Addressing Missingness in Predictive Models That Use Electronic Health Record Data

Electronic health record (EHR) data are increasingly used to develop prediction models that guide clinical decision making at the point of care. These include algorithms that use high-frequency data, like in sepsis prediction, as well as simpler equations, such as the Pooled Cohort Equations for cardiovascular outcome prediction. Although EHR data used in prediction models are often highly granular and more current than other data, there is systematic and nonsystematic missingness in EHR data as there is with most data. Despite growing use for clinical decisions, algorithms implemented in EHRs are mostly unregulated and are often opaque to the user. Guidelines about the development, validation, implementation, and reporting on clinical prediction models are sparse in their recommendations regarding missing data. This article characterizes missingness in EHR data, summarizes methods for attending to missing data when developing prediction models, makes recommendations about validation and implementation of models in practice when data are missing, and identifies research needs in this field.