Wistar Institute, Philadelphia, Pennsylvania, United States of America.
PLoS Med. 2012;9(4):e1001207. doi: 10.1371/journal.pmed.1001207. Epub 2012 Apr 17.
Global programs of anti-HIV treatment depend on sustained laboratory capacity to assess treatment initiation thresholds and treatment response over time. Currently, there is no valid alternative to CD4 count testing for monitoring immunologic responses to treatment, but laboratory cost and capacity limit access to CD4 testing in resource-constrained settings. Thus, methods to prioritize patients for CD4 count testing could improve treatment monitoring by optimizing resource allocation.
Using a prospective cohort of HIV-infected patients (n=1,956) monitored upon antiretroviral therapy initiation in seven clinical sites with distinct geographical and socio-economic settings, we retrospectively apply a novel prediction-based classification (PBC) modeling method. The model uses repeatedly measured biomarkers (white blood cell count and lymphocyte percent) to predict CD4(+) T cell outcome through first-stage modeling and subsequent classification based on clinically relevant thresholds (CD4(+) T cell count of 200 or 350 cells/µl). The algorithm correctly classified 90% (cross-validation estimate=91.5%, standard deviation [SD]=4.5%) of CD4 count measurements <200 cells/µl in the first year of follow-up; if laboratory testing is applied only to patients predicted to be below the 200-cells/µl threshold, we estimate a potential savings of 54.3% (SD=4.2%) in CD4 testing capacity. A capacity savings of 34% (SD=3.9%) is predicted using a CD4 threshold of 350 cells/µl. Similar results were obtained over the 3 y of follow-up available (n=619). Limitations include a need for future economic healthcare outcome analysis, a need for assessment of extensibility beyond the 3-y observation time, and the need to assign a false positive threshold.
Our results support the use of PBC modeling as a triage point at the laboratory, lessening the need for laboratory-based CD4(+) T cell count testing; implementation of this tool could help optimize the use of laboratory resources, directing CD4 testing towards higher-risk patients. However, further prospective studies and economic analyses are needed to demonstrate that the PBC model can be effectively applied in clinical settings. Please see later in the article for the Editors' Summary.
全球抗 HIV 治疗计划依赖于持续的实验室能力，以评估随时间推移的治疗起始阈值和治疗反应。目前，尚无替代 CD4 计数检测的方法来监测治疗引起的免疫反应，但在资源有限的环境中，实验室成本和能力限制了 CD4 检测的应用。因此，通过优化资源分配，优先考虑患者进行 CD4 计数检测的方法可以改善治疗监测。
使用在具有不同地理和社会经济背景的七个临床地点接受抗逆转录病毒治疗启动监测的 HIV 感染患者前瞻性队列（n=1956），我们回顾性地应用了一种新的基于预测的分类（PBC）建模方法。该模型使用反复测量的生物标志物（白细胞计数和淋巴细胞百分比）通过第一阶段建模，然后根据临床相关阈值（CD4+T 细胞计数为 200 或 350 个/µl）进行分类，来预测 CD4（+）T 细胞的结果。该算法在第一年随访中正确分类了 90%（交叉验证估计值=91.5%，标准差[SD]=4.5%）的<200 个/µl 的 CD4 计数测量值；如果仅对预测低于 200 个/µl 阈值的患者进行实验室检测，我们估计 CD4 检测能力可能节省 54.3%（SD=4.2%）。使用 CD4 阈值为 350 个/µl 时，预测可节省 34%（SD=3.9%）的能力。在可获得的 3 年随访中（n=619）得到了类似的结果。限制因素包括需要进行未来的经济医疗保健结果分析、需要评估该方法在 3 年观察时间以外的可扩展性，以及需要指定假阳性阈值。
我们的结果支持将 PBC 建模作为实验室中的分诊点，减少对基于实验室的 CD4（+）T 细胞计数检测的需求；该工具的实施可以帮助优化实验室资源的使用，将 CD4 检测指向高危患者。然而，需要进一步的前瞻性研究和经济分析来证明 PBC 模型可以在临床环境中有效应用。请在文章后面查看编辑总结。
