Effect of a Quality of Care Improvement Initiative in Patients With Acute Coronary Syndrome in Resource-Constrained Hospitals in China: A Randomized Clinical Trial

Yangfeng Wu; Shenshen Li; Anushka Patel; Xian Li; Xin Du; Tao Wu; Yifei Zhao; Lin Feng; Laurent Billot; Eric D Peterson; Mark Woodward; Lingzhi Kong; Yong Huo; Dayi Hu; Kalipso Chalkidou; Runlin Gao

doi:10.1001/jamacardio.2019.0897

. 2019 Apr 17;4(5):418–427. doi: 10.1001/jamacardio.2019.0897

Effect of a Quality of Care Improvement Initiative in Patients With Acute Coronary Syndrome in Resource-Constrained Hospitals in China

A Randomized Clinical Trial

Yangfeng Wu ^1,^2,^✉, Shenshen Li ¹, Anushka Patel ³, Xian Li ^1,⁴, Xin Du ⁵, Tao Wu ¹, Yifei Zhao ¹, Lin Feng ², Laurent Billot ³, Eric D Peterson ⁶, Mark Woodward ^7,⁸, Lingzhi Kong ⁹, Yong Huo ¹⁰, Dayi Hu ¹¹, Kalipso Chalkidou ¹², Runlin Gao ^13,^14,¹⁵, for the CPACS-3 Investigators

¹George Institute for Global Health at Peking University Health Science Center, Beijing, China

²Peking University Clinical Research Institute, Beijing, China

³George Institute for Global Health, University of New South Wales, Sydney, Australia

⁴Faculty of Medicine, University of New South Wales, Sydney, Australia

⁵Beijing Anzhen Hospital, Capital Medical University, Beijing, China

⁶Duke Clinical Research Institute, Duke University Medical Center, Durham, North Carolina

⁷George Institute for Global Health, University of Oxford, Oxford, England

⁸Department of Epidemiology, Johns Hopkins University, Baltimore, Maryland

⁹Chinese Prevention Medical Association, Beijing, China

¹⁰Department of Cardiology, Peking University First Hospital, Beijing, China

¹¹Department of Cardiology, Peking University People’s Hospital, Beijing, China

¹²Global Health and Development, Imperial College, London, United Kingdom

¹³Department of Cardiology, Cardiovascular Institute and Fuwai Hospital, Beijing, China

¹⁴Chinese Academy of Medical Sciences, Beijing, China

¹⁵Peking Union Medical College, Beijing, China

Group Information: The CPACS-3 Investigators appear at the end of the article.

Accepted for Publication: February 17, 2019.

^✉

Corresponding Author: Yangfeng Wu, PhD, The George Institute for Global Health at Peking University Health Science Center, Level 18, Tower B, Horizon Tower, No. 6 Zhichun Rd, Haidian District, Beijing 100088, China (ywu@georgeinstitute.org.cn).

Published Online: April 17, 2019. doi:10.1001/jamacardio.2019.0897

Open Access: This article is published under the JN-OA license and is free to read on the day of publication.

Author Contributions: Dr Y. Wu had full access to all study data and takes responsibility for the integrity of the data and the accuracy of the data analysis.

Concept and design: Y. Wu, Patel, X. Li, Du, Peterson, Woodward, Kong, Hu, Chalkidou, Gao.

Acquisition, analysis, or interpretation of data: Y. Wu, S. Li, Patel, X. Li, Du, T. Wu, Zhao, Feng, Billot, Peterson, Kong, Huo, Gao.

Drafting of the manuscript: Y. Wu, S. Li, Zhao.

Critical revision of the manuscript for important intellectual content: Y. Wu, S. Li, Patel, X. Li, Du, T. Wu, Feng, Billot, Peterson, Woodward, Kong, Huo, Hu, Chalkidou, Gao.

Statistical analysis: X. Li, T. Wu, Billot, Woodward.

Obtained funding: Y. Wu, Gao.

Administrative, technical, or material support: Y. Wu, S. Li, Du, Zhao, Feng, Peterson, Kong, Huo, Gao.

Supervision: Y. Wu, Gao.

Conflict of Interest Disclosures: Drs Y. Wu, S. Li, Patel, X. Li, and Feng reported grants from Sanofi, China during the conduct of the study. Dr Peterson reported grants and personal fees from Sanofi, AstraZeneca, Merck, and Amgen outside the submitted work. Dr Woodward reported personal fees from Amgen and personal fees from Kirin outside the submitted work. No other disclosures were reported.

Funding/Support: CPACS-3 is funded by Sanofi, China, through an unrestricted research grant.

Role of the Funder/Sponsor: Sanofi had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication.

Group Members: CPACS-3 Study steering committee included Kalipso Chalkidou, PhD, National Institute for Health and Clinical Excellence; Runlin Gao (coprincipal investigator), MD, Cardiovascular Institute and Fuwai Hospital); Dayi Hu, MD, Peking University People’s Hospital; Yong Huo, MD, Peking University First Hospital; Yahui Jiao, MD, NHFPC of China; Lingzhi Kong, MD, NHFPC of China; Anushka Patel, The George Institute for Global Health; Eric Peterson, MD, Duke Clinical Research Institute; Fiona Turnbull, MD, The George Institute for Global Health; Mark Woodward, PhD, The George Institute for Global Health; and Yangfeng Wu, MD, PhD (coprincipal investigator), The George Institute for Global Health at PUHSC.

Independent end points adjudication committee: Yu Chen, MD (Navy General Hospital of China), Chun Li, MD, Ming Shen, MD, Yihong Sun, MD, PhD, and Yang Xi, MD, PhD (Peking University People’s Hospital).

