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

ABSTRACT: BACKGROUND:Acute 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. METHODS:We constructed a two-component latent variable mixture model and a linear model using data from a prospective, 653-subject randomized clinical trial of AKI following cardiac surgery (NCT00791648) and included established AKI risk factors and covariates known to affect serum creatinine. We compared model fit, discrimination, power to detect AKI risk factors, and ability to predict AKI between the latent variable mixture model and the linear model. RESULTS:The latent variable mixture model demonstrated superior fit (likelihood ratio of 6.68?×?1071) and enhanced discrimination (permutation test of Spearman's correlation coefficients, p?

SUBMITTER: Smith LE 

PROVIDER: S-EPMC5299779 | biostudies-literature | 2017 Feb

REPOSITORIES: biostudies-literature