Public Health, Xinjiang Medical University, Urumqi, Xinjiang, 830011, China.
Urumqi Maternal and Child Health Hospital, Urumqi, Xinjiang, 830000, China.
BMC Infect Dis. 2024 May 6;24(1):474. doi: 10.1186/s12879-024-09368-z.
Early prediction of mortality in individuals with HIV (PWH) has perpetually posed a formidable challenge. With the widespread integration of machine learning into clinical practice, some researchers endeavor to formulate models predicting the mortality risk for PWH. Nevertheless, the diverse timeframes of mortality among PWH and the potential multitude of modeling variables have cast doubt on the efficacy of the current predictive model for HIV-related deaths. To address this, we undertook a systematic review and meta-analysis, aiming to comprehensively assess the utilization of machine learning in the early prediction of HIV-related deaths and furnish evidence-based support for the advancement of artificial intelligence in this domain.
We systematically combed through the PubMed, Cochrane, Embase, and Web of Science databases on November 25, 2023. To evaluate the bias risk in the original studies included, we employed the Predictive Model Bias Risk Assessment Tool (PROBAST). During the meta-analysis, we conducted subgroup analysis based on survival and non-survival models. Additionally, we utilized meta-regression to explore the influence of death time on the predictive value of the model for HIV-related deaths.
After our comprehensive review, we analyzed a total of 24 pieces of literature, encompassing data from 401,389 individuals diagnosed with HIV. Within this dataset, 23 articles specifically delved into deaths during long-term follow-ups outside hospital settings. The machine learning models applied for predicting these deaths comprised survival models (COX regression) and other non-survival models. The outcomes of the meta-analysis unveiled that within the training set, the c-index for predicting deaths among people with HIV (PWH) using predictive models stands at 0.83 (95% CI: 0.75-0.91). In the validation set, the c-index is slightly lower at 0.81 (95% CI: 0.78-0.85). Notably, the meta-regression analysis demonstrated that neither follow-up time nor the occurrence of death events significantly impacted the performance of the machine learning models.
The study suggests that machine learning is a viable approach for developing non-time-based predictions regarding HIV deaths. Nevertheless, the limited inclusion of original studies necessitates additional multicenter studies for thorough validation.
预测 HIV 感染者（PWH）的死亡率一直是一个巨大的挑战。随着机器学习在临床实践中的广泛应用，一些研究人员致力于构建预测 PWH 死亡风险的模型。然而，PWH 死亡率的时间范围不同，建模变量的潜在数量众多，这使得当前 HIV 相关死亡预测模型的有效性受到质疑。为了解决这个问题，我们进行了系统回顾和荟萃分析，旨在全面评估机器学习在 HIV 相关死亡早期预测中的应用，并为人工智能在该领域的发展提供循证支持。
我们于 2023 年 11 月 25 日系统地检索了 PubMed、Cochrane、Embase 和 Web of Science 数据库。为了评估纳入的原始研究的偏倚风险，我们使用了预测模型偏倚风险评估工具（PROBAST）。在荟萃分析中，我们根据生存和非生存模型进行了亚组分析。此外，我们还使用元回归来探索死亡时间对模型预测 HIV 相关死亡的价值的影响。
经过全面审查，我们共分析了 24 篇文献，其中包含了 401389 名确诊为 HIV 的个体的数据。在这个数据集中，有 23 篇文章专门研究了在医院环境外的长期随访中发生的死亡事件。用于预测这些死亡事件的机器学习模型包括生存模型（COX 回归）和其他非生存模型。荟萃分析的结果表明，在训练集中，使用预测模型预测 HIV 感染者死亡的 c 指数为 0.83（95%置信区间：0.75-0.91）。在验证集中，c 指数略低，为 0.81（95%置信区间：0.78-0.85）。值得注意的是，元回归分析表明，随访时间和死亡事件的发生均不会显著影响机器学习模型的性能。
该研究表明，机器学习是一种可行的方法，可以对 HIV 死亡进行非时间相关的预测。然而，原始研究的纳入数量有限，需要进行更多的多中心研究以进行全面验证。
