Key result
A latent variable mixture model was 94% more powerful at identifying established acute kidney injury risk factors and improved prediction accuracy by 6.8% compared to a traditional linear model in cardiac surgery patients.
Why the study?
Does a latent variable mixture model improve AKI risk factor identification and prediction compared to a traditional linear model in cardiac surgery patients?
Does a latent variable mixture model improve AKI risk factor identification and prediction compared to a traditional linear model in cardiac surgery patients?
Effect estimate: 94% more powerful
p-value: p=<0.001
Latent variable mixture modeling improves the identification of AKI risk factors and the prediction of postoperative serum creatinine changes in cardiac surgery patients compared to traditional linear models.
May enhance AKI prediction after cardiac surgery; extends mixture modeling but leaves open clinical adoption pending validation.
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: ) and enhanced discrimination (permutation test of Spearman's correlation coefficients, p < 0.001) compared to the linear model. The latent variable mixture model was 94% (-13 to 1132%) more powerful (median [range]) at identifying risk factors than the linear model, and demonstrated increased ability to predict change in serum creatinine (relative mean square error reduction of 6.8%). CONCLUSIONS: A latent variable mixture model better fit a clinical cohort of cardiac surgery patients than a linear model, thus providing better assessment of the associations between risk factors of AKI and serum creatinine change and more accurate prediction of AKI. Incorporation of latent variable mixture modeling into AKI research will allow clinicians and investigators to account for clinically meaningful patient heterogeneity resulting from unmeasured variables, and therefore provide improved ability to examine risk factors, measure mechanisms and mediators of kidney injury, and more accurately predict AKI in clinical cohorts.
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Smith et al. (2017) studied Acute kidney injury following cardiac surgery (n=614). Latent variable mixture model vs. Linear model was evaluated on Power to identify established AKI risk factors (94% more powerful, p=<0.001). A latent variable mixture model was 94% more powerful at identifying established acute kidney injury risk factors and improved prediction accuracy by 6.8% compared to a traditional linear model in cardiac surgery patients.
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