this purpose, its use in pharmacovigilance is still in its early stages. Computational linguistics, which involves the automatic manipulation and quantitative analysis of oral or written language, offers a potential method for exploring this content. This study aims to characterize adverse events related to monoclonal antibodies targeting CGRP signaling on Reddit, a large online social media forum, by using computational linguistics. We examined differences in word frequencies from medication-related posts on the Reddit subforum r/Migraine over a 10-year period (2010-2020) using computational linguistics. The study had 2 phases: a validation phase and an application phase. In the validation phase, we compared posts about propranolol and topiramate, as well as posts about each medication against randomly selected posts, to identify known and expected adverse events. In the application phase, we analyzed posts discussing 2 monoclonal antibodies targeting CGRP signaling-erenumab and fremanezumab-to identify potential adverse events for these medications. From 22,467 Reddit r/Migraine posts, we extracted 402 (2%) propranolol posts, 1423 (6.33%) topiramate posts, 468 (2.08%) erenumab posts, and 73 (0.32%) fremanezumab posts. Comparing topiramate against propranolol identified several expected adverse events, for example, "appetite," "weight," "taste," "foggy," "forgetful," and "dizziness." Comparing erenumab against a random selection of terms identified "constipation" as a recurring keyword. Comparing erenumab against fremanezumab identified "constipation," "depression," "vomiting," and "muscle" as keywords. No adverse events were identified for fremanezumab. The validation phase of our study accurately identified common adverse events for oral migraine preventive medications. For example, typical adverse events such as "appetite" and "dizziness" were mentioned in posts about topiramate. When we applied this methodology to monoclonal antibodies targeting CGRP or its receptor-fremanezumab and erenumab, respectively-we found no definite adverse events for fremanezumab. However, notable flagged words for erenumab included "constipation," "depression," and "vomiting." In conclusion, computational linguistics applied to social media may help identify potential adverse events for novel therapeutics. While social media data show promise for pharmacovigilance, further work is needed to improve its reliability and usability.

The Food and Drug Administration’s Safety Surveillance of Adverse Event Cases Involving Cannabinoid Hemp Products, 2019–2023

Objective. To characterize adverse event (AE) cases received by the US Food and Drug Administration (FDA) reportedly involving cannabinoid hemp products (CHPs). Methods. We searched the FDA Adverse Event Reporting System and Center for Food Safety and Applied Nutrition Adverse Event Reporting System for cases reported directly to the FDA from 2019–2023 involving CHPs, which are defined as products containing 1 or more of the following: cannabidiol, delta-8 tetrahydrocannabinol, and emerging cannabinoids (i.e., other intoxicating cannabinoids often synthesized from hemp-derived cannabidiol). We abstracted and summarized information on reporting trends, consumer demographics, reporter information, product information, and AEs. Results. We identified 610 AE cases reportedly involving CHPs containing 1 or more of the following: delta-8 tetrahydrocannabinol (n=355), cannabidiol (n=253), and emerging cannabinoids (n=36). Multiple trends and safety issues were identified, including an annual increase in the number of cases involving intoxicating cannabinoids since 2021, potential quality issues, and the occurrence of fatalities and pediatric exposures. Conclusions. Our findings emphasize the importance of monitoring AE cases received by the FDA involving CHPs as part of a comprehensive surveillance strategy to identify trends and safety issues with these products. ( Am J Public Health. 2024;114(S8):S664–S672. https://doi.org/10.2105/AJPH.2024.307712 )

Real-Time Smoke Detection in Surveillance Videos Using an Enhanced RT-DETR Framework with Triplet Attention and HS-FPN

