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.

翻译：贫血在ICU出院患者中较为常见。然而，哪些患者会出现贫血或从贫血中恢复仍不明确。由于再入院可能通过手术、输血或作为严重疾病的代理指标对血红蛋白水平产生显著影响，因此对该人群进行贫血预测变得复杂。为此，我们提出了一种新颖的贝叶斯联合纵向模型，用于分析血红蛋白随时间的变化趋势，该模型纳入了入院和出院的具体参数效应。这些效应本身取决于患者入院时的血红蛋白水平；因此，特定时间的血红蛋白是该患者截至该时间点完整的入院与出院史的函数。但由于入院或出院效应并不依赖于其自身，该模型仍保持良好定义。我们使用来自罗切斯特流行病学项目的6,876例患者回顾性队列通过交叉验证对模型进行验证，发现该模型能够准确估计血红蛋白水平，并在出院后30天内预测贫血状态和再入院风险，AUC分别为0.82和0.72。