Predicting smoking cessation and its relapse in HIV-infected patients: the Swiss HIV Cohort Study.

Details

Serval ID
serval:BIB_14378C4B4A1C
Type
Article: article from journal or magazin.
Collection
Publications
Institution
Title
Predicting smoking cessation and its relapse in HIV-infected patients: the Swiss HIV Cohort Study.
Journal
Hiv Medicine
Author(s)
Schäfer J., Young J., Bernasconi E., Ledergerber B., Nicca D., Calmy A., Cavassini M., Furrer H., Battegay M., Bucher H.
Working group(s)
Swiss HIV Cohort Study
ISSN
1468-1293 (Electronic)
ISSN-L
1464-2662
Publication state
Published
Issued date
2015
Peer-reviewed
Oui
Volume
16
Number
1
Pages
3-14
Language
english
Notes
Publication types: Journal Article Publication Status: ppublish
Abstract
OBJECTIVES: The aim of the study was to assess whether prospective follow-up data within the Swiss HIV Cohort Study can be used to predict patients who stop smoking; or among smokers who stop, those who start smoking again.
METHODS: We built prediction models first using clinical reasoning ('clinical models') and then by selecting from numerous candidate predictors using advanced statistical methods ('statistical models'). Our clinical models were based on literature that suggests that motivation drives smoking cessation, while dependence drives relapse in those attempting to stop. Our statistical models were based on automatic variable selection using additive logistic regression with component-wise gradient boosting.
RESULTS: Of 4833 smokers, 26% stopped smoking, at least temporarily; because among those who stopped, 48% started smoking again. The predictive performance of our clinical and statistical models was modest. A basic clinical model for cessation, with patients classified into three motivational groups, was nearly as discriminatory as a constrained statistical model with just the most important predictors (the ratio of nonsmoking visits to total visits, alcohol or drug dependence, psychiatric comorbidities, recent hospitalization and age). A basic clinical model for relapse, based on the maximum number of cigarettes per day prior to stopping, was not as discriminatory as a constrained statistical model with just the ratio of nonsmoking visits to total visits.
CONCLUSIONS: Predicting smoking cessation and relapse is difficult, so that simple models are nearly as discriminatory as complex ones. Patients with a history of attempting to stop and those known to have stopped recently are the best candidates for an intervention.
Pubmed
Web of science
Create date
17/01/2015 11:04
Last modification date
20/08/2019 12:42
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