This study addresses the urgent need for an efficient and accurate smoke detection system to enhance safety measures in fire monitoring, industrial safety, and urban surveillance. Given the complexity of detecting smoke in diverse environments and under real-time constraints, our research aims to solve challenges related to low-resolution imagery, limited computational resources, and environmental variability. This study introduces a novel smoke detection system that utilizes the real-time detection Transformer (RT-DETR) architecture to enhance the speed and precision of video analysis. Our system integrates advanced modules, including triplet attention, ADown, and a high-level screening-feature fusion pyramid network (HS-FPN), to address challenges related to low-resolution imagery, real-time processing constraints, and environmental variability. The triplet attention mechanism is essential for detecting subtle smoke features, often overlooked due to their nuanced nature. The ADown module significantly reduces computational complexity, enabling real-time operation on devices with limited resources. Furthermore, the HS-FPN enhances the system’s robustness by amalgamating multi-scale features for reliable detection across various smoke types and sizes. Evaluation using a diverse dataset showcased notable improvements in average precision (AP50) and frames per second (FPS) metrics compared to existing state-of-the-art networks. Ablation studies validated the contributions of each component in achieving an optimal balance between accuracy and operational efficiency. The RT-DETR-based smoke detection system not only meets real-time requirements for applications like fire monitoring, industrial safety, and urban surveillance but also establishes a new performance benchmark in this field.

Traceability for strengthening supply chain systems and enhancing real-time visibility: Focus of NAFDAC on advancing vaccine traceability in Nigeria

This study explores the successful implementation of activities aimed at scaling traceability for COVID-19 and routine immunization (RI) vaccines to the local government area and selected healthcare facilities in Nigeria, funded by the Bill and Melinda Gates Foundation. The study was executed by the National Agency for Food and Drug Administration and Control (NAFDAC) and sought to enhance supply chain systems by deploying advanced traceability mechanisms and ensuring real-time stock visibility. The article discusses the accomplishments, challenges, and regulatory framework of NAFDAC, emphasizing GS1 technology-driven traceability, and presents the results of the field scanning activities conducted in July 2023. The approach involved a phased public sector pilot, showcasing the feasibility and challenges of tracking vaccine movement through the supply chain. The result shows the detection of 43 unique products across 1022 facilities from a total of 110,113 scans, offering valuable insights into vaccine distributions. The strategic goals of the project aligned with developing safety surveillance systems in low- and middle-income countries (LMICs) to facilitate patient access to global health products. Similarly, significant improvement in traceability through automated data capture (barcode scanning) and expanded coverage for COVID-19 and selected RI vaccines in Nigeria was found. Against this background, the information derived from this report will build confidence in patients regarding vaccine authenticity, establish a transparent and robust supply chain, and foster pharmacovigilance capability through integration with the track-and-trace systems. Thus, the study provides invaluable insights and opportunities for global health practitioners, policymakers, and researchers to incorporate track-and-trace into regulatory systems by other national regulatory authorities.

Developing National Information Systems to Monitor COVID-19 Vaccination: A Global Observational Study.

