Search

Background: Appropriate risk stratification of indeterminate pulmonary nodules (IPNs) is necessary to direct diagnostic evaluation. Currently available models were developed in populations with lower cancer prevalence than that seen in thoracic surgery and pulmonology clinics and usually do not allow for missing data. We updated and expanded the Thoracic Research Evaluation and Treatment (TREAT) model into a more generalized, robust approach for lung cancer prediction in patients referred for specialty evaluation., Research Question: Can clinic-level differences in nodule evaluation be incorporated to improve lung cancer prediction accuracy in patients seeking immediate specialty evaluation compared with currently available models?, Study Design and Methods: Clinical and radiographic data on patients with IPNs from six sites (N = 1,401) were collected retrospectively and divided into groups by clinical setting: pulmonary nodule clinic (n = 374; cancer prevalence, 42%), outpatient thoracic surgery clinic (n = 553; cancer prevalence, 73%), or inpatient surgical resection (n = 474; cancer prevalence, 90%). A new prediction model was developed using a missing data-driven pattern submodel approach. Discrimination and calibration were estimated with cross-validation and were compared with the original TREAT, Mayo Clinic, Herder, and Brock models. Reclassification was assessed with bias-corrected clinical net reclassification index and reclassification plots., Results: Two-thirds of patients had missing data; nodule growth and fluorodeoxyglucose-PET scan avidity were missing most frequently. The TREAT version 2.0 mean area under the receiver operating characteristic curve across missingness patterns was 0.85 compared with that of the original TREAT (0.80), Herder (0.73), Mayo Clinic (0.72), and Brock (0.68) models with improved calibration. The bias-corrected clinical net reclassification index was 0.23., Interpretation: The TREAT 2.0 model is more accurate and better calibrated for predicting lung cancer in high-risk IPNs than the Mayo, Herder, or Brock models. Nodule calculators such as TREAT 2.0 that account for varied lung cancer prevalence and that consider missing data may provide more accurate risk stratification for patients seeking evaluation at specialty nodule evaluation clinics., Competing Interests: Financial/Nonfinancial Disclosures The authors have reported to CHEST the following: J. M. I. discloses grants from Guardant Health and GRAIL, prior support for meeting attendance from Intuitive Surgical, planned or issued patents with AstraZeneca and Roche Genentech, and stock or stock options from LumaCyte, LLC. L. T. V. has received consulting fees from Ambu A/S. F. M. receives consulting fees from Medtronic, Johnson & Johnson, and Intuitive and additionally received research funding from Medtronic. The disclosures listed did not have any relation to the content of this manuscript. None declared (C. M. G., M. E. S., V. F. W., A. W. M., M. C. A., C. M., J. C., S. R., O. B. R., R. P., E. S. L., J. C. N., J. D. B., S. A. D., E. L. G.)., (Published by Elsevier Inc.)

Background: Black Americans receive a diagnosis at later stage of lung cancer more often than White Americans. We undertook a population-based study to identify factors contributing to racial disparities in lung cancer stage of diagnosis among low-income adults., Research Question: Which multilevel factors contribute to racial disparities in stage of lung cancer at diagnosis?, Study Design and Methods: Cases of incident lung cancer from the prospective observational Southern Community Cohort Study were identified by linkage with state cancer registries in 12 southeastern states. Logistic regression shrinkage techniques were implemented to identify individual-level and area-level factors associated with distant stage diagnosis. A subset of participants who responded to psychosocial questions (eg, racial discrimination experiences) were evaluated to determine if model predictive power improved., Results: We identified 1,572 patients with incident lung cancer with available lung cancer stage (64% self-identified as Black and 36% self-identified as White). Overall, Black participants with lung cancer showed greater unadjusted odds of distant stage diagnosis compared with White participants (OR,1.29; 95% CI, 1.05-1.59). Greater neighborhood area deprivation was associated with distant stage diagnosis (OR, 1.58; 95% CI, 1.19-2.11). After controlling for individual- and area-level factors, no significant difference were found in distant stage disease for Black vs White participants. However, participants with COPD showed lower odds of distant stage diagnosis in the primary model (OR, 0.72; 95% CI, 0.53-0.98). Interesting and complex interactions were observed. The subset analysis model with additional variables for racial discrimination experiences showed slightly greater predictive power than the primary model., Interpretation: Reducing racial disparities in lung cancer stage at presentation will require interventions on both structural and individual-level factors., (Copyright © 2022 American College of Chest Physicians. Published by Elsevier Inc. All rights reserved.)

