An innovative method to strengthen evidence for potential drug safety signals using Electronic Health Records

Oscanoa, T.J., F. Lizaraso, and A. Carvajal, Hospital admissions due to adverse drug reactions in the elderly. A meta-analysis. Eur J Clin Pharmacol, 2017. 73(6): p. 759-770.

Article  CAS  PubMed  Google Scholar 

Davies, E.C., et al., Adverse drug reactions in hospital in-patients: a prospective analysis of 3695 patient-episodes. PLoS One, 2009. 4(2): p. e4439.

Article  PubMed  PubMed Central  Google Scholar 

Patel, P.B. and T.K. Patel, Mortality among patients due to adverse drug reactions that occur following hospitalisation: a meta-analysis. Eur J Clin Pharmacol, 2019. 75(9): p. 1293-1307.

Article  PubMed  Google Scholar 

Palleria, C., et al., Limitations and obstacles of the spontaneous adverse drugs reactions reporting: Two "challenging" case reports. J Pharmacol Pharmacother, 2013. 4(Suppl 1): p. S66-72.

Article  PubMed  PubMed Central  Google Scholar 

Garcia-Abeijon, P., et al., Factors Associated with Underreporting of Adverse Drug Reactions by Health Care Professionals: A Systematic Review Update. Drug Saf, 2023. 46(7): p. 625-636.

Article  PubMed  PubMed Central  Google Scholar 

Pacurariu, A.C., et al., A description of signals during the first 18 months of the EMA pharmacovigilance risk assessment committee. Drug Saf, 2014. 37(12): p. 1059-66.

Article  PubMed  Google Scholar 

Lester, J., et al., Evaluation of FDA safety-related drug label changes in 2010. Pharmacoepidemiol Drug Saf, 2013. 22(3): p. 302-5.

Article  PubMed  Google Scholar 

Raine, J.M., Risk Management – a European Regulatory View, in Pharmacovigilance, F. Hon. Member ISoP Ronald D. Mann MD, FRCGP, FFPM, FISPE, Elizabeth B. Andrews MPH, PhD, Editor. 2007. p. 553 - 558.

van Hunsel, F., et al., Signals from the Dutch national spontaneous reporting system: Characteristics and regulatory actions. Pharmacoepidemiol Drug Saf, 2021. 30(8): p. 1115-1122.

Article  PubMed  Google Scholar 

Sloane, R., et al., Social media and pharmacovigilance: A review of the opportunities and challenges. Br J Clin Pharmacol, 2015. 80(4): p. 910-20.

Article  PubMed  PubMed Central  Google Scholar 

Topaz, M., et al., Clinicians' Reports in Electronic Health Records Versus Patients' Concerns in Social Media: A Pilot Study of Adverse Drug Reactions of Aspirin and Atorvastatin. Drug Saf, 2016. 39(3): p. 241-50.

Article  CAS  PubMed  Google Scholar 

McGettigan, P., et al., Patient Registries: An Underused Resource for Medicines Evaluation : Operational proposals for increasing the use of patient registries in regulatory assessments. Drug Saf, 2019. 42(11): p. 1343-1351.

Article  PubMed  PubMed Central  Google Scholar 

Trifiro, G., J. Sultana, and A. Bate, From Big Data to Smart Data for Pharmacovigilance: The Role of Healthcare Databases and Other Emerging Sources. Drug Saf, 2018. 41(2): p. 143-149.

Article  PubMed  Google Scholar 

Basile, A.O., A. Yahi, and N.P. Tatonetti, Artificial Intelligence for Drug Toxicity and Safety. Trends Pharmacol Sci, 2019. 40(9): p. 624-635.

Article  CAS  PubMed  PubMed Central  Google Scholar 

Chazard, E., et al., Detection of adverse drug events detection: data aggregation and data mining. Stud Health Technol Inform, 2009. 148: p. 75-84.

PubMed  Google Scholar 

Bates, D.W., et al., Detecting adverse events using information technology. J Am Med Inform Assoc, 2003. 10(2): p. 115-28.

Article  PubMed  PubMed Central  Google Scholar 

Chapman, A.B., et al., Detecting Adverse Drug Events with Rapidly Trained Classification Models. Drug Saf, 2019. 42(1): p. 147-156.

Article  PubMed  PubMed Central  Google Scholar 

Li, F., W. Liu, and H. Yu, Extraction of Information Related to Adverse Drug Events from Electronic Health Record Notes: Design of an End-to-End Model Based on Deep Learning. JMIR Med Inform, 2018. 6(4): p. e12159.

Article  PubMed  PubMed Central  Google Scholar 

Wunnava, S., et al., Adverse Drug Event Detection from Electronic Health Records Using Hierarchical Recurrent Neural Networks with Dual-Level Embedding. Drug Saf, 2019. 42(1): p. 113-122.

Article  PubMed  Google Scholar 

Klopotowska, J.E., et al., Adverse drug events in older hospitalized patients: results and reliability of a comprehensive and structured identification strategy. PLoS One, 2013. 8(8): p. e71045.

Article  CAS  PubMed  PubMed Central  Google Scholar 

Zhao, J., et al., Predictive modeling of structured electronic health records for adverse drug event detection. BMC Med Inform Decis Mak, 2015. 15 Suppl 4(Suppl 4): p. S1.

