Schäfer J, Young J, Bernasconi E, Ledergerber B, Nicca D, Calmy A, Cavassini M, Furrer H, Battegay M, Bucher Hc
Basel Institute for Clinical Epidemiology and Biostatistics, University Hospital Basel, Basel, Switzerland.
HIV Med. 2015 Jan;16(1):3-14. doi: 10.1111/hiv.12165. Epub 2014 May 8.
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.
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.
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.
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.
本研究旨在评估瑞士HIV队列研究中的前瞻性随访数据是否可用于预测戒烟的患者；或在戒烟者中，预测再次开始吸烟的患者。
我们首先使用临床推理构建预测模型（“临床模型”），然后通过先进的统计方法从众多候选预测因素中进行选择来构建模型（“统计模型”）。我们的临床模型基于文献，这些文献表明动机驱动戒烟，而依赖则驱动试图戒烟者的复吸。我们的统计模型基于使用具有逐分量梯度提升的加法逻辑回归进行自动变量选择。
在4833名吸烟者中，26%至少暂时戒烟；在戒烟者中，48%再次开始吸烟。我们的临床和统计模型的预测性能一般。一个将患者分为三个动机组的基本戒烟临床模型，其区分能力几乎与仅包含最重要预测因素（非吸烟就诊次数与总就诊次数之比、酒精或药物依赖、精神疾病合并症、近期住院情况和年龄）的受限统计模型相同。一个基于戒烟前每日最大吸烟量的基本复吸临床模型，其区分能力不如仅包含非吸烟就诊次数与总就诊次数之比的受限统计模型。
预测戒烟和复吸很困难，因此简单模型的区分能力几乎与复杂模型相同。有戒烟尝试史且已知近期已戒烟的患者是干预的最佳候选对象。
