  Multiple imputation (MI) models can be improved by including auxiliary
covariates (AC), but their performance in high-dimensional data is not well
understood. We aimed to develop and compare high-dimensional MI (HDMI)
approaches using structured and natural language processing (NLP)-derived AC in
studies with partially observed confounders. We conducted a plasmode simulation
study using data from opioid vs. non-steroidal anti-inflammatory drug (NSAID)
initiators (X) with observed serum creatinine labs (Z2) and time-to-acute
kidney injury as outcome. We simulated 100 cohorts with a null treatment
effect, including X, Z2, atrial fibrillation (U), and 13 other
investigator-derived confounders (Z1) in the outcome generation. We then
imposed missingness (MZ2) on 50% of Z2 measurements as a function of Z2 and U
and created different HDMI candidate AC using structured and NLP-derived
features. We mimicked scenarios where U was unobserved by omitting it from all
AC candidate sets. Using LASSO, we data-adaptively selected HDMI covariates
associated with Z2 and MZ2 for MI, and with U to include in propensity score
models. The treatment effect was estimated following propensity score matching
in MI datasets and we benchmarked HDMI approaches against a baseline imputation
and complete case analysis with Z1 only. HDMI using claims data showed the
lowest bias (0.072). Combining claims and sentence embeddings led to an
improvement in the efficiency displaying the lowest root-mean-squared-error
(0.173) and coverage (94%). NLP-derived AC alone did not perform better than
baseline MI. HDMI approaches may decrease bias in studies with partially
observed confounders where missingness depends on unobserved factors.
