A Renaissance in Signal Management
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"Renaissance in Signal Management" highlights recently published studies and their insights regarding opportunities to refine signal management processes and leverage different data sources including electronic health records for signal detection. The talk discusses the published work of many teams including CIOMS, EMA, IMI UMC, FDA and others and what the results of those studies offer regarding the relative strengths of various data sources and statistical methods for signal detection. This talk was presented at the 2016 World Drug Safety Congress by Commonwealth Informatics.
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A Renaissance in Signal Management
- 2. v Renaissance in Signal Management § Signal Management § A clear descripCon of the process (CIOMS-VIII) § Allows us to collect process metrics § Make objecCve choices to opCmize the process § Virtuous cycle of process opCmizaCon § beOer process § beOer data § beOer methods Copyright 2016 Commonwealth InformaCcs Inc. Slide 2
- 3. v Key points § Well defined process allows us to measure performance, experiment with changes, and choose those that opCmize the process § We are starCng to have informaCon on the strengths of each data source and method § Moved past the search for the "one" method and "one" data source § Spontaneous reporCng systems conCnue to provide the best signal detecCon performance for rare adverse reacCons § Electronic health records (EHR) are no panacea, but have a role that complements spontaneous reports in both signal detecCon and in signal refinement § Overall signal management performance is affected by the efficiency of iniCal review and signal refinement Copyright 2016 Commonwealth InformaCcs Inc. Slide 3
- 4. v On the shoulders of giants § CollecCvely we benefit from the intellectually rigorous and transparent published work of many teams § CIOMS § European Medicines Agency (EMA) § InnovaCve Medicines IniCaCve (IMI) § PROTECT, Web-RADR, ... § Uppsala Monitoring Centre (UMC) § vigiRank, vigiPoint, ... § US FDA § mini-SenCnel, SenCnel, ... § And others § EU-ADR, OMOP, OHDSI, iMEDS, ... Copyright 2016 Commonwealth InformaCcs Inc. Slide 4
- 5. v CIOMS-VIII Signal Management Process Copyright 2016 Commonwealth InformaCcs Inc. Slide 5 Impact assessment and prioriCzaCon NEED FURTHER INVESTIGATION? Monitor via rouCne pharmacovigilance (if signal is indeterminate) OR Close out (if signal is refuted) INDIVIDUAL CASE ADVERSE EVENT REPORTS • Clinical trials (serious adverse events) • Post-markeCng sources (serious and non- serious adverse events) • Literature report SIGNAL DETECTION IN ADVERSE EVENT REPORTING SYSTEM • Health authority / monitoring center systems • Company databases TRADITIONAL PHARMACOVIGILANCE METHODS • Review of individual cases • Aggregate analyses of case report data, using case counts, crude or adjusted reporCng rates, etc. DATA MINING ALGORITHMS • ProporConal ReporCng RaCo (PRR) • MulC-item Gamma-Poisson Shrinker (MGPS) • Bayesian Confidence PropagaCon Neural Network (BCPNN) TRIAGE OF OUTPUTS • Interpret within the context of all other relevant sources of safety data, disease knowledge, biological plausibility, alternaCve eCologies for suspected adverse drug reacCons, etc. SIGNAL EVALUATION • Case series analysis • Analysis of exisCng clinical trial data • Literature search and review • Pharmacoepidemiologic studies • MechanisCc studies • AddiConal clinical trials • Other types of studies OTHER SAFETY DATA TO BE MONITORED • Non-clinical / pharmacology studies • Non-intervenConal studies • Published literature (study reports, mechanism of acCon, etc.) • Periodic safety reports • InformaCon on drugs in the same class • Other relevant informaCon From Figure 1, page 22, Report of CIOMS Working Group VIII
- 6. v ICSRs – EMA Signaling Study § Study period Sep-2003 to Mar-2007 using Eudravigilance data § 191 chemically disCnct products § 532 adverse reacCons added to the SPC in the period § Searched EMA records to see when the agency first became aware of each of the adverse reacCons § Looked retrospecCvely to find the earliest point when the signal of disproporConate reporCng (SDR) criteria would have occurred § Compared Cme of SDR with Cme of EMA awareness Reference: Alvarez, Y., Hidalgo, A., Maignen, F., & SlaOery, J. (2010). ValidaCon of staCsCcal signal detecCon procedures in EudraVigilance post-authorizaCon data. Drug safety, 33(6), 475-487. Slide 6
