Validation and Improvement of a Machine Learning Model to Predict Interruptions in Antiretroviral Treatment in South Africa

Abstract

Supplemental Digital Content is Available in the Text.

Historical visit attendance as predictor of treatment interruption in South African HIV patients: Extension of a validated machine learning model

“…For the purpose of this study, we considered different operational definitions of treatment interruption on the individual patient level. We assessed longitudinal treatment attendance on a visit by visit basis, by classifying each visit in a patient’s visit trajectory as an interruption in treatment (IIT) if the visit was attended more than 28 days after the scheduled visit date [ 16 , 17 , 22 ]. On a patient level, we investigated the relationship between the longitudinal pattern of visit attendance and a final outcome of patient retention, where patients were considered LTFU if they were 90 days or more late for a scheduled visit at the end of our observation period in accordance with the South African Department of Health guidelines [ 20 ].…”

“…We have previously described a machine learning algorithm able to predict up to two thirds of missed ART clinic visits using only visit attendance and routinely collected clinical information [ 16 , 17 ]. In this model, patterns of historical visits attendance ranked higher than baseline demographic and clinical characteristics when predicting next missed visits [ 17 ].…”

“…We have previously described a machine learning algorithm able to predict up to two thirds of missed ART clinic visits using only visit attendance and routinely collected clinical information [ 16 , 17 ]. In this model, patterns of historical visits attendance ranked higher than baseline demographic and clinical characteristics when predicting next missed visits [ 17 ]. While this model is able to predict the risk of disengagement from care at the level of on an individual patient and visit, the approach is still limited in terms of ability to infer relationships between predictors and interpret both the individual and relative role of potential predictors of treatment interruptions [ 17 ].…”

“…Previously, we identified 13 predictors for ART treatment discontinuation relating to age, baseline clinical characteristics and patterns of visits attendance from routinely collected ART patient records [ 17 ]. Here, we aim to expand the explainability of these predictors as a means of providing more generalised descriptions of the population at risk of IIT and the underlying drivers of risk.…”

Effect of Behaviourally Informed Text Messages to Promote Retention in HIV Care: A Randomized Trial in Ekurhuleni District, South Africa

“…With many countries implementing same-day ART initiation, the challenges with interruption in care and retention are more pronounced in the first six months after ART initiation (5,6). Clinic appointment attendance has emerged as a strong predictor for longer term retention of ART clients since missed appointments can lead to a reluctance to return to care and ultimately result in disengagement from HIV treatment (7)(8)(9). Beyond the risk of disengagement from ART, when care recipients are late for, or miss their scheduled appointments, this may place additional pressure on already constrained clinic staff to trace and bring clients back to care, negatively affecting the quality-of-service delivery (9)(10)(11).…”