Background: The proliferation of genetic profiling has revealed many associations between genetic variations and disease. However, large-scale phenotyping efforts in largely healthy populations, coupled with DNA sequencing, suggest variants currently annotated as pathogenic are more common in healthy populations than previously thought. In addition, novel and rare variants are frequently observed in genes associated with disease both in healthy individuals and those under suspicion of disease. This raises the question of whether these variants can be useful predictors of disease. To answer this question, we assessed the degree to which the presence of a variant in the cardiac potassium channel gene KCNH2 was diagnostically predictive for the autosomal dominant long QT syndrome., Methods: We estimated the probability of a long QT diagnosis given the presence of each KCNH2 variant using Bayesian methods that incorporated variant features such as changes in variant function, protein structure, and in silico predictions. We call this estimate the posttest probability of disease. Our method was applied to over 4000 individuals heterozygous for 871 missense or in-frame insertion/deletion variants in KCNH2 and validated against a separate international cohort of 933 individuals heterozygous for 266 missense or in-frame insertion/deletion variants., Results: Our method was well-calibrated for the observed fraction of heterozygotes diagnosed with long QT syndrome. Heuristically, we found that the innate diagnostic information one learns about a variant from 3-dimensional variant location, in vitro functional data, and in silico predictors is equivalent to the diagnostic information one learns about that same variant by clinically phenotyping 10 heterozygotes. Most importantly, these data can be obtained in the absence of any clinical observations., Conclusions: We show how variant-specific features can inform a prior probability of disease for rare variants even in the absence of clinically phenotyped heterozygotes.

False discovery rates (FDR) are an essential component of statistical inference, representing the propensity for an observed result to be mistaken. FDR estimates should accompany observed results to help the user contextualize the relevance and potential impact of findings. This paper introduces a new user-friendly R pack-age for estimating FDRs and computing adjusted p-values for FDR control. The roles of these two quantities are often confused in practice and some software packages even report the adjusted p-values as the estimated FDRs. A key contribution of this package is that it distinguishes between these two quantities while also offering a broad array of refined algorithms for estimating them. For example, included are newly augmented methods for estimating the null proportion of findings - an important part of the FDR estimation procedure. The package is broad, encompassing a variety of adjustment methods for FDR estimation and FDR control, and includes plotting functions for easy display of results. Through extensive illustrations, we strongly encourage wider reporting of false discovery rates for observed findings., Competing Interests: No competing interests were disclosed., (Copyright: © 2021 Murray MH and Blume JD.)

Background: Recent trials have suggested use of balanced crystalloids may decrease the incidence of major adverse kidney events compared to saline in critically ill adults. The effect of crystalloid composition on biomarkers of early acute kidney injury remains unknown., Methods: From February 15 to July 15, 2016, we conducted an ancillary study to the Isotonic Solutions and Major Adverse Renal Events Trial (SMART) comparing the effect of balanced crystalloids versus saline on urinary levels of neutrophil gelatinase-associated lipocalin (NGAL) and kidney injury molecule-1 (KIM-1) among 261 consecutively-enrolled critically ill adults admitted from the emergency department to the medical ICU. After informed consent, we collected urine 36 ± 12 h after hospital admission and measured NGAL and KIM-1 levels using commercially available ELISAs. Levels of NGAL and KIM-1 at 36 ± 12 h were compared between patients assigned to balanced crystalloids versus saline using a Mann-Whitney U test., Results: The 131 patients (50.2%) assigned to the balanced crystalloid group and the 130 patients (49.8%) assigned to the saline group were similar at baseline. Urinary NGAL levels were significantly lower in the balanced crystalloid group (median, 39.4 ng/mg [IQR 9.9 to 133.2]) compared with the saline group (median, 64.4 ng/mg [IQR 27.6 to 339.9]) (P < 0.001). Urinary KIM-1 levels did not significantly differ between the balanced crystalloid group (median, 2.7 ng/mg [IQR 1.5 to 4.9]) and the saline group (median, 2.4 ng/mg [IQR 1.3 to 5.0]) (P = 0.36)., Conclusions: In this ancillary analysis of a clinical trial comparing balanced crystalloids to saline among critically ill adults, balanced crystalloids were associated with lower urinary concentrations of NGAL and similar urinary concentrations of KIM-1, compared with saline. These results suggest only a modest reduction in early biomarkers of acute kidney injury with use of balanced crystalloids compared with saline., Trial Registration: ClinicalTrials.gov number: NCT02444988 . Date registered: May 15, 2015.

A major challenge emerging in genomic medicine is how to assess best disease risk from rare or novel variants found in disease-related genes. The expanding volume of data generated by very large phenotyping efforts coupled to DNA sequence data presents an opportunity to reinterpret genetic liability of disease risk. Here we propose a framework to estimate the probability of disease given the presence of a genetic variant conditioned on features of that variant. We refer to this as the penetrance, the fraction of all variant heterozygotes that will present with disease. We demonstrate this methodology using a well-established disease-gene pair, the cardiac sodium channel gene SCN5A and the heart arrhythmia Brugada syndrome. From a review of 756 publications, we developed a pattern mixture algorithm, based on a Bayesian Beta-Binomial model, to generate SCN5A penetrance probabilities for the Brugada syndrome conditioned on variant-specific attributes. These probabilities are determined from variant-specific features (e.g. function, structural context, and sequence conservation) and from observations of affected and unaffected heterozygotes. Variant functional perturbation and structural context prove most predictive of Brugada syndrome penetrance., Competing Interests: The authors have declared that no competing interests exist.

