The Thoracic Research Evaluation and Treatment 2.0 Model: A Lung Cancer Prediction Model for Indeterminate Nodules Referred for Specialist Evaluation

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.

Racial Disparities in Lung Cancer Stage of Diagnosis Among Adults Living in the Southeastern United States

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.

Abstract PO-236: Examining racial disparities in lung cancer stage of diagnosis among low-income adults living in the southeastern U.S

Purpose: Black Americans experience poorer lung cancer survival than White Americans. Racial disparities in stage at diagnosis may contribute to these survival differences, but few studies have explored factors leading to racial disparities in lung cancer stage at diagnosis. We aimed to identify multilevel factors contributing to racial disparities in stage of lung cancer presentation. Methods: Using data from the Southern Community Cohort Study (SCCS), we examined factors associated with distant stage among adults diagnosed with incident lung cancer. SCCS participants were prospectively enrolled, primarily from community health centers between 2002 and 2009 across a 12-state area. Incident cancers were identified by linkage with state cancer registries through end of follow-up in 2019. Self-reported social, behavioral, and medical history information were ascertained at baseline via questionnaire. Cumulative exposure smoking histories were identified using the most recent follow-up questionnaires. Residential addresses and National Cancer Institute Comprehensive Cancer Center locations were geocoded, and residential addresses were linked to census data. Logistic and multinomial regression models were used to identify factors predictive of distant stage diagnosis. Penalized regression was used to shrink the predictor space of these models when necessary. Findings were replicated in an independent population. Results: Among 1,672 incident SCCS lung cancer cases (35% White, 61% Black, and 3% other self-reported race), a greater percentage of Black participants than White participants were diagnosed with distant stage lung cancer (56.4% vs 49.4%, respectively). Overall, Black participants had greater odds of distant vs local stage compared to White participants (odds ratio (OR) = 1.28, 95% confidence interval (CI): 1.05-1.58). Greater area deprivation was also associated with distant lung cancer stage (OR = 1.55, 95% CI: 1.17-2.04). After controlling for individual and area-level factors, there was no significant difference in the odds of distant stage disease for Black participants compared to White participants (OR = 1.03, 95% CI: 0.80-1.33). Significant interactions between race and area deprivation index were not observed. However, greater residential distance from a comprehensive cancer center was significantly associated with increased odds of distant stage disease in the final model (OR = 1.04, 95% CI: 1.00-1.08). No significant differences were observed in the odds of distant stage lung cancer among Black and White participants in the independent population. Conclusions: A greater percentage of Black participants were diagnosed with distant stage lung cancer; however, this disparity dissipated after adjusting for individual and area-level factors. Our findings suggest racial disparities in lung cancer stage at diagnosis may be ameliorated with modifiable factors, such as patient access to high quality cancer centers.

Citation Format: Jennifer Richmond, Megan Hollister, Cato M. Milder, Ann G. Schwartz, Jeffrey D. Blume, Melinda C. Aldrich. Examining racial disparities in lung cancer stage of diagnosis among low-income adults living in the southeastern U.S. [abstract]. In: Proceedings of the AACR Virtual Conference: 14th AACR Conference on the Science of Cancer Health Disparities in Racial/Ethnic Minorities and the Medically Underserved; 2021 Oct 6-8. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2022;31(1 Suppl):Abstract nr PO-236.

FDRestimation: Flexible False Discovery Rate Computation in R

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.

Estimating the Posttest Probability of Long QT Syndrome Diagnosis for Rare KCNH2 Variants

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.

FDRestimation: Flexible False Discovery Rate Computation in R

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.

Effect of balanced crystalloids versus saline on urinary biomarkers of acute kidney injury in critically ill adults

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.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12882-021-02236-x.

Missing data and prediction: the pattern submodel

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.

Semi-supervised Machine Learning with MixMatch and Equivalence Classes

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.

A Bayesian method to estimate variant-induced disease penetrance

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.

