The normality assumption was evaluated using both the
Kolmogorov–Smirnov criterion (P > 0.05 for all variables) and
normal probability plots.
Quantitative variables did not follow normal distribution and this remained after transformation.
Quantitative variables were converted into binary variables using
median values as the criterion for separation.
Thus, multivariate
logistic regression analysis was attainable.
Quantitative variables
are presented as median [interquartile range (IR)], whereas categorical
variables are presented as absolute and relative frequencies.
Initially, a bivariate analysis was performed to investigate the
relationship between the time elapsed since the onset of symptoms
until the patient’s arrival at the hospital and each variable
separately.
In bivariate analysis, the time variable is transformed
into a binary variable with a limit of 2 h. Although several investigators
have used the median value (Dracup & Moser 1997;
Perkins-Porras et al. 2009) as the cut-off point, aiming to define
the patient pre-hospital delay, we selected the 13th percentile
(2 h) as the cut-off point of pre-hospital delay in our study based
on our extremely longmedian value (8 h).
In addition, this point
(2 h) has been previously used by other investigators (Goldberg
et al. 2000; Perkins-Porras et al. 2009) and is significantly critical
for the effective patient management.
This is supported by recent
studies indicating the high effectiveness of thrombolytic therapy
or primary angioplasty within the first 90 min of the AMI symptoms
onset, an advantage that disappears after the first 180 min
(Diercks et al. 2008;White & Chew 2008).
Also, the likelihood of
receiving clot-lysing agents and the benefits to be gained are
greatest within the first hours of AMI onset (Goldberg et al.
2000).
The normality assumption was evaluated using both the
Kolmogorov–Smirnov criterion (P > 0.05 for all variables) and
normal probability plots.
Quantitative variables did not follow normal distribution and this remained after transformation.
Quantitative variables were converted into binary variables using
median values as the criterion for separation.
Thus, multivariate
logistic regression analysis was attainable.
Quantitative variables
are presented as median [interquartile range (IR)], whereas categorical
variables are presented as absolute and relative frequencies.
Initially, a bivariate analysis was performed to investigate the
relationship between the time elapsed since the onset of symptoms
until the patient’s arrival at the hospital and each variable
separately.
In bivariate analysis, the time variable is transformed
into a binary variable with a limit of 2 h. Although several investigators
have used the median value (Dracup & Moser 1997;
Perkins-Porras et al. 2009) as the cut-off point, aiming to define
the patient pre-hospital delay, we selected the 13th percentile
(2 h) as the cut-off point of pre-hospital delay in our study based
on our extremely longmedian value (8 h).
In addition, this point
(2 h) has been previously used by other investigators (Goldberg
et al. 2000; Perkins-Porras et al. 2009) and is significantly critical
for the effective patient management.
This is supported by recent
studies indicating the high effectiveness of thrombolytic therapy
or primary angioplasty within the first 90 min of the AMI symptoms
onset, an advantage that disappears after the first 180 min
(Diercks et al. 2008;White & Chew 2008).
Also, the likelihood of
receiving clot-lysing agents and the benefits to be gained are
greatest within the first hours of AMI onset (Goldberg et al.
2000).
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