The heterogeneous wound microbiome varies with wound care pain, dressing type, and inflammatory gene expression.

Wound dressing changes are essential procedures for wound management. However, ~50% of patients experience severe pain during these procedures despite the availability of analgesic medications, indicating a need for novel therapeutics that address underlying causes of pain. Along with other clinical factors, wound pathogens and inflammatory immune responses have previously been implicated in wound pain. To test whether these factors could contribute to severe pain during wound dressing changes, we conducted an exploratory, cross-sectional analysis of patient-reported pain, inflammatory immune responses, and wound microbiome composition in 445 wounds at the time of a study dressing change. We profiled the bacterial composition of 406 wounds using 16S ribosomal RNA amplicon sequencing and quantified gene expression of 13 inflammatory markers in wound fluid using quantitative real-time polymerase chain reaction (qPCR). Neither inflammatory gene expression nor clinically observed inflammation were associated with severe pain, but Corynebacterium and Streptococcus were of lower relative abundance in wounds of patients reporting severe pain than those reporting little or no pain. Wound microbiome composition differed by wound location, and correlated with six of the inflammatory markers, including complement receptor C5AR1, pro-inflammatory cytokine interleukin (IL)1ß, chemokine IL-8, matrix metalloproteinase MMP2, and the antimicrobial peptide encoding cathelicidin antimicrobial peptide. Interestingly, we found a relationship between the wound microbiome and vacuum-assisted wound closure (VAC). These findings identify preliminary, associative relationships between wound microbiota and host factors which motivate future investigation into the directional relationships between wound care pain, wound closure technologies, and the wound microbiome.

Obuchowski-Rockette analysis for multi-reader multi-case (MRMC) readers-nested-in-test study design with unequal numbers of readers.

The Obuchowski-Rockette method has been an important tool for analyzing multi-reader multi-case (MRMC) radiologic imaging data. Although the typical study design for such studies has been the factorial design, where each reader reads each case using each test (modality), sometimes a reader-nested-in-test design is more appropriate. We consider such an example in this talk, where 53 Australian and 15 Singaporean breast radiologists interpreted the same test in their respective locations. In this paper we show how the Obuchowski-Rockette method can be used for analysis of such data, without assuming that the number of readers is the same for each test.

Relationship between Obuchowski-Rockette-Hillis and Gallas methods for analyzing multi-reader diagnostic imaging data with empirical AUC as the reader performance measure.

For analyzing multireader multicase (MRMC) diagnostic imaging data when the reader performance measure of interest is the area under the receiver-operating-characteristic curve (AUC), two popular methods of analysis that allow conclusions to generalize to both the reader and case populations are the method developed by Obuchowski, Rockette and Hillis (ORH) and the method primarily developed by Gallas (Gallas). While the ORH method is a general method that is applicable to most reader performance metrics, the Gallas method is limited to those metrics for which an unbiased variance estimate exists. Previously it was not known if the ORH method could be adapted so as to produce the same variance estimate as the Gallas method. In this paper I show that a recently proposed version of the OR method produces the same unconstrained variance statistic as the Gallas method. However, the two methods differ in their approaches to constraining the variance estimate to be nonnegative and in their degrees-of-freedom estimates. These two differences are discussed and recommendations given. In addition, several contributions to the development of the ORH method are made, including determining sufficient conditions for unbiased variance estimates and providing justification for the ORH variance constraints and covariance estimation method.

Roe and Metz identical-test simulation model for validating multi-reader methods of analysis for comparing different radiologic imaging modalities.