Strong information systems are essential for safe and effective immunization programs. The COVID-19 vaccine rollout presented all immunization information systems (IIS) with challenging demands-requiring in-depth vaccine implementation data at all health system levels in real time. The system development approaches taken by countries were heterogeneous, with some countries opting to adapt existing systems and others implementing new ones. Using data reported by Member States to the World Health Organization (WHO), we aim to develop a global understanding of (1) the types of IIS used to monitor COVID-19 vaccination implemented in 2021 and (2) the approaches taken by countries to develop these systems. We conducted a descriptive analysis of data reported through a supplemental questionnaire of the WHO/United Nations Children's Emergency Fund (UNICEF) Joint Reporting Form on Immunization, collecting data for 2021 on (1) the use of and developmental approaches taken for 7 IIS functions (appointments, aggregate reporting, individual-level reporting, reminders, home-based records, safety surveillance, and stock management), and (2) modifications needed for digital health frameworks to permit COVID-19 vaccination monitoring. In total, 188 of 194 WHO Member States responded to the supplemental questionnaire, with 155 reporting on the IIS-related questions. Among those reporting, for each of the 7 IIS functions explored, greater than 85% of responding countries reported that the system was in place for COVID-19 vaccines. Among responding countries, "aggregate reporting system" was the system most frequently reported as being in place (n=116, 98.3%), while "reminder system" was the least (n=77, 89%). Among the countries reporting using a system, whether an existing system was adapted for COVID-19 vaccines or a new one was developed varied by system. Additionally, two-thirds (n=127, 67.6%) of countries reported establishing at least one new system, ranging from 72% (n=42) in high-income countries (HICs) to 62% (n=16) in low-income countries. Concurrently, 55.3% (n=104) of countries reported adapting at least one system already in place for COVID-19 vaccines, with 62% (n=36) of HICs reporting this compared to about 53% for other income groups. Of those reporting developing new systems, for each of the systems explored, more than 85% of countries reported that they intended to keep new systems specific to COVID-19 vaccines. Further, 147 of the 188 (78.2%) Member States responding to the supplemental questionnaire responded to the digital health frameworks question. Lastly, 31% (n=46) of responding countries reported needing to adapt them for COVID-19 vaccination systems. HICs had a higher percentage. Nearly all countries have adapted existing or developed new IIS to monitor COVID-19 vaccination. The approaches varied, notably by income group. Reflection is needed on how to sustain the investments made in IIS during the pandemic. Continued support for IIS is critical, given their essential role in program monitoring and performance.

81 mRNA COVID-19 vaccine safety among children and adolescents from the Canadian National Vaccine Safety Network

Abstract Background The Canadian National Vaccine Safety Network (CANVAS) expanded its active safety surveillance for COVID-19 vaccines among children and adolescents during the COVID-19 pandemic. Objectives This analysis aimed to determine whether the first or second dose of mRNA COVID-19 vaccination is associated with health events in children and adolescents. Design/Methods Adolescents 15-19 years of age or parents/guardians of children 5-14 years of age were recruited from seven Canadian provinces/territories and followed for the occurrence of health events seven days after doses 1 and 2 or for a seven-day period for unvaccinated participants. The primary outcome was a health event that prevented daily activities, resulted in school absenteeism, and/or required a medical consultation, including emergency department visits or hospitalization. Aged-stratified multivariable logistic regression examined health events associated with mRNA COVID-19 vaccines (BNT162b2, mRNA1273, and mixed [different products for doses 1 and 2]) for children aged 5 to 11 years and adolescents aged 12 to 19 years. Results Between January 2021 and February 2023, a total of 249,418 (Children BNT162b2: 205,744, mRNA-1273: 36, and Adolescent BNT162b2: 39,628, mRNA-1273: 4,010) completed the first dose survey and 128,599 (Children BNT162b2: 109,625, mRNA-1273: 3, mixed: 235, and Adolescent BNT162b2: 17,202, mRNA-1273: 1,155, mixed: 474) had completed the second dose survey. A total of 668 unvaccinated participants (Children 431 and Adolescent 237) completed the control survey. In the week following dose 1, health event rates among vaccinated children and adolescents were <4% and lower than in unvaccinated counterparts (6.9%), and neither BNT162b2 nor mRNA-1273 were associated with health events in either age group. Following dose 2, among vaccinated adolescents health events rates (BNT162b2 4.6%, mRNA-1273 8.5%, mixed 10.4%) were higher than unvaccinated adolescents (3.4%), but were self-limiting and resolved within 7 days. Among children, there was no difference in health event rates between vaccinated and unvaccinated groups. Conclusion For children, mRNA COVID-19 vaccines were not associated with health events preventing daily activities, resulting in school absenteeism and/or requiring medical consultation. Similar to vaccinated adults our findings found vaccinated adolescents reported higher rates of health events after dose 2 mRNA COVID-19 vaccines, but these were self-limiting and resolved quickly. Our findings can be used to inform adolescents and their parents of what to expect following COVID-19 vaccination.

Safety study on adverse events of zanubrutinib based on WHO-VigiAccess and FAERS databases.