Missing data are a common problem for both the construction and implementation of a prediction algorithm. Pattern submodels (PS)-a set of submodels for every missing data pattern that are fit using only data from that pattern-are a computationally efficient remedy for handling missing data at both stages. Here, we show that PS (i) retain their predictive accuracy even when the missing data mechanism is not missing at random (MAR) and (ii) yield an algorithm that is the most predictive among all standard missing data strategies. Specifically, we show that the expected loss of a forecasting algorithm is minimized when each pattern-specific loss is minimized. Simulations and a re-analysis of the SUPPORT study confirms that PS generally outperforms zero-imputation, mean-imputation, complete-case analysis, complete-case submodels, and even multiple imputation (MI). The degree of improvement is highly dependent on the missingness mechanism and the effect size of missing predictors. When the data are MAR, MI can yield comparable forecasting performance but generally requires a larger computational cost. We also show that predictions from the PS approach are equivalent to the limiting predictions for a MI procedure that is dependent on missingness indicators (the MIMI model). The focus of this article is on out-of-sample prediction; implications for model inference are only briefly explored., (© The Author 2018. Published by Oxford University Press.)

Semi-supervised methods have an increasing impact on computer vision tasks to make use of scarce labels on large datasets, yet these approaches have not been well translated to medical imaging. Of particular interest, the MixMatch method achieves significant performance improvement over popular semi-supervised learning methods with scarce labels in the CIFAR-10 dataset. In a complementary approach, Nullspace Tuning on equivalence classes offers the potential to leverage multiple subject scans when the ground truth for the subject is unknown. This work is the first to (1) explore MixMatch with Nullspace Tuning in the context of medical imaging and (2) characterize the impacts of the methods with diminishing labels. We consider two distinct medical imaging domains: skin lesion diagnosis and lung cancer prediction. In both cases we evaluate models trained with diminishing labeled data using supervised, MixMatch, and Nullspace Tuning methods as well as MixMatch with Nullspace Tuning together. MixMatch with Nullspace Tuning together is able to achieve an AUC of 0.755 in lung cancer diagnosis with only 200 labeled subjects on the National Lung Screening Trial and a balanced multi-class accuracy of 77% with only 779 labeled examples on HAM10000. This performance is similar to that of the fully supervised methods when all labels are available. In advancing data driven methods in medical imaging, it is important to consider the use of current state-of-the-art semi-supervised learning methods from the greater machine learning community and their impact on the limitations of data acquisition and annotation.

Increasing reliance on electronic medical records at large medical centers provides unique opportunities to perform population level analyses exploring disease progression and etiology. The massive accumulation of diagnostic, procedure, and laboratory codes in one place has enabled the exploration of co-occurring conditions, their risk factors, and potential prognostic factors. While most of the readily identifiable associations in medical records are (now) well known to the scientific community, there is no doubt many more relationships are still to be uncovered in EMR data. In this paper, we introduce a novel finding index to help with that task. This new index uses data mined from real-time PubMed abstracts to indicate the extent to which empirically discovered associations are already known (i.e., present in the scientific literature). Our methods leverage second-generation p-values, which better identify associations that are truly clinically meaningful. We illustrate our new method with three examples: Autism Spectrum Disorder, Alzheimer's Disease, and Optic Neuritis. Our results demonstrate wide utility for identifying new associations in EMR data that have the highest priority among the complex web of correlations and causalities. Data scientists and clinicians can work together more effectively to discover novel associations that are both empirically reliable and clinically understudied., Competing Interests: The authors have declared that no competing interests exist.

Importance: The United States Preventive Services Task Force (USPSTF) recommends low-dose computed tomography screening for lung cancer. However, USPSTF screening guidelines were derived from a study population including only 4% African American smokers, and racial differences in smoking patterns were not considered., Objective: To evaluate the diagnostic accuracy of USPSTF lung cancer screening eligibility criteria in a predominantly African American and low-income cohort., Design, Setting, and Participants: The Southern Community Cohort Study prospectively enrolled adults visiting community health centers across 12 southern US states from March 25, 2002, through September 24, 2009, and followed up for cancer incidence through December 31, 2014. Participants included African American and white current and former smokers aged 40 through 79 years. Statistical analysis was performed from May 11, 2016, to December 6, 2018., Exposures: Self-reported race, age, and smoking history. Cumulative exposure smoking histories encompassed most recent follow-up questionnaires., Main Outcomes and Measures: Incident lung cancer cases assessed for eligibility for lung cancer screening using USPSTF criteria., Results: Among 48 364 ever smokers, 32 463 (67%) were African American and 15 901 (33%) were white, with 1269 incident lung cancers identified. Among all 48 364 Southern Community Cohort Study participants, 5654 of 32 463 African American smokers (17%) were eligible for USPSTF screening compared with 4992 of 15 901 white smokers (31%) (P < .001). Among persons diagnosed with lung cancer, a significantly lower percentage of African American smokers (255 of 791; 32%) was eligible for screening compared with white smokers (270 of 478; 56%) (P < .001). The lower percentage of eligible lung cancer cases in African American smokers was primarily associated with fewer smoking pack-years among African American vs white smokers (median pack-years: 25.8 [interquartile range, 16.9-42.0] vs 48.0 [interquartile range, 30.2-70.5]; P < .001). Racial disparity was observed in the sensitivity and specificity of USPSTF guidelines between African American and white smokers for all ages. Lowering the smoking pack-year eligibility criteria to a minimum 20-pack-year history was associated with an increased percentage of screening eligibility of African American smokers and with equitable performance of sensitivity and specificity compared with white smokers across all ages (for a 55-year-old current African American smoker, sensitivity increased from 32.2% to 49.0% vs 56.5% for a 55-year-old white current smoker; specificity decreased from 83.0% to 71.6% vs 69.4%; P < .001)., Conclusions and Relevance: Current USPSTF lung cancer screening guidelines may be too conservative for African American smokers. The findings suggest that race-specific adjustment of pack-year criteria in lung cancer screening guidelines would result in more equitable screening for African American smokers at high risk for lung cancer.

