Health Economics and Epidemiology Research Office, Department of Internal Medicine, School of Clinical Medicine, Faculty of Health Sciences, University of the Witwatersrand, 39 Empire Road, Parktown, Johannesburg, South Africa.
Palindrome Data, Cape Town, South Africa.
Sci Rep. 2022 Jul 26;12(1):12715. doi: 10.1038/s41598-022-16062-0.
HIV treatment programs face challenges in identifying patients at risk for loss-to-follow-up and uncontrolled viremia. We applied predictive machine learning algorithms to anonymised, patient-level HIV programmatic data from two districts in South Africa, 2016-2018. We developed patient risk scores for two outcomes: (1) visit attendance ≤ 28 days of the next scheduled clinic visit and (2) suppression of the next HIV viral load (VL). Demographic, clinical, behavioral and laboratory data were investigated in multiple models as predictor variables of attending the next scheduled visit and VL results at the next test. Three classification algorithms (logistical regression, random forest and AdaBoost) were evaluated for building predictive models. Data were randomly sampled on a 70/30 split into a training and test set. The training set included a balanced set of positive and negative examples from which the classification algorithm could learn. The predictor variable data from the unseen test set were given to the model, and each predicted outcome was scored against known outcomes. Finally, we estimated performance metrics for each model in terms of sensitivity, specificity, positive and negative predictive value and area under the curve (AUC). In total, 445,636 patients were included in the retention model and 363,977 in the VL model. The predictive metric (AUC) ranged from 0.69 for attendance at the next scheduled visit to 0.76 for VL suppression, suggesting that the model correctly classified whether a scheduled visit would be attended in 2 of 3 patients and whether the VL result at the next test would be suppressed in approximately 3 of 4 patients. Variables that were important predictors of both outcomes included prior late visits, number of prior VL tests, time since their last visit, number of visits on their current regimen, age, and treatment duration. For retention, the number of visits at the current facility and the details of the next appointment date were also predictors, while for VL suppression, other predictors included the range of the previous VL value. Machine learning can identify HIV patients at risk for disengagement and unsuppressed VL. Predictive modeling can improve the targeting of interventions through differentiated models of care before patients disengage from treatment programmes, increasing cost-effectiveness and improving patient outcomes.
艾滋病毒治疗项目在识别可能失访和病毒载量不受控制的患者方面面临挑战。我们应用预测机器学习算法，对来自南非两个地区的 2016 年至 2018 年匿名的、患者层面的艾滋病毒项目数据进行分析。我们为两个结果开发了患者风险评分：（1）下一次预约就诊日期前 28 天内就诊次数≤28 次；（2）下一次艾滋病毒病毒载量（VL）得到抑制。在多个模型中研究了人口统计学、临床、行为和实验室数据，作为下一次预约就诊和下一次检测 VL 结果的预测变量。评估了三种分类算法（逻辑回归、随机森林和自适应增强）来构建预测模型。数据按 70/30 随机分为训练集和测试集。训练集包含一个平衡的正例和负例集合，分类算法可以从这些集合中学习。将来自未观察测试集的预测变量数据提供给模型，并且对每个预测结果与已知结果进行比较。最后，我们根据敏感性、特异性、阳性和阴性预测值以及曲线下面积（AUC）来估计每个模型的性能指标。共有 445636 名患者被纳入保留模型，363977 名患者被纳入 VL 模型。预测指标（AUC）范围从下一次预约就诊的 0.69 到 VL 抑制的 0.76，这表明模型正确地将 3 名患者中的 2 名患者是否会参加预约，以及大约 4 名患者中的 3 名患者的下一次检测结果是否会被抑制。对两个结果都重要的预测变量包括之前的晚期就诊次数、之前的 VL 检测次数、上次就诊后的时间、当前治疗方案的就诊次数、年龄和治疗持续时间。对于保留，当前机构的就诊次数和下一次预约日期的详细信息也是预测因素，而对于 VL 抑制，其他预测因素包括之前 VL 值的范围。机器学习可以识别可能脱离治疗和 VL 不受抑制的艾滋病毒患者。预测模型可以通过在患者脱离治疗项目之前，通过差异化的护理模式来改善干预措施的针对性，提高成本效益并改善患者的治疗结果。
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