The study endeavors to elucidate AE signals associated with zanubrutinib data from the WHO-VigiAccess and FAERS databases. The aim is to furnish robust scientific evidence to inform clinical practice and regulatory decisions. We meticulously extracted all AE breports tied to zanubrutinib from the both databases, encompassing the period from the drug's market introduction until 30 April 2024. Retrospective quantitative analysis employing ROR, PRR, and BCPNN methodologies were utilized to analysis. The investigation unveiled 1,304 reports from WHO-VigiAccess and 1,141 reports from FAERS related to zanubrutinib. Among the top 30 reported PTs in both databases, those not recorded in the drug label included pyrexia, dizziness, and death. In the FAERS database, signals for zanubrutinib were detected across 19 SOCs. Systems not covered in the drug label included reproductive system and breast disorders, metabolism and nutrition disorders, ear and labyrinth disorders, among others. Unmentioned signals included intestinal perforation, onychoclasis, night sweats, etc. This study confirms common AEs of zanubrutinib and identifies new ones, highlighting the need for careful monitoring and personalized treatment. It also emphasizes the importance of ongoing drug safety surveillance and patient management to maximize therapeutic benefits and minimize risks.

SIMILARITY CHECKING OF CCTV IMAGES USING PEARSON CORRELATION: IMPLEMENTATION WITH PYTHON

Video surveillance technology, such as CCTV, is increasingly common in various applications, including public safety and business surveillance. Analyzing and comparing images from CCTV systems is essential for ensuring safety and security. This research implements the Pearson Correlation method in Python to measure the similarity of CCTV images. Pearson Correlation, which assesses the linear relationship between two variables, is employed to compare the pixel values of two images, resulting in a coefficient that indicates the degree of similarity. We used a quantitative approach with experiments on two scenarios to test the program's effectiveness in measuring image similarity. The results demonstrate that Pearson Correlation is highly effective in distinguishing between identical and other images, providing a more accurate and comprehensive assessment of image similarity compared to histogram analysis. However, the findings are constrained by the specific scenarios and dataset utilized. Further research with more diverse empirical data is required to generalize these results across a broader range of CCTV conditions.

Prediction and Visualization of Total Volatile Basic Nitrogen in Yellow Croaker (Larimichthys polyactis) Using Shortwave Infrared Hyperspectral Imaging.

In the present investigation, we have devised a hyperspectral imaging (HSI) apparatus to assess the chemical characteristics and freshness of the yellow croaker (Larimichthys polyactis) throughout its storage period. This system operates within the shortwave infrared spectrum, specifically ranging from 900 to 1700 nm. A variety of spectral pre-processing techniques, including standard normal variate (SNV), multiple scatter correction, and Savitzky-Golay (SG) derivatives, were employed to augment the predictive accuracy of total volatile basic nitrogen (TVB-N)-which serves as a critical freshness parameter. Among the assessed methodologies, SG-1 pre-processing demonstrated superior predictive accuracy (Rp2 = 0.8166). Furthermore, this investigation visualized freshness indicators as concentration images to elucidate the spatial distribution of TVB-N across the samples. These results indicate that HSI, in conjunction with chemometric analysis, constitutes an efficacious instrument for the surveillance of quality and safety in yellow croakers during its storage phase. Moreover, this methodology guarantees the freshness and safety of seafood products within the aquatic food sector.

A Texting- and Internet-Based Self-Reporting System for Enhanced Vaccine Safety Surveillance With Insights From a Large Integrated Health Care System in the United States: Prospective Cohort Study.

