Owuor Kevin, Turan Janet M, Szychowski Jeff M, Onono Maricianah, Ongeri Linet, Beres Laura K, Helova Anna, Ouma Emmah, Onyando Mercelline, Patel Rena C, Oyaro Patrick, Abuogi Lisa L, Hampanda Karen
Department of Biostatistics, School of Public Health, The University of Alabama at Birmingham, Birmingham, AL, USA.
Sparkman Center for Global Health, School of Public Health, The University of Alabama at Birmingham, Birmingham, AL, USA.
AIDS Behav. 2025 Jul 10. doi: 10.1007/s10461-025-04814-8.
No tool currently exists to predict the cumulative risk of suboptimal clinical outcomes among pregnant and postpartum women with HIV (PPWH). This study sought to develop and validate a parsimonious risk calculator capable of predicting disengagement from care and HIV treatment failure among PPWH. We created the risk calculator using data from 1,331 PPWH from Southwestern Kenya (Homabay, Migori, and Kisumu Counties) in the Mother Infant Visit Adherence and Treatment Engagement Trial. Least absolute shrinkage and selection operator logistic regression retained the most predictive variables from 16 candidate factors to estimate the probability of treatment failure or disengagement from care. Three risk quintiles were calculated. We assessed external validation with an independent dataset (Opt4Mamas; N = 820). Cross-validated area under the curve of receiver operating characteristic (AUROC) and calibration measures assessed model performance. Two unique risk calculators were created - one for PPWH with known HIV diagnosis prior to pregnancy and one for PPWH with new HIV diagnoses. The combined outcome of care disengagement or treatment failure occurred in 43% of PPWH with known diagnosis and 40% with new diagnosis in the development dataset; and 37% with known diagnosis and 13% with new diagnosis in the validation dataset. The calculators included demographic (age, parity, marital status), clinical (virological failure, missed visits, regimen line, gestation age), and psychosocial variables (intimate partner violence, stigma, depression, partner support, disclosure, food insecurity). The model for PPWH with known diagnosis demonstrated better calibration and discrimination (AUROC 0.843 [95% CI 0.805, 0.866]) than the model for PPWH with a new HIV diagnosis (AUROC 0.463 [95% CI 0.347, 0.597]). Mean predicted risk probabilities among PPWH with known HIV diagnosis were: low (6%), moderate (56%), and high (70%). Mean predicted risk probabilities among those with a new HIV diagnosis were: low (31%), moderate (48%), and high (65%). The novel risk calculator for PPWH with a known HIV diagnosis has the potential to identify those who are at risk of sub-optimal HIV treatment and care outcomes for targeted interventions to prevent treatment failure and loss to follow-up.
目前尚无工具可预测感染艾滋病毒的孕妇和产后妇女（PPWH）出现次优临床结局的累积风险。本研究旨在开发并验证一种简洁的风险计算器，能够预测PPWH的治疗中断和艾滋病毒治疗失败情况。我们利用肯尼亚西南部（霍马湾、米戈里和基苏木县）母婴访视依从性和治疗参与试验中1331名PPWH的数据创建了风险计算器。最小绝对收缩和选择算子逻辑回归从16个候选因素中保留了最具预测性的变量，以估计治疗失败或治疗中断的概率。计算了三个风险五分位数。我们使用一个独立数据集（Opt4Mamas；N = 820）进行外部验证。通过交叉验证的受试者工作特征曲线下面积（AUROC）和校准指标评估模型性能。创建了两个独特的风险计算器——一个用于妊娠前已知艾滋病毒诊断的PPWH，另一个用于新诊断艾滋病毒的PPWH。在开发数据集中，已知诊断的PPWH中43%出现了护理中断或治疗失败的综合结局，新诊断的为40%；在验证数据集中，已知诊断的为37%，新诊断的为13%。这些计算器纳入了人口统计学（年龄、产次、婚姻状况）、临床（病毒学失败、错过访视、治疗方案线、孕周）和心理社会变量（亲密伴侣暴力、耻辱感、抑郁、伴侣支持、信息披露、粮食不安全）。已知诊断的PPWH模型比新诊断艾滋病毒的PPWH模型表现出更好的校准和区分能力（AUROC 0.843 [95% CI 0.805, 0.866] 对比 AUROC 0.463 [95% CI 0.347, 0.597]）。已知艾滋病毒诊断的PPWH的平均预测风险概率为：低（6%）、中（56%）和高（70%）。新诊断艾滋病毒的PPWH的平均预测风险概率为：低（31%）、中（48%）和高（65%）。针对已知艾滋病毒诊断的PPWH的新型风险计算器有可能识别出那些有艾滋病毒治疗和护理结局次优风险的人群，以便进行有针对性的干预，预防治疗失败和失访。
