Acute aortic dissection (AAD) is
a medical emergency and should be treated as soon as possible. The
International Registry of Acute Aortic Dissection (IRAD) reported at 2015 a total
mortality of 24.4% in type A dissection and 10.7% in type B dissection (1) . Some
studies reported a relationship between meteorological factors and AAD (2-6).
Nevertheless, the results of the studies are different and one study didn’t
find any relationship between weather condition and acute aortic dissection (7). The aim of the present study is to investigate the possible correlation
between atmospheric pressure, temperature, lunar cycle and the event of aortic
The study design was a retrospective review of prospectively collected
data. The Institutional Review Board of Cologne University approved this study
and waived the need for individual patient consent. Cologne, located in Germany, has its highest point at 118m
and lowest point at 37.5m above sea level. The needed meteorological
information was extracted from the Cologne weather station. All
consecutive patients admitted to our institution with the diagnosis of a acute
aortic dissection Stanford A and B during the time period from January 2006 to
July 2015 were included and analyzed with regard to chronobiological variations
in the timing of occurrence of aortic dissections. Meteorological data, such as
air pressure and air temperature, seasonal differences as well as the presence
of full moon were documented for each day within the study period and evaluated
for potential predictive power of clinical events of interest. A total of 3499
days were analyzed in terms of association of meteorological and seasonal
factors with the incidence of AAD. Along with the analysis of AAD in general, subgroup
analyzes of Stanford A and Stanford B AAD was performed in order to evaluate
potential influence of factors of interest on various types of dissections.
All data were analyzed using IBM SPSS Statistics for Windows, Version 21
(IBM Corp. Released 2012. Armonk, NY: IBM Corp) and are presented as
continuous or categorical variables. Continuous data were evaluated for
normality using one sample Kolmogorov-Smirnov-test and confirmed by histograms.
Continuous variables were expressed as the mean ±
standard deviation in cases of normally distributed variables or median
(interquartile range) in cases of non-normally distributed variables.
Categorical variables are presented as total numbers and percentages.
Continuous data potentially associated with clinical events of interest were
analyzed using unpaired Student t-test for
normally distributed variables and Mann Whitney U-test for non-normally
distributed variables. Pearson’s ?² or Fisher exact tests were used
for categorical data dependent on the minimum expected count in each cross tab.
Binary logistic regression model was applied for analysis of potential
predictors of clinical events of interest. P values