Discovering novel disease comorbidities using electronic medical records

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.

Assessment of Fluorodeoxyglucose F18-Labeled Positron Emission Tomography for Diagnosis of High-Risk Lung Nodules

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.

Evaluation of USPSTF Lung Cancer Screening Guidelines Among African American Adult Smokers

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.

SCN5A (NaV1.5) Variant Functional Perturbation and Clinical Presentation: Variants of a Certain Significance

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_V1.5; 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_V1.5 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_V1.5 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_V1.5 variant electrophysiological abnormalities and help improve Na_V1.5 variant classification.

A Regression Framework for Causal Mediation Analysis with Applications to Behavioral Science

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.

Protein structure aids predicting functional perturbation of missense variants in SCN5A and KCNQ1

Rare variants in the cardiac potassium channel K_V7.1 (KCNQ1) and sodium channel Na_V1.5 (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_V1.5 (n = 356) and K_V7.1 (n = 144) variants with in vitro characterized channel currents gathered from the literature. Here we investigated the ability to predict commonly reported Na_V1.5 and K_V7.1 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_V1.5 peak current (adj. R² = 0.27) and K_V7.1 + KCNE1 half-maximal voltage of activation (adj. R² = 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.

A classical regression framework for mediation analysis: fitting one model to estimate mediation effects

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.

Racial Disparities in Lung Cancer Survival: The Contribution of Stage, Treatment, and Ancestry

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.

Predicting the Functional Impact of KCNQ1 Variants of Unknown Significance

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.

Second-generation p-values: Improved rigor, reproducibility, & transparency in statistical analyses

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.

High-Density Lipoprotein Cholesterol Concentration and Acute Kidney Injury After Cardiac Surgery

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.

Dynamic contrast-enhanced magnetic resonance imaging and diffusion-weighted magnetic resonance imaging for predicting the response of locally advanced breast cancer to neoadjuvant therapy: a meta-analysis

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 (p<0.001). 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.

Acute Kidney Injury as a Risk Factor for Delirium and Coma during Critical Illness

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.

Latent variable modeling improves AKI risk factor identification and AKI prediction compared to traditional methods

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 × 10⁷¹) 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.

Electronic supplementary material

The online version of this article (doi:10.1186/s12882-017-0465-1) contains supplementary material, which is available to authorized users.

A general approach to risk modeling using partial surrogate markers with application to perioperative acute kidney injury

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.

Documentation of an Imperative To Improve Methods for Predicting Membrane Protein Stability

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.

Safety and Efficacy of 68Ga-DOTATATE PET/CT for Diagnosis, Staging, and Treatment Management of Neuroendocrine Tumors

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.

Predicting lung cancer prior to surgical resection in patients with lung nodules

BACKGROUND

Existing predictive models for lung cancer focus on improving screening or referral for biopsy in general medical populations. A predictive model calibrated for use during preoperative evaluation of suspicious lung lesions is needed to reduce unnecessary operations for a benign disease. A clinical prediction model (Thoracic Research Evaluation And Treatment [TREAT]) is proposed for this purpose.

METHODS

We developed and internally validated a clinical prediction model for lung cancer in a prospective cohort evaluated at our institution. Best statistical practices were used to construct, evaluate, and validate the logistic regression model in the presence of missing covariate data using bootstrap and optimism corrected techniques. The TREAT model was externally validated in a retrospectively collected Veteran Affairs population. The discrimination and calibration of the model was estimated and compared with the Mayo Clinic model in both the populations.

RESULTS

The TREAT model was developed in 492 patients from Vanderbilt whose lung cancer prevalence was 72% and validated among 226 Veteran Affairs patients with a lung cancer prevalence of 93%. In the development cohort, the area under the receiver operating curve (AUC) and Brier score were 0.87 (95% confidence interval [CI], 0.83-0.92) and 0.12, respectively compared with the AUC 0.89 (95% CI, 0.79-0.98) and Brier score 0.13 in the validation dataset. The TREAT model had significantly higher accuracy (p < 0.001) and better calibration than the Mayo Clinic model (AUC = 0.80; 95% CI, 75-85; Brier score = 0.17).

