Screening Metrics Research Articles

Dockey: a modern integrated tool for large-scale molecular docking and virtual screening.

Molecular docking is a structure-based and computer-aided drug design approach that plays a pivotal role in drug discovery and pharmaceutical research. AutoDock is the most widely used molecular docking tool for study of protein-ligand interactions and virtual screening. Although many tools have been developed to streamline and automate the AutoDock docking pipeline, some of them still use outdated graphical user interfaces and have not been updated for a long time. Meanwhile, some of them lack cross-platform compatibility and evaluation metrics for screening lead compound candidates. To overcome these limitations, we have developed Dockey, a flexible and intuitive graphical interface tool with seamless integration of several useful tools, which implements a complete docking pipeline covering molecular sanitization, molecular preparation, paralleled docking execution, interaction detection and conformation visualization. Specifically, Dockey can detect the non-covalent interactions between small molecules and proteins and perform cross-docking between multiple receptors and ligands. It has the capacity to automatically dock thousands of ligands to multiple receptors and analyze the corresponding docking results in parallel. All the generated data will be kept in a project file that can be shared between any systems and computers with the pre-installation of Dockey. We anticipate that these unique characteristics will make it attractive for researchers to conduct large-scale molecular docking without complicated operations, particularly for beginners. Dockey is implemented in Python and freely available at https://github.com/lmdu/dockey.

Live Mammographic Screening Interpretation Versus Offline Same-Day Screening Interpretation ata Tertiary Cancer Center.

The aim of this study was to compare screening mammography performance metrics for immediate (live) interpretation versus offline interpretation at a cancer center. An institutional review board-approved, retrospective comparison of screening mammography metrics at a cancer center for January 1, 2018, to December 31, 2019 (live period), and September 1, 2020, to March 31, 2022 (offline period), was performed. Before July 2020, screening examinations were interpreted while patients waited (live period), and diagnostic workup was performed concurrently. After the coronavirus disease 2019 shutdown from March to mid-June 2020, offline same-day interpretation was instituted. Patients with abnormal screening results returned for separate diagnostic evaluation. Screening metrics of positive predictive value 1 (PPV1), cancer detection rate (CDR), and abnormal interpretation rate (AIR) were compared for 17 radiologists who interpreted during both periods. Statistical significance was assessed using χ2 analysis. In the live period, there were 7,105 screenings, 635 recalls, and 51 screen-detected cancers. In the offline period, there were 7,512 screenings, 586 recalls, and 47 screen-detected cancers. Comparison of live screening metrics versus offline metrics produced the following results: AIR, 8.9% (635 of 7,105) versus 7.8% (586 of 7,512) (P= .01); PPV1, 8.0% (51 of 635) versus 8.0% (47 of 586); and CDR, 7.2/1,000 versus 6.3/1,000 (P= .50). When grouped by >10% AIR or <10% AIR for the live period, the >10% AIR group showed a significant decrease in AIR for offline interpretation (from 12.7% to 9.7%, P < .001), whereas the <10% AIR group showed no significant change (from 7.4% to 6.7%, P= .17). Conversion to offline screening interpretation from immediate interpretation at a cancer center was associated with lower AIR and similar CDR and PPV1. This effect was seen largely in radiologists with AIR > 10% in the live setting.

Abstract WP83: An Artificial Intelligence Based Approach To Clinical Trial Recruitment: The Impact Of Viz Recruit On Enrollment In The Embolise Trial

