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

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

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

Spectroscopy of 19^{19}Ne for the thermonuclear 15^{15}O(α,γ\alpha,\gamma)19^{19}Ne and 18^{18}F(p,αp,\alpha)15^{15}O reaction rates

Uncertainties in the thermonuclear rates of the $^{15}$O($\alpha,\gamma$)$^{19}$Ne and $^{18}$F($p,\alpha$)$^{15}$O reactions affect model predictions of light curves from type I X-ray bursts and the amount of the observable radioisotope $^{18}$F produced in classical novae, respectively. To address these uncertainties, we have studied the nuclear structure of $^{19}$Ne over $E_{x} = 4.0 - 5.1$ MeV and $6.1 - 7.3$ MeV using the $^{19}$F($^{3}$He,t)$^{19}$Ne reaction. We find the $J^{\pi}$ values of the 4.14 and 4.20 MeV levels to be consistent with $9/2^{-}$ and $7/2^{-}$ respectively, in contrast to previous assumptions. We confirm the recently observed triplet of states around 6.4 MeV, and find evidence that the state at 6.29 MeV, just below the proton threshold, is either broad or a doublet. Our data also suggest that predicted but yet unobserved levels may exist near the 6.86 MeV state. Higher resolution experiments are urgently needed to further clarify the structure of $^{19}$Ne around the proton threshold before a reliable $^{18}$F($p,\alpha$)$^{15}$O rate for nova models can be determined.Comment: 5 pages, 3 figures, Phys. Rev. C (in press

Urinary Tubular Injury Biomarkers Are Associated With ESRD and Death in the REGARDS Study.

IntroductionUrinary neutrophil gelatinase-associated lipocalin (uNGAL) and urinary kidney injury molecule-1 (uKIM-1) are established markers of subclinical acute kidney injury. In persons with reduced estimated glomerular filtration rate (eGFR) and albuminuria who are at high risk for end-stage renal disease (ESRD) and death, the associations of these urinary markers with incident ESRD or death is an area of active investigation.MethodsAmong 1472 black and white participants from the REasons for Geographic and Racial Differences in Stroke (REGARDS) study with eGFR ≤60 ml/min per 1.73 m2 (Chronic Kidney Disease Epidemiology Collaboration [CKD-EPI] cystatin, 2012) and albumin-to-creatinine ratio (ACR) ≥30 mg/g, we evaluated the associations of baseline uNGAL and uKIM-1 with progression to ESRD and all-cause death. Cox models were sequentially adjusted for urinary creatinine, traditional risk factors, C-reactive protein, ACR, and eGFR.ResultsThere were 257 ESRD events and 819 deaths over a median follow-up of 5.7 and 6.5 years, respectively. In demographic adjusted models, higher levels of uNGAL were associated with increased risk of ESRD and death, but these associations were attenuated in fully adjusted models including baseline eGFR for both ESRD (hazard ratio [HR] = 1.06 per doubling, 95% confidence interval [CI] 0.98-1.14) and death (HR = 1.04, 95% CI = 1.00-1.08). Higher levels of uKIM-1 were associated with increased risk of ESRD and death in demographic-adjusted models, and although attenuated in fully adjusted models, remained statistically significant for both ESRD (HR = 1.24 per doubling, 95% CI = 1.08-1.42) and death (HR = 1.10, 95% CI =1.03-1.19).ConclusionIn this cohort of high-risk patients with baseline eGFR ≤60 ml/min per 1.73 m2 and albuminuria, renal tubular injury is associated with higher mortality and progression to ESRD. Further studies are necessary to investigate the mechanism underlying this increased risk

Rare mutations and potentially damaging missense variants in genes encoding fibrillar collagens and proteins involved in their production are candidates for risk for preterm premature rupture of membranes

Preterm premature rupture of membranes (PPROM) is the leading identifiable cause of preterm birth with ~ 40% of preterm births being associated with PPROM and occurs in 1% - 2% of all pregnancies. We hypothesized that multiple rare variants in fetal genes involved in extracellular matrix synthesis would associate with PPROM, based on the assumption that impaired elaboration of matrix proteins would reduce fetal membrane tensile strength, predisposing to unscheduled rupture. We performed whole exome sequencing (WES) on neonatal DNA derived from pregnancies complicated by PPROM (49 cases) and healthy term deliveries (20 controls) to identify candidate mutations/variants. Genotyping for selected variants from the WES study was carried out on an additional 188 PPROM cases and 175 controls. All mothers were self-reported African Americans, and a panel of ancestry informative markers was used to control for genetic ancestry in all genetic association tests. In support of the primary hypothesis, a statistically significant genetic burden (all samples combined, SKAT-O p-value = 0.0225) of damaging/potentially damaging rare variants was identified in the genes of interest—fibrillar collagen genes, which contribute to fetal membrane strength and integrity. These findings suggest that the fetal contribution to PPROM is polygenic, and driven by an increased burden of rare variants that may also contribute to the disparities in rates of preterm birth among African Americans