CONCLUSION

The validated TREAT model had better diagnostic accuracy than the Mayo Clinic model in preoperative assessment of suspicious lung lesions in a population being evaluated for lung resection.

Elucidating the foundations of statistical inference with 2 x 2 tables

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.

The fellowship effect: how the establishment of a fellowship in female pelvic medicine and reconstructive surgery affected resident vaginal hysterectomy training

OBJECTIVE

We report on trends in resident-performed vaginal hysterectomies before and after the establishment of a female pelvic medicine and reconstructive surgery fellowship at Vanderbilt University Medical Center.

STUDY DESIGN

We examined medical records and resident self-reports concerning all hysterectomies at our institution in an 8-year period: 4 years before fellowship and 4 years after. Route of hysterectomy, resident and fellow involvement, and division of attending surgeon were recorded from the electronic medical record. Resident Accreditation Council for Graduate Medical Education (ACGME) case log data were used to estimate the number of hysterectomies where residents reported themselves as the primary surgeon.

RESULTS

During the 8-year period of this study, 3317 hysterectomies were performed at our institution, 41% (1371) before and 59% (1946) after fellowship. Prior to fellowship, 29% (393) were vaginal, 56% (766) were abdominal, and 15% (212) were laparoscopic/robotic. After addition of fellowship, 23% (449) were vaginal, 31% (597) were abdominal, and 46% (900) were laparoscopic/robotic. Of the total vaginal hysterectomies (TVH), there was resident involvement in 98.0% (385) cases before fellowship and 98.2% (441) cases after fellowship. From the ACGME case log data, the resident identified himself/herself as the primary surgeon in 388 cases before and 393 cases after fellowship. During this time period, medical records indicate a fellow was involved in 42% (189) of TVH, with resident involvement in all but 5 of these procedures.

CONCLUSION

Frequency of resident involvement in TVH cases, either as primary surgeon or team member, remained constant after the addition of the female pelvic medicine and reconstructive surgery fellowship.

Accuracy of FDG-PET to diagnose lung cancer in areas with infectious lung disease: a meta-analysis

IMPORTANCE

Positron emission tomography (PET) combined with fludeoxyglucose F 18 (FDG) is recommended for the noninvasive diagnosis of pulmonary nodules suspicious for lung cancer. In populations with endemic infectious lung disease, FDG-PET may not accurately identify malignant lesions.

OBJECTIVES

To estimate the diagnostic accuracy of FDG-PET for pulmonary nodules suspicious for lung cancer in regions where infectious lung disease is endemic and compare the test accuracy in regions where infectious lung disease is rare.

DATA SOURCES AND STUDY SELECTION

Databases of MEDLINE, EMBASE, and the Web of Science were searched from October 1, 2000, through April 28, 2014. Articles reporting information sufficient to calculate sensitivity and specificity of FDG-PET to diagnose lung cancer were included. Only studies that enrolled more than 10 participants with benign and malignant lesions were included. Database searches yielded 1923 articles, of which 257 were assessed for eligibility. Seventy studies were included in the analysis. Studies reported on a total of 8511 nodules; 5105 (60%) were malignant.

DATA EXTRACTION AND SYNTHESIS

Abstracts meeting eligibility criteria were collected by a research librarian and reviewed by 2 independent reviewers. Hierarchical summary receiver operating characteristic curves were constructed. A random-effects logistic regression model was used to summarize and assess the effect of endemic infectious lung disease on test performance.

MAIN OUTCOME AND MEASURES

The sensitivity and specificity for FDG-PET test performance.

