哮喘联盟

   摘要
   目的：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能预测成功戒烟患者或戒烟成功后再次复发的患者。
   方法：我们首先采用临床推理方法（临床模型）建立预测模型，然后采用可筛选建立大量候选预测因素的高级统计学方法（统计模型）建立预测模型。我们的临床模型基于相关文献，这些文献提示了一些戒烟动机和导致戒烟者复发的依赖因素。我们的统计模型采用特定组件梯度提升的加性回归方法进行自动变量选择。
   结果:在4833例吸烟者中，有26%成功戒烟或至少临时戒烟；因为在戒烟群体中，又有48%复发。本研究使用的临床模型及统计模型均表现出中度预测效果。一个将患者分为三种戒烟动机组的戒烟基本临床模型与正规统计模型预测效果几乎类似，仅能分辨出最主要预测因素（非吸烟就诊率、酒精或药物依赖性、是否合并精神疾病、近期住院史及年龄等）。而基于戒烟前每日最大吸烟数的复发基本临床模型仅在非吸烟就诊率方面预测效果低于正规统计模型。
   结论：预测戒烟成功或复发是非常困难的。因此，简单模型与复杂模型的预测效果几乎类似。曾经尝试戒烟的患者及最近成功戒烟的患者是干预的最佳对象。

 

（刘国梁 审校）
HIV Med. 2014 May 8. doi: 10.1111/hiv.12165. [Epub ahead of print]

 

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

Schäfer J1, Young J, Bernasconi E, Ledergerber B, Nicca D, Calmy A, Cavassini M, Furrer H, Battegay M, Bucher H; Swiss HIV Cohort Study.
 

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.
 

HIV Med. 2014 May 8. doi: 10.1111/hiv.12165. [Epub ahead of print]