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      Chronic kidney disease and the outcomes of fibrinolysis for ST-segment elevation myocardial infarction: A real-world study

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          Abstract

          Background

          In low-resource regions, fibrinolytic therapy is often the only option for ST-elevation myocardial infarction (STEMI) patients as primary percutaneous coronary intervention (PCI) is often not available and patients are hardly transferred to a medical center with PCI capacity within the first 120 minutes. Chronic kidney disease (CKD) is one of the most frequently encountered complications of STEMI. However, the evidence for the efficacy of fibrinolytic therapy in STEMI patients with CKD is still limited. The aim of this study is to test whether CKD modifies the association between fibrinolytic therapy and short-term major adverse cardiovascular events (MACEs) among patients with STEMI.

          Methods and findings

          This is a real-world study analyzing the data from 9508 STEMI patients (mean age: 64.0±12.4 years; male: 70.1%) in the third phase of Clinical Pathways in Acute Coronary Syndromes program (CPACS-3), which is a large study of the management of acute coronary syndromes (ACS) in 101 county hospitals without PCI capacity in China. CKD was defined as an estimated glomerular filtration rate of less than 60 mL/min per 1·73 m 2 at the admission. The primary outcome is short-term MACEs, including all-cause death, recurrent myocardial infarction, or nonfatal stroke. Patients were recruited consecutively between October 2011 and November 2014. Out of them, 1282 patients (13.5%) were classified as having CKD. Compared with non-CKD patients, CKD patients were less likely to receive fibrinolytic therapy than non-CKD patients (26.4% vs. 38.9%, P<0.001), more likely to experience a failed fibrinolytic therapy (32.8% vs. 16.9%), and had a higher risk of short-term MACEs (19.7% vs. 5.6%). After full adjustment, use of fibrinolytic therapy was associated with a significantly lower risk of short-term MACEs in non-CKD patients (relative risk [RR] = 0.87, 95% confidence interval [CI]: 0.76–0.99), but not in CKD patients ( P for interaction = 0.026). Further analysis stratified by the success of fibrinolysis showed that compared with patients who did not receive fibrinolytic therapy, patients with successful fibrinolysis had a lower risk of short-term MACEs that was similar between patients with (RR = 0.71, 95% CI: 0.55–0.82) and without CKD (RR = 0.67, 95% CI: 0.55–0.92), while patients with unsuccessful fibrinolysis had a similarly higher risk in CKD patients (RR = 1.25, 95% CI: 1.09–1.43) and non-CKD patients (RR = 1.30, 95% CI: 1.13–1.50).

          Conclusions

          CKD reduced the likelihood of successful fibrinolysis and increased the risk of short-term MACEs in patients with STEMI. Attention should be paid to how to improve the success rate of fibrinolytic therapy for STEMI patients with CKD.

          Trial registration

          The CPACS-3 study was registered on www.clinicaltrials.gov ( NCT01398228).

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          Most cited references21

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          2017 ESC Guidelines for the management of acute myocardial infarction in patients presenting with ST-segment elevation

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            Global and regional mortality from 235 causes of death for 20 age groups in 1990 and 2010: a systematic analysis for the Global Burden of Disease Study 2010.

