Centre for Clinical Research in Infection and Sexual Health, Institute for Global Health, University College London, Gower Street, London, WC1E 6BT, UK.
BMC Med Res Methodol. 2018 Jun 27;18(1):65. doi: 10.1186/s12874-018-0522-x.
Minimisation of the delay to diagnosis is critical to achieving optimal outcomes for HIV patients and to limiting the potential for further onward infections. However, investigation of diagnosis delay is hampered by the fact that in most newly diagnosed patients the exact timing of infection cannot be determined and so inferences must be drawn from biomarker data.
We develop a Bayesian statistical model to evaluate delay-to-diagnosis distributions in HIV patients without known infection date, based on viral sequence genetic diversity and longitudinal viral load and CD4 count data. The delay to diagnosis is treated as a random variable for each patient and their biomarker data are modelled relative to the true time elapsed since infection, with this dependence used to obtain a posterior distribution for the delay to diagnosis. Data from a national seroconverter cohort with infection date known to within ± 6 months, linked to a database of viral sequences, are used to calibrate the model parameters. An exponential survival model is implemented that allows general inferences regarding diagnosis delay and pooling of information across groups of patients. If diagnoses are only observed within a given window period, then it is necessary to also model incidence as a function of time; we suggest a pragmatic approach to this problem when dealing with data from an established epidemic. The model developed is used to investigate delay-to-diagnosis distributions in men who have sex with men diagnosed with HIV in London in the period 2009-2013 with unknown date of infection.
Cross-validation and simulation analyses indicate that the models developed provide more accurate information regarding the timing of infection than does CD4 count-based estimation. Delay-to-diagnosis distributions were estimated in the London cohort, and substantial differences were observed according to ethnicity.
The combination of all available biomarker data with pooled estimation of the distribution of diagnosis-delays allows for more precise prediction of the true timing of infection in individual patients, and the models developed also provide useful population-level information.
为了使 HIV 患者获得最佳治疗效果并限制进一步传播感染的可能性，将诊断延迟最小化至关重要。然而，由于在大多数新诊断的患者中，确切的感染时间无法确定，因此必须从生物标志物数据中推断，这使得对诊断延迟的研究受到了阻碍。
我们开发了一种贝叶斯统计模型，用于评估无已知感染日期的 HIV 患者的诊断延迟分布，该模型基于病毒序列遗传多样性以及纵向病毒载量和 CD4 计数数据。对于每个患者，诊断延迟被视为随机变量，他们的生物标志物数据相对于从感染开始以来的真实时间进行建模，这种依赖性用于获得诊断延迟的后验分布。使用已知感染日期在±6 个月内的全国血清转化队列的数据对模型参数进行校准，这些数据与病毒序列数据库相关联。实施了一个指数生存模型，该模型允许对诊断延迟进行一般推断，并对患者群体的信息进行汇总。如果仅在给定的窗口期内观察到诊断，则还需要将发病作为时间的函数进行建模；当处理来自既定流行的已有数据时，我们提出了一种解决此问题的实用方法。所开发的模型用于研究 2009-2013 年间在伦敦诊断为 HIV 的男男性行为者的感染日期未知的诊断延迟分布。
交叉验证和模拟分析表明，与基于 CD4 计数的估计相比，所开发的模型提供了更准确的感染时间信息。对伦敦队列进行了诊断延迟分布的估计，并根据种族观察到了显著差异。
将所有可用的生物标志物数据与对诊断延迟分布的汇总估计相结合，可以更精确地预测个体患者的真实感染时间，所开发的模型还提供了有用的人群水平信息。
