Journal article
Latent variable modeling improves AKI risk factor identification and AKI prediction compared to traditional methods
BMC nephrology, Vol.18(1), p.55
02/08/2017
DOI: 10.1186/s12882-017-0465-1
PMCID: PMC5299779
PMID: 28178929
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 x 10(71)) 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.
Details
- Title: Subtitle
- Latent variable modeling improves AKI risk factor identification and AKI prediction compared to traditional methods
- Creators
- Loren E. Smith - Vanderbilt University Medical CenterDerek K. Smith - Vanderbilt University Medical CenterJeffrey D. Blume - Vanderbilt University Medical CenterEdward D. Siew - Vanderbilt University Medical CenterFrederic T. Billings - Vanderbilt University Medical Center
- Resource Type
- Journal article
- Publication Details
- BMC nephrology, Vol.18(1), p.55
- DOI
- 10.1186/s12882-017-0465-1
- PMID
- 28178929
- PMCID
- PMC5299779
- NLM abbreviation
- BMC Nephrol
- ISSN
- 1471-2369
- eISSN
- 1471-2369
- Publisher
- Springer Nature
- Number of pages
- 8
- Grant note
- R01GM112871 / NATIONAL INSTITUTE OF GENERAL MEDICAL SCIENCES; United States Department of Health & Human Services; National Institutes of Health (NIH) - USA; NIH National Institute of General Medical Sciences (NIGMS) LES GM108554; EDS DK92192-07; FTB GM102676; GM112871; UL1 TR000445 / United States National Institutes of Health; United States Department of Health & Human Services; National Institutes of Health (NIH) - USA Vanderbilt University Medical Center Department of Anesthesiology UL1TR000445 / NATIONAL CENTER FOR ADVANCING TRANSLATIONAL SCIENCES; United States Department of Health & Human Services; National Institutes of Health (NIH) - USA; NIH National Center for Advancing Translational Sciences (NCATS) Vanderbilt Center for Kidney Disease IIR 13-073-3 / Veterans Health Affairs
- Language
- English
- Date published
- 02/08/2017
- Academic Unit
- Preventive and Community Dentistry; Anesthesia; Dental Research
- Record Identifier
- 9984949240402771
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