Boileau Catherine, Bruneau Julie, Al-Nachawati Hicham, Lamothe François, Vincelette Jean
Department of Social and Preventive Medicine, University of Montreal, Quebec, Canada.
J Acquir Immune Defic Syndr. 2005 Aug 1;39(4):489-95. doi: 10.1097/01.qai.0000153424.56379.61.
The main goal of this study was to construct a prognostic model for HIV seroconversion among injection drug users (IDUs) using easy-to-measure risk indicators.
Cox proportional hazards regression modeling was used for risk stratification in a heterogeneous population of IDUs with regards to HIV risk-taking behaviors.
Subjects were recruited in a prospective cohort of IDUs followed between September 1992 and October 2001. A total of 1602 men, seronegative at enrollment with at least 1 follow-up visit, were included in the analyses. Only variables that consistently predict HIV seroconversion in several settings were considered. The final model was used to assign a risk score for each participant.
Three risk indicators were included in the risk score to predict HIV seroconversion: unstable housing, average cocaine injections per day, and having shared a syringe with a known HIV-positive partner. Kaplan-Meier survival functions were generated and risk score values stratified in 3 groups. HIV incidence rates per 100 person-years were as follows: 0.91 (95% CI, 0.55-1.52) for the low-risk group, 3.10 (95% CI, 2.49-3.84) for the moderate-risk group, and 7.82 (95% CI, 6.30-9.73) for the high-risk group (log-rank P value < 0.0001).
If validated in other settings, this risk score may improve the prediction of outcome and allow more accurate stratification in clinical trials.
本研究的主要目标是使用易于测量的风险指标构建一个针对注射吸毒者（IDU）HIV血清转化的预后模型。
Cox比例风险回归模型用于对具有不同HIV风险行为的IDU异质人群进行风险分层。
在1992年9月至2001年10月期间随访的IDU前瞻性队列中招募受试者。共有1602名男性纳入分析，这些男性在入组时血清学阴性且至少有1次随访。仅考虑在多个环境中一致预测HIV血清转化的变量。最终模型用于为每个参与者分配一个风险评分。
风险评分中纳入了三个预测HIV血清转化的风险指标：住房不稳定、每天平均注射可卡因次数以及与已知HIV阳性伴侣共用注射器。生成了Kaplan-Meier生存函数，并将风险评分值分为3组。每100人年的HIV发病率如下：低风险组为0.91（95%CI，0.55 - 1.52），中风险组为3.10（95%CI，2.49 - 3.84），高风险组为7.82（95%CI，6.30 - 9.73）（对数秩P值<0.0001）。
如果在其他环境中得到验证，该风险评分可能会改善对结果的预测，并在临床试验中实现更准确的分层。