We introduce and extend the classical regression framework for conducting mediation analysis from the fit of only one model. Using the essential mediation components (EMCs) allows us to estimate causal mediation effects and their analytical variance. This single-equation approach reduces computation time and permits the use of a rich suite of regression tools that are not easily implemented on a system of three equations. Additionally, we extend this framework to non-nested mediation systems, provide a joint measure of mediation for complex mediation hypotheses, propose new visualizations for mediation effects, and explain why estimates of the total effect may differ depending on the approach used. Using data from social science studies, we also provide extensive illustrations of the usefulness of this framework and its advantages over traditional approaches to mediation analysis. The example data are freely available for download online and we include the R code necessary to reproduce our results.

Rare variants in the cardiac potassium channel K V 7.1 ( KCNQ1 ) and sodium channel Na V 1.5 ( SCN5A ) are implicated in genetic disorders of heart rhythm, including congenital long QT and Brugada syndromes (LQTS, BrS), but also occur in reference populations. We previously reported two sets of Na V 1.5 ( n  = 356) and K V 7.1 ( n  = 144) variants with in vitro characterized channel currents gathered from the literature. Here we investigated the ability to predict commonly reported Na V 1.5 and K V 7.1 variant functional perturbations by leveraging diverse features including variant classifiers PROVEAN, PolyPhen-2, and SIFT; evolutionary rate and BLAST position specific scoring matrices (PSSM); and structure-based features including "functional densities" which is a measure of the density of pathogenic variants near the residue of interest. Structure-based functional densities were the most significant features for predicting Na V 1.5 peak current (adj. R 2  = 0.27) and K V 7.1 + KCNE1 half-maximal voltage of activation (adj. R 2  = 0.29). Additionally, use of structure-based functional density values improves loss-of-function classification of SCN5A variants with an ROC-AUC of 0.78 compared with other predictive classifiers (AUC = 0.69; two-sided DeLong test p  = .01). These results suggest structural data can inform predictions of the effect of uncharacterized SCN5A and KCNQ1 variants to provide a deeper understanding of their burden on carriers.

Mediation analysis explores the degree to which an exposure's effect on an outcome is diverted through a mediating variable. We describe a classical regression framework for conducting mediation analyses in which estimates of causal mediation effects and their variance are obtained from the fit of a single regression model. The vector of changes in exposure pathway coefficients, which we named the essential mediation components (EMCs), is used to estimate standard causal mediation effects. Because these effects are often simple functions of the EMCs, an analytical expression for their model-based variance follows directly. Given this formula, it is instructive to revisit the performance of routinely used variance approximations (e.g., delta method and resampling methods). Requiring the fit of only one model reduces the computation time required for complex mediation analyses and permits the use of a rich suite of regression tools that are not easily implemented on a system of three equations, as would be required in the Baron-Kenny framework. Using data from the BRAIN-ICU study, we provide examples to illustrate the advantages of this framework and compare it with the existing approaches.

Introduction: Lung cancer is a leading cause of cancer-related death worldwide. Racial disparities in lung cancer survival exist between blacks and whites, yet they are limited by categorical definitions of race. We sought to examine the impact of African ancestry on overall survival among blacks and whites with NSCLC cases., Methods: Incident cases of NSCLC in blacks and whites from the prospective Southern Community Cohort Study (N = 425) were identified through linkage with state cancer registries in 12 southern states. Vital status was determined by linkage with the National Death Index and Social Security Administration. We evaluated the impact of African ancestry (as estimated by using genome-wide ancestry-informative markers) on overall survival by calculating the time-dependent area under the curve (AUC) for Cox proportional hazards models, adjusting for relevant covariates such as stage and treatment. We replicated our findings in an independent population of NSCLC cases in blacks., Results: Global African ancestry was not significantly associated with overall survival among NSCLC cases. There was no change in model performance when Cox proportional hazards models with and without African ancestry were compared (AUC = 0.79 for each model). Removal of stage and treatment reduced the average time-dependent AUC from 0.79 to 0.65. Similar findings were observed in our replication study., Conclusions: Stage and treatment are more important predictors of survival than African ancestry is. These findings suggest that racial disparities in lung cancer survival may disappear with similar early detection efforts for blacks and whites., (Copyright © 2018 International Association for the Study of Lung Cancer. All rights reserved.)

