Acute of AAD in general, subgroup analyzes of Stanford

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
dissection.

 

 

MATERIAL
AND METHODS

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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.     

 

Statistical
analysis

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 <0.05 were considered statistically significant.     RESULTS A total of 348 patients diagnosed with an AAD were admitted during the time period from January 2006 to July 2015 to our institution. Of them, 255 had Stanford A AAD whereas 87 were diagnosed with Stanford B AAD.  Table 1 shows the seasonal and monthly distribution of AAD events.   In the univariate analysis, there were no statistically significant differences between meteorological parameters on days of AAD events compared to control days. Air temperature (10.20±6.67 vs. 10.80±6.89 °C, p=0.124) and air pressure (1004.28±8.27 vs. 1003.95±8.86 mmHg, p=0.512) were equally distributed between days of AAD events and control days. Also, there were no statistically significant differences in terms of the distribution of months (p=0.171) and seasons (p=0.753) between days on which AAD occurred and control days. The presence of full moon did also not influence the incidence of AAD while full moon was present on 2.3% (n=8) vs. 3.5% (n=111) of event vs. control days (p=0.232). Logistic regression model showed that air pressure (OR 1.004, 95% CI 0.991-1.017, p=0.542), air temperature (OR 0.978, 95% CI 0.949-1.008, p=0.145), season (p=0.918) and month of the event (p=0.175) as well as full moon (OR 1.579, 95% CI 0.763-3.270, p=0.219) were not able to predict AAD events. Figure 1 and 2 demonstrate the seasonal and monthly event of ADD and means atmospheric pressure during the study period.   Also, no predictive power of meteorological data and season was found analyzing their impact on different types of AAD events.   An analysis of the subgroup of Stanford A dissection did also not reveal any dependence on meteorological factors and seasonal changes. In the univariate analysis, there were no statistically significant differences between meteorological parameters on days of Stanford A AAD events compared to control days