Using Multilevel Outcomes to Construct and Select Biomarker Combinations for Single-level Prediction

Biomarker studies may involve a multilevel outcome, such as no, mild, or severe disease. There is often interest in predicting one particular level of the outcome due to its clinical significance. The standard approach to constructing biomarker combinations in this context involves dichotomizing the outcome and using a binary logistic regression model. We assessed whether information can be usefully gained from instead using more sophisticated regression methods. Furthermore, it is often necessary to select among several candidate biomarker combinations. One strategy involves selecting a combination on the basis of its ability to predict the outcome level of interest. We propose an algorithm that leverages the multilevel outcome to inform combination selection. We apply this algorithm to data from a study of acute kidney injury after cardiac surgery, where the kidney injury may be absent, mild, or severe. Using more sophisticated modeling approaches to construct combinations provided gains over the binary logistic regression approach in specific settings. In the examples considered, the proposed algorithm for combination selection tended to reduce the impact of bias due to selection and to provide combinations with improved performance. Methods that utilize the multilevel nature of the outcome in the construction and/or selection of biomarker combinations have the potential to yield better combinations

Developing Biomarker Combinations in Multicenter Studies via Direct Maximization and Penalization

When biomarker studies involve patients at multiple centers and the goal is to develop biomarker combinations for diagnosis, prognosis, or screening, we consider evaluating the predictive capacity of a given combination with the center-adjusted AUC (aAUC), a summary of conditional performance. Rather than using a general method to construct the biomarker combination, such as logistic regression, we propose estimating the combination by directly maximizing the aAUC. Furthermore, it may be desirable to have a biomarker combination with similar predictive capacity across centers. To that end, we allow for penalization of the variability in center-specific performance. We demonstrate good asymptotic properties of the resulting combinations. Simulations provide small-sample evidence that maximizing the aAUC can lead to combinations with greater predictive capacity than combinations constructed via logistic regression. We further illustrate the utility of constructing combinations by maximizing the aAUC while penalizing variability. We apply these methods to data from a study of acute kidney injury after cardiac surgery

Biomarker Combinations for Diagnosis and Prognosis in Multicenter Studies: Principles and Methods

Many investigators are interested in combining biomarkers to predict an outcome of interest or detect underlying disease. This endeavor is complicated by the fact that many biomarker studies involve data from multiple centers. Depending upon the relationship between center, the biomarkers, and the target of prediction, care must be taken when constructing and evaluating combinations of biomarkers. We introduce a taxonomy to describe the role of center and consider how a biomarker combination should be constructed and evaluated. We show that ignoring center, which is frequently done by clinical researchers, is often not appropriate. The limited statistical literature proposes using random intercept logistic regression models, an approach that we demonstrate is generally inadequate and may be misleading. We instead propose using fixed intercept logistic regression, which appropriately accounts for center without relying on untenable assumptions. After constructing the biomarker combination, we recommend using performance measures that account for the multicenter nature of the data, namely the center-adjusted area under the receiver operating characteristic curve. We apply these methods to data from a multicenter study of acute kidney injury after cardiac surgery. Appropriately accounting for center, both in construction and evaluation, may increase the likelihood of identifying clinically useful biomarker combinations

Association of urinary uromodulin with kidney function decline and mortality: the health ABC study .

BackgroundUrine uromodulin (uUMOD) is a protein secreted by the kidney tubule. Recent studies have suggested that higher uUMOD may be associated with improved kidney and mortality outcomes.MethodsUsing a case-cohort design, we evaluated the association between baseline uUMOD levels and ≥ 30% estimated glomerular filtration rate (eGFR) decline, incident chronic kidney disease (CKD), rapid kidney function decline, and mortality using standard and modified Cox proportional hazards regression.ResultsThe median value of uUMOD was 25.8 µg/mL, mean age of participants was 74 years, 48% were women, and 39% were black. Persons with higher uUMOD had lower prevalence of diabetes and coronary artery disease (CAD), and had lower systolic blood pressure. Persons with higher uUMOD also had higher eGFR, lower urinary albumin to creatinine ratio (ACR), and lower C-reactive protein (CRP). There was no association of uUMOD with > 30% eGFR decline. In comparison to those in the lowest quartile of uUMOD, those in the highest quartile had a significantly (53%) lower risk of incident CKD (CI 73%, 18%) and a 51% lower risk of rapid kidney function decline (CI 76%, 1%) after multivariable adjustment. Higher uUMOD was associated with lower risk of mortality in demographic adjusted models, but not after multivariable adjustment.ConclusionHigher levels of uUMOD are associated with lower risk of incident CKD and rapid kidney function decline. Additional studies are needed in the general population and in persons with advanced CKD to confirm these findings.