Purpose: The most frequently used model for simulating multi-reader multi-case (MRMC) data that emulate confidence-of-disease ratings from diagnostic imaging studies has been the Roe and Metz model, proposed by Roe and Metz in 1997 and later generalized by Hillis (2012), Abbey et al. (2013), and Gallas and Hillis (2014). These models have been used for evaluating MRMC analysis and sample size methods. The models suggested in these papers for assessing type I error have been null models, where the expected area under the receiver-operating-characteristic curve across readers is the same for each test. However, for these null models, there are other differences that would not exist if the two tests were identical. None of the papers mentioned above discuss how to formulate a null model that is also an identical-test model, where the two tests are identical in all respects. The purpose of this paper is to show how to formulate a Roe and Metz identical-test model and to show its usefulness for validating the error covariance constraints employed by the Obuchowski-Rockette (1995) method. Approach: For a given Roe-and-Metz model, the corresponding Roe-and-Metz identical-test model is derived by modifying the Roe and Metz null model under the assumption that the two tests are identical. Results: The importance of the Obuchowski-Rockette model constraints for avoiding negative variance estimates is established using data simulated from the Roe and Metz identical-test model. It is also shown that negative variance estimates can occur at nontrivial rates when the two tests are not identical but somewhat "close" to being identical. Conclusions: The findings of this paper are important because it has recently been shown (Hillis, 2022) that the commonly used MRMC method proposed by Gallas (2006) and Gallas et al. (2009) uses the same test statistic as the unconstrained Obuchowski-Rockette method.

Radiologists Help Each Other to Improve Efficiency by Decreasing the Time to Look Up a Patient's Relevant Clinical History in the Electronic Medical Record.

The study aims to prove that it takes less time to look up relevant clinical history from an electronic medical record (EMR) if the information is already provided in a specific space in the EMR by a fellow radiologist. Patients with complex oncological and surgical histories need frequent imaging, and every time a radiologist may spend a significant amount of time looking up the same clinical information as their peers. In collaboration with ACMIO and Radiant Epic team, a space labeled "Specialty Comments" was added to the SNAPSHOT of patient's chart in EMR. For our research purpose, the specialty comment was labeled as boxed history as a variable for data analysis. If the history was not provided in that particular space, it was labeled as without boxed history. Inclusion criteria included outpatients with complex oncological histories undergoing CT chest, abdomen, and pelvis with IV contrast. The time to look up history (LUT) was documented in minutes and seconds. Two assistant professors from Abdominal Imaging provided LUT. A total of 85 cases were included in the study, 39 with boxed history and 46 without boxed history. Comparing averages of the individual reader means for history, mean LUT differed by 2.03 min (without boxed history) versus 0.57 min (with boxed history), p < 0.0001. The t-test and the nonparametric Wilcoxon tests for a difference in the population means were highly significant (p < 0.0001). A history directed to radiologist's needs resulted in a statistically significant decrease in time spent by interpreting radiologists to look through the electronic medical records for patients with complex oncological histories. Availability of history pertinent to radiology has wide-ranging advantages, including quality reporting, decrease in turnaround time, reduction in interpretation errors, and radiologists' continued learning. The space for documenting clinical history may be reproduced, or some similar area may be developed by optimizing the electronic medical records.

A Pilot Study to Assess the Performance of Phase-Sensitive Breast Tomosynthesis.

Background A new modality, phase-sensitive breast tomosynthesis (PBT), may have similar diagnostic performance to conventional breast tomosynthesis but with a reduced radiation dose. Purpose To perform a pilot study of the performance of a novel PBT system compared with conventional digital breast tomosynthesis (DBT) in patients undergoing additional diagnostic imaging workup for breast lesions. Materials and Methods In a prospective study from June 2020 to March 2021, participants with suspicious breast lesions detected at screening DBT or MRI were recruited for additional PBT imaging before additional diagnostic workup or biopsy. In this pilot study, nine radiologists independently evaluated image quality and assessed the likelihood of lesion malignancy by retrospectively evaluating DBT and PBT images in two separate reading sessions. Image quality was rated subjectively using a Likert scale from 1 to 5. Areas under the receiver operating characteristic curve (AUCs) were used to compare the lesion classification (malignant vs benign) performance of the radiologists. Results Images in 50 patients (mean age, 56 years ± 12 [SD]; 49 women) with 52 evaluable lesions (28 malignant) were assessed. For image appearance and general feature visibility, DBT images had a higher total mean image quality score (3.8) than PBT images (2.9), with P < .002 for each comparison. For classification of lesions as benign or malignant, the AUCs were 0.74 for both PBT and DBT. PBT images were acquired at a 24% mean radiation dose reduction (mean, 1.78 mGy vs 2.34 mGy for DBT; P < .001). Conclusion The phase-sensitive breast tomosynthesis system had a 24% lower mean radiation dose compared with digital breast tomosynthesis, although with lower image quality. Diagnostic performance of the system remains to be determined in larger studies. © RSNA, 2022 Online supplemental material is available for this article. See also the editorial by Gao and Moy in this issue.

