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Area of Science:

  • Cardiovascular epidemiology
  • Public health research
  • Preventive medicine

Background:

  • Stroke is a leading cause of death and disability globally and in Japan.
  • Early detection of high-risk individuals is crucial for implementing preventive measures.
  • Existing stroke risk prediction models may not be optimized for the urban Japanese population.

Purpose of the Study:

  • To develop and validate a stroke risk prediction model tailored for the urban Japanese population.
  • To identify key cardiovascular risk factors contributing to stroke incidence in this demographic.
  • To provide a tool for assessing individual stroke risk within the study cohort.

Main Methods:

  • A cohort of 6,641 participants (aged 30-79) without prior stroke or coronary heart disease was followed.
  • The Cox proportional hazard model was employed to estimate stroke incidence risk.
  • Model performance was evaluated using receiver operating characteristic (ROC) curves and Hosmer-Lemeshow statistics.
  • Internal validation was performed using derivation and validation samples.

Main Results:

  • A total of 372 strokes occurred during a median follow-up of 17.1 years.
  • The developed risk model, incorporating age, smoking, blood pressure, fasting glucose, diabetes, chronic kidney disease, and atrial fibrillation, achieved an area under the curve (AUC) of 0.76.
  • The model demonstrated good internal validity with a non-significant p-value for the goodness-of-fit test in both derivation (p=0.21) and validation (p=0.64) samples.
  • Stroke incidence increased progressively with higher risk scores, ranging from 1.1% to 18.6%.

Conclusions:

  • A novel, internally validated stroke risk prediction model was successfully developed for the urban Japanese population.
  • The model effectively utilizes common cardiovascular risk factors to predict stroke incidence.
  • Further research is needed to assess the clinical utility and practical application of this risk model in broader public health settings.