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Hotline update of clinical trials and registries presented at the American College of Cardiology Congress 2014
No full textThis article provides information and commentaries on trials which were presented at the Hotline and Clinical Trial Update Sessions during the Late Breaking Clinical Trial Sessions at the 63rd annual meeting of the American College of Cardiology in Washington, USA, from 29th to 31st March 2014. This article gives an overview on a number of novel clinical trials in the field of cardiovascular medicine, which were presented. Comprehensive summaries have been generated from the oral presentation and the webcasts of the American College of Cardiology, similar to as previously reported and should provide the readers with the most comprehensive information of relevant publications. The discussed studies are US CoreValve, Choice, Symplcity-HTN-3, GRS, ZEUS, GIPS-III, HEAT-PPCI, COPR-2, MSC-HF, POISE-2, SIRS. The data were presented by leading experts in the field.DZHK (Deutsches Zentrum fur Herz-Kreislauf-Forschung
De-escalation of support with veno-arterial extracorporeal membrane oxygenation and Impella for cardiogenic shock: reply
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Heart-rate dependent regulation of intrinsic Contractility and hemodynamics in patients with diastolic heart failure
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Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
Full text link“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Full text linkWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
Prädiktoren für die Diagnose eines Typ-2 Myokardinfarktes
No full textZielsetzung: Die schnelle Unterscheidung zwischen Myokardinfarkt Typ 1 (T1MI) und Myokardinfarkt Typ 2 (T2MI) in der Notaufnahme ist aufgrund unterschiedlicher Pathophysiologie und Therapieoptionen wichtig. Bei beiden Infarkttypen ist eine sofortige Therapie notwendig, aber die Unterscheidung ist wegen ähnlicher Symptomatik und Laborergebnissen erschwert. Das Ziel dieser Arbeit ist es, ein Vorhersagemodell zu entwickeln, um eine schnelle Unterscheidung zwischen beiden Infarkttypen vornehmen zu können und eine Entscheidungshilfe bezüglich der individualisierten Therapie zu bieten.
Methoden: In die BACC-Studie wurden prospektiv 1,548 Patienten eingeschlossen, die sich mit dem Verdacht auf einen Myokardinfarkt in der Notaufnahme vorstellten. Alle Patienten wurden über zwei Jahre nachverfolgt. Um die wichtigsten Prädiktoren für einen T2MI zu ermitteln, wurden Patientencharakteristika, klinische Parameter, Laborwerte und Ereignisse während der Nachbeobachtung analysiert. Aus diesen Daten wurde ein logistisches Regressionsmodell erstellt und eine Rückwärtsselektion durchgeführt. Basierend auf diesen Regressionskoeffizienten wurde ein Vorhersagemodell mit einem Score entwickelt, um die Wahrscheinlichkeit für das Vorliegen eines T2MI anzugeben.
Ergebnisse: Insgesamt wurde bei 99 Patienten ein T2MI diagnostiziert. Unter diesen Patienten betrug die 1-Jahres-Mortalität 13.8%, vergleichbar mit der hohen Mortalität der T1MI Patienten von 9.4%. Weibliches Geschlecht (Beta 1.27 (95% KI 0.67-1.9)), kein ausstrahlender Brustschmerz (Beta 1.62 (95% KI 0.96-2.34)) und eine hs-cTnI Konzentration von ≤ 40.8 ng/L bei Aufnahme (Beta 1.30 (95% KI 0.74-1.89)) waren die stärksten Prädiktoren für einen T2MI. Die Kombination aller drei Prädiktoren führte zu einer Area under the Curve von 0.71. Jedem der drei Prädiktoren wurde in dem Score ein Wert von null oder eins zugeordnet. Bei dem höchsten Wert von drei betrug die Wahrscheinlichkeit für einen T2MI 72%. Wurde ein Wert von zwei ermittelt betrug die Wahrscheinlichkeit 42%, bei einem Wert von eins 17% und bei Vorliegen keines der Prädiktoren 5%.
Fazit: T2MI Patienten sind eine heterogene Population mit einem hohen kardiovaskulären Risiko. Ein Score, basierend auf laborchemischen und klinischen Parametern, könnte bei der Diagnosestellung und der individualisierten Therapieentscheidung der T1MI und T2MI Patienten helfen. Um den Nutzen und die Sicherheit des Scores zu testen, sollte dieser prospektiv untersucht werden.Aims: A rapid differentiation between myocardial infarction type 1 (T1MI) and myocardial infarction type 2 (T2MI) in the emergency department is important, as each pathophysiological variation necessitates a specialized and immediate therapy. The differentiation between the two diagnoses is difficult, as both cause similar symptoms and laboratory constellations. This thesis aims to develop a predictive model to quickly differentiate between the two infarction types and to provide a decision-making tool for an individualized therapy.
Methods: The prospective BACC-study included 1,548 patients who presented to the emergency department with a suspected myocardial infarction. Patients follow-up occurred over a two-year period. To determine the most important predictors of T2MI, patient characteristics, clinical parameters, laboratory results and events during the follow-up period were analyzed. The collected data was used to create a logistic regression model and a backward selection was performed. Based on these regression coefficients, a predictive model with a score was developed to indicate the probability of the presence of a T2MI.
Results: In total, 99 patients were diagnosed with T2MI. Among these patients, the 1-year-mortality was 13.8%, comparable with the high mortality of T1MI patients (9.4%). Female sex (beta 1.27 (95% CI 0.67-1.9)), no radiating chest pain (beta 1.62 (95% CI 0.96-2.34)) and a hs-cTnI concentration of ≤ 40.8 ng/L at admission (beta 1.30 (95% CI 0.74-1.89)) were the strongest predictors for a T2MI. The combination of all three predictors resulted in an area under the curve of 0.71. Each of the three predictors was assigned a value of zero (predictor not applicable) or one (predictor applicable) in the score. For the highest total value (three), the probability of having a T2MI was 72%. If a value of two was determined, the probability was 42%, with a value of one 17% and if none of the predictors were present 5%.
Conclusion: The heterogeneous group of T2MI have a high cardiovascular risk. A score based on laboratory and clinical parameters could aid in the diagnosis and individualized therapy of T1MI and T2MI. To test the benefit and safety of the score, it should be investigated prospectively
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