MATLAB toolbox for ROC analysis of multi-reader multi-case diagnostic imaging studies.

A common study design for comparing the performances of diagnostic imaging tests is to obtain ratings from multiple readers of multiple cases whose true statuses are known. Typically, there is overlap between the tests, readers, and/or cases for which special analytical methods are needed to perform statistical comparisons. We present our new MATLAB MRMCaov toolbox, which is designed for multi-reader multi-case comparisons of two or more diagnostic tests. The toolbox allows for statistical comparison of reader performance metrics, such as area under the receiver operating characteristic curve (ROC AUC), with analysis of variance methods originally proposed by Obuchowski and Rockette (1995) and later unified and improved by Hillis and colleagues (2005, 2007, 2008, 2018). MRMCaov is open-source software with an integrated command-line interface for performing multi-reader multi-case statistical analysis, plotting, and presenting results. Its features (1) ROC AUC, likelihood ratios of positive or negative ratings, sensitivity, specificity, and expected utility reader performance metrics; (2) reader-specific ROC curves; (3) user-definable performance metrics; (4) test-specific estimates of mean performance along with confidence intervals and p-values for statistical comparisons; (5) support for factorial, nested, or partially paired study designs; (6) inference for random or fixed readers and cases; (7) DeLong, jackknife, or unbiased covariance estimation; and (8) compatibility with Microsoft Windows, Mac OS, and Linux.

Identical-test Roe and Metz simulation model for validating multi-reader methods of analysis for comparing different radiologic imaging modalities.

The most frequently used model for simulating multireader multicase (MRMC) data that emulate confidence-of-disease ratings from diagnostic imaging studies has been the Roe and Metz model, proposed by Roe and Metz in 1997 and later generalized by Hillis (2012), Abbey et al (2013) and Gallas and Hillis (2014). All of these models generate continuous confidence-of-disease ratings based on an underlying binormal model for each reader, with the separation between the normal and abnormal rating distributions varying across readers. Numerous studies have used these models for evaluating MRMC analysis and sample size methods. The models suggested in these papers for assessing type I error have been "null" models, where the expected AUC across readers is the same for each test. However, for the null models that have been suggested, there are other differences that would not exist if the two tests were identical. None of the papers cited above discuss how to formulate a null model that is also an "identical-test" model, where the two tests are identical in all respects. The purpose of this paper is to show how to formulate an identical-test model and to discuss the importance of this model. Using the identical-test model, I show through simulations the importance of the Obuchowski-Rockette model constraints to avoid a negative variance estimate, a result which had not previously been empirically demonstrated.

A clinical tool to predict severe pain during wound dressing changes.

ABSTRACT: Dressing changes cause severe pain (ie, 8-10 on a 10-point scale) for approximately one-third (36%) of patients with open skin wounds. No tool exists that allows nurses to predict which patients are likely to experience severe pain during dressing changes. The aim of this study was to develop a clinical tool to predict severe pain during dressing changes using clinically accessible wound and pain predictors and to evaluate the diagnostic validity of this model. Using a cross-sectional design, a one-time study dressing change was conducted by the same wound care nurse on 445 subjects while concurrently measuring patient and wound predictors and pain intensity during the dressing change. Three predictors came out of the study as most useful for a clinical prediction tool: type of dressing, resting wound pain, and expected pain. Algorithms based on these predictors are presented, which can be applied in other settings to predict patients likely to experience severe pain during a dressing change. This is the first study to systematically examine a comprehensive set of wound and patient predictors for their individual and collective associations with pain during dressing changes using precisely defined and rigorously measured study variables. The ability to predict which patients are likely to have severe pain during dressing changes is critically needed so that they can be targeted for preventive pain control strategies.

Determining Roe and Metz model parameters for simulating multireader multicase confidence-of-disease rating data based on real-data or conjectured Obuchowski-Rockette parameter estimates.