Background: EMBOLISE (NCT 04402632) is an ongoing randomized controlled trial investigating the safety and efficacy of middle meningeal artery embolization for the treatment of subacute or chronic subdural hematoma (SDH). We hypothesized that the implementation Viz RECRUIT SDH, an artificial-intelligence (AI)-based mobile recruitment platform, would aid enrollment by automatically pre-screening suspected candidates for SDH and coordinating communication and image sharing among the entire research team. Methods: Patient enrollment in EMBOLISE prior to and after implementation of Viz RECRUIT SDH at a primary (PSC) and comprehensive stroke center (CSC) was compared. Viz RECRUIT SDH automatically detects SDH in head non-contrast computed tomography (NCCT) images and reports the volume, maximum thickness, and midline shift. The EMBOLISE trial was activated on May 5, 2021, and Viz RECRUIT SDH was activated on October 6, 2021; therefore, the pre-Viz cohort consisted of all patients from EMBOLISE to Viz activation (131 days), and the post-Viz cohort consisted of all patients from Viz activation until August 18, 2022 (316 days). For the post-Viz cohort, additional data is presented on AI-based screening metrics. Results: Prior to implementation of the AI-based software, there were 5 patients enrolled (0.99 patients/month). After implementation of the software for trial screening, enrollment increased by 36% to 1.35 patients/month (14 total enrolled), and there were no screen failures. Over the entire post-Viz period, a total of 6804 NCCTs were prescreened by Viz with 208 total SDH detections (3% prevalence). A total of 50 detections were made at the PSC and 158 came from the CSC. 79% of all alerts for suspected SDH were viewed, and 48% of all viewed alerts occurred within 10 minutes. Conclusions: The implementation of an AI-based software for the automatic screening of SDH patients increased the enrollment rate in the EMBOLISE trial. Moreover, in a busy research institution with 13 active studies in 2022, the automatic alerts of all potential patients keep our attention on EMBOLISE with coordinated screening and communication in a single mobile application.

The US Residency Match at 70: What Was, What Is, and What Could Be.

The US Residency Match at 70: What Was, What Is, and What Could Be.

Prediction of successful veno-venous extracorporeal life support liberation using the oxygen challenge test.

The oxygen challenge test (OCT) is an underutilized measure of lung recovery, easily performed prior to proceeding with a trial-off V-V ECLS as part of a weaning algorithm. Evidence-based thresholds for OCT results which support continuing with V-V ECLS weaning are lacking, making interpretation of these tests challenging in clinical practice. We performed a retrospective review of patients commenced on V-V ECLS as a bridge-to-recovery at Vancouver General Hospital from 2015-2019. The absolute PaO2 post-OCT and change in PaO2 proportional to incremental FiO2 change on the ventilator (∆PaO2 ) were evaluated as predictive screening metrics for identifying conditions favorable for successful trial-off of V-V ECLS. An optimal cut-off of PaO2 ≥ 240 mm Hg post-OCT (AUC 0.77) and ∆PaO2 ≥ 250 mm Hg (AUC 0.76) was identified as a threshold for predicting successful trials-off. A total of 26 and 24 patients achieved post-OCT PaO2 and ∆PaO2 thresholds, and 100% of these patients were liberated successfully from ECLS during their admission. The OCT can serve as an effective screen of shunt reduction and native lung recovery which can be used alongside other measures of ventilation to assess for suitability of liberation from V-V ECLS prior to a trial-off. Achieving a PaO2 ≥ 240 mm Hg post-OCT is a strong prognostic indicator for successful liberation from V-V ECLS during ICU admission.

Impact of the COVID-19 Pandemic on Breast Imaging: An Analysis of the National Mammography Database.