Study investigators: Kuandian Central Hospital: Danbing Zhou, BM, Guiying Wang, BM, Shan Xu, MD, Tongbao Sang, BM, and Dongxue Song, BM; Lingyuan Central Hospital: Lijuan Sun, BM, Shuying Bai, BM, Helan Li, DN, and Jinrong Zhang, DN; Huaxian People’s Hospital: Shaoyong Hu, BM, Hecai Ma, BM, Yuezhou Li, BM, Wenli Liu, BM, and Lijing Xiao, BN; Dancheng People’s Hospital: Yuwang Yang, BM, Yuhui Luo, BM, Tianming Yu, BM, and Huanyu Guo, BM; Jingning People’s Hospital: Fei Han, BM, Shuzhi Chen, BN, Hongbin Zhang, BM, Feipeng Wang, BM, and Zhixin Liu, ADM; Pingyu People’s Hospital: Yinling Xu, BM, Wei Wei, BM, Xiang Xue, BM, Wei Wang, BM, and Juanjuan Ma, ADM; Yixian Hospital: Guishan Feng, BM, Yanchao Su, BM, Minzhuo Chen, BM, and Shengli Ji, BM; Donge People’s Hospital: Wenmin Fei, BM, Aihua Liu, BM, Hongfei Si, MD, Lingling Liu, MD, and Hui Zhao, MD; Jizhou Hospital: Zhuci Li, BM, Taihaoliang Tan, BM, Guobin Li, BM, and Yongna Li, BM; Gaoping People’s Hospital: Jun Li, BM, Xiping Zhu, BM, Hailong Wang, BM, Tingting Qin, BM, and Erdan Wang, BM; Yingshang Hospital: Yaying Guo, BM, Kai Chen, BM, Gangtie Liu, BM, Yu Wang, BM, and Qingfeng Wu, BN; Hancheng Hospital: Jiangru Liu, BM, Yanping Pan, BM, Jianhong Liu, BM, Ling Xue, BM, and Rong Fan, BM; Yicheng People’s Hospital: Changming Song, BM, Jiang Xu, BM, Zhimin Yan, BM, Wenquan Zhang, BM, and Xing Ma, BM; Penglai People’s Hospital: Huanyu Qin, MD, Jiashan Liu, BM, Lei Wang, BM, Li Zhao, MD, and Jian Sun, MD; Donghai People’s Hospital: Chunshui Yang, BM, Runfeng Sun, BM, Junhui Yu, BM, and Jianling Zhou, BM; Zhuanghe Central Hospital: Zhun Wen, BM, Yunyao Sui, BM, Xia Zhang, BM, Jinguo Wang, BM, and Jun Li, BM; Songzi People’s Hospital: Yuanguo Lei, ADM, Bin Li, BM, Lei Xia, MD, and Longhui Chen, BM; Linquan Hospital: Lunhua Zhu, BM, Lei Zhang, BM, Zikun Huang, BM, Qixiang Zhang, BM, and Hongjian Sun, BM; Xintai People’s Hospital: Hongyan Zhang, BM, Tian Sun, BM, Bingxin Chen, BM, Nan Li, BM, and Yinxue Li, BM; Xiangfen People’s Hospital: Yun Bai, BM, Junming Qin, BM, Youji Jia, BM, Liang Zhang, BM, and Yanping Lu, BN; Yuanping First People’s Hospital: Yutian Zhang, ADM, Guituan Tan, ADM, Yunzeng Zhang, BM, Qin Han, BM, and Lu Wang, BM; Gaoyang Hospital: Yahui Zhang, BM, Nan Zhao, BM, Jiangming Dai, BM, Yanmin Chen, BM, and Ruiling Zhou, BM; Danjiangkou First Hospital: Gongxing Cao, BM, Junfeng Tan, BM, Hongjiang Chen, BM, Rui Wang, MBME, and Zhidan Li, MBME; Houma People’s Hospital: Xuezhen Zhao, ADM, Shuqin Wang, BM, Lijie Zhang, BM, Jinjuan Wei, BM, and Xianghui Hou, BM; Ruyang People’s Hospital: Jiqiang Ma, BA, Chengning Shen, BM, Jingjing Zhao, BM, and Litao Wu, BM; Qinglong Hospital: Zhanshan Song, BM, Surong Zhao, BM, Jingchun Yang, BM, Shuaiyuan Xu, BM, and Hui Yu, DN; Shenmu Hospital: Shengli Sun, BM, Zhengqing Li, MD, Zhenping Zhao, BM, Baoqing Ma, BM, and Shifu Ren, BM; Central Hospital of Changbai Mountain Protection An d Development Zone: Fengjun Sun, BM, Xiuyan Wang, BM, Hongyan Du, ADM, Wenwen Chen, BM, and Jing Liu, BM; First People’s Hospital of Yitong Man Autonomous County: Haifeng Wang, BM, Chunlai Tian, BM, Qingsong Guan, BM, Danyang Yu, BM, and Chao Sui, BM; Dongfeng Hospital: Wen Cun, BM, Songyan Xiao, AND, Wei Liu, BM, Xiaoxin Li, BM, and Ying Wang, BA; Shenze Hospital: Shimin Wang, ADM, Yulong Song, BM, Can Chen, BM, Weiqiang Zhao, BM, and Xuewei Zhang, BM; Changheng Hongli Hospital: Qingxiao Teng, BM, Feng Qian, BM, Yunjun Wang, BA, Yuwen Li, BN, and Xiangqian Zeng, BM; Linqu People’s Hospital: Fengjuan Li, BM, Jine Feng, BM, Xiuling Yan, BM, Guangting Zhang, BM, and Conghui Ren, BM; Shucheng People’s Hospital: Ligong Song, BM, Bing Hu, BM, Yonghong Ren, BM, Yuanzhi Jin, MD, and Ziyin Wang, BM; Linjiang Hospital: Zhongshan Wang, BM, Weidong Zuo, ADM, Zhaoxin Li, ADM, Xiaobo Yu, BM, and Ying Du, BM; Suixi Hospital: Lianling Wu, Ying Li, Jin Hou, BM, Jicun Liang, BM, and Jianjun Zheng, BM; Lintao People’s Hospital: Qingde Xia, BM, Xiaoqin Zhu, BM, YinXia Zhou, BN, Junxiang Wang, BM, and Xingyu Chen, BM; Huichun Hospital: Lijun Yu, BM, Xinxin Zhao, BM, Chunmei Li, BM, Guixiang Wang, BM, and Hui Yin, BM; Huxian Hospital: Zhaojun Yang, BM, Keli Song, BM, Anqi Dan, BM, and Yamei Yang, ADN; Huinan People’s Hospita:l Yanhui Zhang, BM, Hongyan Guo, BM, Xueyan Yang, BM, Liang Xiao, BM, and Bin Sun, BM; Zhongxiang People’s Hospital: Fuxing Liu, MD, Xiang Zhang, BM, Jingjing Hu, BM, Wei Zhou, BM, and Bo Liu, BM; Qinyuan People’s Hospital: Qingfeng Yang, BM, Guohong Ren, BM, Xiulin Sun, BM, Caihong Zhao, BM, and Qingfeng Gu, BM; Hulunbeier Arongqi People’s Hospital: Chengjun Sui, BM, Shijun Zhang, BM, Yongqing Yang, BM, Jing Guo, BA, and Wenxuan Song, BM; Huanqu People’s Hospital: Junlei Di, BM, Lei Wang, BM, Yaqin He, BM, Yali Yang, BM, and Liping Zhang, BM; Mianning People’s Hospital: Jian Yang, BM, Xueyun Li, BM, Wenying Yuan, BM, Minghui Lu, BM, and Fangfang Ni, ADN; Changzhi Iron and Steel Group Company Limited General Workers Hospital: Liping Wang, BM, Jianying Liu, BM, Xiaopeng Li, BM, Jianqi Yao, BM, and Lijuan Wang, BM; Fuping: Youxing Guo, BM, Ruina Hao, BM, Jun Su, BM, Wangni Kang, AND, and Yuan Wang, ADN; Qingyuan Man Autonomous County People’s Hospital: Chengyuan Zhai, BM, Zhengcai Xu, BM, Yanhui Guan, BM, and Yang Zhou, BM; Tongliao Zhaluteqi People’s Hospital: Guangze Mao, BM, Yongchun Yang, BN, Eerdunjurihe