Dataset Information

Advanced Clinical Decision Support for Vaccine Adverse Event Detection and Reporting.

ABSTRACT: BACKGROUND:Reporting of adverse events (AEs) following vaccination can help identify rare or unexpected complications of immunizations and aid in characterizing potential vaccine safety signals. We developed an open-source, generalizable clinical decision support system called Electronic Support for Public Health-Vaccine Adverse Event Reporting System (ESP-VAERS) to assist clinicians with AE detection and reporting. METHODS:ESP-VAERS monitors patients' electronic health records for new diagnoses, changes in laboratory values, and new allergies following vaccinations. When suggestive events are found, ESP-VAERS sends the patient's clinician a secure electronic message with an invitation to affirm or refute the message, add comments, and submit an automated, prepopulated electronic report to VAERS. High-probability AEs are reported automatically if the clinician does not respond. We implemented ESP-VAERS in December 2012 throughout the MetroHealth System, an integrated healthcare system in Ohio. We queried the VAERS database to determine MetroHealth's baseline reporting rates from January 2009 to March 2012 and then assessed changes in reporting rates with ESP-VAERS. RESULTS:In the 8 months following implementation, 91 622 vaccinations were given. ESP-VAERS sent 1385 messages to responsible clinicians describing potential AEs. Clinicians opened 1304 (94.2%) messages, responded to 209 (15.1%), and confirmed 16 for transmission to VAERS. An additional 16 high-probability AEs were sent automatically. Reported events included seizure, pleural effusion, and lymphocytopenia. The odds of a VAERS report submission during the implementation period were 30.2 (95% confidence interval, 9.52-95.5) times greater than the odds during the comparable preimplementation period. CONCLUSIONS:An open-source, electronic health record-based clinical decision support system can increase AE detection and reporting rates in VAERS.

SUBMITTER: Baker MA

PROVIDER: S-EPMC6642796 | biostudies-literature | 2015 Sep

REPOSITORIES: biostudies-literature

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Advanced Clinical Decision Support for Vaccine Adverse Event Detection and Reporting.

Baker Meghan A MA Kaelber David C DC Bar-Shain David S DS Moro Pedro L PL Zambarano Bob B Mazza Megan M Garcia Crystal C Henry Adam A Platt Richard R Klompas Michael M

Clinical infectious diseases : an official publication of the Infectious Diseases Society of America 20150609 6

<h4>Background</h4>Reporting of adverse events (AEs) following vaccination can help identify rare or unexpected complications of immunizations and aid in characterizing potential vaccine safety signals. We developed an open-source, generalizable clinical decision support system called Electronic Support for Public Health-Vaccine Adverse Event Reporting System (ESP-VAERS) to assist clinicians with AE detection and reporting.<h4>Methods</h4>ESP-VAERS monitors patients' electronic health records fo ...[more]

PMID: 26060294

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Project description:INTRODUCTION:Adverse event (AE) under-reporting has been a recurrent issue raised during health authorities Good Clinical Practices (GCP) inspections and audits. Moreover, safety under-reporting poses a risk to patient safety and data integrity. The current clinical Quality Assurance (QA) practices used to detect AE under-reporting rely heavily on investigator site and study audits. Yet several sponsors and institutions have had repeated findings related to safety reporting, and this has led to delays in regulatory submissions. Recent developments in data management and IT systems allow data scientists to apply techniques such as machine learning to detect AE under-reporting in an automated fashion. OBJECTIVE:In this project, we developed a predictive model that enables Roche/Genentech Quality Program Leads oversight of AE reporting at the program, study, site, and patient level. This project was part of a broader effort at Roche/Genentech Product Development Quality to apply advanced analytics to augment and complement traditional clinical QA approaches. METHOD:We used a curated data set from 104 completed Roche/Genentech sponsored clinical studies to train a machine learning model to predict the expected number of AEs. Our final model used 54 features built on patient (e.g., demographics, vitals) and study attributes (e.g., molecule class, disease area). RESULTS:In order to evaluate model performance, we tested how well it would detect simulated test cases based on data not used for model training. For relevant simulation scenarios of 25%, 50%, and 75% under-reporting on the site level, our model scored an area under the curve (AUC) of the receiver operating characteristic (ROC) curve of 0.62, 0.79, and 0.92, respectively. CONCLUSION:The model has been deployed to evaluate safety reporting performance in a set of ongoing studies in the form of a QA/dashboard cockpit available to Roche Quality Program Leads. Applicability and production performance will be assessed over the next 12-24 months in which we will develop a validation strategy to fully integrate our model into Roche QA processes.

