Evidence map›Paper›PMID 30597747›Full record

ArticleJournal of diabetes2019

Detecting pioglitazone use and risk of cardiovascular events using electronic health record data in a large cohort of Chinese patients with type 2 diabetes.

Shumei Miao, Xiao Dong, Xin Zhang, Shenqi Jing, Xiaoliang Zhang, Tingyu Xu, Li Wang, Xianglin Du, Hua Xu, Yun Liu

Open access · hybridAbstract read
In one paragraph

Article in Journal of diabetes, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed
1.3field-weighted citation impact, top 18% of its field
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

7 citing papers in PubMed, 12 citations in OpenAlex.

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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

10 authors at 5 institutions in 2 countries.

Shumei MiaoDepartment of Information, The First Affiliated Hospital of Nanjing Medical University and Jiangsu Province Hospital, Nanjing, China.
Xiao DongSchool of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, Texas.
Xin ZhangDepartment of Information, The First Affiliated Hospital of Nanjing Medical University and Jiangsu Province Hospital, Nanjing, China.
Shenqi JingDepartment of Information, The First Affiliated Hospital of Nanjing Medical University and Jiangsu Province Hospital, Nanjing, China.
Xiaoliang ZhangDepartment of Information, The First Affiliated Hospital of Nanjing Medical University and Jiangsu Province Hospital, Nanjing, China.
Tingyu XuDepartment of Information, The First Affiliated Hospital of Nanjing Medical University and Jiangsu Province Hospital, Nanjing, China.
Li WangDepartment of Medical Informatics, Medical School, Nantong University, Nantong, China.
Xianglin DuSchool of Public Health, The University of Texas Health Science Center at Houston, Houston, Texas.
Hua XuSchool of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, Texas.
Yun LiuInstitute of Medical Informatics and Management, Nanjing Medical University, Nanjing, China.
Jiangsu Province Hospital · CNNanjing Medical University · CNThe University of Texas Health Science Center at Houston · USNantong University · CNThe University of Texas Health Science Center · US

Funding

2016 Industry Prospecting and Common Key Technology Key Projects of Jiangsu Province Science and Technology Department BE2016002-42016 Projects of Nanjing Science Bureau 2016080032017 Projects of Jiangsu Provincial Department of Finance 2150510UTHealth Innovation for Cancer Prevention Research Training Program Pre-doctoral Fellowship (Cancer Prevention and Research Institute of Texas) RP160015
6 · The paper itself

Abstract

backgroundPioglitazone may have a protective effect against cardiovascular disease risk among type 2 diabetes (T2D) patients, but evidence from China is lacking. This study investigated the association using electronic health records (EHR) data from a Chinese cohort of T2D patients.

methodsAll T2D patients from the First Affiliated Hospital of Nanjing Medical University who were prescribed at least one oral antidiabetic drug and were aged ≥18 years between 1 July 2005 and 30 June 2017 were eligible for inclusion (n = 71 783). Pioglitazone use was determined in 6-month study intervals, with outcome events of myocardial infarction (MI), ischemic stroke, and heart failure. Poisson regression was used to estimate adjusted rate ratios (RRs) with 95% confidence intervals (CIs).

resultsIn multivariable analysis adjusted for potential confounders, pioglitazone use, compared with no use, was associated with a significant 39% decreased risk of MI (RR = 0.61; 95% CI = 0.42-0.90; P = 0.012). Pioglitazone use was also associated with a non-significant reduction in risk of heart failure or stroke. When MI, heart failure, and stroke were combined as a composite outcome, pioglitazone use was associated with a 30% decrease in risk (RR = 0.70; 95% CI = 0.56-0.88; P = 0.002).

conclusionsThis study demonstrates that applying informatics tools to a large EHR database could be a good way to efficiently conduct clinical observational research. In addition, the findings validated the favorable effect of pioglitazone on the risk of MI among T2D patients in China, with the use of pioglitazone decreasing the risk of MI among those with T2D.

Indexed as

Case-Control StudiesChinaDiabetes Mellitus, Type 2Electronic Health RecordsFemaleFollow-Up StudiesHeart FailureHumansHypoglycemic AgentsMaleMiddle AgedMyocardial InfarctionPioglitazonePrognosisRetrospective StudiesStrokeHypoglycemic AgentsPioglitazone2型糖尿病cardiovascular diseaseChinese populationpioglitazonePoisson regressiontype 2 diabetes中国人群吡格列酮心血管疾病泊松回归

Identifiers

PMID30597747
PMCPMC6850402
OpenAlexW2907448314

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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 OpenQuestion graph.