Statins and Contrast-induced Acute Kidney Injury with Coronary Angiography

BackgroundContrast-induced acute kidney injury is an adverse outcome resulting from radiocontrast medium exposure during coronary angiography and percutaneous coronary intervention.MethodsA systematic search was conducted to retrieve studies that investigated the impact of statin exposure before coronary angiography or percutaneous coronary intervention on the development of contrast-induced acute kidney injury. The primary outcome was the development of contrast-induced acute kidney injury. We separately analyzed statin/placebo comparisons and high-/low-dose statin comparisons.ResultsFifteen randomized controlled trials met inclusion criteria: 11 studies with statin-naïve subjects, 2 studies with chronic statin users, and 2 studies with unspecified prior statin exposure. Statin exposure reduced the risk of contrast-induced acute kidney injury relative to placebo (relative risk [RR] 0.63, P = .01) with a nonsignificant reduction in the need for hemodialysis (RR 0.25, P = .08). This benefit was also observed in high-dose versus low-dose statin trials (RR 0.46, P = .004), in statin-naïve patients (RR 0.53, P <.0001), and with all studied statins. Higher statin exposure reduced contrast-induced acute kidney injury in patients with acute coronary syndromes compared with placebo or low-dose statins (RR 0.49, P <.00001), with no significant benefit among patients undergoing elective procedures (RR 0.86, P = .50). Subgroup analyses confirmed the benefit of statins in patients with diabetes, chronic kidney disease, congestive heart failure, and those receiving >140 mL of contrast dye.ConclusionStatin therapy is effective at reducing the risk of contrast-induced acute kidney injury. It should thus be considered, at least on a short-term basis, for patients at increased risk of this complication.     

Links that speak: The global language network and its association with global fame

Languages vary enormously in global importance because of historical, demographic, political, and technological forces. However, beyond simple measures of population and economic power, there has been no rigorous quantitative way to define the global influence of languages. Here we use the structure of the networks connecting multilingual speakers and translated texts, as expressed in book translations, multiple language editions of Wikipedia, and Twitter, to provide a concept of language importance that goes beyond simple economic or demographic measures. We find that the structure of these three global language networks (GLNs) is centered on English as a global hub and around a handful of intermediate hub languages, which include Spanish, German, French, Russian, Portuguese, and Chinese. We validate the measure of a language’s centrality in the three GLNs by showing that it exhibits a strong correlation with two independent measures of the number of famous people born in the countries associated with that language. These results suggest that the position of a language in the GLN contributes to the visibility of its speakers and the global popularity of the cultural content they produce.Of the thousands of languages that have ever been spoken, only a handful have become influential enough to be considered global languages. However, how do we measure the global influence of a language? What are the implications of a world in which only a handful of languages are globally influential?In the past, researchers have used a variety of measures to determine the global influence of a language. Several studies have relied on measures that proxy the global influence of a language using the population and wealth of its speakers (1–4). While wealth and population approximate a language’s influence, as the dissemination of a language has historically required a strong power base (5), such measures fail to capture the global influence of a language: often the speakers of a language, and their wealth, are locally concentrated, making the language locally influential rather than globally influential.An alternative method to measure the global influence of a language is to focus on who speaks that language, and in particular, on how connected the speakers of that language are. In the words of linguist David Crystal, “Why a language becomes a global language has little to do with the number of people who speak it. It is much more to do with who those speakers are.” (5) In the past, Latin was the pan-European language, not because it was the mother tongue of most Europeans, but because it was the language of the Roman Empire and later the language of the Catholic Church, scholars, and educators (5). The use of Latin by well-connected elites set it apart from other languages and helped Latin endure as a universal language for more than 1,000 years.However, can we use these ideas to identify which modern languages are globally influential? If global languages are those connecting international elites, then we can identify the global languages associated with particular elites by mapping their networks of multilingual coexpressions. Examples of multilingual coexpressions include book translations, edits to multiple language editions of Wikipedia, and posting short messages on Twitter (“tweets”) in multiple languages. These coexpressions define networks (Fig. 1) that—even though not representative of the world’s general population—represent a coarse map of the links connecting the elites that participate of these three important global forums, as social connections often require a shared language.Open in a separate windowFig. 1.Visualizations of the three GLNs. The three GLNs contain all language connections that involve at least six users (Twitter and Wikipedia) or six translations and that are significant with P < 0.01.In this paper, we map the global language networks (GLNs) expressed in three large records of linguistic expression, and use the structure of these networks to determine the degree to which each language is global. First, we look at a collection of more than 2.2 million book translations compiled by UNESCO’s Index Translationum project. This dataset allows us to map the network of book translations, which are produced by individuals with a high literary capacity (authors and professional translators) and are shaped by market forces, such as the demand for books in different languages. Each translation from one language to another forms a connection. Next, we map the network of linguistic coexpressions expressed by the community of digitally engaged knowledge specialists that edit Wikipedia. Here, two languages are connected when users that edit an article in one Wikipedia language edition are significantly more likely to also edit an article in another language edition. Finally, we map the network of linguistic coexpressions expressed in Twitter. Here, two languages are connected when users that tweet in a language are also significantly more likely to tweet in another language.These three networks allow us to map the paths of direct and indirect communication between speakers from different languages. Our method formalizes the intuition that certain languages are disproportionately influential because they provide direct and indirect paths of translation among most of the world’s other languages. For example, it is easy for an idea conceived by a Spaniard to reach an Englishman through bilingual speakers of English and Spanish. An idea conceived by a Vietnamese speaker, however, might only reach a Mapudungun speaker in south-central Chile through a circuitous path that connects bilingual speakers of Vietnamese and English, English and Spanish, and Spanish and Mapudungun. In both cases, however, English and Spanish are still involved in the flow of information, indicating that they act as global languages. In the first example, Spanish and English have a direct involvement because communication is flowing among their speakers. In the latter case, the involvement is indirect and emerges from the lack of speakers that can communicate in both Vietnamese and Mapudungun. These indirect connections make multilingual speakers of global languages globally influential, as they mediate the flow of information not only among each other, but also, among people with whom they do not share a language (6).     