PurposeThe aim of this study was to quantify the initial decline and subsequent rebound in breast cancer screening metrics throughout the coronavirus disease 2019 (COVID-19) pandemic.MethodsScreening and diagnostic mammographic examinations, biopsies performed, and cancer diagnoses were extracted from the ACR National Mammography Database from March 1, 2019, through May 31, 2021. Patient (race and age) and facility (regional location, community type, and facility type) demographics were collected. Three time periods were used for analysis: pre-COVID-19 (March 1, 2019, to May 31, 2019), peak COVID-19 (March 1, 2020, to May 31, 2020), and COVID-19 recovery (March 1, 2021, to May 31, 2021). Analysis was performed at the facility level and overall between time periods.ResultsIn total, 5,633,783 screening mammographic studies, 1,282,374 diagnostic mammographic studies, 231,390 biopsies, and 69,657 cancer diagnoses were analyzed. All peak COVID-19 metrics were less than pre-COVID-19 volumes: 36.3% of pre-COVID-19 for screening mammography, 57.9% for diagnostic mammography, 47.3% for biopsies, and 48.7% for cancer diagnoses. There was some rebound during COVID-19 recovery as a percentage of pre-COVID-19 volumes: 85.3% of pre-COVID-19 for screening mammography, 97.8% for diagnostic mammography, 91.5% for biopsies, and 92.0% for cancer diagnoses. Across various metrics, there was a disproportionate negative impact on older women, Asian women, facilities in the Northeast, and facilities affiliated with academic medical centers.ConclusionsCOVID-19 had the greatest impact on screening mammography volumes, which have not returned to pre-COVID-19 levels. Cancer diagnoses declined significantly in the acute phase and have not fully rebounded, emphasizing the need to increase outreach efforts directed at specific patient population and facility types.

Analysis of the likelihood of depression versus distress screening to identify need for intervention.

1544 Background: Psychosocial assessments are increasingly used to evaluate a patient-centered approach to quality cancer care delivery. Value-based oncology programs endorse screening metrics at every encounter. To comply with expectations of these programs, our cancer center utilizes two standardized tools: Patient Health Questionnaire (PHQ) to screen for depression at every encounter; National Comprehensive Cancer Network Distress Thermometer (NCCNDT) to screen for acute distress at clinically meaningful intervals. In 2021, oncology patients completed, on average, 5 annual appointments at Sidney Kimmel Cancer Center (SKCC), with a median appointment frequency of once every 19 days. Given the high encounter-per-patient ratio, we aimed to assess utility of frequent screening leading to supportive intervention. Methods: A retrospective analysis was conducted of medical oncology patients seeking care at SKCC with a completed depression and/or distress screening, as recorded in the patient’s electronic health record, between 1/1/2021 and 12/31/2021. This analysis intended to evaluate the percentage of patients whose scores indicate need for intervention. Patients who received more than one screening were attributed the highest score recorded during the measurement period. Results: A total 13,342 patients were screened at least once for either depression (n = 7,433), distress (n = 1,325), or both (n = 4,584). 3% of all patients screened ever met the intervention threshold (IT) for depression; 33% met the IT for distress. Of the patients who received both types of screenings, 31% met the IT for distress without meeting the threshold for depression. Those 1,418 patients would not have been referred for intervention through depression screening alone. Conclusions: This analysis highlights routine depression screening among a cancer population with a high encounter-per-patient ratio may not be sensitive in identifying need for supportive intervention. It also suggests that distress screening is more likely to lead to a supportive intervention than depression screening alone. This analysis combined with the anecdotal assessment by social workers supports the value of distress at clinically meaningful intervals over depression screening at each encounter. [Table: see text]

Guidance on evaluating parametric model uncertainty at decision-relevant scales

Abstract. Spatially distributed hydrological models are commonly employed to optimize the locations of engineering control measures across a watershed. Yet, parameter screening exercises that aim to reduce the dimensionality of the calibration search space are typically completed only for gauged locations, like the watershed outlet, and use screening metrics that are relevant to calibration instead of explicitly describing the engineering decision objectives. Identifying parameters that describe physical processes in ungauged locations that affect decision objectives should lead to a better understanding of control measure effectiveness. This paper provides guidance on evaluating model parameter uncertainty at the spatial scales and flow magnitudes of interest for such decision-making problems. We use global sensitivity analysis to screen parameters for model calibration, and to subsequently evaluate the appropriateness of using multipliers to adjust the values of spatially distributed parameters to further reduce dimensionality. We evaluate six sensitivity metrics, four of which align with decision objectives and two of which consider model residual error that would be considered in spatial optimizations of engineering designs. We compare the resulting parameter selection for the basin outlet and each hillslope. We also compare basin outlet results for four calibration-relevant metrics. These methods were applied to a RHESSys ecohydrological model of an exurban forested watershed near Baltimore, MD, USA. Results show that (1) the set of parameters selected by calibration-relevant metrics does not include parameters that control decision-relevant high and low streamflows, (2) evaluating sensitivity metrics at the basin outlet misses many parameters that control streamflows in hillslopes, and (3) for some multipliers, calibrating all parameters in the set being adjusted may be preferable to using the multiplier if parameter sensitivities are significantly different, while for others, calibrating a subset of the parameters may be preferable if they are not all influential. Thus, we recommend that parameter screening exercises use decision-relevant metrics that are evaluated at the spatial scales appropriate to decision making. While including more parameters in calibration will exacerbate equifinality, the resulting parametric uncertainty should be important to consider in discovering control measures that are robust to it.

