Evidence map›Paper›PMID 30949829›Full record

ArticleInternal and emergency medicine2019

Identifying patients with refusal of percutaneous coronary intervention for acute myocardial infarction: a classification and regression tree analysis.

Manyan Wu, Long Li, Sufang Li, Yuxia Cui, Dan Hu, Junxian Song, Chongyou Lee, Hong Chen

Abstract read
PubMed Publisher
In one paragraph

Article in Internal and emergency medicine, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
0.2field-weighted citation impact, top 47% 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

1 citing paper in PubMed, 1 citations in OpenAlex.

  1. 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

8 authors at 1 institution in 1 country.

Manyan WuDepartment of Cardiology, Beijing Key Laboratory of Early Prediction and Intervention of Acute Myocardial Infarction, Center for Cardiovascular Translational Research, Peking University People's Hospital, Xizhimen South Rd No.11, Xicheng District, Beijing, 100044, China.
Long LiDepartment of Cardiology, Beijing Key Laboratory of Early Prediction and Intervention of Acute Myocardial Infarction, Center for Cardiovascular Translational Research, Peking University People's Hospital, Xizhimen South Rd No.11, Xicheng District, Beijing, 100044, China.
Sufang LiDepartment of Cardiology, Beijing Key Laboratory of Early Prediction and Intervention of Acute Myocardial Infarction, Center for Cardiovascular Translational Research, Peking University People's Hospital, Xizhimen South Rd No.11, Xicheng District, Beijing, 100044, China.
Yuxia CuiDepartment of Cardiology, Beijing Key Laboratory of Early Prediction and Intervention of Acute Myocardial Infarction, Center for Cardiovascular Translational Research, Peking University People's Hospital, Xizhimen South Rd No.11, Xicheng District, Beijing, 100044, China.
Dan HuDepartment of Cardiology, Beijing Key Laboratory of Early Prediction and Intervention of Acute Myocardial Infarction, Center for Cardiovascular Translational Research, Peking University People's Hospital, Xizhimen South Rd No.11, Xicheng District, Beijing, 100044, China.
Junxian SongDepartment of Cardiology, Beijing Key Laboratory of Early Prediction and Intervention of Acute Myocardial Infarction, Center for Cardiovascular Translational Research, Peking University People's Hospital, Xizhimen South Rd No.11, Xicheng District, Beijing, 100044, China.
Chongyou LeeDepartment of Cardiology, Beijing Key Laboratory of Early Prediction and Intervention of Acute Myocardial Infarction, Center for Cardiovascular Translational Research, Peking University People's Hospital, Xizhimen South Rd No.11, Xicheng District, Beijing, 100044, China.
Hong ChenDepartment of Cardiology, Beijing Key Laboratory of Early Prediction and Intervention of Acute Myocardial Infarction, Center for Cardiovascular Translational Research, Peking University People's Hospital, Xizhimen South Rd No.11, Xicheng District, Beijing, 100044, China. chenhongbj@medmail.com.cn.ORCID http://orcid.org/0000-0003-4997-7368
Peking University · CN

Funding

National Natural Science Foundation of China 81770356
6 · The paper itself

Abstract

The purpose of the present study is to develop and validate a prediction tool to identify patients who refuse to receive percutaneous coronary intervention (PCI) rapidly. We developed a risk stratification model using the derivation cohort of 288 patients with ST segment elevation myocardial infarction (STEMI) in our hospital and validated it in a prospective cohort of 115 patients. There were 52 (18.1%) patients and 18 (15.7%) patients who refused PCI among derivation and validation cohort, respectively. A classification and regression tree (CART) analysis and multivariate logistic regression were used for statistical analysis. The decision-making factors for refusal of PCI were also investigated. The CART analysis and logistic regression both showed that self-rated mild symptom was the most significant predictor of not choosing PCI. The model generated three risk groups. The high-risk group included: self-rated mild symptoms; self-rated severe symptom, glomerular filtration rate < 60 ml/min/1.73m

Indexed as

AgedAnalysis of VarianceChinaCohort StudiesFemaleHumansLogistic ModelsMaleMiddle AgedOdds RatioPercutaneous Coronary InterventionRegression AnalysisRetrospective StudiesRisk FactorsStatistics, NonparametricST Elevation Myocardial InfarctionAcute myocardial infarctionClinical prediction rulePercutaneous coronary intervention

Identifiers

PMID30949829
OpenAlexW2933468907

What OpenQuestion holds

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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.