TY - JOUR
T1 - Examining the effect of prescription sequence on developing adverse drug reactions
T2 - The case of renal failure in diabetic patients
AU - Davazdahemami, Behrooz
AU - Delen, Dursun
N1 - Publisher Copyright:
© 2019 Elsevier B.V.
PY - 2019/5/1
Y1 - 2019/5/1
N2 - Objectives: While the effect of medications in development of Adverse Drug Reactions (ADRs) have been widely studied in the past, the literature lacks sufficient coverage in investigating whether the sequence in which [ADR-prone] drugs are prescribed (and administered) can increase the chances of ADR development. The present study investigates this potential effect by applying emergent sequential pattern mining techniques to electronic health records. Materials and methods: Using longitudinal medication and diagnosis records from more than 377,000 diabetic patients, in this study, we assessed the possible effect of prescription sequences in developing acute renal failure as a prevalent ADR among this group of patients. Relying on emergent sequential pattern mining, two statistical case-control approaches were designed and employed for this purpose. Results: The results taken from the two employed approaches (i.e. 76.7% total agreement and 68.4% agreement on the existence of some significant effect) provide evidence for the potential effect of prescription sequence on ADRs development evidenced by the discovery that certain sequential patterns occurred more frequently in one group of patients than the other. Conclusion: Given the significant effects shown by our data analyses, we believe that design and implementation of automated clinical decision support systems to constantly monitor patients’ medication transactions (and the sequence in which they are administered) and make appropriate alerts to prevent certain possible ADRs, may decrease ADR occurrences and save lives and money.
AB - Objectives: While the effect of medications in development of Adverse Drug Reactions (ADRs) have been widely studied in the past, the literature lacks sufficient coverage in investigating whether the sequence in which [ADR-prone] drugs are prescribed (and administered) can increase the chances of ADR development. The present study investigates this potential effect by applying emergent sequential pattern mining techniques to electronic health records. Materials and methods: Using longitudinal medication and diagnosis records from more than 377,000 diabetic patients, in this study, we assessed the possible effect of prescription sequences in developing acute renal failure as a prevalent ADR among this group of patients. Relying on emergent sequential pattern mining, two statistical case-control approaches were designed and employed for this purpose. Results: The results taken from the two employed approaches (i.e. 76.7% total agreement and 68.4% agreement on the existence of some significant effect) provide evidence for the potential effect of prescription sequence on ADRs development evidenced by the discovery that certain sequential patterns occurred more frequently in one group of patients than the other. Conclusion: Given the significant effects shown by our data analyses, we believe that design and implementation of automated clinical decision support systems to constantly monitor patients’ medication transactions (and the sequence in which they are administered) and make appropriate alerts to prevent certain possible ADRs, may decrease ADR occurrences and save lives and money.
KW - Adverse drug events
KW - Adverse drug reactions
KW - Electronic health records
KW - Emergent pattern mining
KW - Prescriptions sequence
UR - http://www.scopus.com/inward/record.url?scp=85062228322&partnerID=8YFLogxK
U2 - 10.1016/j.ijmedinf.2019.02.010
DO - 10.1016/j.ijmedinf.2019.02.010
M3 - Article
C2 - 30914182
AN - SCOPUS:85062228322
SN - 1386-5056
VL - 125
SP - 62
EP - 70
JO - International Journal of Medical Informatics
JF - International Journal of Medical Informatics
ER -