Bellón Juan Ángel, de Dios Luna Juan, King Michael, Nazareth Irwin, Motrico Emma, GildeGómez-Barragán María Josefa, Torres-González Francisco, Montón-Franco Carmen, Sánchez-Celaya Marta, Díaz-Barreiros Miguel Ángel, Vicens Catalina, Moreno-Peral Patricia
Instituto de Investigación Biomédica de Málaga (IBIMA), associate professor, Departamento de Medicina Preventiva y Psiquiatría, Universidad de Málaga, Málaga, Spain.
Departamento de Bioestadística.
Br J Gen Pract. 2017 Apr;67(657):e280-e292. doi: 10.3399/bjgp17X690245.
Little is known about the risk of progressing to hazardous alcohol use in abstinent or low-risk drinkers.
To develop and validate a simple brief risk algorithm for the onset of hazardous alcohol drinking (HAD) over 12 months for use in primary care.
Prospective cohort study in 32 health centres from six Spanish provinces, with evaluations at baseline, 6 months, and 12 months.
Forty-one risk factors were measured and multilevel logistic regression and inverse probability weighting were used to build the risk algorithm. The outcome was new occurrence of HAD during the study, as measured by the AUDIT.
From the lists of 174 GPs, 3954 adult abstinent or low-risk drinkers were recruited. The 'predictAL-10' risk algorithm included just nine variables (10 questions): province, sex, age, cigarette consumption, perception of financial strain, having ever received treatment for an alcohol problem, childhood sexual abuse, AUDIT-C, and interaction AUDIT-C*Age. The c-index was 0.886 (95% CI = 0.854 to 0.918). The optimal cutoff had a sensitivity of 0.83 and specificity of 0.80. Excluding childhood sexual abuse from the model (the 'predictAL-9'), the c-index was 0.880 (95% CI = 0.847 to 0.913), sensitivity 0.79, and specificity 0.81. There was no statistically significant difference between the c-indexes of predictAL-10 and predictAL-9.
The predictAL-10/9 is a simple and internally valid risk algorithm to predict the onset of hazardous alcohol drinking over 12 months in primary care attendees; it is a brief tool that is potentially useful for primary prevention of hazardous alcohol drinking.
对于戒酒者或低风险饮酒者发展为有害饮酒的风险了解甚少。
开发并验证一种简单的简短风险算法,用于预测初级保健中12个月内有害饮酒(HAD)的发生情况。
在西班牙六个省份的32个健康中心进行的前瞻性队列研究,在基线、6个月和12个月时进行评估。
测量了41个风险因素,并使用多水平逻辑回归和逆概率加权法构建风险算法。结局是研究期间通过酒精使用障碍识别测试(AUDIT)测量的新发生的有害饮酒情况。
从174名全科医生的名单中,招募了3954名成年戒酒者或低风险饮酒者。“predictAL - 10”风险算法仅包括九个变量(10个问题):省份、性别、年龄、香烟消费量、对经济压力的感知、是否曾因酒精问题接受过治疗、童年性虐待、AUDIT - C以及AUDIT - C*年龄的交互作用。c指数为0.886(95%置信区间 = 0.854至0.918)。最佳截断值的灵敏度为0.83,特异度为0.80。从模型中排除童年性虐待(“predictAL - 9”)后,c指数为0.880(95%置信区间 = 0.847至0.913),灵敏度为0.79,特异度为0.81。predictAL - 10和predictAL - 9的c指数之间没有统计学上的显著差异。
predictAL - 10/9是一种简单且内部有效的风险算法,用于预测初级保健参与者12个月内有害饮酒的发生情况;它是一种简短的工具,对有害饮酒的初级预防可能有用。