Latent variable modeling improves AKI risk factor identification and AKI prediction compared to traditional methods

摘要

BackgroundAcute kidney injury (AKI) is diagnosed based on postoperative serum creatinine change, but AKI models have not consistently performed well, in part due to the omission of clinically important but practically unmeasurable variables that affect creatinine. We hypothesized that a latent variable mixture model of postoperative serum creatinine change would partially account for these unmeasured factors and therefore increase power to identify risk factors of AKI and improve predictive accuracy.