Wu, BM, Zhanmin Sun, BN, and Limin Li, ADN; Pingyin People’s Hospital: Yandong Yin, BM, Zhengbao Li, BM, Guoqiang Zhao, BM, Huali Zhang, MD, and Luanluan Yang, MD; Wuhua People’s Hospital: Renquan Zhang, BM, Maosen Wen, BM, Junen Gu, BM, Weibiao Zeng, ADM, and Junliang Zhang, ADM; Tongyu First Hospital: Fengwu Sun, BM, Tong Yan, BM, Yan Li, BM, Qingyu Sun, BM, and Dejie Qi, BM; Yinan People’s Hospital: Changsheng Lu, BM, Hongguo Liu, BM, Xiaoli Zhao, BM, and Luyang Wang, BM; Mianxian People’s Hospital: Zhe Zhang, BM, Baohua Su, BM, Xuee Zhou, BN, Xiaodan Gao, BN, and Jianxun Zhang, BM; Taishan People’s Hospital: Hui Chen, BM, Shengkai Chen, BM, and Zichuan Lin, BM; Weichang Man and Menggu Autonomous County Hospital: Yifeng Hu, BM, Yanming Yang, BM, Lijie Tang, BM, Yajing Kang, BM, and Yumin Jing, BM; Lingshi People’s Hospital: Shiwen Zhang, BM, Jian Wang, BM, Xiaoqin Wu, BN, Meiling Fan, BM, and Xujing Han, BM; Chengwu People’s Hospital: Tianting Zhang, BM, Fengqin Liu, BM, Chong Liu, BM, Lijuan Wang, BM, and Dandan Meng, BA; Hengtai People’s Hospital: Gang Ma, BM, Hongbin Zhang, ADM, Xichun Gao, MD, Xianlian Xu, MD, and Xiaoming Bao, MD; Zanhuang Hospital: Guojun Wang, BM, Liying Wang, BM, Yongjun Zhao, BM, Yanling Zhang, BM, and Pingli Liu, BM; Lixin People’s Hospital: Chong Wang, BM, Junwei Xu, BM, Fangtao Ma, MD, Gan Huang, BM, and Biao Liu, BM; Dazu People’s Hospital: Xiaopeng Ji, BM, He Huang, BM, Lin Pan, BM, Liangtai Peng, BM, and Fei Xiao, BM; Qianan Hospital: Yangang Zhu, BM, Hongjiang Zhou, BM, Yan Liu, AND, Bing Sun, BM; Gucheng People’s Hospital: Yongbin He, MD, Yonghong Chen, MD, Qunfeng Wan, BM, Shijia Dong, BM, and Zhichao Yang, BM; Wushe People’s Hospital: Wusheng Liu, BM, Xuewen Liu, ADM, Baofu Li, ADM, Xiaojun Zhang, ADM, and Xiaochun Wang, BM; Chifeng Balinzuoqi Hospital: Hai Chen, BM, Rulin Li, BM, and Xue Zhang, ADM; Hunyuan People’s Hospital: Zhilan He, BM, Jizhi Wang, BM, and Yanlu Pei, BM; Lianzhou People’s Hospital: Dengpeng Huang, BM, Wengeng Wang, BM, Xiaoshe Chen, BM, and Shaojun Wang, BM; Wafangdian Central Hospital: Xiangpin Li, BM, Hainan Wang, BM, Liangliang Yu, BM, Chunqiang Yu, BM, and Yunxiong Liu, BM; Beizhen People’s Hospital: Gang Qiu, BM, Yan Chang, ADM, Feng Xia, BM, Liwen Ma, BM, and Song Yang, BM; Yingcheng People’s Hospital: Zhongdao Zhang, BM, and Libing Zhang, MD; Wuchuan People’s Hospital: Yuanming Yi, BM, Xuelian Deng, BN, Zhangli Lin, AND, Huafeng Chen, BN, and Guanhong Wu, BN; Xuyong People’s Hospital: Zhengye Li, BM, Li Huang, ADM, Hong Li, BM, Jiang Long, BM, and Zhenyan Ke, BM; Gaoyou People’s Hospital: Zhengzhang Li, MD, Zhe Shen, BM, Haoping Xue, BM, Shi Cheng, BM, and Xiaohe Feng, MD; Zichang Hospital: Yanmei Xu, BM, Xiaoyong Hao, BM, Runting Jing, BM, Yacheng Li, BM, and Yongjun Xue, BM; Fengxiang Hospital: Kai Yang, BM, Xisheng Zhou, BM, Cangwei Gou, BM, and Fei Zheng, BM; Pingxiang Hospital: Ruishuang Zhang, BM, Shengjiang Li, BM, Libo Wang, BM, Xiaoping Bai, BM, and Lingyan Wu, BM; Tongliao Huolinguole Hospital: Guofu Chen, BM, Gang Cao, BM, Shuangshuang Liu, ADM, Haishan Zhang, BM, and Zhihui Zhang, BM; Xixia People’s Hospital: Fenglou Zhang, ADM, Yujie Pan, BM, Junfang Zhang, BM, Chunqiang Pang, BM, and Hongzhao Yang, MD; Wuwei People’s Hospital: Kaibao Wang, BM, Caizang Zheng, BM, Yihong Fang, BM, and Yinong Fang, BM; Puan People’s Hospital: Jiang Liu, BM, Anhua Rong, BM, Xianqiang Zhang, BM, Pengxia Chen, AND, and Lang Chen, BA; Shangcheng People’s Hospital: Baiwu Zhou, BM, Chenhui Xiong, BM, Zhiying Lin, AND, Wei Wang, BM, and Zuyu Yu, BN; Woyang People’s Hospital: Shuhua Yuan, BM, Juan Yang, BA, Xiao Liu, BM, Minghua Zhai, BM, and Yong Xu, BM; Dongtai People’s Hospital: Xiaohong Wu, BS, Liping Shu, ADM, Shiping Xu, BM, Ning Xu, ADM, and Chengjun Yao, BM; Cangxi People’s Hospital: Jian Han, BM, Rui Wang, BM, Qiong Cao, BM, Shuke Chun, BM, and Xianfa Li, DM; Dejiang People’s Hospital: Xia Zhang, BM, Guichao Li, BM, Yan Chen, ADN, Haitang Xu, BM, and Yonghong Zhang, BS; Zhuanglang People’s Hospital: Yingxu Li, DM, Lailu Wang, BM, Rui Niu, BM, Sheng Yang, DN, and Lili Liu, DM; Huoshan Hospital: Yunwu Zhu, BA, Jun Li, MD, Yi Xiang, BM, Jun Cai, BM, and Huiying Liu, BN; Xixiang Hospital: Fan Zhou, ADM, Xinxia Li, BM, Jing Zhao, BM, Linyan Huang, BM, and Juan Meng, MD; Quzhou Hospital: Shikui Zhu, BM, Shaohua Sun, BM, Heying Li, BM, and Yang Zhao, BM; Loufan People’s Hospital: Jiangang Li, ADM, Jianping Li, DM, Jie Li, ADM, Shuxiong Guo, AND, and Weiwei Fan, DN; Tangxian Hospital: Longhui Di, MD, Huibin Qi, BM, and Hongbo Zhang, BM; Yidu First Hospital: Zhibin Peng, BM, Shilei Deng, BM, Bohua Li, BM, Zemin Yang, BM, and Xiaobo Pi, BM; Yuqing People’s Hospital: Congzhi Zhang, BM, Hua Zhang, BM, Yanmei Yang, BM, Song Guo, BM, and Zaihong Yang, BM; Weining People’s Hospital: Qilin Shen, BM, Congshu Luo, BM, Xiaojun Lu, BN, Juan Guan, ADN, and Jia Ren, ADN; Jianshi People’s Hospital: Dazhi Qian, BM, Mingfu Ma, BM, Shuihong Huang, BN, Mingzhou Hou, BM, and Hong Qiao, ADN; Guxian People’s Hospital: Haifeng Jia, BM, Chunhui Shi, BM, Zhipeng Fan, BM, Qian Han, BM, and Xuehui Li, BM; Gaolan People’s Hospital: Wenming Yang, BM, Xiaofang Liu, AND, Guiyong Zhao, BM, Kehui Gu, BM, and Wei Wei, BM; and Puxian People’s Hospital: Yaoji Chen, BM, Wenlong Zhou, BM, Zengchang Wang, BM, Huafeng Zhao, BM, and Lihong Cao, BM.

