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Development and validation of a prediction tool to support engagement in HIV care among young people ages 10–24 years in Kenya

INTRODUCTION: Loss to follow-up (LTFU) among adolescents and young adults living with HIV (AYALWH) is a barrier to optimal health and HIV services. We developed and validated a clinical prediction tool to identify AYALWH at risk of LTFU. METHODS: We used electronic medical records (EMR) of AYALWH ag...

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Detalles Bibliográficos
Autores principales: Wilson, Kate, Agot, Kawango, Dyer, Jessica, Badia, Jacinta, Kibugi, James, Bosire, Risper, Neary, Jillian, Inwani, Irene, Beima-Sofie, Kristin, Shah, Seema, Chakhtoura, Nahida, John-Stewart, Grace, Kohler, Pamela
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10313055/
https://www.ncbi.nlm.nih.gov/pubmed/37390119
http://dx.doi.org/10.1371/journal.pone.0286240
Descripción
Sumario:INTRODUCTION: Loss to follow-up (LTFU) among adolescents and young adults living with HIV (AYALWH) is a barrier to optimal health and HIV services. We developed and validated a clinical prediction tool to identify AYALWH at risk of LTFU. METHODS: We used electronic medical records (EMR) of AYALWH ages 10 to 24 in HIV care at 6 facilities in Kenya and surveys from a subset of participants. Early LTFU was defined as >30 days late for a scheduled visit in the last 6 months, which accounts for clients with multi-month refills. We developed a tool combining surveys with EMR (‘survey-plus-EMR tool’), and an ‘EMR-alone’ tool to predict high, medium, and low risk of LTFU. The survey-plus-EMR tool included candidate sociodemographics, partnership status, mental health, peer support, any unmet clinic needs, WHO stage, and time in care variables for tool development, while the EMR-alone included clinical and time in care variables only. Tools were developed in a 50% random sample of the data and internally validated using 10-fold cross-validation of the full sample. Tool performance was evaluated using Hazard Ratios (HR), 95% Confidence Intervals (CI), and area under the curve (AUC) ≥ 0.7 for good performance and ≥0.60 for modest performance. RESULTS: Data from 865 AYALWH were included in the survey-plus-EMR tool and early LTFU was (19.2%, 166/865). The survey-plus-EMR tool ranged from 0 to 4, including PHQ-9 ≥5, lack of peer support group attendance, and any unmet clinical need. High (3 or 4) and medium (2) prediction scores were associated with greater risk of LTFU (high, 29.0%, HR 2.16, 95%CI: 1.25–3.73; medium, 21.4%, HR 1.52, 95%CI: 0.93–2.49, global p-value = 0.02) in the validation dataset. The 10-fold cross validation AUC was 0.66 (95%CI: 0.63–0.72). Data from 2,696 AYALWH were included in the EMR-alone tool and early LTFU was 28.6% (770/2,696). In the validation dataset, high (score = 2, LTFU = 38.5%, HR 2.40, 95%CI: 1.17–4.96) and medium scores (1, 29.6%, HR 1.65, 95%CI: 1.00–2.72) predicted significantly higher LTFU than low-risk scores (0, 22.0%, global p-value = 0.03). Ten-fold cross-validation AUC was 0.61 (95%CI: 0.59–0.64). CONCLUSIONS: Clinical prediction of LTFU was modest using the surveys-plus-EMR tool and the EMR-alone tool, suggesting limited use in routine care. However, findings may inform future prediction tools and intervention targets to reduce LTFU among AYALWH.