. 2020 Jan-Mar;32(1):123–132. doi: 10.5935/0103-507X.20200018

Table 3.

Comparison between biomarkers, prediction models, and combined models for early acute kidney injury diagnosis in adult intensive care units

Characteristic	Characteristic sub-groups	Malhotra et al.⁽²⁵⁾	Flechet et al.^*⁽²⁶⁾	Deng et al.^†⁽²⁸⁾	Chiofolo et al.⁽²⁹⁾	Zimmerman et al.^‡⁽³⁰⁾
Biomarkers used for comparison		Not applicable	sNGAL	sCysC and uNAG	Not applicable	Not applicable
Discrimination of biomarkers (AUROC)		Not applicable	For NGAL in validation NGAL cohort: 0.74 (0.74 - 0.74)	0.756 (0.723 - 0.789)	Not applicable	Not applicable
Discrimination of prediction models (AUROC)	Development cohort	Not reported	0.86 (0.86 - 0.86)	0.821 (0.792 - 0.850)	0.949 (0.943 - 0.954)	Not reported
	Internal validation cohort	0.792 (0.697 - 0.887)	0.86 (0.86 - 0.86)	0.821 (0.792 - 0.850)	0.882 (0.867 - 0.897)	0.78
	External validation cohort	0.81 (0.78 - 0.83)	0.81 (0.81 - 0.81)	Not applicable	Not applicable	Not applicable
Discrimination of prediction models with biomarkers (AUROC)		Not applicable	For combined model in validation NGAL cohort: 0.80 (0.80 - 0.80)	0.836 (0.808 - 0.864)	Not applicable	Not applicable

sNGAL - serum neutrophil gelatinase-associated lipocalin; sCysC - serum cystatin C; uNAG - urinary N-acetyl-β-D-glucosaminidase; AUROC - area under the receiver operating characteristics; NGAL - neutrophil gelatinase-associated lipocalin.

Data from Flechet et al. are only reported for the prediction model for any acute kidney injury on the first day;

^†

data from Deng et al. are only reported for the prediction model for any acute kidney injury;

^‡

data for Zimmerman et al. are only reported for the multivariate logistic regression model derived with backward selection.