RESULTS

Heterogeneity for sensitivity (I2 = 87%) and specificity (I2 = 82%) was observed across studies. The pooled (unadjusted) sensitivity was 89% (95% CI, 86%-91%) and specificity was 75% (95% CI, 71%-79%). There was a 16% lower average adjusted specificity in regions with endemic infectious lung disease (61% [95% CI, 49%-72%]) compared with nonendemic regions (77% [95% CI, 73%-80%]). Lower specificity was observed when the analysis was limited to rigorously conducted and well-controlled studies. In general, sensitivity did not change appreciably by endemic infection status, even after adjusting for relevant factors.

CONCLUSIONS AND RELEVANCE

The accuracy of FDG-PET for diagnosing lung nodules was extremely heterogeneous. Use of FDG-PET combined with computed tomography was less specific in diagnosing malignancy in populations with endemic infectious lung disease compared with nonendemic regions. These data do not support the use of FDG-PET to diagnose lung cancer in endemic regions unless an institution achieves test performance accuracy similar to that found in nonendemic regions.

Racial variation in the quality of surgical care for prostate cancer

PURPOSE

Difference in the quality of care may contribute to the less optimal prostate cancer treatment outcomes among black men compared with white men. We determined whether a racial quality of care gap exists in surgical care for prostate cancer, as evidenced by racial variation in the use of high volume surgeons and facilities, and in the quality of certain outcome measures of care.

MATERIALS AND METHODS

We performed cross-sectional and cohort analyses of administrative data from the Healthcare Cost and Utilization Project all-payer State Inpatient Databases, encompassing all nonfederal hospitals in Florida, Maryland and New York State from 1996 to 2007. Included in analysis were men 18 years old or older with a diagnosis of prostate cancer who underwent radical prostatectomy. We compared the use of surgeons and/or hospitals in the top quartile of annual volume for this procedure, inpatient blood transfusion, complications, mortality and length of stay between black and white patients.

RESULTS

Of 105,972 patients 81,112 (76.5%) were white, 14,006 (13.2%) were black, 6,999 (6.6%) were Hispanic and 3,855 (3.6%) were all other. In mixed effects multivariate models, black men had markedly lower use of high volume hospitals (OR 0.73, 95% CI 0.70-0.76) and surgeons (OR 0.67, 95% CI 0.64-0.70) compared to white men. Black men also had higher odds of blood transfusion (OR 1.08, 95% CI 1.01-1.14), longer length of stay (OR 1.07, 95% CI 1.06-1.07) and inpatient mortality (OR 1.73, 95% CI 1.02-2.92).

CONCLUSIONS

Using an all-payer data set, we identified concerning potential quality of care gaps between black and white men undergoing radical prostatectomy for prostate cancer.

Locally advanced breast cancer: MR imaging for prediction of response to neoadjuvant chemotherapy--results from ACRIN 6657/I-SPY TRIAL

Purpose

This study was designed to determine (1) rates of clinically meaningful tumor reduction in breast tumor size following neoadjuvant chemotherapy (NAC), (2) which receptor subtypes and MRI phenotypes are associated with clinically meaningful tumor reduction, and (3) whether MRI phenotype impacts concordance between pathologic and MRI size.

Methods

We analyzed data from the I-SPY TRIAL, a multicenter, prospective NAC trial. Reduction in tumor size from >4 to ≤4 cm was considered clinically meaningful, as crossing this threshold was considered a reasonable cutoff for potential breast conservation therapy (BCT). MRI phenotypes were scored between one (well-defined) and five (diffuse) on pre-NAC MRIs.

Results

Of 174 patients with tumors >4 cm, 141 (81 %) had clinically meaningful tumor reduction. Response to therapy varied by MRI phenotype (p = 0.003), with well-defined phenotypes more likely than diffuse phenotypes to have clinically meaningful tumor shrinkage (91 vs. 72 %, p = 0.037). Her2+ and triple-negative (Tneg) tumors had the highest rate of clinically meaningful tumor reduction (p = 0.005). The concordance between tumor diameter on MRI and surgical pathology was highest for Her2+ and Tneg tumors, especially among tumors with solid imaging phenotypes (p = 0.004).