Background: Accurately predicting the impact of rare nonsynonymous variants on disease risk is an important goal in precision medicine. Variants in the cardiac sodium channel SCN5A (protein Na V 1.5; voltage-dependent cardiac Na+ channel) are associated with multiple arrhythmia disorders, including Brugada syndrome and long QT syndrome. Rare SCN5A variants also occur in ≈1% of unaffected individuals. We hypothesized that in vitro electrophysiological functional parameters explain a statistically significant portion of the variability in disease penetrance., Methods: From a comprehensive literature review, we quantified the number of carriers presenting with and without disease for 1712 reported SCN5A variants. For 356 variants, data were also available for 5 Na V 1.5 electrophysiological parameters: peak current, late/persistent current, steady-state V1/2 of activation and inactivation, and recovery from inactivation., Results: We found that peak and late current significantly associate with Brugada syndrome ( P <0.001; ρ=-0.44; Spearman rank test) and long QT syndrome disease penetrance ( P <0.001; ρ=0.37). Steady-state V1/2 activation and recovery from inactivation associate significantly with Brugada syndrome and long QT syndrome penetrance, respectively. Continuous estimates of disease penetrance align with the current American College of Medical Genetics classification paradigm., Conclusions: Na V 1.5 in vitro electrophysiological parameters are correlated with Brugada syndrome and long QT syndrome disease risk. Our data emphasize the value of in vitro electrophysiological characterization and incorporating counts of affected and unaffected carriers to aid variant classification. This quantitative analysis of the electrophysiological literature should aid the interpretation of Na V 1.5 variant electrophysiological abnormalities and help improve Na V 1.5 variant classification., (© 2018 American Heart Association, Inc.)

Objectives: Acute kidney injury frequently complicates critical illness and is associated with high morbidity and mortality. Frailty is common in critical illness survivors, but little is known about the impact of acute kidney injury. We examined the association of acute kidney injury and frailty within a year of hospital discharge in survivors of critical illness., Design: Secondary analysis of a prospective cohort study., Setting: Medical/surgical ICU of a U.S. tertiary care medical center., Patients: Three hundred seventeen participants with respiratory failure and/or shock., Interventions: None., Measurements and Main Results: Acute kidney injury was determined using Kidney Disease Improving Global Outcomes stages. Clinical frailty status was determined using the Clinical Frailty Scale at 3 and 12 months following discharge. Covariates included mean ICU Sequential Organ Failure Assessment score and Acute Physiology and Chronic Health Evaluation II score as well as baseline comorbidity (i.e., Charlson Comorbidity Index), kidney function, and Clinical Frailty Scale score. Of 317 patients, 243 (77%) had acute kidney injury and one in four patients with acute kidney injury was frail at baseline. In adjusted models, acute kidney injury stages 1, 2, and 3 were associated with higher frailty scores at 3 months (odds ratio, 1.92; 95% CI, 1.14-3.24; odds ratio, 2.40; 95% CI, 1.31-4.42; and odds ratio, 4.41; 95% CI, 2.20-8.82, respectively). At 12 months, a similar association of acute kidney injury stages 1, 2, and 3 and higher Clinical Frailty Scale score was noted (odds ratio, 1.87; 95% CI, 1.11-3.14; odds ratio, 1.81; 95% CI, 0.94-3.48; and odds ratio, 2.76; 95% CI, 1.34-5.66, respectively). In supplemental and sensitivity analyses, analogous patterns of association were observed., Conclusions: Acute kidney injury in survivors of critical illness predicted worse frailty status 3 and 12 months postdischarge. These findings have important implications on clinical decision making among acute kidney injury survivors and underscore the need to understand the drivers of frailty to improve patient-centered outcomes.

