Development of a Risk Score to Predict Short-term Smoking Relapse Following an Inpatient Smoking Cessation Intervention

Asia Pac J Public Health. 2024 May;36(4):329-336. doi: 10.1177/10105395241240949. Epub 2024 Mar 30.

Abstract

This study aimed to investigate the factors affecting smoking relapse and to develop predictive models among Korean national 5-day smoking cessation program participants. The subjects were 518 smokers and follow-up was continued for 6 months after discharge. A predictive logistic model and risk score were developed from the multivariate logistic models and compared using the area under the receiver operating characteristic curve (area under the curve [AUC]). The smoking relapse rate within 6 months after program participation was 38.4%. The AUCs of the logistic regression model and risk score model were similar (odds ratio [OR] = 0.69; 0.69, respectively) in the development data set, and those of the risk score model were similar between the development and validation data sets (OR = 0.68). The risk score used by the six risk factors could predict smoking relapse among participants who attended a 5-day inpatient smoking cessation program.

Keywords: prediction; risk factors; smoking cessation.

MeSH terms

  • Adult
  • Female
  • Humans
  • Inpatients / psychology
  • Inpatients / statistics & numerical data
  • Logistic Models
  • Male
  • Middle Aged
  • Recurrence*
  • Republic of Korea
  • Risk Assessment
  • Risk Factors
  • Smoking / epidemiology
  • Smoking / psychology
  • Smoking Cessation* / psychology
  • Smoking Cessation* / statistics & numerical data