Discussion

NAC allows most patients with large breast tumors to have clinically meaningful tumor reduction, meaning response that would impact ability to undergo BCT. However, response varies by imaging and tumor subtypes. Concordance between tumor size on MRI and surgical pathology was higher in well-defined tumors, especially those with a Tneg subtype, and lower in HR+ diffuse tumors.

Electronic supplementary material

The online version of this article (doi:10.1245/s10434-013-3038-y) contains supplementary material, which is available to authorized users.

Locally advanced breast cancer: MR imaging for prediction of response to neoadjuvant chemotherapy--results from ACRIN 6657/I-SPY TRIAL

PURPOSE

To compare magnetic resonance (MR) imaging findings and clinical assessment for prediction of pathologic response to neoadjuvant chemotherapy (NACT) in patients with stage II or III breast cancer.

MATERIALS AND METHODS

The HIPAA-compliant protocol and the informed consent process were approved by the American College of Radiology Institutional Review Board and local-site institutional review boards. Women with invasive breast cancer of 3 cm or greater undergoing NACT with an anthracycline-based regimen, with or without a taxane, were enrolled between May 2002 and March 2006. MR imaging was performed before NACT (first examination), after one cycle of anthracyline-based treatment (second examination), between the anthracycline-based regimen and taxane (third examination), and after all chemotherapy and prior to surgery (fourth examination). MR imaging assessment included measurements of tumor longest diameter and volume and peak signal enhancement ratio. Clinical size was also recorded at each time point. Change in clinical and MR imaging predictor variables were compared for the ability to predict pathologic complete response (pCR) and residual cancer burden (RCB). Univariate and multivariate random-effects logistic regression models were used to characterize the ability of tumor response measurements to predict pathologic outcome, with area under the receiver operating characteristic curve (AUC) used as a summary statistic.

RESULTS

Data in 216 women (age range, 26-68 years) with two or more imaging time points were analyzed. For prediction of both pCR and RCB, MR imaging size measurements were superior to clinical examination at all time points, with tumor volume change showing the greatest relative benefit at the second MR imaging examination. AUC differences between MR imaging volume and clinical size predictors at the early, mid-, and posttreatment time points, respectively, were 0.14, 0.09, and 0.02 for prediction of pCR and 0.09, 0.07, and 0.05 for prediction of RCB. In multivariate analysis, the AUC for predicting pCR at the second imaging examination increased from 0.70 for volume alone to 0.73 when all four predictor variables were used. Additional predictive value was gained with adjustments for age and race.

CONCLUSION

MR imaging findings are a stronger predictor of pathologic response to NACT than clinical assessment, with the greatest advantage observed with the use of volumetric measurement of tumor response early in treatment.

An evidential approach to non-inferiority clinical trials

We present likelihood methods for defining the non-inferiority margin and measuring the strength of evidence in non-inferiority trials using the 'fixed-margin' framework. Likelihood methods are used to (1) evaluate and combine the evidence from historical trials to define the non-inferiority margin, (2) assess and report the smallest non-inferiority margin supported by the data, and (3) assess potential violations of the constancy assumption. Data from six aspirin-controlled trials for acute coronary syndrome and data from an active-controlled trial for acute coronary syndrome, Organisation to Assess Strategies for Ischemic Syndromes (OASIS-2) trial, are used for illustration. The likelihood framework offers important theoretical and practical advantages when measuring the strength of evidence in non-inferiority trials. Besides eliminating the influence of sample spaces and prior probabilities on the 'strength of evidence in the data', the likelihood approach maintains good frequentist properties. Violations of the constancy assumption can be assessed in the likelihood framework when it is appropriate to assume a unifying regression model for trial data and a constant control effect including a control rate parameter and a placebo rate parameter across historical placebo controlled trials and the non-inferiority trial. In situations where the statistical non-inferiority margin is data driven, lower likelihood support interval limits provide plausibly conservative candidate margins.

