PMID- 23161894
OWN - NLM
STAT- MEDLINE
DCOM- 20130710
LR  - 20211021
IS  - 1527-974X (Electronic)
IS  - 1067-5027 (Print)
IS  - 1067-5027 (Linking)
VI  - 20
IP  - 3
DP  - 2013 May 1
TI  - Comparative analysis of pharmacovigilance methods in the detection of adverse 
      drug reactions using electronic medical records.
PG  - 420-6
LID - 10.1136/amiajnl-2012-001119 [doi]
AB  - OBJECTIVE: Medication  safety requires that each drug be monitored throughout its 
      market life as early detection of adverse drug reactions (ADRs) can lead to 
      alerts that prevent patient harm. Recently, electronic medical records (EMRs) 
      have emerged as a valuable resource for pharmacovigilance. This study examines 
      the use of retrospective medication orders and inpatient laboratory results 
      documented in the EMR to identify ADRs. METHODS: Using 12 years of EMR data from 
      Vanderbilt University Medical Center (VUMC), we designed a study to correlate 
      abnormal laboratory results with specific drug administrations by comparing the 
      outcomes of a drug-exposed group and a matched unexposed group. We assessed the 
      relative merits of six pharmacovigilance measures used in spontaneous reporting 
      systems (SRSs): proportional reporting ratio (PRR), reporting OR (ROR), Yule's Q 
      (YULE), the chi(2) test (CHI), Bayesian confidence propagation neural networks 
      (BCPNN), and a gamma Poisson shrinker (GPS). RESULTS: We systematically evaluated 
      the methods on two independently constructed reference standard datasets of 
      drug-event pairs. The dataset of Yoon et al contained 470 drug-event pairs (10 
      drugs and 47 laboratory abnormalities). Using VUMC's EMR, we created another 
      dataset of 378 drug-event pairs (nine drugs and 42 laboratory abnormalities). 
      Evaluation on our reference standard showed that CHI, ROR, PRR, and YULE all had 
      the same F score (62%). When the reference standard of Yoon et al was used, ROR 
      had the best F score of 68%, with 77% precision and 61% recall. CONCLUSIONS: 
      Results suggest that EMR-derived laboratory measurements and medication orders 
      can help to validate previously reported ADRs, and detect new ADRs.
FAU - Liu, Mei
AU  - Liu M
AD  - Department of Computer Science, New Jersey Institute of Technology, Newark, New 
      Jersey, USA.
FAU - McPeek Hinz, Eugenia Renne
AU  - McPeek Hinz ER
FAU - Matheny, Michael Edwin
AU  - Matheny ME
FAU - Denny, Joshua C
AU  - Denny JC
FAU - Schildcrout, Jonathan Scott
AU  - Schildcrout JS
FAU - Miller, Randolph A
AU  - Miller RA
FAU - Xu, Hua
AU  - Xu H
LA  - eng
GR  - R01 CA141307/CA/NCI NIH HHS/United States
GR  - R01 LM007995/LM/NLM NIH HHS/United States
GR  - T15 LM007450/LM/NLM NIH HHS/United States
GR  - UL1 RR024975/RR/NCRR NIH HHS/United States
PT  - Comparative Study
PT  - Journal Article
DEP - 20121117
PL  - England
TA  - J Am Med Inform Assoc
JT  - Journal of the American Medical Informatics Association : JAMIA
JID - 9430800
SB  - IM
MH  - Algorithms
MH  - Drug-Related Side Effects and Adverse Reactions/*diagnosis
MH  - *Electronic Health Records
MH  - Humans
MH  - *Pharmacovigilance
MH  - Product Surveillance, Postmarketing/methods
PMC - PMC3628053
EDAT- 2012/11/20 06:00
MHDA- 2013/07/11 06:00
PMCR- 2014/05/01
CRDT- 2012/11/20 06:00
PHST- 2012/11/20 06:00 [entrez]
PHST- 2012/11/20 06:00 [pubmed]
PHST- 2013/07/11 06:00 [medline]
PHST- 2014/05/01 00:00 [pmc-release]
AID - amiajnl-2012-001119 [pii]
AID - 10.1136/amiajnl-2012-001119 [doi]
PST - ppublish
SO  - J Am Med Inform Assoc. 2013 May 1;20(3):420-6. doi: 10.1136/amiajnl-2012-001119. 
      Epub 2012 Nov 17.