Evidence mapPaperPMID 34519186Full record

ArticleJournal of Korean medical science2021

Treatment Patterns of Type 2 Diabetes Assessed Using a Common Data Model Based on Electronic Health Records of 2000-2019.

Kyung Ae Lee, Heung Yong Jin, Yu Ji Kim, Yong-Jin Im, Eun-Young Kim, Tae Sun Park

Abstract read
In one paragraph

Article in Journal of Korean medical science, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.

0numbers the graph read from it
0cells of the map it votes in
17citing papers in PubMed
field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

17 citing papers in PubMed.

  1. Trial
  2. Trial
  3. Trial
  4. Article
  5. Article
  6. Article
  7. Article
  8. Article
  9. Review
  10. Treatment Preferences for Novel Type 2 Diabetes Oral Medications: Insights from the Asian Diabetes Patient Preference Study.Diabetes therapy : research, treatment and education of diabetes and related disorders · 2025
    Article
  11. Article
  12. Observational
  13. Observational
  14. Article
  15. Article
  16. Article
  17. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

Kyung Ae LeeDivision of Endocrinology and Metabolism, Department of Internal Medicine, Research Institute of Clinical Medicine of Jeonbuk National University-Biomedical Research Institute of Jeonbuk National University Hospital, Jeonbuk National University Medical School, Jeonju, Korea.ORCID https://orcid.org/0000-0003-3700-8279
Heung Yong JinDivision of Endocrinology and Metabolism, Department of Internal Medicine, Research Institute of Clinical Medicine of Jeonbuk National University-Biomedical Research Institute of Jeonbuk National University Hospital, Jeonbuk National University Medical School, Jeonju, Korea.ORCID https://orcid.org/0000-0002-1841-2092
Yu Ji KimDivision of Endocrinology and Metabolism, Department of Internal Medicine, Research Institute of Clinical Medicine of Jeonbuk National University-Biomedical Research Institute of Jeonbuk National University Hospital, Jeonbuk National University Medical School, Jeonju, Korea.ORCID https://orcid.org/0000-0003-4100-5839
Yong-Jin ImCenter for Clinical Pharmacology, Biochemical Research Institute, Jeonbuk National University Hospital, Jeonju, Korea.ORCID https://orcid.org/0000-0003-4304-9747
Eun-Young KimCenter for Clinical Pharmacology, Biochemical Research Institute, Jeonbuk National University Hospital, Jeonju, Korea.ORCID https://orcid.org/0000-0001-5885-9473
Tae Sun ParkDivision of Endocrinology and Metabolism, Department of Internal Medicine, Research Institute of Clinical Medicine of Jeonbuk National University-Biomedical Research Institute of Jeonbuk National University Hospital, Jeonbuk National University Medical School, Jeonju, Korea. pts@jbnu.ac.kr.ORCID https://orcid.org/0000-0001-7216-8468

Funding

Korea Health Industry Development Institute HI19C0543
6 · The paper itself

Abstract

backgroundReal-world data analysis is useful for identifying treatment patterns. Understanding drug prescription patterns of type 2 diabetes mellitus may facilitate diabetes management. We aimed to analyze treatment patterns of type 2 diabetes mellitus using Observational Medical Outcomes Partnership Common Data Model based on electronic health records.

methodsThis retrospective, observational study employed electronic health records of patients who visited Jeonbuk National University Hospital in Korea during January 2000-December 2019. Data were transformed into the Observational Medical Outcomes Partnership Common Data Model and analyzed using R version 4.0.3 and ATLAS ver. 2.7.6. Prescription frequency for each anti-diabetic drug, combination therapy pattern, and prescription pattern according to age, renal function, and glycated hemoglobin were analyzed.

resultsThe number of adults treated for type 2 diabetes mellitus increased from 1,867 (2.0%) in 2000 to 9,972 (5.9%) in 2019. In the early 2000s, sulfonylurea was most commonly prescribed (73%), and in the recent years, metformin has been most commonly prescribed (64%). Prescription rates for DPP4 and SGLT2 inhibitors have increased gradually over the past few years. Monotherapy prescription rates decreased, whereas triple and quadruple combination prescription rates increased steadily. Different drug prescription patterns according to age, renal function, and glycated hemoglobin were observed. The proportion of patients with HbA1c ≤ 7% increased from 31.1% in 2000 to 45.6% in 2019, but that of patients visiting the emergency room for severe hypoglycemia did not change over time.

conclusionMedication utilization patterns have changed significantly over the past 20 years with an increase in the use of newer drugs and a shift to combination therapies. In addition, various prescription patterns were demonstrated according to the patient characteristics in actual practice. Although glycemic control has improved, the proportion within the target is still low, underscoring the need to improve diabetes management.

Indexed as

AdolescentAdultAgedDatabases, FactualDiabetes Mellitus, Type 2Dipeptidyl-Peptidase IV InhibitorsElectronic Health RecordsFemaleGlomerular Filtration RateHumansHypoglycemic AgentsMaleMetforminMiddle AgedPractice Patterns, Physicians'Republic of KoreaDipeptidyl-Peptidase IV InhibitorsHypoglycemic AgentsMetforminSodium-Glucose Transporter 2 InhibitorsSulfonylurea CompoundsCommon Data ModelDiabetes Mellitus Type 2Electronic Health RecordsHypoglycemic Agents

Identifiers

PMID34519186
PMCPMC8438187

What Socratic holds

Texttitle and abstract
LicenceCC BY-NC
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.