Aortic Arch Calcification in Ungated Lung Computed Tomography Scans as Predictors of Atherosclerotic Cardiovascular Disease: Methodology and Reproducibility in a Multi-Ethnic Study of Atherosclerosis.

Aortic arch calcium (AAC), which is frequently detected on ungated lung computed tomography (CT) due to a large field of view, can serve as a marker of subclinical atherosclerotic burden. Our study sought to validate novel cardiac screening metrics of subclinical atherosclerosis by evaluating the inter- and intra-observer reproducibility of AAC measurements with ungated lung CT. The authors randomly selected 100 ungated lung CT scans from the Multi-Ethnic Study of Atherosclerosis cohort. A Bland-Altman plot analysis was used to test inter- and intra-reader reproducibility, after measuring the total arch calcium score and arch calcium volume. The intra-reader reproducibility for the total arch calcium score and arch calcium volume in all subjects was excellent at 99% and 97%, respectively. The inter-reader reproducibility for the total arch calcium score and volume in all subjects was similarly excellent at 97% and 96%, respectively. The high reproducibility of ungated lung CT suggests a potential new method of stratifying the atherosclerotic cardiovascular disease risk among patients undergoing lung CT without requiring additional scanning. This methodology helps promote routine reporting of AAC and coronary artery calcium based on millions of ungated CT images acquired for lung screening purposes.

Age, Race, and Digital Divide Index are Associated With Video Visit Completion for Patients Seen in Radiation Oncology

<h3>Purpose/Objective(s)</h3> The COVID-19 pandemic drove rapid adoption of telehealth across medicine, including radiation oncology, to minimize in-person contact. This exposed patient barriers to telehealth access and created care disparities in the absence of specific access assistance which have been little studied. A better understanding of these telehealth access barriers will help direct assistance resources to improve access in radiation oncology patients in the future. <h3>Materials/Methods</h3> Data for all unique patient visits at a large academic medical center between 4/1/2020 and 6/30/2020 were generated, including visit type (in person, video, audio only), age, race, ethnicity, rural/urban home address (zip code assessment by Federal Office of Rural Health Policy), and Digital Divide Index (DDI; incorporates metrics of technology and internet access, age, disability, and educational attainment in a geographic area). A multivariable logistic regression model evaluated the association of these variables with successful video visit completion. <h3>Results</h3> A total of 1990 visits by patients seen in radiation oncology for 1698 unique individuals were assessed. The median age was 66 (IQR: 14); patients were 38.1% female and 89.4% Caucasian. The median age of those completing at least one video visit was 64 (IQR: 15) vs. 68 (IQR: 14) for those who completed audio visits only (<i>P</i> < 0.001). Among African American patients, 22.7% completed at least one video visit, while 36.7% of Caucasian patients completed at least one video visit (<i>P</i> = 0.003). In multivariable analyses, patient age (OR: 0.965; 95% CI 0.956-0.973; <i>P</i> < 0.001), DDI (OR: 0.986; 95% CI 0.976-0.996; <i>P</i> = 0.006), and race (OR: 0.472; 0.294-0.756; <i>P</i> = 0.039) predicted completion of at least one video visit. Those who were older, had higher DDI (greater digital divide), or African American race were less likely to complete a video visit. Gender, ethnicity, and rural/urban home address were not significantly associated with video visit completion. <h3>Conclusion</h3> This work is to our knowledge the first characterization of telehealth usage in radiation oncology patients and demonstrates associations between patient characteristics and video visit completion that have implications for clinical practice and future research. First, older patients, those with minority backgrounds, and those from areas with less access to technology (high digital divide) are less likely to be able to access care through video visits. Second, audio visits were a significant proportion of visits early in the pandemic, and subsequent data should be analyzed to determine their importance as a means to continue providing virtual care to prevent further care disparities. Third, future research is needed to address barriers to care experienced by elderly and minority patients as well as those from communities with high DDI. Fourth, researchers should explore markers of access barriers such as DDI and other potential pre-visit screening metrics for use in directing resources to those in need.