Purpose: The most frequently used model for simulating multireader multicase (MRMC) data that emulates confidence-of-disease ratings from diagnostic imaging studies has been the Roe and Metz (RM) model, proposed by Roe and Metz in 1997 and later generalized by Hillis (2012), Abbey et al. (2013), and Gallas and Hillis (2014). A problem with these models is that it has been difficult to set model parameters such that the simulated data are similar to MRMC data encountered in practice. To remedy this situation, Hillis (2018) mapped parameters from the RM model to Obuchowski-Rockette (OR) model parameters that describe the distribution of the empirical AUC outcomes computed from the RM model simulated data. We continue that work by providing the reverse mapping, i.e., by deriving an algorithm that expresses RM parameters as functions of the OR empirical AUC distribution parameters. Approach: We solve for the corresponding RM parameters in terms of the OR parameters using numerical methods. Results: An algorithm is developed that results in, at most, one solution of RM parameter values that correspond to inputted OR parameter values. The algorithm can be implemented using an R software function. Examples are provided that illustrate the use of the algorithm. A simulation study validates the algorithm. Conclusions: The resulting algorithm makes it possible to easily determine RM model parameter values such that simulated data emulate a specific real-data study. Thus, MRMC analysis methods can be empirically tested using simulated data similar to that encountered in practice.

A phase sensitive x-ray breast tomosynthesis system: Preliminary patient images with cancer lesions.

Phase-sensitive x-ray imaging continues to attract research for its ability to visualize weakly absorbing details like those often encountered in biology and medicine. We have developed and assembled the first inline-based high-energy phase sensitive breast tomosynthesis (PBT) system, which is currently undergoing patient imaging testing at a clinical site. The PBT system consists of a microfocus polychromatic x-ray source and a direct conversion-based flat panel detector coated with a 1 mm thick amorphous selenium layer allowing a high detective quantum efficiency at high energies. The PBT system scans a compressed breast over 15° with 9 angular projection views. The high-energy scan parameters are carefully selected to ensure similar or lower mean glandular dose levels to the clinical standard of care systems. Phase retrieval and data binning are applied to the phase contrast angular projection views and a filtered back-projection algorithm is used to reconstruct the final images. This article reports the distributions of radiation dose versus thickness of the compressed breasts at 59 and 89 kV and sample PBT images acquired from 3 patients. Preliminary PBT images demonstrate the feasibility of this new imaging modality to acquire breast images at lower radiation dose as compared to the clinical digital breast tomosynthesis system with enhanced lesion characteristics (i.e. lesion spiculation and margins).

Development and preclinical evaluation of a patient-specific high energy x-ray phase sensitive breast tomosynthesis system.

BACKGROUND: This article reports the first x-ray phase sensitive breast tomosynthesis (PBT) system that is aimed for direct translation to clinical practice for the diagnosis of breast cancer. PURPOSE: To report the preclinical evaluation and comparison of the newly built PBT system with a conventional digital breast tomosynthesis (DBT) system. METHODS AND MATERIALS: The PBT system is developed based on a comprehensive inline phase contrast theoretical model. The system consists of a polyenergetic microfocus x-ray source and a flat panel detector mounted on an arm that is attached to a rotating gantry. It acquires nine projections over a 15° angular span in a stop-and-shoot manner. A dedicated phase retrieval algorithm is integrated with a filtered back-projection method that reconstructs tomographic slices. The American College of Radiology (ACR) accreditation phantom, a contrast detail (CD) phantom and mastectomy tissue samples were imaged at the same glandular dose levels by both the PBT and a standard of care DBT system for image quality characterizations and comparisons. RESULTS: The PBT imaging scores with the ACR phantom are in good to excellent range and meet the quality assurance criteria set by the Mammography Quality Standard Act. The CD phantom image comparison and associated statistical analyses from two-alternative forced-choice reader studies confirm the improvement offered by the PBT system in terms of contrast resolution, spatial resolution, and conspicuity. The artifact spread function (ASF) analyses revealed a sizable lateral spread of metal artifacts in PBT slices as compared to DBT slices. Signal-to-noise ratio values for various inserts of the ACR and CD phantoms further validated the superiority of the PBT system. Mastectomy sample images acquired by the PBT system showed a superior depiction of microcalcifications vs the DBT system. CONCLUSION: The PBT imaging technology can be clinically employed for improving the accuracy of breast cancer screening and diagnosis.

