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Quantifying the Severity of Adverse Drug Reactions Using Social Media: Network Analysis.

ABSTRACT: Adverse drug reactions (ADRs) affect the health of hundreds of thousands of individuals annually in the United States, with associated costs of hundreds of billions of dollars. The monitoring and analysis of the severity of ADRs is limited by the current qualitative and categorical systems of severity classification. Previous efforts have generated quantitative estimates for a subset of ADRs but were limited in scope because of the time and costs associated with the efforts. The aim of this study is to increase the number of ADRs for which there are quantitative severity estimates while improving the quality of these severity estimates. We present a semisupervised approach that estimates ADR severity by using social media word embeddings to construct a lexical network of ADRs and perform label propagation. We used this method to estimate the severity of 28,113 ADRs, representing 12,198 unique ADR concepts from the Medical Dictionary for Regulatory Activities. Our Severity of Adverse Events Derived from Reddit (SAEDR) scores have good correlations with real-world outcomes. The SAEDR scores had Spearman correlations of 0.595, 0.633, and -0.748 for death, serious outcome, and no outcome, respectively, with ADR case outcomes in the Food and Drug Administration Adverse Event Reporting System. We investigated different methods for defining initial seed term sets and evaluated their impact on the severity estimates. We analyzed severity distributions for ADRs based on their appearance in boxed warning drug label sections, as well as for ADRs with sex-specific associations. We found that ADRs discovered in the postmarketing period had significantly greater severity than those discovered during the clinical trial (P<.001). We created quantitative drug-risk profile (DRIP) scores for 968 drugs that had a Spearman correlation of 0.377 with drugs ranked by the Food and Drug Administration Adverse Event Reporting System cases resulting in death, where the given drug was the primary suspect. Our SAEDR and DRIP scores are well correlated with the real-world outcomes of the entities they represent and have demonstrated utility in pharmacovigilance research. We make the SAEDR scores for 12,198 ADRs and the DRIP scores for 968 drugs publicly available to enable more quantitative analysis of pharmacovigilance data.

SUBMITTER: Lavertu A

PROVIDER: S-EPMC8569532 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:BackgroundSocial media is a potential source of information on postmarketing drug safety surveillance that still remains unexploited nowadays. Information technology solutions aiming at extracting adverse reactions (ADRs) from posts on health forums require a rigorous evaluation methodology if their results are to be used to make decisions. First, a gold standard, consisting of manual annotations of the ADR by human experts from the corpus extracted from social media, must be implemented and its quality must be assessed. Second, as for clinical research protocols, the sample size must rely on statistical arguments. Finally, the extraction methods must target the relation between the drug and the disease (which might be either treated or caused by the drug) rather than simple co-occurrences in the posts.ObjectiveWe propose a standardized protocol for the evaluation of a software extracting ADRs from the messages on health forums. The study is conducted as part of the Adverse Drug Reactions from Patient Reports in Social Media project.MethodsMessages from French health forums were extracted. Entity recognition was based on Racine Pharma lexicon for drugs and Medical Dictionary for Regulatory Activities terminology for potential adverse events (AEs). Natural language processing-based techniques automated the ADR information extraction (relation between the drug and AE entities). The corpus of evaluation was a random sample of the messages containing drugs and/or AE concepts corresponding to recent pharmacovigilance alerts. A total of 2 persons experienced in medical terminology manually annotated the corpus, thus creating the gold standard, according to an annotator guideline. We will evaluate our tool against the gold standard with recall, precision, and f-measure. Interannotator agreement, reflecting gold standard quality, will be evaluated with hierarchical kappa. Granularities in the terminologies will be further explored.ResultsNecessary and sufficient sample size was calculated to ensure statistical confidence in the assessed results. As we expected a global recall of 0.5, we needed at least 384 identified ADR concepts to obtain a 95% CI with a total width of 0.10 around 0.5. The automated ADR information extraction in the corpus for evaluation is already finished. The 2 annotators already completed the annotation process. The analysis of the performance of the ADR information extraction module as compared with gold standard is ongoing.ConclusionsThis protocol is based on the standardized statistical methods from clinical research to create the corpus, thus ensuring the necessary statistical power of the assessed results. Such evaluation methodology is required to make the ADR information extraction software useful for postmarketing drug safety surveillance.International registered report identifier (irrid)RR1-10.2196/11448.

Project description:BackgroundAdverse drug reactions (ADRs) are an important cause of morbidity and mortality. Classical Pharmacovigilance process is limited by underreporting which justifies the current interest in new knowledge sources such as social media. The Adverse Drug Reactions from Patient Reports in Social Media (ADR-PRISM) project aims to extract ADRs reported by patients in these media. We identified 5 major challenges to overcome to operationalize the analysis of patient posts: (1) variable quality of information on social media, (2) guarantee of data privacy, (3) response to pharmacovigilance expert expectations, (4) identification of relevant information within Web pages, and (5) robust and evolutive architecture.ObjectiveThis article aims to describe the current state of advancement of the ADR-PRISM project by focusing on the solutions we have chosen to address these 5 major challenges.MethodsIn this article, we propose methods and describe the advancement of this project on several aspects: (1) a quality driven approach for selecting relevant social media for the extraction of knowledge on potential ADRs, (2) an assessment of ethical issues and French regulation for the analysis of data on social media, (3) an analysis of pharmacovigilance expert requirements when reviewing patient posts on the Internet, (4) an extraction method based on natural language processing, pattern based matching, and selection of relevant medical concepts in reference terminologies, and (5) specifications of a component-based architecture for the monitoring system.ResultsConsidering the 5 major challenges, we (1) selected a set of 21 validated criteria for selecting social media to support the extraction of potential ADRs, (2) proposed solutions to guarantee data privacy of patients posting on Internet, (3) took into account pharmacovigilance expert requirements with use case diagrams and scenarios, (4) built domain-specific knowledge resources embeding a lexicon, morphological rules, context rules, semantic rules, syntactic rules, and post-analysis processing, and (5) proposed a component-based architecture that allows storage of big data and accessibility to third-party applications through Web services.ConclusionsWe demonstrated the feasibility of implementing a component-based architecture that allows collection of patient posts on the Internet, near real-time processing of those posts including annotation, and storage in big data structures. In the next steps, we will evaluate the posts identified by the system in social media to clarify the interest and relevance of such approach to improve conventional pharmacovigilance processes based on spontaneous reporting.