Wolfe, D., et al., Incidence, causes, and consequences of preventable adverse drug reactions occurring in inpatients: A systematic review of systematic reviews. PLoS One, 2018. 13(10): p. e0205426.

Article  PubMed  PubMed Central  Google Scholar 

Luo, Y., et al., Natural Language Processing for EHR-Based Pharmacovigilance: A Structured Review. Drug Saf, 2017. 40(11): p. 1075-1089.

Article  PubMed  Google Scholar 

Gonzalez-Hernandez, G., et al., Capturing the Patient's Perspective: a Review of Advances in Natural Language Processing of Health-Related Text. Yearb Med Inform, 2017. 26(1): p. 214-227.

Article  CAS  PubMed  PubMed Central  Google Scholar 

van Laar, S.A., et al., An Electronic Health Record Text Mining Tool to Collect Real-World Drug Treatment Outcomes: A Validation Study in Patients With Metastatic Renal Cell Carcinoma. Clin Pharmacol Ther, 2020. 108(3): p. 644-652.

Article  PubMed  PubMed Central  Google Scholar 

Abedian Kalkhoran, H., et al., A text-mining approach to study the real-world effectiveness and potentially fatal immune-related adverse events of PD-1 and PD-L1 inhibitors in older patients with stage III/IV non-small cell lung cancer. BMC Cancer, 2023. 23(1): p. 247.

Article  CAS  PubMed  PubMed Central  Google Scholar 

van Laar, S.A., et al., Application of Electronic Health Record Text Mining: Real-World Tolerability, Safety, and Efficacy of Adjuvant Melanoma Treatments. Cancers (Basel), 2022. 14(21).

Ventola, C.L., Big Data and Pharmacovigilance: Data Mining for Adverse Drug Events and Interactions. P T, 2018. 43(6): p. 340-351.

PubMed  PubMed Central  Google Scholar 

Chen, S., et al., Natural Language Processing to Automatically Extract the Presence and Severity of Esophagitis in Notes of Patients Undergoing Radiotherapy. JCO Clin Cancer Inform, 2023. 7: p. e2300048.

Article  PubMed  Google Scholar 

Chazard, E., et al., The ADE scorecards: a tool for adverse drug event detection in electronic health records. Stud Health Technol Inform, 2011. 166: p. 169-79.

PubMed  Google Scholar 

Pacurariu, A.C., et al., Useful Interplay Between Spontaneous ADR Reports and Electronic Healthcare Records in Signal Detection. Drug Saf, 2015. 38(12): p. 1201-10.

Article  PubMed  PubMed Central  Google Scholar 

Patadia, V.K., et al., Evaluating performance of electronic healthcare records and spontaneous reporting data in drug safety signal detection. Int J Clin Pharm, 2015. 37(1): p. 94-104.

Article  CAS  PubMed  Google Scholar 

Coloma, P.M., et al., A reference standard for evaluation of methods for drug safety signal detection using electronic healthcare record databases. Drug Saf, 2013. 36(1): p. 13-23.

Article  CAS  PubMed  Google Scholar 

Bate, A., et al., Hypothesis-free signal detection in healthcare databases: finding its value for pharmacovigilance. Ther Adv Drug Saf, 2019. 10: p. 2042098619864744.

Article  PubMed  PubMed Central  Google Scholar 

Leegwater, E., et al., Hypokalaemia in patients treated with intravenous flucloxacillin: Incidence and risk factors. Br J Clin Pharmacol, 2022. 88(6): p. 2938-2945.

Article  CAS  PubMed  Google Scholar 

Zand Irani, A., et al., Flucloxacillin and paracetamol induced pyroglutamic acidosis. BMJ Case Rep, 2021. 14(1).

(PRAC), P.R.A.C., PRAC recommendations on signals adopted at the 25–29 Sep 2017 PRAC meeting (EMA/PRAC/610975/2017). 2017.

Human, T.C.G.f.M.R.a.D.P.-. Flucloxacillin: Scientific Conclusions and Grounds for the Variation to the Terms of the Marketing Authorisation(s), . 2020, European Medicines Agency.

Rolfes, L., F. van Hunsel, and E. van Puijenbroek, Response to the validity and reliability of a signal impact assessment tool: statistical issue to avoid misinterpretation. Pharmacoepidemiol Drug Saf, 2016. 25(10): p. 1217.

Article  PubMed  Google Scholar 

Davis, S.E., et al., Use of Electronic Health Record Data for Drug Safety Signal Identification: A Scoping Review. Drug Saf, 2023. 46(8): p. 725-742.

Article  PubMed  Google Scholar 

Patadia, V.K., et al., Can Electronic Health Records Databases Complement Spontaneous Reporting System Databases? A Historical-Reconstruction of the Association of Rofecoxib and Acute Myocardial Infarction. Front Pharmacol, 2018. 9: p. 594.

Article  PubMed  PubMed Central  Google Scholar 

Chandler, R.E., Nintedanib and ischemic colitis: Signal assessment with the integrated use of two types of real-world evidence, spontaneous reports of suspected adverse drug reactions, and observational data from large health-care databases. Pharmacoepidemiol Drug Saf, 2020. 29(8): p. 951-957.

Article  CAS  PubMed  PubMed Central  Google Scholar 

Rolfes, L., et al., The validity and reliability of a signal impact assessment tool. Pharmacoepidemiol Drug Saf, 2016. 25(7): p. 815-9.

Article  PubMed 

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