Elevated Urinary Tissue Inhibitor of Metalloproteinase-2 and Insulin-Like Growth Factor Binding Protein-7 Predict Drug-Induced Acute Kidney Injury.

BACKGROUND: Urinary tissue inhibitor of metalloproteinase-2 (TIMP2) and insulin-like growth factor binding protein-7 (IGFBP7) predict severe acute kidney injury (AKI) in critical illness. Earlier but subtle elevation of either biomarker from nephrotoxicity may predict drug-induced AKI. METHODS: A prospective study involving serial urine collection in patients treated with vancomycin, aminoglycosides, amphotericin, foscarnet, or calcineurin inhibitors was performed. Urinary TIMP2 and IGFBP7, both absolute levels and those normalized with urine creatinine, were examined in days leading to AKI onset by KDIGO criteria in cases or at final day of nephrotoxic therapy in non-AKI controls, who were matched for age, baseline kidney function, and nephrotoxic exposure. RESULTS: Urinary biomarker analyses were performed in 21 AKI patients and 28 non-AKI matched-controls; both groups had comparable baseline kidney function and duration of nephrotoxic drug therapy. Significantly higher absolute, normalized, and composite levels of TIMP2 and IGFBP7 were observed in AKI cases versus controls as early as 2-3 days before AKI onset (all P<0.05); >70% of patients with corresponding levels above 75th percentile developed AKI. Normalized TIMP2 at 2-3 days pre-AKI predicted AKI with the highest average AUROC of 0.81, followed by that of composite [TIMP2]x[IGFBP7] (0.78) after cross-validation. [TIMP2]x[IGFBP7] >0.01 (ng/mL)2/1000 predicted AKI with a sensitivity of 79% and specificity of 60%. CONCLUSION: Elevated urinary TIMP2 or IGFBP7 predicts drug-induced AKI with a lead-time of 2-3 days; an opportune time for interventions to reduce nephrotoxicity.

Health Care Analytics With Time-Invariant and Time-Variant Feature Importance to Predict Hospital-Acquired Acute Kidney Injury: Observational Longitudinal Study.

BACKGROUND: Acute kidney injury (AKI) develops in 4% of hospitalized patients and is a marker of clinical deterioration and nephrotoxicity. AKI onset is highly variable in hospitals, which makes it difficult to time biomarker assessment in all patients for preemptive care. OBJECTIVE: The study sought to apply machine learning techniques to electronic health records and predict hospital-acquired AKI by a 48-hour lead time, with the aim to create an AKI surveillance algorithm that is deployable in real time. METHODS: The data were sourced from 20,732 case admissions in 16,288 patients over 1 year in our institution. We enhanced the bidirectional recurrent neural network model with a novel time-invariant and time-variant aggregated module to capture important clinical features temporal to AKI in every patient. Time-series features included laboratory parameters that preceded a 48-hour prediction window before AKI onset; the latter's corresponding reference was the final in-hospital serum creatinine performed in case admissions without AKI episodes. RESULTS: The cohort was of mean age 53 (SD 25) years, of whom 29%, 12%, 12%, and 53% had diabetes, ischemic heart disease, cancers, and baseline eGFR <90 mL/min/1.73 m2, respectively. There were 911 AKI episodes in 869 patients. We derived and validated an algorithm in the testing dataset with an AUROC of 0.81 (0.78-0.85) for predicting AKI. At a 15% prediction threshold, our model generated 699 AKI alerts with 2 false positives for every true AKI and predicted 26% of AKIs. A lowered 5% prediction threshold improved the recall to 60% but generated 3746 AKI alerts with 6 false positives for every true AKI. Representative interpretation results produced by our model alluded to the top-ranked features that predicted AKI that could be categorized in association with sepsis, acute coronary syndrome, nephrotoxicity, or multiorgan injury, specific to every case at risk. CONCLUSIONS: We generated an accurate algorithm from electronic health records through machine learning that predicted AKI by a lead time of at least 48 hours. The prediction threshold could be adjusted during deployment to optimize recall and minimize alert fatigue, while its precision could potentially be augmented by targeted AKI biomarker assessment in the high-risk cohort identified.

Cumulative iodinated contrast exposure for computed tomography during acute kidney injury and major adverse kidney events.

