Evidence map›Paper›PMID 33845863›Full record

ArticleImplementation science : IS2021

A stepped wedge cluster randomized control trial to evaluate the implementation and effectiveness of optimized initiatives in improving quality of care for ST segment elevation myocardial infarction in response to the COVID-19 outbreak.

Shuduo Zhou, Xuejie Dong, Fangjing Liu, Yan Zhang, Dahai Yue, Qiang Zhou, Yinzi Jin, Zhi-Jie Zheng

Abstract readClinical Trial Protocol
In one paragraph

Article in Implementation science : IS, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
9citing papers in PubMed, 1 pooled it
–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

9 citing papers in PubMed, 1 synthesis or guideline pooled it.

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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

8 authors.

Shuduo ZhouDepartment of Global Health, School of Public Health, Peking University, 38 Xue Yuan Road, Haidian District, Beijing, 100191, China.
Xuejie DongDepartment of Global Health, School of Public Health, Peking University, 38 Xue Yuan Road, Haidian District, Beijing, 100191, China.
Fangjing LiuDepartment of Global Health, School of Public Health, Peking University, 38 Xue Yuan Road, Haidian District, Beijing, 100191, China.
Yan ZhangInstitute of Cardiovascular Disease, Peking University First Hospital, Beijing, China.
Dahai YueUniversity of Maryland, College Park, USA.
Qiang ZhouShenzhen Center for Prehospital Care, Shenzhen, China.
Yinzi JinDepartment of Global Health, School of Public Health, Peking University, 38 Xue Yuan Road, Haidian District, Beijing, 100191, China. yzjin@bjmu.edu.cn.ORCID 0000-0003-0634-3955
Zhi-Jie ZhengDepartment of Global Health, School of Public Health, Peking University, 38 Xue Yuan Road, Haidian District, Beijing, 100191, China.

Funding

China Medical Board No. #20-376National Outstanding Youth Science Fund Project of National Natural Science Foundation of China 71904004
6 · The paper itself

Abstract

backgroundThe National Chest Pain Center Accreditation Program (CHANGE) is the first hospital-based, multifaceted, nationwide quality improvement (QI) initiative, to monitor and improve the quality of the ST segment elevation myocardial infarction (STEMI) care in China. The QI initiatives, as implementation strategies, include a bundle of evidence-based interventions adapted for implementation in China. During the pandemic of coronavirus disease 2019 (COVID-19), fear of infection with severe acute respiratory syndrome coronavirus 2, national lockdowns, and altered health care priorities have highlighted the program's importance in improving STEMI care quality. This study aims to minimize the adverse impact of the COVID-19 pandemic on the quality of STEMI care, by developing interventions that optimize the QI initiatives, implementing and evaluating the optimized QI initiatives, and developing scale-up activities of the optimized QI initiatives in response to COVID-19 and other public health emergencies.

methodsA stepped wedge cluster randomized control trial will be conducted in three selected cities of China: Wuhan, Suzhou, and Shenzhen. Two districts have been randomly selected in each city, yielding a total of 24 registered hospitals. This study will conduct a rollout in these hospitals every 3 months. The 24 hospitals will be randomly assigned to four clusters, and each cluster will commence the intervention (optimized QI initiatives) at one of the four steps. We will conduct hospital-based assessments, questionnaire surveys among health care providers, community-based household surveys, and key informant interviews during the trial. All outcome measures will be organized using the RE-AIM (reach, effectiveness, adoption, implementation, maintenance) framework, including implementation outcomes, service outcomes (e.g., treatment time), and patient outcomes (e.g., in-hospital mortality and 1-year complication). The Consolidated Framework for Implementation Research framework will be used to identify factors that influence implementation of the optimized QI interventions. DISCUSSION: The study findings could be translated into a systematic solution to implementing QI initiatives in response to COVID-19 and future potential major public health emergencies. Such actionable knowledge is critical for implementors of scale-up activities in low- and middle-income settings.

trial registrationChiCTR 2100043319 . Registered on 10 February 2021.

Indexed as

COVID-19Quality ImprovementST Elevation Myocardial InfarctionChinaHumansMulticenter Studies as TopicRandomized Controlled Trials as TopicSARS-CoV-2Time-to-TreatmentCFIRChinaCOVID-19OptimizedQuality-improvedRE-AIMST segment elevation myocardial infarction

Identifiers

PMID33845863
PMCPMC8040354

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Registered trials

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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.