Project description:BackgroundNumerous observational studies suggest that preventable adverse drug reactions are a significant burden in healthcare, but no meta-analysis using a standardised definition for adverse drug reactions exists. The aim of the study was to estimate the percentage of patients with preventable adverse drug reactions and the preventability of adverse drug reactions in adult outpatients and inpatients.MethodsStudies were identified through searching Cochrane, CINAHL, EMBASE, IPA, Medline, PsycINFO and Web of Science in September 2010, and by hand searching the reference lists of identified papers. Original peer-reviewed research articles in English that defined adverse drug reactions according to WHO's or similar definition and assessed preventability were included. Disease or treatment specific studies were excluded. Meta-analysis on the percentage of patients with preventable adverse drug reactions and the preventability of adverse drug reactions was conducted.ResultsData were analysed from 16 original studies on outpatients with 48797 emergency visits or hospital admissions and from 8 studies involving 24128 inpatients. No studies in primary care were identified. Among adult outpatients, 2.0% (95% confidence interval (CI): 1.2-3.2%) had preventable adverse drug reactions and 52% (95% CI: 42-62%) of adverse drug reactions were preventable. Among inpatients, 1.6% (95% CI: 0.1-51%) had preventable adverse drug reactions and 45% (95% CI: 33-58%) of adverse drug reactions were preventable.ConclusionsThis meta-analysis corroborates that preventable adverse drug reactions are a significant burden to healthcare among adult outpatients. Among both outpatients and inpatients, approximately half of adverse drug reactions are preventable, demonstrating that further evidence on prevention strategies is required. The percentage of patients with preventable adverse drug reactions among inpatients and in primary care is largely unknown and should be investigated in future research.

Project description:Introduction: The panorama of drug-induced ototoxicity has widened in the last decades; moreover, post-marketing data are necessary to gain a better insight on ototoxic adverse drug reactions (ADRs). The aim of this study was to perform an analysis of ADR reports describing drug-induced ototoxicity from the Italian spontaneous reporting system (SRS). Methods: As a measure of disproportionality, we calculated the reporting odds ratios (RORs) and 95% confidence intervals (CIs) with a case/non-case methodology. Cases were all suspected ADR reports regarding drug-induced ototoxicity collected into the Italian SRS from 2001 to 2017. Non-cases included all other ADRs reported in the same period. Results: Of 325,980 reports, 652 included at least one ototoxic ADR, compared with 325,328 non-cases. Statistically significant adjusted RORs were found for drugs for cardiovascular disorders, urologicals, teriparatide, amikacin, prulifloxacin, rifampicin and isoniazid, cisplatin, hormone antagonists, tacrolimus, pomalidomide, tramadol, and antidepressants. Significant adjusted RORs in relation to tinnitus were also observed for doxazosin (ROR 5.55, 95% CI 2.06-14.93), bisoprolol (4.28, 1.59-11.53), nebivolol (8.06, 3.32-19.56), ramipril (3.96, 2.17-7.23), irbesartan (19.60, 9.19-41.80), betamethasone (4.01, 1.28-12.52), moxifloxacin (4.56, 1.71-12.34), ethambutol (12.25, 3.89-38.57), efavirenz (16.82, 5.34-52.96), sofosbuvir/ledipasvir (5.95, 1.90-18.61), etoposide (7.09, 2.63-19.12), abatacept (6.51, 2.42-17.53), indometacin (6.30, 2.02-19.72), etoricoxib (5.00, 2.23-11.23), tapentadol (4.37, 1.09-17.62), and timolol combinations (23.29, 9.53-56.95). Moreover, significant adjusted RORs for hypoacusis regarded clarithromycin (3.95, 1.86-8.40), azithromycin (10.23, 5.03-20.79), vancomycin (6.72, 2.14-21.11), methotrexate (3.13, 1.00-9.81), pemetrexed (4.38, 1.40-13.76), vincristine (5.93, 1.88-18.70), vinorelbine (21.60, 8.83-52.82), paclitaxel (2.34, 1.03-5.30), rituximab (3.20, 1.19-8.63), interferon alfa-2b (17.44, 8.56-35.53), thalidomide (16.92, 6.92-41.38), and deferasirox (41.06, 20.07-84.01). Conclusions: This study is largely consistent with results from literature. Nevertheless, propafenone, antituberculars, hormone antagonists, teriparatide, tramadol, and pomalidomide are unknown for being ototoxic. Hypoacusis after the use of vinorelbine, methotrexate, and pemetrexed is unexpected, such as tinnitus related with etoposide, nebivolol, betamethasone, abatacept, sofosbuvir/ledipasvir, and tapentadol, but these considerations require further investigation to better define the risk due to the paucity of data. Moreover, physicians should be aware of the clinical significance of ototoxicity and be conscious about the importance of their contribution to spontaneous reporting.

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Quantifying the Severity of Adverse Drug Reactions Using Social Media: Network Analysis.

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