Smith Loren E, Smith Derek K, Blume Jeffrey D, Siew Edward D, Billings Frederic T
Department of Anesthesiology, Vanderbilt University Medical Center, 1211 21st Avenue South, Nashville, TN, 37205, USA.
Department of Biostatistics, Vanderbilt University Medical Center, Nashville, TN, USA.
BMC Nephrol. 2017 Feb 8;18(1):55. doi: 10.1186/s12882-017-0465-1.
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.
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.
The latent variable mixture model demonstrated superior fit (likelihood ratio of 6.68 × 10) 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%).
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.
急性肾损伤（AKI）是根据术后血清肌酐变化来诊断的，但AKI模型的表现并不一致，部分原因是遗漏了影响肌酐的临床上重要但实际难以测量的变量。我们假设，术后血清肌酐变化的潜在变量混合模型将部分解释这些未测量因素，从而提高识别AKI危险因素的效能并提高预测准确性。
我们使用来自一项针对心脏手术后AKI的前瞻性、653例受试者的随机临床试验（NCT00791648）的数据构建了一个双成分潜在变量混合模型和一个线性模型，并纳入了已确定的AKI危险因素和已知影响血清肌酐的协变量。我们比较了潜在变量混合模型和线性模型之间的模型拟合度、区分度、检测AKI危险因素的效能以及预测AKI的能力。
与线性模型相比，潜在变量混合模型显示出更好的拟合度（似然比为6.68×10）和更强的区分度（Spearman相关系数的置换检验，p<0.001）。潜在变量混合模型在识别危险因素方面比线性模型强大94%（中位数[范围]为-13%至1132%），并且显示出更强的预测血清肌酐变化的能力（相对均方误差降低6.8%）。
与线性模型相比，潜在变量混合模型更适合心脏手术患者的临床队列，从而能更好地评估AKI危险因素与血清肌酐变化之间的关联，并更准确地预测AKI。将潜在变量混合模型纳入AKI研究将使临床医生和研究人员能够考虑由未测量变量导致的具有临床意义的患者异质性，从而提高检查危险因素、测量肾损伤机制和介质的能力，并更准确地预测临床队列中的AKI。