Importance: Clinicians rely heavily on fluorodeoxyglucose F18-labeled positron emission tomography (FDG-PET) imaging to evaluate lung nodules suspicious for cancer. We evaluated the performance of FDG-PET for the diagnosis of malignancy in differing populations with varying cancer prevalence., Objective: To determine the performance of FDG-PET/computed tomography (CT) in diagnosing lung malignancy across different populations with varying cancer prevalence., Design, Setting, and Participants: Multicenter retrospective cohort study at 6 academic medical centers and 1 Veterans Affairs facility that comprised a total of 1188 patients with known or suspected lung cancer from 7 different cohorts from 2005 to 2015., Exposures: 18F fluorodeoxyglucose PET/CT imaging., Main Outcome and Measures: Final diagnosis of cancer or benign disease was determined by pathological tissue diagnosis or at least 18 months of stable radiographic follow-up., Results: Most patients were male smokers older than 60 years. Overall cancer prevalence was 81% (range by cohort, 50%-95%). The median nodule size was 22 mm (interquartile range, 15-33 mm). Positron emission tomography/CT sensitivity and specificity were 90.1% (95% CI, 88.1%-91.9%) and 39.8% (95% CI, 33.4%-46.5%), respectively. False-positive PET scans occurred in 136 of 1188 patients. Positive predictive value and negative predictive value were 86.4% (95% CI, 84.2%-88.5%) and 48.7% (95% CI, 41.3%-56.1%), respectively. On logistic regression, larger nodule size and higher population cancer prevalence were both significantly associated with PET accuracy (odds ratio, 1.027; 95% CI, 1.015-1.040 and odds ratio, 1.030; 95% CI, 1.021-1.040, respectively). As the Mayo Clinic model-predicted probability of cancer increased, the sensitivity and positive predictive value of PET/CT imaging increased, whereas the specificity and negative predictive value dropped., Conclusions and Relevance: High false-positive rates were observed across a range of cancer prevalence. Normal PET/CT scans were not found to be reliable indicators of the absence of disease in patients with a high probability of lung cancer. In this population, aggressive tissue acquisition should be prioritized using a comprehensive lung nodule program that emphasizes advanced tissue acquisition techniques such as CT-guided fine-needle aspiration, navigational bronchoscopy, and endobronchial ultrasonography.

Verifying that a statistically significant result is scientifically meaningful is not only good scientific practice, it is a natural way to control the Type I error rate. Here we introduce a novel extension of the p-value-a second-generation p-value (pδ)-that formally accounts for scientific relevance and leverages this natural Type I Error control. The approach relies on a pre-specified interval null hypothesis that represents the collection of effect sizes that are scientifically uninteresting or are practically null. The second-generation p-value is the proportion of data-supported hypotheses that are also null hypotheses. As such, second-generation p-values indicate when the data are compatible with null hypotheses (pδ = 1), or with alternative hypotheses (pδ = 0), or when the data are inconclusive (0 < pδ < 1). Moreover, second-generation p-values provide a proper scientific adjustment for multiple comparisons and reduce false discovery rates. This is an advance for environments rich in data, where traditional p-value adjustments are needlessly punitive. Second-generation p-values promote transparency, rigor and reproducibility of scientific results by a priori specifying which candidate hypotheses are practically meaningful and by providing a more reliable statistical summary of when the data are compatible with alternative or null hypotheses.

This meta-analysis assesses the prognostic value of quantitative dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) and diffusion-weighted MRI (DW-MRI) performed during neoadjuvant therapy (NAT) of locally advanced breast cancer. A systematic literature search was conducted to identify studies of quantitative DCE-MRI and DW-MRI performed during breast cancer NAT that report the sensitivity and specificity for predicting pathological complete response (pCR). Details of the study population and imaging parameters were extracted from each study for subsequent meta-analysis. Metaregression analysis, subgroup analysis, study heterogeneity, and publication bias were assessed. Across 10 studies that met the stringent inclusion criteria for this meta-analysis (out of 325 initially identified studies), we find that MRI had a pooled sensitivity of 0.91 [95% confidence interval (CI), 0.80 to 0.96] and specificity of 0.81(95% CI, 0.68 to 0.89) when adjusted for covariates. Quantitative DCE-MRI exhibits greater specificity for predicting pCR than semiquantitative DCE-MRI ([Formula: see text]). Quantitative DCE-MRI and DW-MRI are able to predict, early in the course of NAT, the eventual response of breast tumors, with a high level of specificity and sensitivity. However, there is a high degree of heterogeneity in published studies highlighting the lack of standardization in the field.