Multi-reader multi-case analysis of variance software for diagnostic performance comparison of imaging modalities.

A common study design for comparing the diagnostic performance of imaging modalities is to obtain modality-specific ratings from multiple readers of multiple cases whose true statuses are known. Typically, there is overlap between the modalities, readers, and/or cases for which special analytical methods are needed to perform statistical comparisons. We describe our new R software package MRMCaov, which is designed for multi-reader multi-case comparisons of two or more imaging modalities. The software allows for the comparison of reader performance metrics, such as area under the receiver operating characteristic curve (ROC AUC), with analysis of variance methods originally proposed by Obuchowski and Rockette (1995) and later unified and improved by Hillis and colleagues (2005, 2007, 2008, 2018). MRMCaov is an open-source package with an integrated command-line interface for performing multi-reader multi-case statistical analysis, plotting, and presenting results. Features of the package include (1) ROC curves estimated parametrically or non-parametrically; (2) reader-specific ROC curves and performance metrics; (3) user-definable performance metrics; (4) modality-specific estimates of mean performance along with confidence intervals and p-values for statistical comparisons; (5) support for factorial, nested, or partially paired study designs; (6) inference for random readers and cases, random readers and fixed cases, or fixed readers and random cases; (7) DeLong, jackknife, or unbiased covariance estimation; and (8) compatibility with Microsoft Windows, Mac OS, and Linux.

Determining Roe and Metz model parameters for simulating multireader multicase confidence-of-disease rating data based on real-data or conjectured Obuchowski-Rockette parameter estimates.

The most frequently used model for simulating MRMC data that emulate confidence-of-disease ratings from diagnostic imaging studies has been the Roe and Metz model, proposed in 1997. The RM model generates continuous confidence-of-diseases ratings based on an underlying equal-variance binormal model for each reader, with the separation between the normal and abnormal rating distributions varying across readers. A problem with the RM model is that the parameters are expressed in terms of the rating distributions, as opposed to the reader performance outcomes. Because MRMC analysis results are almost always expressed in terms of the reader performance outcomes, and not in terms of the rating data distributions, it has been difficult to assess how similar the simulated data are to MRMC data encountered in practice. To remedy this situation, recently Hillis (in 2018) derived formulas expressing parameters that describe the distribution of empirical AUC outcomes computed from RM simulated data as functions of the RM parameters. An examination of these values revealed several problems with the realism of the simulated data. This paper continues that work by providing the inverse mapping, i.e., by deriving an algorithm that expresses the RM parameters as functions of the AUC empirical distribution parameters. This result will enable the creation of a recalibrated RM model that more closely emulates real-data studies.

Interpretation time for screening mammography as a function of the number of computer-aided detection marks.

Purpose: Computer-aided detection (CAD) alerts radiologists to findings potentially associated with breast cancer but is notorious for creating false-positive marks. Although a previous paper found that radiologists took more time to interpret mammograms with more CAD marks, our impression was that this was not true in actual interpretation. We hypothesized that radiologists would selectively disregard these marks when present in larger numbers. Approach: We performed a retrospective review of bilateral digital screening mammograms. We use a mixed linear regression model to assess the relationship between number of CAD marks and ln (interpretation time) after adjustment for covariates. Both readers and mammograms were treated as random sampling units. Results: Ten radiologists, with median experience after residency of 12.5 years (range 6 to 24) interpreted 1832 mammograms. After accounting for number of images, Breast Imaging Reporting and Data System category, and breast density, the number of CAD marks was positively associated with longer interpretation time, with each additional CAD mark proportionally increasing median interpretation time by 4.35% for a typical reader. Conclusions: We found no support for our hypothesis that radiologists will selectively disregard CAD marks when they are present in larger numbers.

Estimating latent reader-performance variability using the Obuchowski-Rockette method.