Trends in estradiol during critical illness are associated with mortality independent of admission estradiol

BACKGROUND

We have previously demonstrated that elevated serum estradiol (E(2)) at intensive care unit (ICU) admission is associated with death in the critically ill, regardless of sex. However, little is known about how changes in initial E(2) during the course of care might signal increasing patient acuity or risk of death. We hypothesized that changes from baseline serum E(2) during the course of critical illness are more strongly associated with mortality than a single E(2) level at admission.

STUDY DESIGN

A prospective cohort of 1,408 critically ill or injured nonpregnant adult patients requiring ICU care for ≥48 hours with admission and subsequent E(2) levels was studied. Demographics, illness severity, and E(2) levels were examined, and the probability of mortality was modeled with multivariate logistic regression. Changes in E(2) were examined by both analysis of variance and logistic regression.

RESULTS

Overall mortality was 14.1% [95% confidence interval (CI) 12.3% to 16%]. Both admission and subsequent E(2) levels were independently associated with mortality [admission E(2) odds ratio 1.1 (CI 1.0 to 1.2); repeat estradiol odds ratio 1.3 (CI 1.2 to1.4)], with subsequent values being stronger. Changes in E(2) were independently associated with mortality [odds ratio 1.1 (CI 1.0 to 1.16)] and improved regression model performance. The regression model produced an area under the receiver operating characteristic curve of 0.80 (CI 0.77 to 0.83).

CONCLUSIONS

Although high admission levels of E(2) are associated with mortality, changes from baseline E(2) in critically ill or injured adults are independently associated with mortality. Future studies of E(2) dynamics may yield new indicators of patient acuity and illuminate underlying mechanisms for targeted therapy.

Percutaneous radiofrequency ablation of painful osseous metastases: a multicenter American College of Radiology Imaging Network trial

BACKGROUND

The study was conducted to determine whether radiofrequency ablation (RFA) can safely reduce pain from osseous metastatic disease.

METHODS

The single-arm prospective trial included patients with a single painful bone metastasis with unremitting pain with a score >50 on a pain scale of 0-100. Percutaneous computed tomography-guided RFA of the bone metastasis to temperatures >60 degrees C was performed. Endpoints were the toxicity and pain effects of RFA before and at 2 weeks, 1 month, and 3 months after RFA.

RESULTS

Fifty-five patients completed RFA. Grade 3 toxicities occurred in 3 of 55 (5%) patients. RFA reduced pain at 1 and 3 months for all pain assessment measures. The average increase in pain relief from pre-RFA to 1-month follow-up is 26.3 (95% confidence interval [CI], 17.7-34.9; P < .0001), and the increase from pre-RFA to 3-month follow-up is 16.38 (95% CI, 3.4-29.4; P = .02). The average decrease in pain intensity from pre-RFA to 1-month follow-up was 26.9 (P < .0001) and 14.2 for 3-month follow-up (P = .02). The odds of lower pain severity at 1-month follow-up were 14.0 (95% CI, 2.3-25.7; P < .0001) times higher than at pre-RFA, and the odds at 3-month follow-up were 8.0 (95% CI, 0.9-15.2; P < .001) times higher than at pre-RFA. The average increase in mood from pre-RFA to 1-month follow-up was 19.9 (P < .0001) and 14.9 to 3-month follow-up (P = .005).

CONCLUSIONS

This cooperative group trial strongly suggests that RFA can safely palliate pain from bone metastases.

Reasons women at elevated risk of breast cancer refuse breast MR imaging screening: ACRIN 6666

PURPOSE

To determine reasons for nonparticipation in a trial of supplemental screening with magnetic resonance (MR) imaging after mammography and ultrasonography (US).