SMS text messaging- and internet-based self-reporting systems can supplement existing vaccine safety surveillance systems, but real-world participation patterns have not been assessed at scale. This study aimed to describe the participation rates of a new SMS text messaging- and internet-based self-reporting system called the Kaiser Permanente Side Effect Monitor (KPSEM) within a large integrated health care system. We conducted a prospective cohort study of Kaiser Permanente Southern California (KPSC) patients receiving a COVID-19 vaccination from April 23, 2021, to July 31, 2023. Patients received invitations through flyers, SMS text messages, emails, or patient health care portals. After consenting, patients received regular surveys to assess adverse events up to 5 weeks after each dose. Linkage with medical records provided demographic and clinical data. In this study, we describe KPSEM participation rates, defined as providing consent and completing at least 1 survey within 35 days of COVID-19 vaccination. Approximately, 8% (164,636/2,091,975) of all vaccinated patients provided consent and completed at least 1 survey within 35 days. The lowest participation rates were observed for parents of children aged 12-17 years (1349/152,928, 0.9% participation rate), and the highest participation was observed among older adults aged 61-70 years (39,844/329,487, 12.1%). Persons of non-Hispanic White race were more likely to participate compared with other races and ethnicities (13.1% vs 3.9%-7.5%, respectively; P<.001). In addition, patients residing in areas with a higher neighborhood deprivation index were less likely to participate (5.1%, 16,503/323,122 vs 10.8%, 38,084/352,939 in the highest vs lowest deprivation quintiles, respectively; P<.001). Invitations through the individual's Kaiser Permanente health care portal account and by SMS text message were associated with the highest participation rate (19.2%, 70,248/366,377 and 10.5%, 96,169/914,793, respectively), followed by email (19,464/396,912, 4.9%) and then QR codes on flyers (25,882/2,091,975, 1.2%). SMS text messaging-based surveys demonstrated the highest sustained daily response rates compared with internet-based surveys. This real-world prospective study demonstrated that a novel digital vaccine safety self-reporting system implemented through an integrated health care system can achieve high participation rates. Linkage with participants' electronic health records is another unique benefit of this surveillance system. We also identified lower participation among selected vulnerable populations, which may have implications when interpreting data collected from similar digital systems.

Lenalidomide in Transfusion-Dependent IPSS Low- or Intermediate-1-Risk Myelodysplastic Syndromes and Isolated Del(5q): Results of a European Postauthorization Safety Surveillance Study

BackgroundThis noninterventional postauthorization safety study assessed the safety and effectiveness of lenalidomide in patients with transfusion-dependent, International Prognostic Scoring System (IPSS) Low- or Intermediate (Int)-1-risk myelodysplastic syndromes (MDS) associated with isolated deletion of 5q (del[5q]) who were treated in routine care. Patients and MethodsEligible adult patients in the lenalidomide cohort had transfusion-dependent, IPSS Low- or Int-1-risk MDS and isolated del(5q) and had received ≥ 1 dose of lenalidomide between 2014 and 2022. The primary endpoint was the 24-month cumulative incidence of acute myeloid leukemia (AML) progression. Overall survival (OS) was estimated by Kaplan–Meier analysis and safety data were collected. ResultsIn total, 296 patients received ≥ 1 dose of lenalidomide (lenalidomide cohort, safety population) and 277 had received ≥ 1 complete cycle of lenalidomide (primary population). In the safety population, 44.3% of patients completed 3-year follow-up and 55.1% discontinued, with 33.1% discontinuing due to death. In the primary population, 24-month cumulative incidence of AML progression was 12.7% (95% confidence interval, 8.9%-17.1%) and estimated OS probability was 78.3% at 24 months and 63.9% at 36 months. Grade 3/4 treatment-emergent adverse events were experienced by 67.2% of the safety population, and these led to discontinuation in 35.5% of patients. There were no new safety signals. ConclusionThese real-world data support the established benefit-risk profile of lenalidomide in transfusion-dependent IPSS Low- or Int-1-risk MDS with isolated del(5q).

