Prescription pattern analysis of Type 2 Diabetes Mellitus: a cross-sectional study in Isfahan, Iran.

IF 4 3区生物学 Q1 MATHEMATICAL & COMPUTATIONAL BIOLOGY

Biodata Mining Pub Date : 2023-10-20 DOI:10.1186/s13040-023-00344-y

Elnaz Ziad, Somayeh Sadat, Farshad Farzadfar, Mohammad-Reza Malekpour

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引用次数: 0

Abstract

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.

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2型糖尿病的处方模式分析：伊朗伊斯法罕的一项横断面研究。

背景：2型糖尿病（T2DM）患者服用多种药物的风险更高，更容易受到不合理处方的影响；因此，药物治疗模式的监测非常重要。本研究的主要目的是强调T2DM患者目前的处方模式，并将其与现有的糖尿病医疗护理标准进行比较。第二个目的是分析年龄和性别是否会影响处方模式。方法：这项横断面研究使用伊朗健康保险组织（IHIO）处方数据库进行。它是通过关联规则挖掘（ARM）技术FP Growth进行挖掘的，目的是找到与抗糖尿病药物合用的药物。该算法在解剖治疗化学（ATC）分类系统的不同级别上实现，该系统根据药物的解剖、药理学、治疗和化学特性为其分配不同的代码，以深入分析共同处方模式。结果：共分析914652例患者的处方，其中91505例为糖尿病患者。根据我们的研究结果，处方脂质修饰剂（C10）（56.3%）、作用于肾素-血管紧张素系统的药物（C09）（48.9%）、抗血栓药物（B01）（35.7%）和β-阻断剂（C07）（30.1%）与糖尿病药物的处方有显著相关性。我们的研究还表明，女性糖尿病患者服用甲状腺制剂的几率更高，而且患者年龄越大，就越容易服用与神经病变相关的药物。此外，研究结果表明，阿司匹林和糖尿病药物之间的相关性存在性别差异，这种差异在老年时变得不那么明显。结论：本研究中发现的几乎所有关联规则都具有临床意义，证明了ARM在发现联合处方模式方面的潜力。此外，实现基于层次的ARM在检测难以发现的规则方面是有效的。此外，医生开出的大多数药物都符合糖尿病医疗保健标准。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Biodata Mining MATHEMATICAL & COMPUTATIONAL BIOLOGY-

CiteScore

7.90

自引率

0.00%

发文量

审稿时长

23 weeks

期刊介绍： BioData Mining is an open access, open peer-reviewed journal encompassing research on all aspects of data mining applied to high-dimensional biological and biomedical data, focusing on computational aspects of knowledge discovery from large-scale genetic, transcriptomic, genomic, proteomic, and metabolomic data. Topical areas include, but are not limited to: -Development, evaluation, and application of novel data mining and machine learning algorithms. -Adaptation, evaluation, and application of traditional data mining and machine learning algorithms. -Open-source software for the application of data mining and machine learning algorithms. -Design, development and integration of databases, software and web services for the storage, management, retrieval, and analysis of data from large scale studies. -Pre-processing, post-processing, modeling, and interpretation of data mining and machine learning results for biological interpretation and knowledge discovery.