Decreases in cardiac catheter laboratory workload during the COVID‐19 level 4 lockdown in New Zealand

An increase in coronavirus disease (COVID‐19) infections prompted Level 4 lockdown throughout New Zealand from 25 March 2020. We have investigated trends in coronary and electrophysiology (EP) procedures before and during this lockdown. The number of acute procedures for ST elevation myocardial infarction remained stable. In contrast, the number of in‐patient angiograms and percutaneous intervention procedures fell by 53% compared with the previous 4 weeks in 2020 and by 56% compared with the corresponding period in 2019. Further study is required to determine the reasons for these trends.     

血液纤维蛋白原和糖化血红蛋白水平与冠状动脉Gensini积分的相关性分析

目的：分析血液纤维蛋白原和糖化血红蛋白水平与冠状动脉Gensini积分之间的相关性。方法回顾分析261例行冠状动脉造影及相关实验室检查的患者,根据Gensini积分及冠状动脉内径狭窄程度将患者分为A组（对照组,狭窄＜50％,共46例）,B组（冠状动脉狭窄≥50％,Gensini积分＜20,共122例）、C组（冠状动脉狭窄≥50％, Gensini积分≥20,共93例）,分析3组间血液纤维蛋白原和糖化血红蛋白等指标与Gensini积分的相关性。结果 A、B、C三组的血液纤维蛋白原、糖化血红蛋白、同型半胱氨酸、尿酸浓度均依次增高,差异有统计学意义（均 P＜0.05）;215例冠心病的纤维蛋白原和糖化血红蛋白与Gensini积分间均呈正相关（r＝0.17、0.15,P＜0.05）。结论血液纤维蛋白原和糖化血红蛋白浓度越高,冠状动脉狭窄越严重,可用于冠心病的早期危险性评估。