Data Sharing Statement: See Supplement 5.

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Corresponding author.

PMCID: PMC6537808 PMID: 30994898

Key Points

Question

What is the effect of a multifaceted quality of care improvement initiative for acute coronary syndrome (ACS) on major adverse cardiovascular events in low-resource hospitals in China?

Findings

In this stepped-wedge cluster randomized clinical trial that included 101 hospitals and 29 346 patients with ACS, the in-hospital rates of major adverse cardiovascular events were 4.4% in the control phase and 3.9% in the intervention phase; the difference was not statistically significant after adjusting for cluster and temporal trends.

Meaning

Among patients with ACS in low-resource hospitals in China, a multifaceted quality of care initiative did not reduce the in-hospital major adverse cardiovascular events compared with usual care.

Abstract

Importance

Prior observational studies suggest that quality of care improvement (QCI) initiatives can improve the clinical outcomes of acute coronary syndrome (ACS). To our knowledge, this has never been demonstrated in a well-powered randomized clinical trial.

Objective

To determine whether a clinical pathway–based, multifaceted QCI intervention could improve clinical outcomes among patients with ACS in resource-constrained hospitals in China.

Design, Setting, Participants

This large, stepped-wedge cluster randomized clinical trial was conducted in nonpercutaneous coronary intervention hospitals across China and included all patients older than 18 years and with a final diagnosis of ACS who were recruited consecutively between October 2011 and December 2014. We excluded patients who died before or within 10 minutes of hospital arrival. We recruited 5768 and 0 eligible patients for the control and intervention groups, respectively, in step 1, 4326 and 1365 in step 2, 3278 and 3059 in step 3, 1419 and 4468 in step 4, and 0 and 5645 in step 5.

Interventions

The intervention included establishing a QCI team, training clinical staff, implementing ACS clinical pathways, sequential site performance assessment and feedback, online technical support, and patient education. The usual care was the control that was compared.

Main Outcomes and Measures

The primary outcome was the incidence of in-hospital major adverse cardiovascular events (MACE), comprising all-cause mortality, reinfarction/myocardial infarction, and nonfatal stroke. Secondary outcomes included 16 key performance indicators (KPIs) and the composite score developed from these KPIs.

Results

Of 29 346 patients (17 639 men [61%]; mean [SD] age for control, 64.1 [11.6] years; mean [SD] age for intervention, 63.9 [11.7] years) who were recruited from 101 hospitals, 14 809 (50.5%) were in the control period and 14 537 (49.5%) were in the intervention period. There was no significant difference in the incidence of in-hospital MACE between the intervention and control periods after adjusting for cluster and time effects (3.9% vs 4.4%; odds ratio, 0.93; 95% CI, 0.75-1.15; P = .52). The intervention showed a significant improvement in the composite KPI score (mean [SD], 0.69 [0.22] vs 0.61 [0.23]; P < .01) and in 7 individual KPIs, including the early use of antiplatelet therapy and the use of appropriate secondary prevention medicines at discharge. No unexpected adverse events were reported.

Conclusions and Relevance

Among resource-constrained Chinese hospitals, introducing a multifaceted QCI intervention had no significant effect on in-hospital MACE, although it improved a few of the care process indicators of evidence-based ACS management.

Trial Registration

ClinicalTrials.gov identifier: NCT01398228

This randomized clinical trial explores the effect of a clinical pathway–based, multifaceted quality of care initiative intervention on clinical outcomes among patients with acute coronary syndromes in resource-constrained hospitals in China.

Introduction

Cardiovascular disease accounts for almost a third of all deaths worldwide and is the leading cause of death in China.^1,2 Compared with the previous decade, China is predicted to experience a 69% increase in the incidence of acute coronary disease between 2010 and 2019, amounting to nearly 8 million additional episodes of myocardial infarction (MI) or unstable angina pectoris.³ Given that more than two-thirds of cardiovascular events will occur in adults younger than 65 years,^2,3 this rapidly escalating burden of acute coronary syndromes (ACS) will have profound economic and social implications for China.^4,5

Despite the widespread promulgation and endorsement of ACS treatment guidelines^6,7 and the strong evidence base underpinning many guideline recommendations,^8,9,10 their translation into clinical practice remains suboptimal, globally. This is particularly true for low-income and middle-income countries^11,12,13,14 and for nontertiary hospitals where financial, technical, and staff resources are more limited.^13,14 In China, nontertiary regional hospitals account for 40% of all hospitals in the country and provide first-line care for 900 million patients annually.¹⁵

Many strategies have been proposed to narrow evidence practice gaps in ACS care, including clinical pathways and patient education, as well as data audits and feedback.^{11,16,17,18,19} Among these, clinical pathways have been studied most extensively, with good evidence to associate pathway use with a reduction in in-hospital complications.²⁰ Consequently, clinical pathways have been incorporated into routine practice in many high-income countries and are also highly promoted in low-income and middle-income countries.^20,21 However, this practice is largely supported by effects on surrogate process outcomes^11,22; these effects on clinical outcomes have been largely derived from observational studies.¹⁰ To our knowledge, the effects of such programs on clinical events, such as cardiovascular death, reinfarction, or other diseases or complications, have not previously been studied in a randomized clinical trial and thus remain uncertain.

Since 2009, the Chinese government has initiated a new round of health care reforms.²³ One objective is to strengthen the health care system, which regards nontertiary county hospitals as regional centers.^23,24As an official implementation research project of the National Health Commission (former Ministry of Health), the third phase of the Clinical Pathways for Acute Coronary Syndromes in China (CPACS-3) was initiated to evaluate a clinical pathway–based, multifaceted quality of care improvement intervention aimed at improving clinical outcomes among patients with ACS in resource-constrained hospitals.

Methods

Study Design

The study design has been previously published.²⁵ Briefly, CPACS-3 was a stepped-wedge cluster randomized clinical trial among resource-constrained hospitals in China (Figure 1). The primary objective was to determine whether routinely using a clinical pathway–based, multifaceted quality of care initiative (QCI) led to a measurable reduction in the number of in-hospital major adverse cardiovascular events (MACE) in patients with ACS presenting to resource-limited hospitals in China. The secondary objectives were to determine: (1) whether the QCI would improve the quality of care and (2) any major facilitators and barriers to the implementation and uptake of the interventions in these settings. To be eligible, hospitals had to be nontertiary centers with (1) more than 90 minutes taken to transfer a patient with ACS to the nearest large tertiary hospital with a cardiac catheterization laboratory, (2) no plans to develop the capacity for onsite percutaneous coronary intervention (PCI) within the next 4 years, (3) more than 40 patients with ACS hospitalized every 6 months, and (4) no participation in another hospital QCI. Patients with ACS in eligible hospitals that agreed to participate were consecutively enrolled in 5 6-month steps (ie, cycles). No intervention was applied in the first cycle in all participating hospitals. Study hospitals were randomly allocated to 4 wedges. Each wedge commenced the intervention in one of the 4 remaining cycles. All hospitals were on the intervention in the last cycle. The intervention was applied at the hospital level, with outcomes measured at the patient level. A stepped-wedge design was chosen mainly because it was anticipated that the study would be beneficial and receipt of the intervention was the strong preference of all participating hospitals and the government officials in charge of the project. The Peking University institutional review board reviewed and approved the study and all participating patients provided written informed consent. The trial protocol and statistical analysis plan are available in Supplement 1-3.