Background: Surrogate outcomes are often utilized when disease outcomes are difficult to directly measure. When a biological threshold effect exists, surrogate outcomes may only represent disease in specific subpopulations. We refer to these outcomes as "partial surrogate outcomes." We hypothesized that risk models of partial surrogate outcomes would perform poorly if they fail to account for this population heterogeneity. We developed criteria for predictive model development using partial surrogate outcomes and demonstrate their importance in model selection and evaluation within the clinical example of serum creatinine, a partial surrogate outcome for acute kidney injury., Methods: Data from 4737 patients who underwent cardiac surgery at a major academic center were obtained. Linear and mixture models were fit on maximum 2-day serum creatinine change as a surrogate for estimated glomerular filtration rate at 90 days after surgery (eGFR90), adjusted for known AKI risk factors. The AUC for eGFR90 decline and Spearman's rho were calculated to compare model discrimination between the linear model and a single component of the mixture model deemed to represent the informative subpopulation. Simulation studies based on the clinical data were conducted to further demonstrate the consistency and limitations of the procedure., Results: The mixture model was highly favored over the linear model with BICs of 2131.3 and 5034.3, respectively. When model discrimination was evaluated with respect to the partial surrogate, the linear model displays superior performance ( p  < 0.001); however, when it was evaluated with respect to the target outcome, the mixture model approach displays superior performance (AUC difference p  = 0.002; Spearman's difference p  = 0.020). Simulation studies demonstrate that the nature of the heterogeneity determines the magnitude of any advantage the mixture model., Conclusions: Partial surrogate outcomes add complexity and limitations to risk score modeling, including the potential for the usual metrics of discrimination to be misleading. Partial surrogacy can be potentially uncovered and appropriately accounted for using a mixture model approach. Serum creatinine behaved as a partial surrogate outcome consistent with two patient subpopulations, one representing patients whose injury did not exceed their renal functional reserve and a second population representing patients whose injury did exceed renal functional reserve., Competing Interests: This study was approved by the Vanderbilt University IRB #120372.NAThe authors declare that they have no competing interests.Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Background: Acute kidney injury (AKI) after cardiac surgery is associated with increased short- and long-term mortality. Inflammation, oxidative stress, and endothelial dysfunction and damage play important roles in the development of AKI. High-density lipoproteins (HDLs) have anti-inflammatory and antioxidant properties and improve endothelial function and repair. Statins enhance HDL's anti-inflammatory and antioxidant capacities. We hypothesized that a higher preoperative HDL cholesterol concentration is associated with decreased AKI after cardiac surgery and that perioperative statin exposure potentiates this association., Methods and Results: We tested our hypothesis in 391 subjects from a randomized clinical trial of perioperative atorvastatin to reduce AKI after cardiac surgery. A 2-component latent variable mixture model was used to assess the association between preoperative HDL cholesterol concentration and postoperative change in serum creatinine, adjusted for known AKI risk factors and suspected confounders. Interaction terms were used to examine the effects of preoperative statin use, preoperative statin dose, and perioperative atorvastatin treatment on the association between preoperative HDL and AKI. A higher preoperative HDL cholesterol concentration was independently associated with a decreased postoperative serum creatinine change ( P =0.02). The association between a high HDL concentration and an attenuated increase in serum creatinine was strongest in long-term statin-using patients ( P =0.008) and was further enhanced with perioperative atorvastatin treatment ( P =0.004) and increasing long-term statin dose ( P =0.003)., Conclusions: A higher preoperative HDL cholesterol concentration was associated with decreased AKI after cardiac surgery. Preoperative and perioperative statin treatment enhanced this association, demonstrating that pharmacological potentiation is possible during the perioperative period., Clinical Trial Registration: URL: http://www.clinicaltrials.gov. Unique Identifier: NCT00791648., (© 2017 The Authors. Published on behalf of the American Heart Association, Inc., by Wiley.)

Background: An emerging standard-of-care for long-QT syndrome uses clinical genetic testing to identify genetic variants of the KCNQ1 potassium channel. However, interpreting results from genetic testing is confounded by the presence of variants of unknown significance for which there is inadequate evidence of pathogenicity., Methods and Results: In this study, we curated from the literature a high-quality set of 107 functionally characterized KCNQ1 variants. Based on this data set, we completed a detailed quantitative analysis on the sequence conservation patterns of subdomains of KCNQ1 and the distribution of pathogenic variants therein. We found that conserved subdomains generally are critical for channel function and are enriched with dysfunctional variants. Using this experimentally validated data set, we trained a neural network, designated Q1VarPred, specifically for predicting the functional impact of KCNQ1 variants of unknown significance. The estimated predictive performance of Q1VarPred in terms of Matthew's correlation coefficient and area under the receiver operating characteristic curve were 0.581 and 0.884, respectively, superior to the performance of 8 previous methods tested in parallel. Q1VarPred is publicly available as a web server at http://meilerlab.org/q1varpred., Conclusions: Although a plethora of tools are available for making pathogenicity predictions over a genome-wide scale, previous tools fail to perform in a robust manner when applied to KCNQ1. The contrasting and favorable results for Q1VarPred suggest a promising approach, where a machine-learning algorithm is tailored to a specific protein target and trained with a functionally validated data set to calibrate informatics tools., (© 2017 American Heart Association, Inc.)

Rationale: Acute kidney injury may contribute to distant organ dysfunction. Few studies have examined kidney injury as a risk factor for delirium and coma., Objectives: To examine whether acute kidney injury is associated with delirium and coma in critically ill adults., Methods: In a prospective cohort study of intensive care unit patients with respiratory failure and/or shock, we examined the association between acute kidney injury and daily mental status using multinomial transition models adjusting for demographics, nonrenal organ failure, sepsis, prior mental status, and sedative exposure. Acute kidney injury was characterized daily using the difference between baseline and peak serum creatinine and staged according to Kidney Disease Improving Global Outcomes criteria. Mental status (normal vs. delirium vs. coma) was assessed daily with the Confusion Assessment Method for the ICU and Richmond Agitation-Sedation Scale., Measurements and Main Results: Among 466 patients, stage 2 acute kidney injury was a risk factor for delirium (odds ratio [OR], 1.55; 95% confidence interval [CI], 1.07-2.26) and coma (OR, 2.04; 95% CI, 1.25-3.34) as was stage 3 injury (OR for delirium, 2.56; 95% CI, 1.57-4.16) (OR for coma, 3.34; 95% CI, 1.85-6.03). Daily peak serum creatinine (adjusted for baseline) values were also associated with delirium (OR, 1.35; 95% CI, 1.18-1.55) and coma (OR, 1.44; 95% CI, 1.20-1.74). Renal replacement therapy modified the association between stage 3 acute kidney injury and daily peak serum creatinine and both delirium and coma., Conclusions: Acute kidney injury is a risk factor for delirium and coma during critical illness.

