Evidence map›Paper›PMID 34039268›Full record

Observational studyBMC cardiovascular disorders2021

Current trends in optimal medical therapy after PCI and its influence on clinical outcomes in China.

Jian Zhang, Jing-Yan Hao, Rui Jing, Jing-Jing Liu, Cheng-Ye Di, Yu-Jie Lu, Peng Gao, Ya-Jie Wang, Rui-Fei Yang, Wen-Hua Lin

Open access · goldAbstract readObservational Study
In one paragraph

Observational study in BMC cardiovascular disorders, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
0.5field-weighted citation impact, top 34% 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

4 citing papers in PubMed, 4 citations in OpenAlex.

  1. Trial
  2. Article
  3. Article
  4. 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

10 authors at 2 institutions in 1 country.

Jian ZhangDepartment of Cardiology I, TEDA International Cardiovascular Hospital, Tianjin, 300457, China.
Jing-Yan HaoCollege of Clinical Cardiovascular Disease, Tianjin Medical University, Tianjin, China.
Rui JingDepartment of Cardiology I, TEDA International Cardiovascular Hospital, Tianjin, 300457, China.
Jing-Jing LiuDepartment of Cardiology I, TEDA International Cardiovascular Hospital, Tianjin, 300457, China.
Cheng-Ye DiDepartment of Cardiology I, TEDA International Cardiovascular Hospital, Tianjin, 300457, China.
Yu-Jie LuDepartment of Cardiology I, TEDA International Cardiovascular Hospital, Tianjin, 300457, China.
Peng GaoDepartment of Cardiology I, TEDA International Cardiovascular Hospital, Tianjin, 300457, China.
Ya-Jie WangDepartment of Cardiology I, TEDA International Cardiovascular Hospital, Tianjin, 300457, China.
Rui-Fei YangDepartment of Cardiology I, TEDA International Cardiovascular Hospital, Tianjin, 300457, China.
Wen-Hua LinDepartment of Cardiology I, TEDA International Cardiovascular Hospital, Tianjin, 300457, China. linwernhua@sina.com.
TEDA International Cardiovascular Hospital · CNTianjin Medical University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundLimited data were available on the current trends in optimal medical therapy (OMT) after PCI and its influence on clinical outcomes in China. We aimed to evaluate the utilization and impact of OMT on the main adverse cardiovascular and cerebrovascular events (MACCEs) in post-PCI patients and analyzed the factors predictive of OMT after discharge.

methodsWe collected data from 3812 individuals from 2016.10 to 2017.09 at TEDA International Cardiovascular Hospital. They were classified into an OMT group and a non-OMT group according to their OMT status, which was defined as the combination of dual antiplatelet therapy, statins, β-blockers, angiotensin-converting enzyme inhibitors or angiotensin receptor blockers after PCI. Multivariable Cox regression models were developed to assess the association between OMT and MACCEs, defined as all-cause mortality, nonfatal myocardial infarction, stroke, and target vessel revascularization. A logistic regression model was established to analyze the factors predictive of OMT.

resultsOur results revealed that the proportion of patients receiving OMT and its component drugs decreased over time. A total of 36.0% of patients were still adherent to OMT at the end of follow-up. Binary logistic regression analysis revealed that baseline OMT (P < 0.001, OR = 52.868) was the strongest predictor of OMT after PCI. The Cox hazard model suggested that smoking after PCI was associated with the 1-year risk of MACCE (P = 0.001, HR = 2.060, 95% CI 1.346-3.151), while OMT (P = 0.001, HR = 0.486, 95% CI 0.312-0.756) was an independent protective factor against postoperative MACCEs.

conclusionsThere was still a gap between OMT utilization after PCI and the recommendations in the evidence-based guidelines. Sociodemographic and clinical factors influence the application of OMT. The management of OMT and smoking cessation after PCI should be emphasized.

Indexed as

AgedCardiovascular AgentsChinaComorbidityCoronary DiseaseDrug UtilizationFemaleHumansMaleMedication AdherenceMiddle AgedPercutaneous Coronary InterventionPractice Patterns, Physicians'Prospective StudiesRisk AssessmentRisk FactorsCardiovascular AgentsCoronary heart diseaseOptimal medical therapyPost-PCIPredictorsPrognosis

Identifiers

PMID34039268
PMCPMC8157424
OpenAlexW3165320539

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.