Figure 1. — ITT indicates intention to treat.

^aOne hospital from wedge 2 and 1 hospital from wedge 4 dropped out in cycle 2.

Patients

All patients older than 18 years with a final diagnosis of ACS at discharge or death were recruited consecutively in 2 batches of hospitals. The first batch included 76 hospitals and recruited study patients from October 9, 2011, to May 31, 2014, and the second batch included 25 hospitals recruiting patients between June 1, 2012, and December 29, 2014. We excluded patients who died before or within 10 minutes of hospital arrival.

Randomization

The randomization was done centrally among all 101 hospitals, with stratification by province, before initiating the intervention in the first-wedge hospitals in the first batch. The allocation codes were concealed by the statistician separately and would be given to the project manager who was in charge of the initiation of the intervention when it began. Because the second batch of hospitals started roughly 6 months after the first batch of hospitals, the intervention in these hospitals also initiated 6 months later in each wedge.

Data Collection

A trained hospital staff member who was not involved in treating patients with ACS was responsible for collecting and entering data into a dedicated web-based data management system. Data for each patient were collected from medical records and from survivors before hospital discharge. The data included sociodemographic information; symptoms and signs relating to the presenting ACS; medical history; electrocardiographic results; biomarker findings; investigations performed; treatments administered before admission, during hospitalization, and at death or hospital discharge; final diagnosis and discharge status; major in-hospital clinical events; personal insurance status; and the total cost of hospitalization. Data quality was maintained through in-person and online study monitoring activities.

Intervention

The intervention was a multifaceted QCI comprising 6 components: the establishment of a QCI team, chaired by the hospital director and including the department chiefs for emergency, general medicine/cardiology, and the medical services administration; implementation of clinical pathways for managing different subtypes of ACS that were developed in CPACS-2 and tailored to fit the hospital when necessary²⁶; regular reports provided every 6 months on key performance indicators (KPIs) that used information collected through the study data management system through which hospitals could self-assess their peer-ranked clinical performance; technical training and a compulsory test for medical staff engaged in ACS care; a web-based online technical support to get advice from senior cardiologists; and patient educational materials on ACS clinical manifestation, treatment, secondary prevention, and lifestyle modification.

The fidelity of the study intervention components was monitored at each site by the clinical associates from the study coordinating center at the George Institute for Global Health at Peking University Health Science Center in Beijing, China, at the beginning, middle, and end of the study. The participating and passing rates of the technical training for participating physicians and nurses were obtained from the background records from the online system for training.

Outcomes

The primary outcome of the study was in-hospital MACE, defined as all-cause mortality, MI, or recurrent MI and nonfatal stroke. We chose all-cause mortality rather than cardiac death because our definition of in-hospital all-cause mortality not only included patients who died in the hospital but also those who were discharged against medical advice and died within 1 week and those who transferred to upper-level hospitals but died within 24 hours. For the 2 latter cases, we were not able to collect reliable data to confirm cause of death. Recurrent MI during hospitalization was classified as an event during which a hospitalized patient with MI demonstrated a rise of the cardiac biomarker (troponin or creatine kinase myocardial band) at least once above the 99th percentile reference limit or the value increased more than 20% compared with the former measurement and with at least 1 of the following 3 criteria: new symptoms of ischemia, new significant ST-T wave changes, and imaging evidence of new regional wall motion abnormality. All primary outcome events were adjudicated by an independent committee masked to the hospital’s randomization status.

Secondary outcomes were a patient-level composite score of the KPIs and each of the individual 16 KPIs of ACS care (the definitions of KPIs are provided in eTable 1 in Supplement 4). The patient-level KPI composite score was calculated by allocating a score of 1 for each of the binary KPIs achieved, adding these, and dividing by the number of KPIs relevant to that individual. Accordingly, length of hospital stay was the only KPI not used for the calculation of the composite score. Because of the changes in clinical guidelines and also in our study intervention, we added 3 new KPIs after the trial initiation: the percentage of patients receiving dual antiplatelet therapy, loading dose dual antiplatelet therapy, and intensive statin therapy. This change was made before the statistical analysis plan was finalized and the database locked but after the study protocol was published.²⁵

Sample Size

Assuming a primary outcome event rate of 8% and a 2-sided 5% significance test, 96 hospitals and 40 patients per 6-month cycle from each hospital would provide 98% and 85% power to detect relative risk reductions of 20% and 15%, respectively. The control period event rate was based on that observed in the published CPACS study among nontertiary hospitals.¹³ The sample size calculations also assumed that there was no delay in the effects of the intervention and that the intraclass correlation coefficient was 0.10. To account for dropout, we aimed to recruit from 104 hospitals.²⁵

Data Analysis

The primary analysis was performed according to the intention-to-treat principle. All analyses on outcomes were at the individual level but accounted for the clustering of patients at the hospital level. Comparisons of baseline characteristics between intervention and control participants were conducted using the t test and χ² test.

There were a few variables, such as education, health insurance, and smoking status, that had missing data. We disclosed number of patients with missing data in Table 1 and Table 2 but calculated the proportions for each classification without including patients with missing data. In the multivariable analyses, we treated these variables as categorical and the patients with missing data as a separate subgroup of patients.

Table 1. Characteristics of CPACS-3 Study Participants by Randomized Allocation.

Characteristic	No. (%)		Difference (95% CI)^a	P Value
Characteristic	Control (n = 14 809)	Intervention (n = 14 537)	Difference (95% CI)^a	P Value
Age, mean (SD), y	64.1 (11.64)	63.9 (11.72)	−0.2 (−0.4 to 0.1)	.28
Male	8888 (60.0)	8751 (60.2)	0.2 (−0.9 to 1.3)	.75
Education^b
Illiteracy	3479 (30.6)	3132 (27.6)	−3.0 (−5.2 to −0.8)	<.001
Primary	3426 (30.1)	3760 (33.1)	3.0 (0.9 to 5.2)
Secondary	2656 (23.3)	2842 (25.0)	1.7 (−0.6 to 4.0)
High school and above	1815 (16.0)	1616 (14.2)	−1.7 (−4.1 to 0.7)
Farmer^b	9062 (64.3)	8816 (66.1)	1.9 (0.7 to 3.0)	.001
Health insurance^b	10 892 (94.8)	9817 (95.5)	0.6 (0.1 to 1.2)	.03
Diagnosis
STEMI	5383 (36.3)	4911 (33.8)	−2.6 (−3.7 to −1.5)	<.001
NSTEMI	2191 (14.8)	2512 (17.3)	2.5 (1.7 to 3.3)
UAP	7235 (48.9)	7114 (48.9)	0.1 (−1.1 to 1.2)
Smoking status^b
Never smoked	9440 (64.8)	9131 (64.4)	−0.4 (−1.8 to 0.9)	.63
Ex-smoker	1509 (10.4)	1467 (10.3)	−0.0 (−2.2 to 2.2)
Smoking	3611 (24.8)	3580 (25.3)	0.5 (−1.6 to 2. 5)
History of disease
Myocardial infarction	1290 (8.7)	1174 (8.1)	−0.6 (−2.8 to 1.6)	.05
Angina	2995 (20.2)	3226 (22.2)	2.0 (−0.1 to 4.0)	<.001
Heart failure	601 (4.1)	560 (3.9)	−0.2 (−2.4 to 2.0)	.37
Stroke	1140 (7.7)	1165 (8.0)	0.3 (−1.9 to 2.5)	.31
Transient ischemic attack	152 (1.1)	242 (1.8)	0.7 (−1.6 to 3.0)	<.001
Diabetes	1973 (13.3)	2025 (13.9)	0.6 (−1.5 to 2.7)	.13
Hypertension^c	10159 (68.6)	9804 (67.4)	−1.2 (−2.2 to −0.1)	.03
Dyslipidemia^d	2024 (13.7)	1975 (13.6)	−0.1 (−0.9 to 0.7)	.84
Physical signs at presentation
SBP <90 mm Hg	391 (2.6)	357 (2.5)	−0.2 (−0.6 to 0.2)	.32
Heart rate ≥100 beats/min	1673 (11.3)	1642 (11.3)	−0.0 (−0.7 to 0.7)	>.99
Hospital characteristics
No. of beds
>1000 (n = 9)	1179 (8.0)	758 (5.2)	−2.8 (−3.3 to −2.2)	<.001
501-1000 (n = 33)	8612 (58.2)	8618 (59.3)	1.1 (0.0 to 2.3)
201-500 (n = 47)	4382 (29.6)	4664 (32.1)	2.5 (1.4 to 3.6)
≤200 (n = 12)	636 (4.3)	497 (3.4)	−0.9 (−1.3 to −0.4)

