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Predicting smoking cessation and its relapse in HIV ‐infected patients: the S wiss HIV C ohort S tudy
Author(s) -
Schäfer J,
Young J,
Bernasconi E,
Ledergerber B,
Nicca D,
Calmy A,
Cavassini M,
Furrer H,
Battegay M,
Bucher HC
Publication year - 2015
Publication title -
hiv medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.53
H-Index - 79
eISSN - 1468-1293
pISSN - 1464-2662
DOI - 10.1111/hiv.12165
Subject(s) - medicine , smoking cessation , logistic regression , clinical trial , human immunodeficiency virus (hiv) , statistical model , statistics , family medicine , pathology , mathematics
Objectives The aim of the study was to assess whether prospective follow‐up data within the S wiss HIV C ohort S tudy 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.