Gebrezgi Merhawi T, Fennie Kristopher P, Sheehan Diana M, Ibrahimou Boubakari, Jones Sandra G, Brock Petra, Ladner Robert A, Trepka Mary Jo
Department of Epidemiology, Robert Stempel College of Public Health and Social Work, Florida International University, Miami, Florida, USA.
Division of Natural Sciences, New College of Florida, Sarasota, Florida, USA.
Int J STD AIDS. 2020 Mar;31(3):244-253. doi: 10.1177/0956462419893538. Epub 2020 Feb 9.
: Identifying PLHIV in HIV care who are at particular risk of non-retention in care is an important element in improving their HIV care outcomes. The purpose of this study was to develop a risk prediction tool to identify PLHIV at risk of non-retention in care over the course of the next year.
: We used stepwise logistic regression to assess sociodemographic, clinical and behavioral predictors of non-retention in HIV care. Retention in care was defined as having evidence of at least two encounters with an HIV care provider (or CD4 or viral load lab tests as a proxy measure for the encounter), at least 3 months apart within a year. We validated the risk prediction tool internally using the bootstrap method.
: The risk prediction tool included a total of six factors: age group, race, poverty level, homelessness, problematic alcohol/drug use and viral suppression status. The total risk score ranged from 0 to 17. Compared to those in the lowest quartile (0 risk score), those who were in the middle two quartiles (score 1–4) and those in the upper quartile (>4 risk score) were more likely not to be retained in care (odds ratio [OR] 1.63 [CI; 1.39–1.92] and OR 4.82 [CI; 4.04–5.78] respectively). The discrimination ability for the prediction model was 0.651.
: We found that increased risk for non-retention in care can be predicted with routinely available variables. Since the discrimination of the tool was low, future studies may need to include more prognostic factors in the risk prediction tool.
识别处于艾滋病毒护理中特别容易失访风险的艾滋病毒感染者是改善其艾滋病毒护理结果的重要因素。本研究的目的是开发一种风险预测工具，以识别在接下来的一年中处于护理失访风险的艾滋病毒感染者。
我们使用逐步逻辑回归来评估艾滋病毒护理中失访的社会人口学、临床和行为预测因素。护理留存定义为有证据表明至少与艾滋病毒护理提供者接触过两次（或使用CD4或病毒载量实验室检测作为接触的替代指标），在一年内间隔至少3个月。我们使用自助法在内部验证了风险预测工具。
风险预测工具总共包括六个因素：年龄组、种族、贫困水平、无家可归、酒精/药物使用问题和病毒抑制状态。总风险评分范围为0至17。与最低四分位数（风险评分为0）的人相比，中间两个四分位数（评分1 - 4）和最高四分位数（风险评分>4）的人更有可能失访（优势比[OR]分别为1.63[CI；1.39 - 1.92]和OR 4.82[CI；4.04 - 5.78]）。预测模型的辨别能力为0.651。
我们发现可以用常规可用变量预测护理失访风险增加。由于该工具的辨别能力较低，未来的研究可能需要在风险预测工具中纳入更多预后因素。