Scalable incident detection via natural language processing and probabilistic language models

Post marketing safety surveillance depends in part on the ability to detect concerning clinical events at scale. Spontaneous reporting might be an effective component of safety surveillance, but it requires awareness and understanding among healthcare professionals to achieve its potential. Reliance on readily available structured data such as diagnostic codes risks under-coding and imprecision. Clinical textual data might bridge these gaps, and natural language processing (NLP) has been shown to aid in scalable phenotyping across healthcare records in multiple clinical domains. In this study, we developed and validated a novel incident phenotyping approach using unstructured clinical textual data agnostic to Electronic Health Record (EHR) and note type. It’s based on a published, validated approach (PheRe) used to ascertain social determinants of health and suicidality across entire healthcare records. To demonstrate generalizability, we validated this approach on two separate phenotypes that share common challenges with respect to accurate ascertainment: (1) suicide attempt; (2) sleep-related behaviors. With samples of 89,428 records and 35,863 records for suicide attempt and sleep-related behaviors, respectively, we conducted silver standard (diagnostic coding) and gold standard (manual chart review) validation. We showed Area Under the Precision-Recall Curve of ~ 0.77 (95% CI 0.75–0.78) for suicide attempt and AUPR ~ 0.31 (95% CI 0.28–0.34) for sleep-related behaviors. We also evaluated performance by coded race and demonstrated differences in performance by race differed across phenotypes. Scalable phenotyping models, like most healthcare AI, require algorithmovigilance and debiasing prior to implementation.

A prospective, observational, multi-center, post-marketing safety surveillance study of the GSK combined vaccine against diphtheria, tetanus, pertussis, poliomyelitis, and Haemophilus influenzae type b invasive infections (DTaP-IPV/Hib) in South Korean infants

ABSTRACT In South Korea, a combined vaccine against diphtheria, tetanus, pertussis, poliomyelitis, and Haemophilus influenzae type b invasive infections (DTaP-IPV/Hib) is available since 2018 for vaccination of infants from the age of 2 months. This prospective, observational, non-comparative, post-marketing study evaluated the real-world safety of DTaP-IPV/Hib primary vaccination in eligible South Korean infants from the age of 2 months between 2018 and 2022. Infants were followed up for 30 days after each vaccine dose to assess the proportion of infants experiencing any adverse event (AE), including adverse drug reactions (ADRs), unexpected AEs, and serious AEs/serious ADRs (SAEs/SADRs). Of 660 infants vaccinated during the study period, 646 were included in the total safety cohort. A total of 194 AEs were reported in 143 (22.1%) infants; 158 AEs occurred after the first dose in 130 (20.1%) infants, 21 after the second dose in 20 (13.4%) infants, and 11 after the third dose in ten (8.1%) infants. The most frequent AEs by Medical Dictionary for Regulatory Activities Preferred Terms terminology were pyrexia (13.3%), injection site swelling (5.1%), and irritability (1.7%). Most of the AEs were mild, resolved without a medical visit, and were classified as possibly related to vaccination. The incidence proportions of ADRs, unexpected AEs, and SAEs/SADRs were 19.4%, 4.3%, and 0.9%, respectively. All SAEs/SADRs resolved after hospitalization or emergency room visit, and one event was possibly related to vaccination. These results are in line with the approved label and other national/international studies, confirming the acceptable safety profile of DTaP-IPV/Hib in the South Korean pediatric population.

Awareness Regarding Medical Device Safety among Physicians: A Knowledge, Attitude, and Practice Survey

