Pre-hospital Delay after Acute Ischemic Stroke in Central Urban China: Prevalence and Risk Factors

Mol Neurobiol. 2017 May;54(4):3007-3016. doi: 10.1007/s12035-016-9750-4. Epub 2016 Mar 31.

Abstract

Timely thrombolytic treatment is paramount after acute ischemic stroke (AIS); however, a large proportion of patients experience substantial delays in presentation to hospital. This study evaluates the prevalence and risk factors in pre-hospital delays after AIS in central urban China. AIS patients from 66 hospitals in 13 major cities across Hubei Province, between October 1, 2014 and January 31, 2015 were interviewed and their medical records were reviewed to identify those who suffered pre-hospital delays. Bivariate and multivariate analyses were undertaken to determine the prevalence rates and the risk factors associated with pre-hospital delays. A total of 1835 patients were included in the analysis, with 69.3 % patients reportedly arrived at hospital 3 or more hours after onset and 55.3 % patients arrived 6 or more hours after onset. Factors associated with increased pre-hospital delays for 3 or more hours were as follows: patient had a history of stroke (odds ratio (OR), 1.319, P = 0.028), onset location was at home (OR, 1.573, P = 0.002), and patients rather than someone else noticed the symptom onset first (OR, 1.711; P < 0.001). In contrast, knowing someone who had suffered a stroke, considering any kind of the symptoms as severe, transferring from a community-based hospital factors, calling emergency number (120), and shorter distance from the onset place to the first hospital were independently associated with decreased pre-hospital delays. These findings indicate that pre-hospital delays after AIS are common in urban central China, and future intervention programs should be focused on public awareness of stroke and appropriate response.

Keywords: Acute ischemia stroke; China; Pre-hospital delay; Prevalence.

MeSH terms

  • Aged
  • Brain Ischemia / epidemiology*
  • China / epidemiology
  • Cities / epidemiology*
  • Demography
  • Female
  • Hospitals / statistics & numerical data*
  • Humans
  • Logistic Models
  • Male
  • Middle Aged
  • Prevalence
  • Risk Factors
  • Socioeconomic Factors
  • Stroke / epidemiology*