Pilangorgi Sahar Souri, Khodakarim Soheila, Shayan Zahra, Nejat Mehdi
Department of Biostatistics, School of Medicine, Shiraz University of Medical Sciences, Shiraz, Iran.
Vice Chancellor for Health Affairs, Shiraz University of Medical Sciences, Shiraz, Iran.
BMC Public Health. 2025 Mar 12;25(1):979. doi: 10.1186/s12889-025-22096-6.
In many epidemiological HIV studies, patients are frequently monitored over time to predict their survival by examining their CD4 levels repeatedly. This study aims to evaluate factors related to longitudinal CD4 count and the risk of death among HIV-infected patients using Bayesian joint models.
The information of patients who were infected with HIV in Fars Province, from 2011 to 2016 and followed up until 2022 was used in this study. A joint model of count longitudinal outcome and time to death is used to model information of HIV patients.
The majority of patients were male (67.8%) with a median age of 34 years. During the follow-up, 212 patients (28.0%) died. The age-standardized mortality and incidence rates from 2011 to 2016 were 0.496 and 2.49 per 100,000 person-years respectively. The 1-year and 5-year survival rates are 91% (95%CI: 89%, 93%) and 79% (95%CI: 77%, 82%) respectively. There is a significant association in this model between the CD4 cell count and the risk of death. Age, addiction, and men were all significantly linked to CD4 cell count. Age was positively correlated with the risk of death. Men, those with hepatitis B and history of addiction had a higher risk of death.
This study uses the power of Bayesian joint models to explore the complex relationship between changes in CD4 counts over time and the risk of death in patients with HIV. Our findings highlight a strong and statistically significant connection between CD4 cell count and mortality risk. By modeling CD4 counts alongside survival data, we offer a deeper understanding of the factors influencing patient outcomes over time, significantly enhancing traditional separate modeling methods. This comprehensive approach leads to more accurate predictions, ultimately aiding in better-informed clinical decisions for HIV care.
在许多艾滋病病毒流行病学研究中，经常对患者进行长期监测，通过反复检测其CD4水平来预测他们的生存情况。本研究旨在使用贝叶斯联合模型评估与艾滋病病毒感染患者纵向CD4计数及死亡风险相关的因素。
本研究使用了2011年至2016年在法尔斯省感染艾滋病病毒并随访至2022年的患者信息。采用计数纵向结局和死亡时间的联合模型来模拟艾滋病病毒患者的信息。
大多数患者为男性（67.8%），中位年龄为34岁。随访期间，212名患者（28.0%）死亡。2011年至2016年的年龄标准化死亡率和发病率分别为每10万人年0.496和2.49。1年和5年生存率分别为91%（95%置信区间：89%，93%）和79%（95%置信区间：77%，82%）。该模型中CD4细胞计数与死亡风险之间存在显著关联。年龄、成瘾和男性均与CD4细胞计数显著相关。年龄与死亡风险呈正相关。男性、患有乙型肝炎和有成瘾史的人死亡风险更高。
本研究利用贝叶斯联合模型的力量，探索了随时间变化的CD4计数变化与艾滋病病毒患者死亡风险之间的复杂关系。我们的研究结果突出了CD4细胞计数与死亡风险之间强有力的统计学显著联系。通过将CD4计数与生存数据一起建模，我们对随时间影响患者结局的因素有了更深入的理解，显著改进了传统的单独建模方法。这种综合方法能带来更准确的预测，最终有助于为艾滋病护理做出更明智的临床决策。