Abstract Background: With the huge inflow of medical devices into the healthcare market, it is imperative to monitor the device safety throughout its period of usage, with the help of a systematic continual surveillance mechanism. Although the Materiovigilance Program of India has been formally introduced since 2015, there exists a dearth of awareness regarding its existence and functioning among the major stakeholders of patient safety. Addressing this gap, the study probes the awareness regarding materiovigilance among the doctors in eastern India. Methods: A cross-sectional questionnaire-based study was carried out using a 20-item structured survey tool to evaluate the knowledge, attitude, and practice of medical professionals regarding safety surveillance of medical devices. Responses obtained were statistically analyzed and interpreted. Results: Principal elements of medical device adverse event (MDAE) reporting were known to around 40% of respondents. However, the time frame for reporting an event/incident was found to be grossly unclear. Over 30% of respondents were aware of the effect of MDAEs and that its reporting could enhance patient safety. Items related to practice domains revealed that although around 50% of respondents of either category encountered MDAEs in their practice setup, only 10.95% of faculty and 6.41% of residents have reported it. Less than 5% of the respondents were ever trained on how to report MDAEs. The mean domain score for knowledge was on higher mark for the faculty, while that for attitude and practice domains were higher for the residents. Conclusion: There was a gross dearth of awareness regarding materiovigilance among the doctors. Enhanced sensitization regarding medical device safety surveillance in all healthcare levels can definitely help the situation ensuring patient safety.

From regional signal to alert in addictovigilance

France has several monitoring systems that form the foundation of its health safety surveillance. This system is designed for the early detection of signals and their swift into an action system that enables timely, appropriate, and effective interventions to protect public health. These signals are considered alerts when sufficiently validated after an initial risk assessment and if they represent a potential threat to public health, necessitating an appropriate response. The French Addictovigilance Network (FAN) was established specifically to address complications associated with the use of psychoactive substances (PAS), both medicinal and non-medicinal, excluding alcohol and tobacco. This system, unique in Europe, provides information on one of the most complex vigilance systems due to its scope, and the risks associated with it are becoming an increasingly significant public health issue. FAN is at the forefront of identifying potential signals. It has been working closely with regional partners since 1990, particularly through a multi-source approach that combines data from various sources, in collaboration with local professionals. After outlining the missions of the addictovigilance centres and the general principles of signal detection in addictovigilance, this article will present recent examples involving similar products but different identifications or modes of consumption: synthetic cannabinoids, nitazene, pregabalin, methylphenidate. These examples demonstrate that while there are regional disparities, the risk of occurrence in other French regions should not be overlooked, and information should be rapidly communicated at the national level for preventive action.

Increased Identification of Vaccines for Vaccine Safety Surveillance Through Linkage With the Minnesota Immunization Information Connection as of December 31, 2023.

The HealthPartners' Vaccine Safety Datalink (VSD) team maintains standardized files of vaccines from medical and pharmacy claims and electronic health records (established data sources) for safety surveillance. Since 2021, for selected vaccines, data from the Minnesota Immunization Information Connection (MIIC), Minnesota's immunization information system, have been added to the HealthPartners' VSD files. We examined how MIIC data have enhanced the identification of novel and routine vaccines. We describe the approach to incorporating MIIC data. We determined and compared the number and proportion of vaccines identified from established data sources with the additional capture of vaccine data identified from MIIC, in which age group and period of observation varied by vaccine. As of December 31, 2023, of 1 099 411 people in the HealthPartners' VSD cohort, 1 001 400 people (91%) were linked with an MIIC record. Across all data sources, for the full cohort, >2.7 million COVID-19 vaccine doses were recorded since 2020, >4000 mpox vaccine doses since 2022, >7.3 million influenza vaccine doses since 2004, >600 000 human papillomavirus (HPV) vaccine doses since 2006, and >1.1 million diphtheria and tetanus toxoids and acellular pertussis (DTaP) vaccine doses since 2004. For COVID-19 vaccines, about 30% of vaccine doses were exclusively captured from MIIC, with the remaining 70% from established data sources. For the mpox vaccine, about 42% were exclusively from MIIC. For influenza, HPV, and DTaP vaccines, about 20%, 14%, and 17%, respectively, were exclusively identified from MIIC. Incorporation of data from state immunization information systems into existing vaccine data files can enhance monitoring on the safety of novel vaccines administered outside traditional health care settings and can enhance data quality for routine childhood and adult vaccines.

