Nomogram to predict successful smoking cessation in a Chinese outpatient population

The study aimed to establish and internally validate a nomogram to predict successful smoking cessation in a Chinese outpatient population.

A total of 278 participants were included, and data were collected from March 2016 to December 2018. Predictors for successful smoking cessation were evaluated by 3-month sustained abstinence rates. Least absolute shrinkage and selection operator (LASSO) regression was used to select variables for the model to predict successful smoking cessation, and multivariable logistic regression analysis was performed to establish a novel predictive model. The discriminatory ability, calibration, and clinical usefulness of the nomogram were determined by the concordance index (C-index), calibration plot, and decision curve analysis, respectively. Internal validation with bootstrapping was performed.

The nomogram included living with a smoker or experiencing workplace smoking, number of outpatient department visits, reason for quitting tobacco, and varenicline use. The nomogram demonstrated valuable predictive performance, with a C-index of 0.816 and good calibration. A high C-index of 0.804 was reached with interval validation. Decision curve analysis revealed that the nomogram for predicting successful smoking cessation was clinically significant when intervention was conducted at a successful cessation of smoking possibility threshold of 19%.

This novel nomogram for successful smoking cessation can be conveniently used to predict successful cessation of smoking in outpatients.