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Abbreviations: CVD, cardiovascular disease; NSTEMI, non–ST-elevation myocardial infarction; SBP, systolic blood pressure; STEMI, ST-elevation myocardial infarction; UAP, unstable angina pectoris.

^{^a}

Crude difference = intervention minus control.

^{^b}

Data missing: education, 6620 cases (22.6%); farmer, 1921 cases (6.5%); insurance, 7577 cases (25.8%); smoking status, 608 cases (2.1%).

^{^c}

Including patients with medical history of hypertension, a systolic blood pressure of 140 mm Hg higher, or a diastolic blood pressure of 90 mm Hg or higher when presenting at the hospital.

^{^d}

Including patients with medical history of dyslipidemia, total cholesterol level of 240 mg/dL or higher, or low-density lipoprotein cholesterol level of 160 mg/dL or higher (to convert to millimoles per liter, multiply by 0.0259) when presenting at the hospital.

Table 2. Observed Rates and Means of Primary and Secondary Outcomes by Randomized Allocation and Corresponding Mean Differences and Adjusted Odds Ratios in Intervention and Control Periods.

Outcomes	ICC^a	Cases, No. (%)		Cluster-Adjusted		Primary Analysis
Outcomes	ICC^a	Intervention (n = 14 537)	Control (n = 14 809)	Difference (95% CI)	Odds Ratio or β Coefficient (95% CI)^b	Difference (95% CI)	Odds Ratio or β Coefficient (95% CI)^b
Primary outcome
In-hospital MACE	0.01	559 (3.8)	655 (4.4)	−0.6 (−1.1 to −0.1)	0.87 (0.75 to 1.01)	−0.3 (−0.8 to 0.2)	0.93 (0.75 to 1.15)
Secondary outcome
Composite score of KPIs to mean (SD)	0.34	0.69 (0.22)	0.61 (0.231)	0.1 (0.1 to 0.1)	0.08 (0.06 to 0.10)b	0 (0 to 0.1)	0.04 (0.02 to 0.06)^b
Single KPIs, in-hospital therapy
Aspirin	0.07	13 334 (91.7)	13 241 (89.4)	2.6 (1.9 to 3.3)	1.32 (1.15 to 1.50)	0.1 (−0.6 to 0.8)	1.01 (0.80 to 1.28)
Clopidogrel	0.23	10 913 (75.1)	8891 (60.0)	16.7 (15.7 to 17.8)	2.12 (1.84 to 2.45)	4.3 (3.3 to 5.4)	1.21 (1.02 to 1.44)
Statin	0.09	12 501 (86.0)	12 479 (84.3)	2.6 (1.8 to 3.5)	1.21 (1.08 to 1.35)	0.5 (−0.4 to 1.4)	1.04 (0.87 to 1.24)
Dual antiplatelet	0.22	10 725 (73.8)	8680 (58.6)	16.6 (15.5 to 17.7)	2.08 (1.81 to 2.39)	4.4 (3.3 to 5.5)	1.21 (1.03 to 1.43)
Loading dose dual antiplatelet	0.21	5768 (39.7)	3563 (24.1)	15.5 (14. to 16.5)	2.13 (1.70 to 2.66)	4.5 (3.46 to 5.5)	1.24 (0.93 to 1.66)
High-intensive statin	0.35	4954 (34.1)	5524 (37.3)	−3.6 (−4.7 to −2.6)	0.85 (0.70 to 1.03)	3.3 (2.2 to 4.4)	1.16 (0.95 to 1.42)
STEMI receiving reperfusion	0.15	1414 (48.9)	1683 (52.2)	−1.8 (−4.4 to 0.7)	0.93 (0.81 to 1.06)	−2.2 (−4.7 to 0.3)	0.92 (0.72 to 1.16)
Acceptable D2N time	0.12	539 (37.4)	516 (30.0)	5.7 (2.5 to 9.0)	1.30 (1.02 to 1.65)	2.4 (−0.9 to 5.7)	1.12 (0.77 to 1.62)
Single KPIs, discharge therapy
Aspirin	0.20	11 975 (85.5)	11 565 (81.5)	4.7 (3.9 to 5.6)	1.40 (1.08 to 1.81)	5.5 (4.7 to 6.4)	1.48 (1.14 to 1.93)
Clopidogrel	0.26	9824 (70.1)	7718 (54.4)	17.3 (16.2 to 18.4)	2.07 (1.74 to 2.47)	7.3 (6.2 to 8.4)	1.36 (1.12 to 1.64)
β-Blocker	0.11	8358 (59.7)	7458 (52.5)	8.5 (7.3 to 9.6)	1.41 (1.21 to 1.65)	7.6 (6.5 to 8.8)	1.36 (1.17 to 1.59)
Statin	0.18	11 532 (82.3)	11 166 (78.7)	5.3 (4.4 to 6.2)	1.38 (1.13 to 1.70)	4.6 (3.7 to 5.6)	1.33 (1.06 to 1.67)
ACEI or ARB (in LVSD ones)	0.21	1382 (50.6)	1295 (47.9)	4.1 (1.4 to 6.7)	1.18 (1.00 to 1.38)	6.0 (3.3 to 8.6)	1.27 (1.05 to 1.53)
Single KPIs, other
First ECG in time	0.18	9020 (62.0)	7768 (52.5)	10.3 (9.2 to 11.5)	1.52 (1.28 to 1.81)	2.8 (1.7 to 4.0)	1.12 (0.90 to 1.39)
Diagnosis consistent with ECG and biomarker findings	0.03	9957 (83.7)	9825 (84.3)	−0.3 (−1.2 to 0.7)	0.98 (0.87 to 1.11)	−2.3 (−3.2 to −1.3)	0.85 (0.71 to 1.01)
Length of stay to mean (SD)	0.11	8.8 (5.41)	9.6 (5.65)	−0.7 (−0.9 to −0.4)	−0.67 (−0.92 to −0.43)^c	−0.1 (−0.4 to 0.2)	−0.12 (−0.44 to 0.20)^c
In-hospital cost to mean (SD), $	0.19	1486.8 (1914)	1383.8 (1869.9)	134.7 (31.7 to 237.6)	134.7 (31.7 to 237.6)^c	−88.6 (−282.2 to 104.9)	−88.6 (−282.2 to 104.9)^c

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Abbreviations: ACEI, angiotensin-converting enzyme inhibitor; ARB, angiotensin receptor blocker; D2N, door to needle; ECG, electrocardiograph; ICC, intraclass correlation coefficient; KPI, key performance indicator; LVSD, left ventricular systolic dysfunction; MACE, major adverse cardiovascular event; STEMI, ST-elevation myocardial infarction.

