Inadequate cerebral blood flow (CBF) after head injury is an important cause of secondary ischaemic damage. Rapid identification of episodes of hypo- or hyperperfusion would allow timely intervention and would possibly improve outcome. Despite a large number of methods to estimate CBF, this concept is only marginally implemented in clinical practice. The methods to detect such episodes are limited for technical reasons, but also because the thresholds of ischaemia and hyperaemia are variable after head injury. Furthermore, we are not always able to manipulate CBF in a controlled manner. Accordingly, it is not surprising that attempts to compare a CBF-targeted strategy with another management option have failed to demonstrate a clear benefit. Methods need to be developed that allow either identification of thresholds for critically low or high CBF in individual patients, allow monitoring oxygen extraction fraction, representing circulatory reserve, or alternatively provide a measure of the volume of ischaemic or hyperaemic brain.
In a recent cohort of patients with severe TBI, the time spent with a CPP below the CPPopt-derived LLR is related to mortality. Despite aggressive CPP- and ICP-oriented therapies, TBI patients with a fatal outcome spend a significant amount of time with a CPP below their individualised CPPopt, indicating a possible therapeutic target.
Background Perioperative myocardial infarction/injury (PMI) occurring in the first 48h following noncardiac surgery is a frequent cardiac complication. Better understanding of the underlying aetiologies is urgently needed. Aim To explore the association of different aetiologies of PMI with long term outcomes. Methods In this prospective multicenter observational study, PMI aetiology was centrally adjudicated and hierarchically classified by two independent physicians based on all information obtained during clinically-indicated PMI work-up including cardiac imaging among consecutive high-risk patients undergoing major noncardiac surgery. PMI aetiology was classified into “extracardiac” if caused by a primarily extracardiac disease such as severe sepsis or pulmonary embolism; and “cardiac”, further subtyped into type 1 myocardial infarction (T1MI), tachyarrhythmia, acute heart failure (AHF), or likely type 2 myocardial infarction (lT2MI). Major adverse cardiac events (MACE) including T1MI, AHF (both only from day 3 to avoid inclusion bias), life-threatening arrhythmia, and cardiovascular death as well as all-cause death were assessed during 365-days follow-up. Results PMI occurred in 1016/7754 patients (13.1%). At least one MACE occurred in 684/7754 patients (8.8%) and 818/7754 patients died (10.5%) within 365 days. MACE and all-cause death occurred in 51% (95% CI 31–60) and 38% (95% CI 29–47), 41% (95% CI 28–51) and 27% (95% CI 16–34), 57% (95% CI 41–69) and 40% (95% CI 25–53), 64% (95% CI 45–76) and 49% (95% CI 30–62), as well as 25% (95% CI 22–28%) and 17% (95% CI 14–20) of patients with extracardiac PMI, T1MI, tachyarrhythmia, AHF, and lT2MI, respectively. These associations were confirmed in multivariable analysis. Conclusion At 365 days, most PMI aetiologies have unacceptably high rates of MACE and all-cause death, highlighting the urgent need for more intensive treatments. Funding Acknowledgement Type of funding sources: Other. Main funding source(s): Swiss National Science FoundationRoche Diagnostics
Fraud is as old as Mankind. There are an enormous number of historical documents which show the interaction between truth and untruth; therefore it is not really surprising that the prevalence of publication discrepancies is increasing. More surprising is that new cases especially in the medical field generate such a huge astonishment. In financial mathematics a statistical tool for detection of fraud is known which uses the knowledge of Newcomb and Benford regarding the distribution of natural numbers. This distribution is not equal and lower numbers are more likely to be detected compared to higher ones. In this investigation all numbers contained in the blinded abstracts of the 2009 annual meeting of the Swiss Society of Anesthesia and Resuscitation (SGAR) were recorded and analyzed regarding the distribution. A manipulated abstract was also included in the investigation. The χ(2)-test was used to determine statistical differences between expected and observed counts of numbers. There was also a faked abstract integrated in the investigation. A p<0.05 was considered significant. The distribution of the 1,800 numbers in the 77 submitted abstracts followed Benford's law. The manipulated abstract was detected by statistical means (difference in expected versus observed p<0.05). Statistics cannot prove whether the content is true or not but can give some serious hints to look into the details in such conspicuous material. These are the first results of a test for the distribution of numbers presented in medical research.
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