Project description:ImportanceIn the US, safety concern has been identified as a primary barrier to initiating the human papillomavirus (HPV) vaccine. It is unclear if the public's sentiment concerning HPV vaccine safety aligns with postmarketing vaccine safety surveillance data.ObjectiveTo perform a parallel assessment of trends in HPV vaccine safety concerns and HPV vaccine adverse event (AE) reporting.Design, setting, and participantsThis study was a cross-sectional analysis of the National Immunization Survey (NIS) and Vaccine Adverse Event Reporting System (VAERS). Participants in the NIS were adolescents aged 13 to 17 years. AEs were reported to VAERS by patients, health care clinicians, or other sources. Statistical analysis was performed from October 2020 to May 2021.Main outcomes and measuresSecular trends in HPV vaccine safety concerns and spontaneous AE reporting for HPV vaccination from 2015 to 2018.ResultsCaregivers of 39 364 unvaccinated adolescents with a mean (SD) age of 15.57 (0.08) years (26 996 White adolescents [62.9%], 22 707 male adolescents [56.1%], 11 392 privately insured [62.6%], and 32 674 above the poverty level [79.3%]) reported their reasons for not initiating the HPV vaccine series in the 2015-2018 NIS. Citing safety concerns as the primary reason for not initiating the HPV vaccine series increased from 13.0% (95% CI, 12.1%-14.0%) in 2015 to 23.4% (95% CI, 21.8%-25.0%) in 2018 (P for trend < .001), equating to a change from 170 046 to 259 157 US adolescents not initiating the vaccine because of safety concerns. The proportion of parents citing safety concerns as the main reason for HPV vaccine hesitancy increased in 30 states. The largest increases (more than 200%) were observed in California, Hawaii, South Dakota, and Mississippi. During 2015 to 2018, 16 621 AE reports following HPV vaccination were reported to VAERS. The AE reporting rate per 100 000 doses distributed decreased from 44.7 in 2015 to 29.4 in 2018 (P for trend < .001). The serious AE reporting rate, including those leading to hospitalizations, disability, life-threatening condition, or death did not change.Conclusions and relevanceIn this descriptive cross-sectional study, a rise in citing safety concerns was observed among parents with HPV vaccine hesitancy, contrary to the nonserious and serious AE reporting trends. These findings suggest an urgent need to combat the rising sentiment of safety concerns among caregivers to increase HPV vaccine confidence.

Project description:BackgroundVaccine adverse event reporting system (VAERS) was established in the United States (U.S.) as an early warning system with a main purpose of collecting post-marketing Adverse events following immunizations (AEFIs) reports to monitor the vaccine safety and to mitigate the risks from vaccines. During the coronavirus diseases 2019 (COVID-19) pandemic, VAERS got more attention as its important role in monitoring the safety of the vaccines. The aim of this study was to investigate VAERS patterns, reported AEFI, vaccines, and impact of different pandemics since its inception.MethodsThis was an observational study using VARES data from 2/7/1990 to 12/11/2021. Patterns of reports over years were first described, followed by a comparison of reports statistics per year. Furthermore, a comparison of incidents (death, ER visits, etc.) statistics over years, in addition to statistics of each vaccine were calculated. Moreover, each incident's statistics for each vaccine were calculated and top vaccines were reported. All analyses were conducted using R (Version 1.4.1717) and Excel for Microsoft 365.ResultsThere were 1,396,280 domestic and 346,210 non-domestic reports during 1990-2021, including 228 vaccines. For both domestic and non-domestic reports, year of 2021 had the highest reporting rate (48.52 % and 70.33 %), in addition a notable change in AEFIs patterns were recorded during 1991, 1998, 2000, 2006, 2009, 2011, and 2017. AEFIs were as follow: deaths (1.00 % and 4.08 %), ER or doctor visits (13.37 % and 2.27 %), hospitalizations (5.84 % and 27.78 %), lethal threat (1.42 % and 4.38 %), and disabilities (1.4 % and 7.96 %). Pyrexia was the top reported symptom during the past 31 years, except for 2021 where headache was the top one. COVID-19 vaccines namely Moderna, Pfizer-Biontech, and Janssen were the top 3 reported vaccines with headache, pyrexia, and fatigue as the top associated AEFIs. Followed by Zoster, Seasonal Influenza, Pneumococcal, and Human papillomavirus vaccines.ConclusionsThe large data available in VARES make it a useful tool for detecting and monitoring vaccine AEFIs. However, its usability relies on understating the limitations of this surveillance system, the impact of governmental regulations, availability of vaccines, and public health recommendations on the reporting rate.

Dataset Information

Advanced Clinical Decision Support for Vaccine Adverse Event Detection and Reporting.

Publications

Advanced Clinical Decision Support for Vaccine Adverse Event Detection and Reporting.

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