Background: 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., Methods: 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., Results: The latent variable mixture model demonstrated superior fit (likelihood ratio of 6.68 × 10 71 ) 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%)., Conclusions: 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.

There is a compelling and growing need to accurately predict the impact of amino acid mutations on protein stability for problems in personalized medicine and other applications. Here the ability of 10 computational tools to accurately predict mutation-induced perturbation of folding stability (ΔΔG) for membrane proteins of known structure was assessed. All methods for predicting ΔΔG values performed significantly worse when applied to membrane proteins than when applied to soluble proteins, yielding estimated concordance, Pearson, and Spearman correlation coefficients of <0.4 for membrane proteins. Rosetta and PROVEAN showed a modest ability to classify mutations as destabilizing (ΔΔG < -0.5 kcal/mol), with a 7 in 10 chance of correctly discriminating a randomly chosen destabilizing variant from a randomly chosen stabilizing variant. However, even this performance is significantly worse than for soluble proteins. This study highlights the need for further development of reliable and reproducible methods for predicting thermodynamic folding stability in membrane proteins.

Unlabelled: Our purpose was to evaluate the safety and efficacy of (68)Ga-DOTATATE PET/CT compared with (111)In-pentetreotide imaging for diagnosis, staging, and restaging of pulmonary and gastroenteropancreatic neuroendocrine tumors., Methods: (68)Ga-DOTATATE PET/CT and (111)In-pentetreotide scans were obtained for 78 of 97 consecutively enrolled patients with known or suspected pulmonary or gastroenteropancreatic neuroendocrine tumors. Safety and toxicity were measured by comparing vital signs, serum chemistry values, or acquisition-related medical complications before and after (68)Ga-DOTATATE injection. Added value was determined by changes in treatment plan when (68)Ga-DOTATATE PET/CT results were added to all prior imaging, including (111)In-pentetreotide. Interobserver reproducibility of (68)Ga-DOTATATE PET/CT scan interpretation was measured between blinded and nonblinded interpreters., Results: (68)Ga-DOTATATE PET/CT and (111)In-pentetreotide scans were significantly different in impact on treatment (P < 0.001). (68)Ga-DOTATATE PET/CT combined with CT or liver MRI changed care in 28 of 78 (36%) patients. Interobserver agreement between blinded and nonblinded interpreters was high. No participant had a trial-related event requiring treatment. Mild, transient events were tachycardia in 1, alanine transaminase elevation in 1, and hyperglycemia in 2 participants. No clinically significant arrhythmias occurred. (68)Ga-DOTATATE PET/CT correctly identified 3 patients for peptide-receptor radiotherapy incorrectly classified by (111)In-pentetreotide., Conclusion: (68)Ga-DOTATATE PET/CT was equivalent or superior to (111)In-pentetreotide imaging in all 78 patients. No adverse events requiring treatment were observed. (68)Ga-DOTATATE PET/CT changed treatment in 36% of participants. Given the lack of significant toxicity, lower radiation exposure, and improved accuracy compared with (111)In-pentetreotide, (68)Ga-DOTATATE imaging should be used instead of (111)In-pentetreotide imaging where available., (© 2016 by the Society of Nuclear Medicine and Molecular Imaging, Inc.)

To many, the foundations of statistical inference are cryptic and irrelevant to routine statistical practice. The analysis of 2 x 2 contingency tables, omnipresent in the scientific literature, is a case in point. Fisher's exact test is routinely used even though it has been fraught with controversy for over 70 years. The problem, not widely acknowledged, is that several different p-values can be associated with a single table, making scientific inference inconsistent. The root cause of this controversy lies in the table's origins and the manner in which nuisance parameters are eliminated. However, fundamental statistical principles (e.g., sufficiency, ancillarity, conditionality, and likelihood) can shed light on the controversy and guide our approach in using this test. In this paper, we use these fundamental principles to show how much information is lost when the tables origins are ignored and when various approaches are used to eliminate unknown nuisance parameters. We present novel likelihood contours to aid in the visualization of information loss and show that the information loss is often virtually non-existent. We find that problems arising from the discreteness of the sample space are exacerbated by p-value-based inference. Accordingly, methods that are less sensitive to this discreteness - likelihood ratios, posterior probabilities and mid-p-values - lead to more consistent inferences.