We describe how the Obuchowski-Rockette (OR) method of analysis for multi-reader diagnostic studies can be used to estimate the variability of latent reader-performance outcomes, such as the area under the ROC curve (AUC). For a specific reader the latent or true reader performance outcome can conceptually be thought of as the average of the estimates that would result if the reader were to read a very large number of case samples. We note that for the sample sizes used in typical diagnostic studies, the latent reader-performance outcome is equal to the observed outcome minus measurement error. An often-cited study that assesses the variability of various reader-performance outcomes, including the AUC, is the study by Craig Beam et. al., "Variability in the Interpretation of Screening Mammograms by US Radiologists," published in 1996. However, a problem with this type of study is that the variability estimates include measurement error. Thus this approach overestimates latent reader variability and gives variability estimates that are dependent on case sample size. The proposed method overcomes this problem. We illustrate the proposed method for 29 radiologists in Jordan, with each reading 60 chest computed tomography (CT) scans. Using the OR method we estimate the middle 95% range for latent AUC values to be 0.07; i.e., we estimate that 95% of radiologists differ by less than 0.07 in their ability to successfully discriminate between a pair of diseased and nondiseased cases. In contrast, the estimate for the 95% range for the observed AUCs is 0.18. Thus we see how the conventional method of describing variability of reader performance estimates can greatly overstate the variability of the true abilities of the readers.

Multireader sample size program for diagnostic studies: demonstration and methodology.

The software "Multireader sample size program for diagnostic studies," written by Kevin Schartz and Stephen Hillis, performs sample size computations for diagnostic reader-performance studies. The program computes the sample size needed to detect a specified difference in a reader-performance measure between two imaging modalities when using the analysis methods initially proposed by Dorfman, Berbaum, and Metz, and Obuchowski and Rockette, and later unified and improved by Hillis and colleagues. A commonly used reader-performance measure is the area under the receiver-operating-characteristic curve. The program has an easy-to-use step-by-step intuitive interface that walks the user through the entry of the needed information. It can be used with several different study designs, inference procedures, hypotheses, and input and output formats. The program is functional in Windows, OS X, and Linux. The methodology underlying the software is discussed for the most common diagnostic study design, where each reader evaluates each case using each modality.

Severe pain during wound care procedures: A cross-sectional study protocol.

AIM: The aim of this study is to: (a) develop and evaluate a model to predict severe pain during wound care procedures (WCPs) so that high-risk patients can be targeted for specialized dressings and preventive pain control; and (b) identify biological factors associated with severe pain during WCPs so that novel pain control strategies can be developed. BACKGROUND: Wound care procedures such as dressing changes can cause moderate to severe pain in 74% of patients, with nearly half (36%) of all patients experiencing severe pain (rated as 8-10 on a 10-point numeric rating scale) during dressing change. Additionally, clinicians have little direction with current guidelines regarding pain control during WCPs including the selection of the appropriate advanced wound dressings and the appropriate use of analgesics. DESIGN: This is a cross-sectional study. METHODS: The National Institute of Nursing Research approved and funded the study June of 2015 and the appropriate Institutional Review Board approved all study protocols prior to funding. Study enrolment is underway at the University of Iowa Hospitals and Clinics with a target of 525 participants. Potential participants must be adults (21+ years) and have a nonburn, nondiabetic foot, full-thickness wound. The research team performs a one-time study dressing change on enrolled participants and collects all study data. DISCUSSION: This study will allow the development of a tool for clinicians to use to predict severe pain during WCPs and identify biological factors significantly associated with severe pain during WCPs.

Relationship between Roe and Metz simulation model for multireader diagnostic data and Obuchowski-Rockette model parameters.

For the typical diagnostic radiology study design, each case (ie, patient) undergoes several diagnostic tests (or modalities) and the resulting images are interpreted by several readers. Often, each reader is asked to assign a confidence-of-disease rating to each case for each test, and the diagnostic tests are compared with respect to reader-performance outcomes that are functions of the reader receiver operating characteristic (ROC) curves, such as the area under the ROC curve. These reader-performance outcomes are frequently analyzed using the Obuchowski and Rockette method, which allows conclusions to generalize to both the reader and case populations. The simulation model proposed by Roe and Metz (RM) in 1997 emulates confidence-of-disease data collected from such studies and has been an important tool for empirically evaluating various reader-performance analysis methods. However, because the RM model parameters are expressed in terms of a continuous decision variable rather than in terms of reader-performance outcomes, it has not been possible to evaluate the realism of the RM model. I derive the relationships between the RM and Obuchowski-Rockette model parameters for the empirical area under the ROC curve reader-performance outcome. These relationships make it possible to evaluate the realism of the RM parameter models and to assess the performance of Obuchowski-Rockette parameter estimates. An example illustrates the application of the relationships for assessing the performance of a proposed upper one-sided confidence bound for the Obuchowski-Rockette test-by-reader variance component, which is useful for sample size estimation.