MATERIALS AND METHODS

Women(n = 2809) at elevated risk of breast cancer were enrolled in the American College of Radiology Imaging Network 6666 US Screening Protocol at 21 institutions. Fourteen institutions met technical and experience requirements for this institutional review board-approved, HIPAA-compliant substudy of supplemental screening with MR imaging. Those women who had completed 0-, 12-, and 24-month screenings with mammography combined with US were considered for a single contrast material-enhanced MR examination within 8 weeks after completing the 24-month mammography-US screening. A total of 1593 women had complete MR substudy registration data: 378 of them were ineligible for the study, and 1215 had analyzable data. Reasons for nonparticipation were determined. Demographic data were compared between study participants and nonparticipants.

RESULTS

Of 1215 women with analyzable data, 703 (57.9%), with a mean age of 54.8 years, were enrolled in the MR substudy and 512 (42.1%) declined participation. Women with a 25% or greater lifetime risk of breast cancer were more likely to participate (odds ratio, 1.53; 95% confidence interval: 1.10, 2.12). Of 512 nonparticipants, 130 (25.4%) refused owing to claustrophobia; 93 (18.2%), owing to time constraints; 62 (12.1%), owing to financial concerns; 47 (9.2%), because their physician would not provide a referral and/or did not believe MR imaging was indicated; 40 (7.8%), because they were not interested; 39 (7.6%), because they were medically intolerant to MR imaging; 29 (5.7%), because they did not want to undergo intravenous injection; 27 (5.3%), owing to additional biopsy or other procedures that might be required subsequently; 21 (4.1%), owing to MR imaging scheduling constraints; 11 (2.2%), because of the travel required; seven (1.4%), owing to gadolinium-related risks or allergies; and six (1.2%), for unknown reasons.

CONCLUSION

Of 1215 women with elevated breast cancer risk who could, according to protocol guidelines, undergo breast MR imaging, only 57.9% agreed to participate.

Treatment of overactive bladder in women

OBJECTIVES

The Vanderbilt Evidence-based Practice Center systematically reviewed evidence on treatment of overactive bladder (OAB), urge urinary incontinence, and related symptoms. We focused on prevalence and incidence, treatment outcomes, comparisons of treatments, modifiers of outcomes, and costs.

DATA

We searched PubMed, MEDLINE, EMBASE, and CINAHL.

REVIEW METHODS

We included studies published in English from January 1966 to October 2008. We excluded studies with fewer than 50 participants, fewer than 75 percent women, or lack of relevance to OAB. Of 232 included publications, 20 were good quality, 145 were fair, and 67 poor. We calculated weighted averages of outcome effects and conducted a mixed-effects meta-analysis to investigate outcomes of pharmacologic treatments across studies.

RESULTS

OAB affects more than 10 to 15 percent of adult women, with 5 to 10 percent experiencing urge urinary incontinence (UUI) monthly or more often. Six available medications are effective in short term studies: estimates from meta-analysis models suggest extended release forms (taken once a day) reduce UUI by 1.78 (95 percent confidence interval (CI): 1.61, 1.94) episodes per day, and voids by 2.24 (95 percent CI: 2.03, 2.46) per day. Immediate release forms (taken twice or more a day) reduce UUI by 1.46 (95 percent CI: 1.28, 1.64), and voids by 2.17 (95 percent CI: 1.81, 2.54). As context, placebo reduces UUI episodes by 1.08 (95 percent CI: 0.86, 1.30), and voids by 1.48 (95 percent CI: 1.19, 1.71) per day. No one drug was definitively superior to others, including comparison of newer more selective agents to older antimuscarinics. Current evidence is insufficient to guide choice of other therapies including sacral neuromodulation, instillation of oxybutynin, and injections of botulinum toxin. Acupuncture was the sole complementary and alternative medicine treatment, among reflexology and hypnosis, with early evidence of benefit. The strength of the evidence is insufficient to fully inform choice of these treatments. Select behavioral interventions were associated with symptom improvements comparable to medications. Limited evidence suggests no clear benefit from adding behavioral interventions at the time of initiation of pharmacologic treatment.

