Electronic medical record-based prediction models developed and deployed in the HIV care continuum: a systematic review

Abstract

ObjectiveTo assess the methodological issues in prediction models developed using electronic medical records (EMR) and their early-stage clinical impact on the HIV care continuum.MethodsA systematic search of entries in PubMed and Google Scholar was conducted between January 1, 2010, and January 17, 2022, to identify studies developing and deploying EMR-based prediction models. We used the CHARMS (Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modeling Studies), PROBAST (Prediction Model Risk of Bias Assessment Tool), and TRIPOD (Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis) statements to assess the methodological issues. In addition, we consulted reporting guidelines for early-stage clinical evaluation of decision support systems to assess the clinical impact of the models.ResultsThe systematic search yielded 35 eligible articles: 24 (68.6%) aimed at model development and 11 (31.4%) for model deployment. The majority of these studies predicted an individual's risk of carrying HIV (n = 12/35, 34.3%), the risk of interrupting HIV care (n = 9/35), and the risk of virological failure (n = 7/35). The methodological assessment for those 24 studies found that they were rated as high risk (n = 6/24), some concerns (n = 14/24), and a low risk of bias (n = 4/24). Several studies didn't report the number of events (n = 14/24), missing data management (n = 12/24), inadequate reporting of statistical performance (n = 18/24), or lack of external validation (n = 21/24) in their model development processes. The early-stage clinical impact assessment for those 9/11-deployed models showed improved care outcomes, such as HIV screening, engagement in care, and viral load suppression.ConclusionsEMR-based prediction models have been developed, and some are practically deployed as clinical decision support tools in the HIV care continuum. Overall, while early-stage clinical impact is observed with those deployed models, it is important to address methodological concerns and assess their potential clinical impact before widespread implementation.Systematic review registration: PROSPERO CRD42023454765.