  Anemia is common in patients post-ICU discharge. However, which patients will
develop or recover from anemia remains unclear. Prediction of anemia in this
population is complicated by hospital readmissions, which can have substantial
impacts on hemoglobin levels due to surgery, blood transfusions, or being a
proxy for severe illness. We therefore introduce a novel Bayesian joint
longitudinal model for hemoglobin over time, which includes specific parametric
effects for hospital admission and discharge. These effects themselves depend
on a patient's hemoglobin at time of hospitalization; therefore hemoglobin at a
given time is a function of that patient's complete history of admissions and
discharges up until that time. However, because the effects of an admission or
discharge do not depend on themselves, the model remains well defined. We
validate our model on a retrospective cohort of 6,876 patients from the
Rochester Epidemiology Project using cross-validation, and find it accurately
estimates hemoglobin and predicts anemic status and hospital readmission in the
30 days post-discharge with AUCs of .82 and .72, respectively.