CONCLUSIONS

OAB and associated symptoms are common. Treatment effects are modest. Quality of life and treatment satisfaction measures suggest such improvements can be important to women. The amount of high quality literature available is meager for helping guide women's choices. Gaps include weak or absent data about long-term followup, poorly characterized and potentially concerning harms, information about best choices to minimize side effects, and study of how combinations of approaches may best be used. This is problematic since the condition is chronic and a single treatment modality is unlikely to fully resolve symptoms for most women.

Prostate cancer: sextant localization at MR imaging and MR spectroscopic imaging before prostatectomy--results of ACRIN prospective multi-institutional clinicopathologic study

PURPOSE

To determine the incremental benefit of combined endorectal magnetic resonance (MR) imaging and MR spectroscopic imaging, as compared with endorectal MR imaging alone, for sextant localization of peripheral zone (PZ) prostate cancer.

MATERIALS AND METHODS

This prospective multicenter study, conducted by the American College of Radiology Imaging Network (ACRIN) from February 2004 to June 2005, was institutional review board approved and HIPAA compliant. Research associates were required to follow consent guidelines approved by the Office for Human Research Protection and established by the institutional review boards. One hundred thirty-four patients with biopsy-proved prostate adenocarcinoma and scheduled to undergo radical prostatectomy were recruited at seven institutions. T1-weighted, T2-weighted, and spectroscopic MR sequences were performed at 1.5 T by using a pelvic phased-array coil in combination with an endorectal coil. Eight readers independently rated the likelihood of the presence of PZ cancer in each sextant by using a five-point scale-first on MR images alone and later on combined MR-MR spectroscopic images. Areas under the receiver operating characteristic curve (AUCs) were calculated with sextant as the unit of analysis. The presence or absence of cancer at centralized histopathologic evaluation of prostate specimens was the reference standard. Reader-specific receiver operating characteristic curves for values obtained with MR imaging alone and with combined MR imaging-MR spectroscopic imaging were developed. The AUCs were estimated by using Mann-Whitney statistics and appropriate 95% confidence intervals.

RESULTS

Complete data were available for 110 patients (mean age, 58 years; range, 45-72 years). MR imaging alone and combined MR imaging-MR spectroscopic imaging had similar accuracy in PZ cancer localization (AUC, 0.60 vs 0.58, respectively; P > .05). AUCs for individual readers were 0.57-0.63 for MR imaging alone and 0.54-0.61 for combined MR imaging-MR spectroscopic imaging.

CONCLUSION

In patients who undergo radical prostatectomy, the accuracy of combined 1.5-T endorectal MR imaging-MR spectroscopic imaging for sextant localization of PZ prostate cancer is equal to that of MR imaging alone.

Bounding Sample Size Projections for the Area Under a ROC Curve

Studies of diagnostic tests are often designed with the goal of estimating the area under the receiver operating characteristic curve (AUC) because the AUC is a natural summary of a test's overall diagnostic ability. However, sample size projections dealing with AUCs are very sensitive to assumptions about the variance of the empirical AUC estimator, which dependens on two correlation parameters. While these correlation parameters can be estimated from available data, in practice it is hard to find reliable estimates before the study is conducted. Here we derive achievable bounds on the projected sample size that are free of these two correlation parameters. The lower bound is the smallest sample size that would yield the desired level of precision for some model, while the upper bound is the smallest sample size that would yield the desired level of precision for all models. These bounds are important reference points when designing a single or multi-arm study; they are the absolute minimum and maximum sample size that would ever be required. When the study design includes multiple readers or interpreters of the test, we derive bounds pertaining to the average reader AUC and the 'pooled' or overall AUC for the population of readers. These upper bounds for multireader studies are not too conservative when several readers are involved.