Evidence map›Paper›PMID 32075822›Full record

ArticleBMJ open2020

Predicting value of white cell count and total bilirubin on clinical outcomes in patients with ST-elevation myocardial infarction following percutaneous coronary intervention: a cohort study.

Munire Tuxun, Qian Zhao, Yang Xiang, Fen Liu, Chun-Fang Shan, Xin-Rong Zhou, Ning Song, Ajiguli Waisiding, Xue-He Zhang, Gulandanmu Aihemaiti and 2 more

Open access · goldAbstract read
In one paragraph

Article in BMJ open, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
8citing papers in PubMed, 2 pooled it
1.5field-weighted citation impact, top 16% 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

8 citing papers in PubMed, 2 syntheses or guidelines pooled it, 12 citations in OpenAlex.

  1. Pooled it
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  6. Review
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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

12 authors at 1 institution in 1 country.

Munire TuxunDepartment of Cardiology, First Affiliated Hospital of Xinjiang Medical University, Urumqi, China.
Qian ZhaoDepartment of Cardiology, First Affiliated Hospital of Xinjiang Medical University, Urumqi, China.
Yang XiangDepartment of Cardiology, First Affiliated Hospital of Xinjiang Medical University, Urumqi, China.
Fen LiuDepartment of Cardiology, First Affiliated Hospital of Xinjiang Medical University, Urumqi, China.
Chun-Fang ShanDepartment of Cardiology, First Affiliated Hospital of Xinjiang Medical University, Urumqi, China.
Xin-Rong ZhouDepartment of Cardiology, First Affiliated Hospital of Xinjiang Medical University, Urumqi, China.
Ning SongDepartment of Cardiology, First Affiliated Hospital of Xinjiang Medical University, Urumqi, China.
Ajiguli WaisidingDepartment of Cardiology, First Affiliated Hospital of Xinjiang Medical University, Urumqi, China.
Xue-He ZhangDepartment of Cardiology, First Affiliated Hospital of Xinjiang Medical University, Urumqi, China.
Gulandanmu AihemaitiDepartment of Cardiology, First Affiliated Hospital of Xinjiang Medical University, Urumqi, China.
Yi-Ning YangDepartment of Cardiology, First Affiliated Hospital of Xinjiang Medical University, Urumqi, China lixm505@163.com yangyn5126@163.com.
Xiao-Mei LiDepartment of Cardiology, First Affiliated Hospital of Xinjiang Medical University, Urumqi, China lixm505@163.com yangyn5126@163.com.ORCID 0000-0002-8770-3838
Xinjiang Medical University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectivesA combined equation based on white cell count (WCC) and total bilirubin (TB) was assessed for its ability to predict adverse clinical outcomes in patients with acute ST-segment elevation myocardial infarction (STEMI) with primary percutaneous coronary intervention (PCI).

designA single-centre, prospective cohort study.

settingThe First Affiliated Hospital of Xinjiang Medical University.

methodA total of 615 patients with STEMI postprimary PCI were enrolled. WCC and TB were collected at admission. Logistic regression was used to determine the combined equation. The primary endpoints were in-hospital mortality and major adverse cardiovascular events (MACE), which composed of cardiac death, cardiac shock, malignant arrhythmia (ventricular tachycardia, ventricular fibrillation), severe cardiac insufficiency, non-fatal myocardial infarction, angina pectoris readmission, severe cardiac insufficiency (cardiac III-IV level), stent restenosis and target vessels revascularisation during the hospitalisation and 36 months follow-up period.

result77 patients occurred in MACE during the hospitalisation (17 in-hospital mortality). WCC and TB were taken as an independent variables to make a category of logistic regression analysis of in-hospital MACE, the logistic regression model was: logit (P)=-8.00+0.265 WCC+0.077 TB, the combination of WCC and TB was more valuable on evaluating the in-hospital mortality (area under the curve 0.804, 95% CI 0.678 to 0.929, p<0.001). Multivariate logistic regression analysis showed that combined detection was an independent risk factor for in-hospital MACE (OR 5.85, 95% CI 3.425 to 9.990, p=0.032). During the follow-up period, 172 patients (29.5%) developed MACE. But the combined detection did not predict the long-term clinical outcome.

conclusionThe combination of WCC and TB is an independent predictor for in-hospital outcomes in patients with STEMI than single detection.

Indexed as

Leukocyte CountPercutaneous Coronary InterventionAgedBilirubinChinaFemaleHeart DiseasesHospital MortalityHospitalsHumansLeukocytesLogistic ModelsMaleMiddle AgedOdds RatioPatient ReadmissionBilirubincoronary heart diseaseischaemic heart diseasemyocardial infarction

Identifiers

PMID32075822
PMCPMC7044918
OpenAlexW3007451038

What OpenQuestion holds

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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 OpenQuestion graph.