Predicting smoking cessation and its relapse in HIV‐infected patients: the Swiss HIV Cohort Study. Issue 1 (8th May 2014)

• Record Type: Journal Article
• Authors: Schäfer, J; Young, J; Bernasconi, E; Ledergerber, B; Nicca, D; Calmy, A; Cavassini, M; Furrer, H; Battegay, M; Bucher, HC; Swiss HIV Cohort Study
• Is Part Of: HIV medicine. Volume 16:Issue 1(2015:Jan.)
• Page Start: 3
• Page End: 14
• Publication Date: 2014-05-08
• Subjects: HIV infections -- Treatment -- Periodicals; HIV-positive persons -- Periodicals; HIV infections -- Treatment -- Decision making -- Periodicals; 616.9792
• DOI: 10.1111/hiv.12165
• Languages: English
• ISSNs: 1464-2662

Abstract

<abstract abstract-type="main"> <title> <x xml:space="preserve">Abstract</x> </title> <sec id="hiv12165-sec-0001" sec-type="section"> <title>Objectives</title> <p>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.</p> </sec> <sec id="hiv12165-sec-0002" sec-type="section"> <title>Methods</title> <p>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.</p> </sec> <sec id="hiv12165-sec-0003" sec-type="section"> <title>Results</title> <p>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.</p> </sec> <sec id="hiv12165-sec-0004" sec-type="section"> <title>Conclusions</title> <p>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.</p> </sec> </abstract>