Motivation: In predicting HIV therapy outcomes, a critical clinical question is whether using historical information can enhance predictive capabilities compared with current or latest available data analysis. This study analyses whether historical knowledge, which includes viral mutations detected in all genotypic tests before therapy, their temporal occurrence, and concomitant viral load measurements, can bring improvements. We introduce a method to weigh mutations, considering the previously enumerated factors and the reference mutation-drug Stanford resistance tables. We compare a model encompassing history (H) with one not using it (NH). Results: The H-model demonstrates superior discriminative ability, with a higher ROC-AUC score (76.34%) than the NH-model (74.98%). Significant Wilcoxon test results confirm that incorporating historical information improves consistently predictive accuracy for treatment outcomes. The better performance of the H-model might be attributed to its consideration of latent HIV reservoirs, probably obtained when leveraging historical information. The findings emphasize the importance of temporal dynamics in mutations, offering insights into HIV infection complexities. However, our result also shows that prediction accuracy remains relatively high even when no historical information is available. Supplementary information: Supplementary material is available.

翻译：动机：在预测HIV疗法结果时，一个关键的临床问题是：与基于当前或最新可用数据的分析相比，利用历史信息能否提升预测能力？本研究分析了历史知识（包括治疗前所有基因型检测中发现的病毒突变、其出现时间顺序及伴随的病毒载量测量值）是否能带来改进。我们提出了一种突变权重计算方法，综合考虑上述因素及参考突变-药物斯坦福耐药性表格。我们比较了包含历史信息的模型（H）与不包含历史信息的模型（NH）。结果：H模型展现出更优的判别能力，其ROC-AUC评分（76.34%）高于NH模型（74.98%）。Wilcoxon检验结果显著，证实整合历史信息能持续提升治疗结果的预测准确性。H模型的更优表现可能归因于其对潜伏HIV储库的考量——这很可能源于对历史信息的利用。研究结果强调了突变时序动态的重要性，为理解HIV感染的复杂性提供了新视角。然而，我们的结果也表明，即使在缺乏历史信息的情况下，预测准确性仍保持较高水平。补充信息：补充材料可获取。