            Reliable and timely information on the leading causes of death in populations, and how these are changing, is a crucial input into health policy debates. In the Global Burden of Diseases, Injuries, and Risk Factors Study 2010 (GBD 2010), we aimed to estimate annual deaths for the world and 21 regions between 1980 and 2010 for 235 causes, with uncertainty intervals (UIs), separately by age and sex. We attempted to identify all available data on causes of death for 187 countries from 1980 to 2010 from vital registration, verbal autopsy, mortality surveillance, censuses, surveys, hospitals, police records, and mortuaries. We assessed data quality for completeness, diagnostic accuracy, missing data, stochastic variations, and probable causes of death. We applied six different modelling strategies to estimate cause-specific mortality trends depending on the strength of the data. For 133 causes and three special aggregates we used the Cause of Death Ensemble model (CODEm) approach, which uses four families of statistical models testing a large set of different models using different permutations of covariates. Model ensembles were developed from these component models. We assessed model performance with rigorous out-of-sample testing of prediction error and the validity of 95% UIs. For 13 causes with low observed numbers of deaths, we developed negative binomial models with plausible covariates. For 27 causes for which death is rare, we modelled the higher level cause in the cause hierarchy of the GBD 2010 and then allocated deaths across component causes proportionately, estimated from all available data in the database. For selected causes (African trypanosomiasis, congenital syphilis, whooping cough, measles, typhoid and parathyroid, leishmaniasis, acute hepatitis E, and HIV/AIDS), we used natural history models based on information on incidence, prevalence, and case-fatality. We separately estimated cause fractions by aetiology for diarrhoea, lower respiratory infections, and meningitis, as well as disaggregations by subcause for chronic kidney disease, maternal disorders, cirrhosis, and liver cancer. For deaths due to collective violence and natural disasters, we used mortality shock regressions. For every cause, we estimated 95% UIs that captured both parameter estimation uncertainty and uncertainty due to model specification where CODEm was used. We constrained cause-specific fractions within every age-sex group to sum to total mortality based on draws from the uncertainty distributions. In 2010, there were 52·8 million deaths globally. At the most aggregate level, communicable, maternal, neonatal, and nutritional causes were 24·9% of deaths worldwide in 2010, down from 15·9 million (34·1%) of 46·5 million in 1990. This decrease was largely due to decreases in mortality from diarrhoeal disease (from 2·5 to 1·4 million), lower respiratory infections (from 3·4 to 2·8 million), neonatal disorders (from 3·1 to 2·2 million), measles (from 0·63 to 0·13 million), and tetanus (from 0·27 to 0·06 million). Deaths from HIV/AIDS increased from 0·30 million in 1990 to 1·5 million in 2010, reaching a peak of 1·7 million in 2006. Malaria mortality also rose by an estimated 19·9% since 1990 to 1·17 million deaths in 2010. Tuberculosis killed 1·2 million people in 2010. Deaths from non-communicable diseases rose by just under 8 million between 1990 and 2010, accounting for two of every three deaths (34·5 million) worldwide by 2010. 8 million people died from cancer in 2010, 38% more than two decades ago; of these, 1·5 million (19%) were from trachea, bronchus, and lung cancer. Ischaemic heart disease and stroke collectively killed 12·9 million people in 2010, or one in four deaths worldwide, compared with one in five in 1990; 1·3 million deaths were due to diabetes, twice as many as in 1990. The fraction of global deaths due to injuries (5·1 million deaths) was marginally higher in 2010 (9·6%) compared with two decades earlier (8·8%). This was driven by a 46% rise in deaths worldwide due to road traffic accidents (1·3 million in 2010) and a rise in deaths from falls. Ischaemic heart disease, stroke, chronic obstructive pulmonary disease (COPD), lower respiratory infections, lung cancer, and HIV/AIDS were the leading causes of death in 2010. Ischaemic heart disease, lower respiratory infections, stroke, diarrhoeal disease, malaria, and HIV/AIDS were the leading causes of years of life lost due to premature mortality (YLLs) in 2010, similar to what was estimated for 1990, except for HIV/AIDS and preterm birth complications. YLLs from lower respiratory infections and diarrhoea decreased by 45-54% since 1990; ischaemic heart disease and stroke YLLs increased by 17-28%. Regional variations in leading causes of death were substantial. Communicable, maternal, neonatal, and nutritional causes still accounted for 76% of premature mortality in sub-Saharan Africa in 2010. Age standardised death rates from some key disorders rose (HIV/AIDS, Alzheimer's disease, diabetes mellitus, and chronic kidney disease in particular), but for most diseases, death rates fell in the past two decades; including major vascular diseases, COPD, most forms of cancer, liver cirrhosis, and maternal disorders. For other conditions, notably malaria, prostate cancer, and injuries, little change was noted. Population growth, increased average age of the world's population, and largely decreasing age-specific, sex-specific, and cause-specific death rates combine to drive a broad shift from communicable, maternal, neonatal, and nutritional causes towards non-communicable diseases. Nevertheless, communicable, maternal, neonatal, and nutritional causes remain the dominant causes of YLLs in sub-Saharan Africa. Overlaid on this general pattern of the epidemiological transition, marked regional variation exists in many causes, such as interpersonal violence, suicide, liver cancer, diabetes, cirrhosis, Chagas disease, African trypanosomiasis, melanoma, and others. Regional heterogeneity highlights the importance of sound epidemiological assessments of the causes of death on a regular basis. Bill & Melinda Gates Foundation. Copyright © 2012 Elsevier Ltd. All rights reserved.
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              A modified poisson regression approach to prospective studies with binary data.

