Clinical features associated with pre-hospital time delay in XXXX .

@entity1 @entity101 @entity6 @entity167 @entity951 @entity453 @entity955 @entity583
BACKGROUND: The pre-hospital time delay in @entity583 ( @entity583 ) is still a challenging problem since for many @entity1 there are long intervals between the onset of symptoms and the initiation of therapy. The aim of this study was to verify which, among several clinical variables, are associated with a prolonged pre-hospital time delay. METHODS: Five hundred and twenty-six unselected @entity1 with @entity583 and consecutively admitted to three coronary care units were enrolled. The pre-hospital time delay was defined as the time interval from the onset of symptoms to admission to the coronary care unit. Clinical variables included: age, gender, body mass index, level of education, @entity167 consumption, smoking habits, regular physical activity, history of @entity101 , @entity6 , history of @entity453 (documented history of @entity951 and/or previous @entity583 ), chronic @entity955 , Q-wave @entity583 and off hours onset of symptoms. After univariate analysis, multivariable regression analysis was used. RESULTS: The mean age of the @entity1 was 66.6 +/- 12.1 years and 28.7% were female. The median pre-hospital time interval was 200 min (95% confidence interval 60-1140). For 342 @entity1 the pre-hospital time interval was < or = 6 hours and for 184 @entity1 it was > 6 hours. Those variables which, at univariate analysis, were found to significantly influence the pre-hospital delay were analyzed using a multivariable regression model where the dependent variable was the pre-hospital time interval. @entity955 (p = 0.010), a history of @entity453 (p = 0.017), @entity6 (p = 0.016) and age > or = 70 years (p = 0.009) were found to be independently associated with a prolonged prehospital time interval. Similar results were obtained when considering only Q-wave @entity583 . As expected, the thrombolytic therapy rate was much lower in @entity1 with a longer pre-hospital time delay. CONCLUSIONS: The present study shows that, in case of @entity583 , the time interval between the onset of symptoms and a @entity1 's arrival to hospital is still far from being optimal. This is especially true for older @entity1 with @entity6 , a history of @entity453 or chronic @entity955 . Cardiologists should be aware of this problem and should implement adequate educational strategies addressed to those @entity1 at highest risk.
