PMID- 37864248
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20231119
IS  - 1756-0381 (Print)
IS  - 1756-0381 (Electronic)
IS  - 1756-0381 (Linking)
VI  - 16
IP  - 1
DP  - 2023 Oct 20
TI  - Prescription pattern analysis of Type 2 Diabetes Mellitus: a cross-sectional 
      study in Isfahan, Iran.
PG  - 29
LID - 10.1186/s13040-023-00344-y [doi]
LID - 29
AB  - BACKGROUND: Patients with Type 2 Diabetes Mellitus (T2DM) are at a higher risk of 
      polypharmacy and more susceptible to irrational prescriptions; therefore, 
      pharmacological therapy patterns are important to be monitored. The primary 
      objective of this study was to highlight current prescription patterns in T2DM 
      patients and compare them with existing Standards of Medical Care in Diabetes. 
      The second objective was to analyze whether age and gender affect prescription 
      patterns. METHOD: This cross-sectional study was conducted using the Iran Health 
      Insurance Organization (IHIO) prescription database. It was mined by an 
      Association Rule Mining (ARM) technique, FP-Growth, in order to find 
      co-prescribed drugs with anti-diabetic medications. The algorithm was implemented 
      at different levels of the Anatomical Therapeutic Chemical (ATC) classification 
      system, which assigns different codes to drugs based on their anatomy, 
      pharmacological, therapeutic, and chemical properties to provide an in-depth 
      analysis of co-prescription patterns. RESULTS: Altogether, the prescriptions of 
      914,652 patients were analyzed, of whom 91,505 were found to have diabetes. 
      According to our results, prescribing Lipid Modifying Agents (C10) (56.3%), 
      Agents Acting on The Renin-Angiotensin System (C09) (48.9%), Antithrombotic 
      Agents (B01) (35.7%), and Beta Blocking Agents (C07) (30.1%) were meaningfully 
      associated with the prescription of Drugs Used in Diabetes. Our study also 
      revealed that female diabetic patients have a higher lift for taking Thyroid 
      Preparations, and the older the patients were, the more they were prone to take 
      neuropathy-related medications. Additionally, the results suggest that there are 
      gender differences in the association between aspirin and diabetes drugs, with 
      the differences becoming less pronounced in old age. CONCLUSIONS: Almost all of 
      the association rules found in this research were clinically meaningful, proving 
      the potential of ARM for co-prescription pattern discovery. Moreover, 
      implementing level-based ARM was effective in detecting difficult-to-spot rules. 
      Additionally, the majority of drugs prescribed by physicians were consistent with 
      the Standards of Medical Care in Diabetes.
CI  - (c) 2023. BioMed Central Ltd., part of Springer Nature.
FAU - Ziad, Elnaz
AU  - Ziad E
AD  - School of Industrial and Systems Engineering, Tarbiat Modares University, Tehran, 
      Islamic Republic of Iran.
FAU - Sadat, Somayeh
AU  - Sadat S
AD  - Centre for Analytics and Artificial Intelligence Engineering, University of 
      Toronto, Toronto, Canada. Somayeh.sadat@utoronto.ca.
FAU - Farzadfar, Farshad
AU  - Farzadfar F
AD  - Non-Communicable Diseases Research Center, Endocrinology and Metabolism 
      Population Sciences Institute, Tehran University of Medical Sciences, Tehran, 
      Islamic Republic of Iran.
FAU - Malekpour, Mohammad-Reza
AU  - Malekpour MR
AD  - Non-Communicable Diseases Research Center, Endocrinology and Metabolism 
      Population Sciences Institute, Tehran University of Medical Sciences, Tehran, 
      Islamic Republic of Iran.
LA  - eng
PT  - Journal Article
DEP - 20231020
PL  - England
TA  - BioData Min
JT  - BioData mining
JID - 101319161
PMC - PMC10588025
OTO - NOTNLM
OT  - Association Rule Mining
OT  - Diabetes
OT  - Multimorbidity
OT  - Prescription pattern
COIS- The authors declare no competing interests.
EDAT- 2023/10/21 05:42
MHDA- 2023/10/21 05:43
PMCR- 2023/10/20
CRDT- 2023/10/20 23:53
PHST- 2023/02/27 00:00 [received]
PHST- 2023/09/20 00:00 [accepted]
PHST- 2023/10/21 05:43 [medline]
PHST- 2023/10/21 05:42 [pubmed]
PHST- 2023/10/20 23:53 [entrez]
PHST- 2023/10/20 00:00 [pmc-release]
AID - 10.1186/s13040-023-00344-y [pii]
AID - 344 [pii]
AID - 10.1186/s13040-023-00344-y [doi]
PST - epublish
SO  - BioData Min. 2023 Oct 20;16(1):29. doi: 10.1186/s13040-023-00344-y.