Urine neutrophil gelatinase-associated lipocalin is an early marker of acute kidney injury in critically ill children: a prospective cohort study

INTRODUCTION: Serum creatinine is a late marker of acute kidney injury (AKI). Urine neutrophil gelatinase-associated lipocalin (uNGAL) is an early marker of AKI, where the timing of kidney injury is known. It is unknown whether uNGAL predicts AKI in the general critical care setting. We assessed the ability of uNGAL to predict AKI development and severity in critically ill children. METHODS: This was a prospective cohort study of critically ill children. Children aged between 1 month and 21 years who were mechanically ventilated and had a bladder catheter inserted were eligible. Patients with end-stage renal disease or who had just undergone kidney transplantation were excluded. Patients were enrolled within 24 to 48 hours of initiation of mechanical ventilation. Clinical data and serum creatinine were collected daily for up to 14 days from enrollment, and urine was collected once daily for up to 4 days for uNGAL measurement. AKI was graded using pRIFLE (pediatric modified Risk, Injury, Failure, Loss, End Stage Kidney Disease) criteria. Day 0 was defined as the day on which the AKI initially occurred, and pRIFLEmax was defined as the worst pRIFLE AKI grade recorded during the study period. The χ(2 )test was used to compare associations between categorical variables. Mann-Whitney and Kruskal-Wallis tests were used to compare continuous variables between groups. Diagnostic characteristics were evaluated by calculating sensitivity and specificity, and constructing receiver operating characteristic curves. RESULTS: A total of 140 patients (54% boys, mean ± standard deviation Pediatric Risk of Mortality II score 15.0 ± 8.0, 23% sepsis) were included. Mean and peak uNGAL concentrations increased with worsening pRIFLEmax status (P < 0.05). uNGAL concentrations rose (at least sixfold higher than in controls) in AKI, 2 days before and after a 50% or greater rise in serum creatinine, without change in control uNGAL. The parameter uNGAL was a good diagnostic marker for AKI development (area under the receiver operating characteristic curve [AUC] 0.78, 95% confidence interval [CI] 0.62 to 0.95) and persistent AKI for 48 hours or longer (AUC 0.79, 95% CI 0.61 to 0.98), but not for AKI severity, when it was recorded after a rise in serum creatinine had occurred (AUC 0.63, 95% CI 0.44 to 0.82). CONCLUSION: We found uNGAL to be a useful early AKI marker that predicted development of severe AKI in a heterogeneous group of patients with unknown timing of kidney injury

First Post-Operative Urinary Kidney Injury Biomarkers and Association with the Duration of AKI in the TRIBE-AKI Cohort

Background: We previously demonstrated that assessment of the duration of AKI, in addition to magnitude of rise in creatinine alone, adds prognostic information for long-term survival. We evaluated whether post-operative kidney injury biomarkers in urine collected immediately after cardiac surgery associate with duration of serum creatinine elevation. Methods: We studied 1199 adults undergoing cardiac surgery in a prospective cohort study (TRIBE-AKI) and examined the association between the levels of five urinary biomarkers individually at 0–6 hours after surgery: interleukin-18 (IL-18), neutrophil gelatinase-associated lipocalin (NGAL), kidney injury molecule-1 (KIM-1), liver fatty acid binding protein (L-FABP) and albumin with duration of serum creatinine-based AKIN criteria for AKI (0 (no AKI), 1–2, 3–6, ≥7 days). Results: Overall, 407 (34%) patients had at least stage 1 AKI, of whom 251 (61.7%) had duration of 1–2 days, 118 (28.9%) had duration 3–6 days, and 38 (9.3%) had duration of ≥7 days. Higher concentrations of all biomarkers (per log increase) were independently associated with a greater odds of a longer duration of AKI; odds ratios and 95% confidence intervals using ordinal logistic regression were the following: IL-18: 1.22, 1.13–1.32; KIM-1: 1.36, 1.21–1.52; albumin 1.20, 1.09–1.32; L-FABP 1.11, 1.04–1.19; NGAL 1.06, 1.00–1.14). AKI duration of 7 days or longer was associated with a 5-fold adjusted risk of mortality at 3 years. Conclusions: There was an independent dose-response association between urinary levels of injury biomarkers immediately after cardiac surgery and longer duration of AKI. Duration of AKI was also associated with long term mortality. Future studies should explore the potential utility of these urinary kidney injury biomarkers to enrich enrollment of patients at risk for longer duration of AKI into trials of interventions to prevent or treat post-operative AKI.</p