The Food and Drug Administration's (FDA's) Drug Safety Surveillance During the COVID-19 Pandemic.

INTRODUCTION: On 4 February, 2020, the Secretary of the Department of Health and Human Services declared a public health emergency related to coronavirus disease 2019 (COVID-19), and on 27 March, 2020 declared circumstances existed to justify the authorization of the emergency use of drug and biological products (hereafter, "drugs") for COVID-19. At the outset of the pandemic with uncertainty relating to the virus, many drugs were being used to treat or prevent COVID-19, resulting in the US Food and Drug Administration's (FDA's) need to initiate heightened surveillance across these drugs. OBJECTIVE: We aimed to describe the FDA's approach to monitoring the safety of drugs to treat or prevent COVID-19 across multiple data sources and the subsequent actions taken by the FDA to protect public health. METHODS: The FDA conducted surveillance of adverse event and medication error data using the FDA Adverse Event Reporting System, biomedical literature, FDA-American College of Medical Toxicology COVID-19 Toxicology Investigators Consortium Pharmacovigilance Project Sub-registry, and the American Association of Poison Control Centers National Poison Data System. RESULTS: From 4 February, 2020, through 31 January, 2022, we identified 22,944 unique adverse event cases worldwide and 1052 unique medication error cases domestically with drugs to treat or prevent COVID-19. These were from the FDA Adverse Event Reporting System (22,219), biomedical literature (1107), FDA-American College of Medical Toxicology COVID-19 Toxicology Investigator's Consortium Sub-registry (638), and the National Poison Data System (32), resulting in the detection of several important safety issues. CONCLUSIONS: Safety surveillance using near real-time data was critical during the COVID-19 pandemic because the FDA monitored an unprecedented number of drugs to treat or prevent COVID-19. Additionally, the pandemic prompted the FDA to accelerate innovation, forging new collaborations and leveraging data sources to conduct safety surveillance to respond to the pandemic.

Development of an algorithm to detect methotrexate wrong frequency error using computerized health care data.

PURPOSE: We validated an algorithm to detect frequency errors in computerized healthcare data and estimated the incidence of these errors in an integrated healthcare system. METHODS: We applied Sentinel System analytic tools on the electronic health records of Kaiser Permanente, Northern California, January 1, 2010, through May 30, 2015,to identify rheumatoid arthritis (RA) patients with new use of methotrexate (365-day baseline period). We identified potential methotrexate frequency errors using ICD-9 code 995.20 (adverse drug event), Current Procedural Terminology (CPT) code 96409 for injection of leucovorin and prescription refill patterns. We performed chart review to confirm the frequency errors, assessed performance for detecting frequency errors, and estimated the incidence of chart-confirmed errors. RESULTS: The study included 24,529 methotrexate dispensings among 3,668 RA patients. Among these, 722 (3%) had one dispensing and 23,807 (97.1%) had ≥2 dispensings during 1-year follow-up period. We flagged 653 (2.7%) with a potential medication error (46 with one dispensing and 607 with ≥2 dispensings). We sampled 94 for chart review, and confirmed three methotrexate errors. All three confirmed frequency errors involved a first methotrexate dispensing followed by injected rescue therapy, leucovorin, (positive predictive value, 60%; 95% confidence interval [CI], 15-95%). No potential errors were found among patients with ≥2 dispensings. We estimated the frequency error incidence among one methotrexate dispensing to be 0.4% (95%CI, 0.1% to 1.2%). CONCLUSION: Rescue therapy is a specific indicator of methotrexate overdose among first methotrexate dispensings. This method is generalizable to other medications with serious adverse events treated with antidotes.

Pediatric drug surveillance and the Food and Drug Administration's adverse event reporting system: an overview of reports, 2003-2007.

PURPOSE: Our objective was to examine the numbers and characteristics of US pediatric adverse events (AEs) reported to the Food and Drug Administration (FDA)'s adverse event reporting system (AERS) for 5 years following implementation of the Best Pharmaceuticals for Children Act (BPCA) in 2002. METHODS: We analyzed reports in AERS received by FDA from January 1, 2003 to January 1, 2008 for overall numbers, age, gender, and seriousness of outcome in children and adults. Pediatric and adult age groups (<2, 2-10, 11-17, 18-50, and >50 years of age) were further evaluated for most frequently reported suspect drug classes and AEs. RESULTS: Seventy-two percent of 815 267 crude count reports had specified age information. Six percent of the total reports with age information reported age <18 years. Numbers of AEs being reported for children have remained steady, while those for adults have increased. The proportion of serious AEs reported was similar for pediatrics as compared to adults. Frequently reported suspect drug classes noted in pediatric age groups that were not observed in adults included anticonvulsants, attention deficit hyperactivity disorder (ADHD), anti-acne, and respiratory medications. CONCLUSIONS: This overview highlights the need for strengthening the passive drug surveillance system from a pediatric perspective, as well as investing in more active surveillance systems. Drug safety initiatives to better capture risk information in order to balance the risk/benefit of drug use in children.

An evaluation of a data mining signal for amyotrophic lateral sclerosis and statins detected in FDA's spontaneous adverse event reporting system.

BACKGROUND: We detected disproportionate reporting of amyotrophic lateral sclerosis (ALS) with HMG-CoA-reductase inhibitors (statins) in the Food and Drug Administration's (FDA) spontaneous adverse event (AE) reporting system (AERS). PURPOSE: To describe the original ALS signal and to provide additional context for interpreting the signal by conducting retrospective analyses of data from long-term, placebo-controlled clinical trials of statins. METHODS: The ALS signal was detected using the multi-item gamma Poisson shrinker (MGPS) algorithm. All AERS cases of ALS reported in association with use of a statin were individually reviewed by two FDA neurologists. Manufacturers of lovastatin, pravastatin, simvastatin, fluvastatin, atorvastatin, cerivastatin, and rosuvastatin were requested to provide the number of cases of ALS diagnosed during all of their placebo-controlled statin trials that were at least 6 months in duration. RESULTS: There were 91 US and foreign reports of ALS with statins in AERS. The data mining signal scores for ALS and statins ranged from 8.5 to 1.6. Data were obtained from 41 statin clinical trials ranging in duration from 6 months to 5 years and representing approximately 200,000 patient-years of exposure to statin and approximately 200,000 patient-years of exposure to placebo. Nine cases of ALS were reported in statin-treated patients and 10 cases in placebo-treated patients. CONCLUSIONS: Although we observed a data mining signal for ALS with statins in FDA's AERS, retrospective analyses of 41 statin clinical trials did not reveal an increased incidence of ALS in subjects treated with a statin compared with placebo.