^{^a}

The ICCs are obtained using generalized estimating equations models with exchangeable working correlation structure.

^{^b}

Odds ratios and β coefficients represent the effect of intervention compared with control and are calculated as the difference of proportions or means in marginal effects (intervention group minus control group) in a generalized estimating equation model to account for within-hospital clustering with logit link function for binary outcomes or with identity link function for continuous outcomes. In the primary analysis model, time was taken into account as a fixed effect. The 95% CIs for difference in proportions are obtained through normal approximation of the adjusted proportions.

^{^c}

β Coefficient.

To analyze intervention effects, generalized estimating equation models were used to account for the clustering within hospitals.²⁷ The primary model included a fixed effect for time and a binary variable for the effect of the intervention. Within-cluster correlations were modeled using generalized estimating equations with an exchangeable working correlation structure. Sensitivity analyses included a model without the effect of time and a model in which time was considered a continuous variable as well as a model with the interaction between time and intervention. The intervention effects were summarized as the resulting odds ratios and difference of proportions for binary outcomes or mean differences for continuous outcomes. We further conducted covariates-adjusted analyses, including patient-level baseline covariates and hospital-level covariates, using 3-level generalized linear-mixed models with hospital and province as the second and third levels, respectively.

The effect of the intervention on in-hospital MACE and the composite KPI score was analyzed according to the following prespecified baseline subgroups: subtypes of ACS, sex, and age. We did not execute any interim analysis. We did not adjust for the multiple testing in our analyses on secondary outcomes; therefore, these analyses should be considered exploratory.

All statistical tests were 2-tailed. The intervention effects for the primary and secondary outcomes were considered significant at P = .05. All analyses were conducted using SAS, version 9.4 (SAS Institute).

Results

Patient Recruitment

Of 120 eligible hospitals recommended through local health authorities, 19 declined to participate. Before the initiation of the intervention, 2 hospitals withdrew from the study. A total of 29 346 patients with ACS were recruited. Of them, 14 809 patients (50.5%) were recruited before hospitals received the QCI interventions (control) and 14 537 (49.5%) were recruited after the intervention was initiated (Figure 1).

Patient Characteristics

The patient characteristics were generally similar in the control and intervention groups (Table 1). The study participants were age 19 to 102 years, with a mean (SD) age of 64.0 (11.6) years. Consistent with previous reports from China, ST-segment elevation MI accounted for only about one-third of events, while unstable angina pectoris accounted for about one-half.

Effects on Primary and Secondary Outcomes

The difference in in-hospital MACE between patients recruited in the intervention period and those in the control period was not significant after adjusting for the clustering effect and time trend (difference, −6; 95% CI, −1.1 to −0.1) (Table 2). The model with the time-by-treatment interaction showed no statistically significant effect of intervention, and the interaction term was also not significant. The temporal change in the unadjusted rates of in-hospital MACE is shown in Figure 2.

For secondary outcomes, the composite score of KPIs was significantly higher in the intervention group and the difference remained significant after adjusting for the clustering effect and time trend. Among the 16 single KPIs, all those on discharge therapy were significantly improved by the intervention. With respect to in-hospital care, significant intervention effects were only observed for the early use of clopidogrel and dual antiplatelet therapy in the primary analysis (Table 2). The in-hospital cost did not increase in the intervention period in the primary analysis. We repeated all the previously described analyses with further adjusting for multiple variables at patient, hospital, and province levels and the results remained unchanged (eTable 2 in Supplement 4).

Subgroup Analysis

The prespecified subgroup analyses of the primary outcome are shown in Figure 3. There was no evidence of a differential effect of intervention on in-hospital MACE by age, sex, or subtype of ACS. The site monitoring data and the records from the online system for training showed that more than 90% hospitals implemented intervention components, but the fidelity to individual components was variable (eTable 3 in Supplement 4).

Discussion

In this stepped-wedge cluster randomized clinical trial, we found that implementing the QCI improved many process indicators significantly. However, these improvements were generally moderate and did not translate into a significant change in the rate of in-hospital MACE. The results align with findings from the recent systematic review that analyzed 670 reports from 337 studies of 118 strategies to improve health care clinician practices in low-income and middle-income countries.²⁸ The review concluded that the effect size of these strategies varied substantially but was typically moderate, most strategies had low-quality evidence, and the results emphasized the need for better methods to study the effectiveness of interventions.²⁸

Looking at the changes by types of KPIs may help to understand why our study did not achieve a significant reduction in the clinical outcome. Most KPIs that improved significantly were discharge medical therapies, which cannot influence in-hospital clinical outcomes. For the in-hospital management KPIs, the early use of clopidogrel, dual antiplatelet therapy, and loading dose dual antiplatelet therapy all increased by 15% in the intervention group compared with the control, but these changes were primarily driven by the change in the use of clopidogrel alone. However, reperfusion therapy (those receiving the reperfusion therapy and those with an acceptable door-to-needle time), statin use (early use and high dose), and aspirin use (early use and loading dose) showed no significant differences between intervention and control. A significant reduction in in-hospital MACE is unlikely to be achieved solely by a modest increase in clopidogrel use.

The failure to change the clinical outcome might also be due to the intervention itself being incapable to generate clinically meaningful changes. The fidelity of intervention implementation in our study was generally adequate but demonstrated some variability between hospitals regarding individual components. The recently published ACS Quality Improvement in Kerala randomized clinical trial that used a similar design as our study also found that the locally adapted quality improvement kit did not improve clinical outcomes.²⁹

Why should the intervention be effective at improving some KPIs but not others? First, some of the KPIs had an already high rate of use before the intervention was initiated, so there was limited scope for improvement. For example, 13 241 (89.4%) and 12 479 patients (84.3%) in this study, respectively, had been administered aspirin and statins early before the intervention initiated. By contrast, only 8891 patients (60%) received clopidogrel early, leaving much room for the intervention to improve matters. The proportion of use at discharge for 4 evidence-based secondary prevention treatments was generally between 50% and 80% at baseline and all showed significant increase in intervention. The systematic review by Rowe et al²⁸ also found that baseline outcome level was inversely associated with effect size. Second, with the advances in interventional therapy, thrombolytic therapy has been declining worldwide.^12,30 In fact, thrombolytic therapy is now seldom seen in tertiary hospitals.³⁰ Current guidelines tend to encourage patients with MI in primary care to be transferred to medical centers with catheter laboratories for primary PCI.⁷ This “new” trend may discourage physicians at primary care or non-PCI hospitals to use thrombolytic therapy, although it is highly recommended and encouraged in the hospitals in this study. Third, given the poor physician-patient relationship in China,³¹ the risk of an unsuccessful opening of the culprit vessels by thrombolytic therapy, as well as the higher risk of bleeding, prevents physicians from suggesting thrombolytic therapy. Finally, the fact that only about half of the participating hospitals were very active in implementing the study interventions suggested that quality of care has not become a real goal of hospital management in many Chinese hospitals. If the performance review in hospital management would have still been linked to hospital income, but not the quality of care measurement,³² it would be hard to expect any significant improvement in quality of care among patients. Our findings call for a better health care system that provides the foundation for the QCI to take a real effect.