              G Zou (2004)
              Relative risk is usually the parameter of interest in epidemiologic and medical studies. In this paper, the author proposes a modified Poisson regression approach (i.e., Poisson regression with a robust error variance) to estimate this effect measure directly. A simple 2-by-2 table is used to justify the validity of this approach. Results from a limited simulation study indicate that this approach is very reliable even with total sample sizes as small as 100. The method is illustrated with two data sets.
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                Author and article information

                Contributors
                Role: Data curationRole: Formal analysisRole: InvestigationRole: MethodologyRole: Writing – original draftRole: Writing – review & editing
                Role: MethodologyRole: Writing – review & editing
                Role: MethodologyRole: Writing – review & editing
                Role: Formal analysisRole: Writing – review & editing
                Role: Writing – review & editing
                Role: Funding acquisitionRole: MethodologyRole: Writing – review & editing
                Role: Data curationRole: Funding acquisitionRole: InvestigationRole: MethodologyRole: Project administrationRole: ResourcesRole: Supervision
                Role: Editor
                Journal
                PLoS One
                PLoS One
                plos
                plosone
                PLoS ONE
                Public Library of Science (San Francisco, CA USA )
                1932-6203
                19 January 2021
                2021
                : 16
                : 1
                : e0245576
                Affiliations
                [1 ] Peking University Clinical Research Institute, Peking University First Hospital, Beijing, China
                [2 ] The George Institute for Global Health, University of New South Wales, Sydney, Australia
                [3 ] Department of Cardiology, Erasmus Medical College, Rotterdam, The Netherlands
                [4 ] The George Institute for Global Health at Peking University Health Science Center, Beijing, China
                [5 ] Clinical Epidemiology and EBM Center, Beijing Friendship Hospital, Capital Medical University, Beijing, China
                [6 ] Department of Cardiology, Fuwai Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
                [7 ] Department of Epidemiology and Biostatistics, School of Public Health, Peking University Health Science Center, Beijing, China
                Fondazione IRCCS Policlinico San Matteo, ITALY
                Author notes

                Competing Interests: The authors declare no competing interests. There are no other relevant declarations relating to employment, consultancy, patents, products in development or marketed products etc. to be made by Sanofi, China. This does not alter our adherence to PLOS ONE policies on sharing data and materials.

                Author information
                https://orcid.org/0000-0001-7527-1022
                https://orcid.org/0000-0002-4451-1932
                Article
                PONE-D-20-21065
                10.1371/journal.pone.0245576
                7815111
                33465135
                a3636af5-8527-4236-8cc1-69cc2fab0336
                © 2021 Xie et al

                This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

                History
                : 7 July 2020
                : 5 January 2021
                Page count
                Figures: 1, Tables: 4, Pages: 13
                Funding
                Funded by: funder-id http://dx.doi.org/10.13039/100011421, Sanofi China Investment Company;
                Source of funding used to support the research and creation of the article is from Sanofi, China, through an unrestricted research grant. The George Institute for Global Health at PUHSC sponsored the study and owns the data. However, the authors are solely responsible for the design and conduct of this study, all study analyses, the drafting and editing of the manuscript, and its final contents. The funding source 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.
                Categories
                Research Article
                Medicine and Health Sciences
                Nephrology
                Renal Diseases
                Chronic Kidney Disease
                Medicine and Health Sciences
                Hematology
                Blood Coagulation
                Fibrinolysis
                Medicine and Health Sciences
                Pharmaceutics
                Drug Therapy
                Cardiovascular Therapy
                Medicine and Health Sciences
                Cardiology
                Myocardial Infarction
                Medicine and Health Sciences
                Epidemiology
                Medical Risk Factors
                Medicine and Health Sciences
                Medical Conditions
                Cardiovascular Diseases
                Cardiovascular Disease Risk
                Medicine and Health Sciences
                Cardiology
                Cardiovascular Medicine
                Cardiovascular Diseases
                Cardiovascular Disease Risk
                Medicine and Health Sciences
                Health Care
                Health Care Facilities
                Hospitals
                Medicine and Health Sciences
                Cardiology
                Heart Rate
                Custom metadata
                Data cannot be shared publicly because the original data involved sensitive information of patients. Data are available on request from the corresponding author (contact via wuyf@ 123456bjmu.edu.cn ) or the Peking University IRB (contact via llwyh@ 123456bjmu.edu.cn ).

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