- 7. v ICSRs – EMA Signaling Study § Used PRR 025 > 1 to define a signal of disproporConate reporCng (SDR) § 6356 SDRs § 1561 SDRs survived first triage and required further invesCgaCon § 217 SDRs represented real issues found earlier by SDR that were eventually added to SPC § 1561 / 217 = 7.2 invesCgaCons per signal § 405 IME ADRs added to the SPCs § 217 (54%) found earlier by SDR than exisCng methods § 79 found later by SDR § 109 not signalled by SDR in the study period Reference: Alvarez, Y., Hidalgo, A., Maignen, F., & SlaOery, J. (2010). ValidaCon of staCsCcal signal detecCon procedures in EudraVigilance post-authorizaCon data. Drug safety, 33(6), 475-487. Slide 7
- 9. v Electronic Health Records (EHR) § IMI-PROTECT § Work Package 3 Sub-package 10, "Signal detecCon in Electronic Health Records" § Key points § Developed and tested a quesConnaire for structured assessment of temporally associated drug-event combinaCons § Demonstrated that exploratory analysis of electronic medical records can detect important potenCal safety signals § An expert triage step is required to achieve an acceptable false posiCve rate (as with signal detecCon from ICSRs) Copyright 2016 Commonwealth InformaCcs Inc. Slide 9 Reference: Cederholm, S., Hill, G., Asiimwe, A., Bate, A., Bhayat, F., Brobert, G. P., ... & Norén, G. N. (2014). Structured Assessment for ProspecCve IdenCficaCon of Safety Signals in Electronic Medical Records: EvaluaCon in the Health Improvement Network. Drug safety, 38(1), 87-100.
- 10. v Signal detecCon from EHR - Methods § 42 drugs randomly selected § Up to 20 events per drug selected from those with a temporal associaCon § AssociaCon determined by calibrated self-controlled cohort analysis § Data from UK primary care electronic medical records § The Health Improvement Network (THIN) § 6 assessors evaluated 7 drugs each § up to 20 medical events per drug § using a pre-specified quesConnaire Copyright 2016 Commonwealth InformaCcs Inc. Slide 10 Reference: Cederholm, S., Hill, G., Asiimwe, A., Bate, A., Bhayat, F., Brobert, G. P., ... & Norén, G. N. (2014). Structured Assessment for ProspecCve IdenCficaCon of Safety Signals in Electronic Medical Records: EvaluaCon in the Health Improvement Network. Drug safety, 38(1), 87-100.
- 13. v CIOMS-VIII Signal Management Process Copyright 2016 Commonwealth InformaCcs Inc. Slide 13 Impact assessment and prioriCzaCon NEED FURTHER INVESTIGATION? Monitor via rouCne pharmacovigilance (if signal is indeterminate) OR Close out (if signal is refuted) INDIVIDUAL CASE ADVERSE EVENT REPORTS • Clinical trials (serious adverse events) • Post-markeCng sources (serious and non- serious adverse events) • Literature report SIGNAL DETECTION IN ADVERSE EVENT REPORTING SYSTEM • Health authority / monitoring center systems • Company databases TRADITIONAL PHARMACOVIGILANCE METHODS • Review of individual cases • Aggregate analyses of case report data, using case counts, crude or adjusted reporCng rates, etc. DATA MINING ALGORITHMS • ProporConal ReporCng RaCo (PRR) • MulC-item Gamma-Poisson Shrinker (MGPS) • Bayesian Confidence PropagaCon Neural Network (BCPNN) TRIAGE OF OUTPUTS • Interpret within the context of all other relevant sources of safety data, disease knowledge, biological plausibility, alternaCve eCologies for suspected adverse drug reacCons, etc. SIGNAL EVALUATION • Case series analysis • Analysis of exisCng clinical trial data • Literature search and review • Pharmacoepidemiologic studies • MechanisCc studies • AddiConal clinical trials • Other types of studies OTHER SAFETY DATA TO BE MONITORED • Non-clinical / pharmacology studies • Non-intervenConal studies • Published literature (study reports, mechanism of acCon, etc.) • Periodic safety reports • InformaCon on drugs in the same class • Other relevant informaCon From Figure 1, page 22, Report of CIOMS Working Group VIII
- 17. v Concluding points § Metrics from the signal management process provide data for process improvement § We have the research results to guide integraCon of data sources and appropriate methods leveraging their complementary strengths § Spontaneous reporCng systems conCnue to provide the best signal detecCon performance for rare adverse reacCons § Electronic health records (EHR) are no panacea, but have a role that complements spontaneous reports in both signal detecCon and in signal refinement § The expert decision making during iniCal review and signal refinement is a key area for aOenCon Copyright 2016 Commonwealth InformaCcs Inc. Slide 17