Developing large language models to detect adverse drug events in posts on X

ABSTRACT Adverse drug events (ADEs) are one of the major causes of hospital admissions and are associated with increased morbidity and mortality. Post-marketing ADE identification is one of the most important phases of drug safety surveillance. Traditionally, data sources for post-marketing surveillance mainly come from spontaneous reporting system such as the Food and Drug Administration Adverse Event Reporting System (FAERS). Social media data such as posts on X (formerly Twitter) contain rich patient and medication information and could potentially accelerate drug surveillance research. However, ADE information in social media data is usually locked in the text, making it difficult to be employed by traditional statistical approaches. In recent years, large language models (LLMs) have shown promise in many natural language processing tasks. In this study, we developed several LLMs to perform ADE classification on X data. We fine-tuned various LLMs including BERT-base, Bio_ClinicalBERT, RoBERTa, and RoBERTa-large. We also experimented ChatGPT few-shot prompting and ChatGPT fine-tuned on the whole training data. We then evaluated the model performance based on sensitivity, specificity, negative predictive value, positive predictive value, accuracy, F1-measure, and area under the ROC curve. Our results showed that RoBERTa-large achieved the best F1-measure (0.8) among all models followed by ChatGPT fine-tuned model with F1-measure of 0.75. Our feature importance analysis based on 1200 random samples and RoBERTa-Large showed the most important features are as follows: “withdrawals”/”withdrawal”, “dry”, “dealing”, “mouth”, and “paralysis”. The good model performance and clinically relevant features show the potential of LLMs in augmenting ADE detection for post-marketing drug safety surveillance.

Safeguarding Patients in the AI Era: Ethics at the Forefront of Pharmacovigilance.

Artificial intelligence is increasingly being used in pharmacovigilance. However, the use of artificial intelligence in pharmacovigilance raises ethical concerns related to fairness, non-discrimination, compliance, and responsibility as the central ethical principles in risk assessment and regulatory requirements. This paper explores these concerns and provides a roadmap to how to address these challenges by considering data collection, privacy protection, transparency and accountability, model training, and explainability in artificial intelligence decision making for drug safety surveillance. A number of responsible approaches have been identified including an ethics framework and best practices to enhance artificial intelligence use in healthcare. The document also recognizes some initiatives that have demonstrated the importance of ethics in artificial intelligence pharmacovigilance. Nevertheless, the major needs mentioned in this paper are transparency, accountability, data protection, and fairness, which stress the necessity of collaboration to construct a cognitive framework aimed at integrating ethical artificial intelligence into pharmacovigilance. In conclusion, innovation should be balanced with ethical responsibility to enhance public health outcomes as well as patient safety.

A novel HCR-CRISPR/Cas12a immunosensor for the sensitive detection of pesticide residues in animal-derived foods

Pesticides are widely used in the cultivation and breeding of agricultural products. However, pesticide residue monitoring has focused mainly on fruits and vegetables and has not focused on animal-derived foods. Therefore, a novel approach for determining pesticide residues in animal-derived food products via an immunosensor based on a chain hybridization reaction (HCR) with CRISPR/Cas12a is presented. Triazophos pesticide was used as a research object. This method establishes a competitive immunoreaction system in which OVA-hapten is encapsulated on the surface of the enzyme label plate, competing with pesticide molecules bound to antibodies on the surface of the nanogold composite probe. Integrating the immunoreaction with two signal amplification mechanisms, HCR and CRISPR/Cas12a, ensures heightened method sensitivity while maintaining enhanced convenience and speed. The method's detection range spans from 0.01 to 50 ng/mL, with a remarkably low detection limit of 0.013 ng/mL. Moreover, in practical sample applications, the assay exhibited a robust correlation with GC‒MS. This approach uniquely offers real-time, cost-effective, and onsite data, providing a more comprehensive and timely foundation for establishing standards on pesticide residues in animal-derived foods, bolstering food safety surveillance, and upholding public health. Moreover, the proposed HCR-CRISPR/Cas12a immunosensor effectively improved the sensitivity and accuracy of pesticides detection, offering a promising and versatile platform for food safety detection.