Longitudinal associations between mental health conditions and overactive bladder in women veterans.

BACKGROUND: One in 5 recently deployed US women veterans report overactive bladder symptoms. Mental health conditions such as depression and anxiety commonly co-occur in women with overactive bladder, but temporal relationships between these outcomes have not been well studied, and the mechanism behind this association is unknown. The Women Veterans Urinary Health Study, a nationwide longitudinal study in recently deployed women veterans, was designed to better understand relationships between overactive bladder and mental health conditions. OBJECTIVE: We sought to estimate the 1-year incidence and remission of overactive bladder and to identify the impact of depression, anxiety, posttraumatic stress disorder, and prior sexual assault on 1-year overactive bladder incidence and remission rates. STUDY DESIGN: Participants of this 1-year prospective cohort study were female veterans separated from military service who had returned from Iraq or Afghanistan deployment within the previous 2 years. Eligible women were identified through the Defense Manpower Data Center and recruited by mail and telephone. Telephone screening confirmed participants were ambulatory, community-dwelling veterans and excluded those with urinary tract fistula, congenital abnormality, or cancer; pelvic radiation; spinal cord injury; multiple sclerosis; Parkinson disease; stroke; or current/recent pregnancy. Data collection included computer-assisted telephone interviews performed at enrollment and 1 year later. The interview assessed demographic and military service characteristics; urinary symptoms and treatment; depression, anxiety, and posttraumatic stress disorder symptoms and treatment; and a lifetime history of sexual assault. Overactive bladder was identified if at least moderately bothersome urgency urinary incontinence and/or urinary frequency symptoms were reported on Urogenital Distress Inventory items. Exposures included depression, anxiety, posttraumatic stress disorder, and lifetime sexual assault, assessed at baseline using validated questionnaires (including the Patient Health Questionnaire and Posttraumatic Stress Disorder Checklist). Associations between exposures and overactive bladder incidence and remission were estimated using propensity score adjusted logistic regression models. RESULTS: In all, 1107 (88.0%) of 1258 eligible participants completed 1-year interviews. Median age was 29 (range 20-67) years and 53% were nulliparous. Overactive bladder was identified at baseline in 242 (22%), and 102 (9.2%), 218 (19.7%), 188 (17.0%), and 287 (25.9%) met criteria for baseline depression, anxiety, posttraumatic stress disorder, and lifetime sexual assault, respectively. At 1 year, overactive bladder incidence was 10.5% (95% confidence interval, 8.6-12.8%), and remission of overactive bladder was 36.9% (95% confidence interval, 30.8-43.4%). New overactive bladder occurred more often in women with baseline anxiety (21% vs 9%), posttraumatic stress disorder (19% vs 9%) and lifetime sexual assault (16% vs 9%) (all: P < .01). After adjustment, anxiety (odds ratio, 2.4; 95% confidence interval, 1.4-4.1) and lifetime sexual assault (odds ratio, 1.7; 95% confidence interval, 1.0-2.8) predicted 1-year incident overactive bladder. Overactive bladder remission occurred less often in those with baseline depression (19% vs 41%, P < .01) and anxiety (29% vs 42%, P = .03). After adjustment, depression decreased 1-year overactive bladder remission risk (odds ratio, 0.37; 95% confidence interval, 0.16-0.83). Overactive bladder treatment was uncommon and not associated with remission. CONCLUSION: Anxiety, depression, and prior sexual assault-common postdeployment problems for women veterans-influence the natural history of overactive bladder. Providers should screen for mental health conditions and sexual assault in women with newly diagnosed or persistent overactive bladder.