OBJECTIVES: To determine if contrast-enhanced CT imaging performed in patients during their episode of AKI contributes to major adverse kidney events (MAKE). METHODS: A propensity score-matched analysis of 1127 patients with AKI defined by KDIGO criteria was done. Their mean age was 63 ± 16 years with 56% males. A total of 419 cases exposed to CT contrast peri-AKI were matched with 798 non-exposed controls for 14 covariates including comorbidities, acute illnesses, and initial AKI severity; outcomes including MAKE and renal recovery in hospital were compared using bivariate analysis and logistic regression. MAKE was a composite of mortality, renal replacement therapy, and doubling of serum creatinine on discharge over baseline; renal recovery was classified as early versus late based on a 7-day timeline from AKI onset to nadir creatinine or cessation of renal replacement therapy in survivors. RESULTS: Sixty-two patients received cumulatively > 100 mL of CT contrast, 143 patients had > 50-100 mL, and 214 patients had 50 mL or less; MAKE occurred in 34%, 17%, and 21%, respectively, as compared with 20% in non-exposed controls (p = 0.008 for patients with > 100 mL contrast versus none). More contrast-exposed patients experienced late renal recovery (27% versus 20%) and longer hospital days (median 10 versus 8) than non-exposed patients (all p < 0.01). On multivariate analysis, cumulative CT contrast > 100 mL was independently associated with MAKE (odds ratio 2.39 versus non-contrast, adjusted for all confounders, p = 0.005); cumulative CT contrast under 100 mL was not associated with MAKE. CONCLUSIONS: High cumulative volume of CT contrast administered to patients with AKI is associated with worse short-term renal outcomes and delayed renal recovery. KEY POINTS: • Cumulative intravenous iodinated contrast for CT imaging of more than 100 mL, during an episode of acute kidney injury, was independently associated with worse renal outcomes and less renal recovery. • These adverse outcomes including renal replacement therapy were not more frequent in similar patients who received cumulatively 100 mL or less of CT contrast, compared with non-exposed patients. • More patients with CT contrast exposure during acute kidney injury experienced delayed renal recovery.

Serial Quantification of Urinary Protein Biomarkers to Predict Drug-induced Acute Kidney Injury.

BACKGROUND: Drug-induced Acute Kidney Injury (AKI) develops in 10-15% of patients who receive nephrotoxic medications. Urinary biomarkers of renal tubular dysfunction may detect nephrotoxicity early and predict AKI. METHODS: We prospectively studied patients who received aminoglycosides, vancomycin, amphotericin, or calcineurin inhibitors, and collected their serial urine while on therapy. Patients who developed drug-induced AKI (fulfilling KDIGO criteria) were matched with non-AKI controls in a 1:2 ratio. Their urine samples were batch-analyzed at time-intervals leading up to AKI onset; the latter benchmarked against the final day of nephrotoxic therapy in non- AKI controls. Biomarkers examined include clusterin, beta-2-microglobulin, KIM1, MCP1, cystatin-C, trefoil-factor- 3, NGAL, interleukin-18, GST-Pi, calbindin, and osteopontin; biomarkers were normalized with corresponding urine creatinine. RESULTS: Nine of 84 (11%) patients developed drug-induced AKI. Biomarkers from 7 AKI cases with pre-AKI samples were compared with those from 14 non-AKI controls. Corresponding mean ages were 55(±17) and 52(±16) years; baseline eGFR were 99(±21) and 101(±24) mL/min/1.73m2 (all p=NS). Most biomarker levels peaked before the onset of AKI. Median levels of 5 biomarkers were significantly higher in AKI cases than controls at 1-3 days before AKI onset (all µg/mmol): clusterin [58(8-411) versus 7(3-17)], beta-2-microglobulin [1632(913-3823) versus 253(61-791)], KIM1 [0.16(0.13-0.76) versus 0.07(0.05-0.15)], MCP1 [0.40(0.16-1.90) versus 0.07(0.04-0.17)], and cystatin-C [33(27-2990) versus 11(7-19)], all p<0.05; their AUROC for AKI prediction were >0.80 (confidence intervals >0.50), with average accuracy highest for clusterin (86%), followed by beta-2-microglobulin, cystatin-C, MCP1, and KIM1 (57%) after cross-validation. CONCLUSION: Serial surveillance of these biomarkers could improve the lead time for nephrotoxicity detection by days.