Innovative Digital Tools and Surveillance Systems for the Timely Detection of Adverse Events at the Point of Care: A Proof-of-Concept Study
- 239 Downloads
Introduction and Objective
Regulatory authorities often receive poorly structured safety reports requiring considerable effort to investigate potential adverse events post hoc. Automated question-and-answer systems may help to improve the overall quality of safety information transmitted to pharmacovigilance agencies. This paper explores the use of the VACC-Tool (ViVI Automated Case Classification Tool) 2.0, a mobile application enabling physicians to classify clinical cases according to 14 pre-defined case definitions for neuroinflammatory adverse events (NIAE) and in full compliance with data standards issued by the Clinical Data Interchange Standards Consortium.
The validation of the VACC-Tool 2.0 (beta-version) was conducted in the context of a unique quality management program for children with suspected NIAE in collaboration with the Robert Koch Institute in Berlin, Germany. The VACC-Tool was used for instant case classification and for longitudinal follow-up throughout the course of hospitalization. Results were compared to International Classification of Diseases, Tenth Revision (ICD-10) codes assigned in the emergency department (ED).
From 07/2013 to 10/2014, a total of 34,368 patients were seen in the ED, and 5243 patients were hospitalized; 243 of these were admitted for suspected NIAE (mean age: 8.5 years), thus participating in the quality management program. Using the VACC-Tool in the ED, 209 cases were classified successfully, 69 % of which had been missed or miscoded in the ED reports. Longitudinal follow-up with the VACC-Tool identified additional NIAE.
Mobile applications are taking data standards to the point of care, enabling clinicians to ascertain potential adverse events in the ED setting and during inpatient follow-up. Compliance with Clinical Data Interchange Standards Consortium (CDISC) data standards facilitates data interoperability according to regulatory requirements.
- 3.Miller ER, Haber P, Hibbs B, Broder K. Surveillance for adverse events following immunization using the Vaccine Adverse Event Reporting System (VAERS). 2014 1st April 2014. http://www.cdc.gov/vaccines/pubs/surv-manual/chpt21-surv-adverse-events.html#f25. Accessed 1 Jan 2016.
- 4.Uppsala Monitoring Centre. To improve worldwide patient safety. 2015. http://www.who-umc.org/. Accessed 2 Jan 2016.
- 12.Bailey C, Peddie D, Wickham ME, Badke K, Small SS, Doyle-Waters MM, et al. Adverse drug event reporting systems:a systematic review. Br J Clin Pharmacol. 2016 [Epub ahead of print].Google Scholar
- 13.Clinical Data Interchange Standards Consortium. Analysis Data Model (ADaM): data structure for adverse event analysis. 2012. http://www.cdisc.org/system/files/all/standard_category/application/pdf/adam_ae_final_v1.pdf. Accessed 5 Jun 2016.
- 14.Beresniak A, Schmidt A, Proeve J, Bolanos E, Patel N, Ammour N, et al. Cost-benefit assessment of using electronic health records data for clinical research versus current practices: contribution of the electronic health records for clinical research (EHR4CR) European project. Contemp Clin Trials. 2016;46:85–91.CrossRefPubMedGoogle Scholar
- 25.World Health Organization. Causality assessment of an adverse event following immunization (AEFI). 2013. http://www.who.int/vaccine_safety/publications/aefi_manual.pdf?ua=1. Accessed 28 Apr 2016.
- 27.Sejvar JJ, Kohl KS, Bilynsky R, Blumberg D, Cvetkovich T, Galama J, et al. Encephalitis, myelitis, and acute disseminated encephalomyelitis (ADEM): case definitions and guidelines for collection, analysis, and presentation of immunization safety data. Vaccine. 2007;25(31):5771–92.CrossRefPubMedGoogle Scholar
- 32.Clinical Data Interchange Standards Consortium. Clinical Data Acquisition Standards Harmonization (CDASH). 2016.; http://www.cdisc.org/cdash. Accessed 9 Mar 2016.
- 36.The Brighton Collaboration. AEFI case definition document. https://brightoncollaboration.org/public/what-we-do/setting-standards/case-definitions/process/main/02/link/Case_Definition_Format_Template.pdf. Accessed 3 Jan 2016.
- 37.Clinical Data Interchange Standards Consortium. Study Data Tabulation Model (SDTM). http://www.cdisc.org/sdtm. Accessed 10 Jan 2016.
- 38.Clinical Data Interchange Standards Consortium. Clinical Data Acquisition Standards Harmonization (CDASH). http://www.cdisc.org/cdash. Accessed 10 Jan 2016.
- 39.Clinical Data Interchange Standards Consortium. Biomedical Research Integrated Domain Group (BRIDG). http://www.cdisc.org/bridg. Accessed 10 Jan 2016.
- 40.U.S. Food and Drug Administration. Providing regulatory submissions in electronic format: standardized study data. December 2014. http://www.fda.gov/downloads/Drugs/GuidanceComplianceRegulatoryInformation/Guidances/UCM292334.pdf. Accessed 9 Mar 2016.
- 41.U.S. Food and Drug Administration. Study data standards resources. October 2015. http://www.fda.gov/forindustry/datastandards/studydatastandards/default.htm. Accessed 9 Mar 2016.
- 43.U.S. Food and Drug Administration. Statistical guidance on reporting results from studies evaluating diagnostic tests. 2007. http://www.fda.gov/RegulatoryInformation/Guidances/ucm071148.htm. Accessed 10 Jan 2016.
- 44.Donner A, Rotundi MA. Sample size requirements for interval estimation of the kappa statistic for interobserver agreement studies with a binary outcome and multiple raters. Int J Biostat. 2010;6(1):Article 31.Google Scholar
- 45.Hall MA. Correlation-based feature subset selection for machine learning. Hamilton: University of Waikato; 1998.Google Scholar
- 46.Kullback S. Letter to the eitor: the Kullback-Leibler distance. Am Stat. 1987;41(4):340–1.Google Scholar
- 48.Miller E, Haber P, Hibbs B, Broder K. Surveillance for adverse events following immunization using the Vaccine Adverse Event Reporting System (VAERS). 2014. http://www.cdc.gov/vaccines/pubs/surv-manual/chpt21-surv-adverse-events.html#f5. Accessed 17 June 2016.
- 55.World Health Organization. International Statistical Classification of Diseases and Related Health Problems 10th Revision (ICD-10)-WHO Version for 2016. http://apps.who.int/classifications/icd10/browse/2016/en#/G51. Accessed 25 Feb 2016.
- 60.Rath B, Gidudu JF, Anyoti H, Bollweg B, Caubel P, Chen YH, et al. Facial nerve palsy including Bell’s palsy: case definitions and guidelines for collection, analysis, and presentation of immunisation safety data. Vaccine. 2016 [Epub ahead of print].Google Scholar
- 64.Twilt M. Precision medicine: the new era in medicine. 2016. http://dx.doi.org/10.1016/j.ebiom.2016.02.009. Accessed 17 June 2016.