Strengths and Limitations

This study has many strengths. To our knowledge, it is the first well-powered randomized clinical trial to evaluate the effectiveness of the QCIs in reducing clinical outcomes.²⁵ The study design, conduct, and data analyses were overseen by an experienced steering committee composed of international experts in cardiology, epidemiology, and biostatistics. All study end points were adjudicated by an independent committee and the study process was closely monitored by a quality control team. The Ministry of Health provided support to ensure that participating hospitals cooperated well, and only 2 of them withdrew during the study.

The study also has several limitations. First, the event rate for the primary end point was lower than that estimated from the previous CPACS-1 study, which led to this study being underpowered. It may be because of the advantages in clinical management of ACS, as well as the fact that the events in CPACS-1 were not adjudicated. However, the post hoc power analysis indicates that the study was still powered to detect a relative reduction of the primary outcome of at least 19%. Second, the proportion of unstable angina pectoris in the patients in this study was high (14 349 [49%]) compared with that reported in other countries,^10,29,33 which could contribute to the overall low event rates. However, the high proportion of unstable angina pectoris in this study was comparable with the CPACS-1 study (46%) as well as other previous reports among Chinese patients with ACS.^12,13 It is unclear why the proportion in Chinese patients was higher than that in other countries. Third, because of the technical constraints in hospitals at this level, many patients are often transferred to larger medical centers for better medical services. That would limit the ability for the intervention to take effect (there is not enough time for the intervention) and also prevent us from understanding the effect (eg, the causes of death, for which no data were available). In fact, 4108 patients (14%) in this study were transferred to higher-level hospitals.

By focusing on in-hospital MACE as the primary outcome, the effectiveness of the study intervention may have been underestimated, as the most significant improvements were observed on discharge therapies. We anticipate that ongoing follow-up of the patients will determine whether the intervention may have longer-term effects on clinical outcomes.

Conclusions

Among resource-constrained Chinese hospitals, introducing a multifaceted QCI did not affect in-hospital MACE, although it improved a few of the care process indicators of evidence-based ACS management, especially at the time of hospital discharge.

Supplement 1.

Statistical analysis plan.

Click here for additional data file.^{(553.3KB, pdf)}

Supplement 2.

Original protocol.

Click here for additional data file.^{(1.6MB, pdf)}

Supplement 3.

Translation of the original protocol.

Click here for additional data file.^{(1,012.9KB, pdf)}

Supplement 4.

eTable 1. Definitions of 16 KPIs of ACS care.

eTable 2. The corresponding mean differences and cluster- and time- and covariates-adjusted Odds Ratios in intervention and control periods.

eTable 3. Fidelity of the intervention components in CAPCS-3 study, 101 hospitals.

Click here for additional data file.^{(169.8KB, pdf)}

Supplement 5.

Data sharing statement.

Click here for additional data file.^{(21.7KB, pdf)}

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27.Hussey MA, Hughes JP. Design and analysis of stepped wedge cluster randomized trials. Contemp Clin Trials. 2007;28(2):182-191. Review. doi: 10.1016/j.cct.2006.05.007 [DOI] [PubMed] [Google Scholar]
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32.Liu X, Mills A. The effect of performance-related pay of hospital doctors on hospital behaviour: a case study from Shandong, China. Hum Resour Health. 2005;3:11. doi: 10.1186/1478-4491-3-11 [DOI] [PMC free article] [PubMed] [Google Scholar]
33.Mustafa J, Hussain A, Malik MA, Warraich S. Acute coronary syndrome and its sub-types among patients admitted in chest pain unit. JSZMC. 2017;8(2):1181-1184. http://www.jszmc.com/Files_pdf/JSZMCVol08No02/1181.pdf. Accessed October 15, 2018. [Google Scholar]

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Supplement 1.

Statistical analysis plan.

Click here for additional data file.^{(553.3KB, pdf)}

Supplement 2.

Original protocol.

Click here for additional data file.^{(1.6MB, pdf)}

Supplement 3.

Translation of the original protocol.

Click here for additional data file.^{(1,012.9KB, pdf)}

Supplement 4.

eTable 1. Definitions of 16 KPIs of ACS care.

eTable 2. The corresponding mean differences and cluster- and time- and covariates-adjusted Odds Ratios in intervention and control periods.

eTable 3. Fidelity of the intervention components in CAPCS-3 study, 101 hospitals.

Click here for additional data file.^{(169.8KB, pdf)}

Supplement 5.

Data sharing statement.

Click here for additional data file.^{(21.7KB, pdf)}

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[hoi190017r33] 33.Mustafa J, Hussain A, Malik MA, Warraich S. Acute coronary syndrome and its sub-types among patients admitted in chest pain unit. JSZMC. 2017;8(2):1181-1184. http://www.jszmc.com/Files_pdf/JSZMCVol08No02/1181.pdf. Accessed October 15, 2018. [Google Scholar]

PERMALINK

Effect of a Quality of Care Improvement Initiative in Patients With Acute Coronary Syndrome in Resource-Constrained Hospitals in China

Yangfeng Wu, PhD

Shenshen Li, MPH

Anushka Patel, MD

Xian Li, MSc

Xin Du, MD

Tao Wu, MSc

Yifei Zhao, MSc

Lin Feng, BM

Laurent Billot, MSc

Eric D Peterson, MD

Mark Woodward, PhD

Lingzhi Kong, MD

Yong Huo, MD

Dayi Hu, MD

Kalipso Chalkidou, PhD

Runlin Gao, MD

Key Points

Question

Findings

Meaning

Abstract

Importance

Objective

Design, Setting, Participants

Interventions

Main Outcomes and Measures

Results

Conclusions and Relevance

Trial Registration

Introduction

Methods

Study Design

Figure 1. Study Flow Chart.

Patients

Randomization

Data Collection

Intervention

Outcomes

Sample Size

Data Analysis

Table 1. Characteristics of CPACS-3 Study Participants by Randomized Allocation.

Table 2. Observed Rates and Means of Primary and Secondary Outcomes by Randomized Allocation and Corresponding Mean Differences and Adjusted Odds Ratios in Intervention and Control Periods.

Results

Patient Recruitment

Patient Characteristics

Effects on Primary and Secondary Outcomes

Figure 2. Unadjusted Rates of Major Adverse Cardiovascular Events by Wedge and Cycle.

Subgroup Analysis

Figure 3. Subgroup Analysis for the Effect of Intervention on Major Adverse Cardiovascular Events (MACE), Cluster, and Time Adjusted (Primary Model).

Discussion

Strengths and Limitations

Conclusions

References

Associated Data

Supplementary Materials

ACTIONS

PERMALINK

RESOURCES

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