Disparities in Same-Day Diagnostic Imaging in Breast Cancer Screening: Impact of an Immediate-Read Screening Mammography Program Implemented During the COVID-19 Pandemic.

BACKGROUND. The need for second visits between screening mammography and diagnostic imaging contributes to disparities in the time to breast cancer diagnosis. During the COVID-19 pandemic, an immediate-read screening mammography program was implemented to reduce patient visits and decrease time to diagnostic imaging. OBJECTIVE. The purpose of this study was to measure the impact of an immediate-read screening program with focus on disparities in same-day diagnostic imaging after abnormal findings are made at screening mammography. METHODS. In May 2020, an immediate-read screening program was implemented whereby a dedicated breast imaging radiologist interpreted all screening mammograms in real time; patients received results before discharge; and efforts were made to perform any recommended diagnostic imaging during the visit (performed by different radiologists). Screening mammographic examinations performed from June 1, 2019, through October 31, 2019 (preimplementation period), and from June 1, 2020, through October 31, 2020 (postimplementation period), were retrospectively identified. Patient characteristics were recorded from the electronic medical record. Multivariable logistic regression models incorporating patient age, race and ethnicity, language, and insurance type were estimated to identify factors associated with same-day diagnostic imaging. Screening metrics were compared between periods. RESULTS. A total of 8222 preimplementation and 7235 postimplementation screening examinations were included; 521 patients had abnormal screening findings before implementation, and 359 after implementation. Before implementation, 14.8% of patients underwent same-day diagnostic imaging after abnormal screening mammograms. This percentage increased to 60.7% after implementation. Before implementation, patients who identified their race as other than White had significantly lower odds than patients who identified their race as White of undergoing same-day diagnostic imaging after receiving abnormal screening results (adjusted odds ratio, 0.30; 95% CI, 0.10-0.86; p = .03). After implementation, the odds of same-day diagnostic imaging were not significantly different between patients of other races and White patients (adjusted odds ratio, 0.92; 95% CI, 0.50-1.71; p = .80). After implementation, there was no significant difference in race and ethnicity between patients who underwent and those who did not undergo same-day diagnostic imaging after receiving abnormal results of screening mammography (p > .05). The rate of abnormal interpretation was significantly lower after than it was before implementation (5.0% vs 6.3%; p < .001). Cancer detection rate and PPV1 (PPV based on positive findings at screening examination) were not significantly different before and after implementation (p > .05). CONCLUSION. Implementation of the immediate-read screening mammography program reduced prior racial and ethnic disparities in same-day diagnostic imaging after abnormal screening mammograms. CLINICAL IMPACT. An immediate-read screening program provides a new paradigm for improved screening mammography workflow that allows more rapid diagnostic workup with reduced disparities in care.

Symmetry and sex differences in knee kinematics and ACL elongation in healthy collegiate athletes during high‐impact activities revealed through dynamic biplane radiography

The objectives of this study were to determine symmetry and sex differences in knee kinematics and anterior cruciate ligament (ACL) elongation waveforms in healthy athletes without a history of a knee injury during fast running, drop jump, and 180° internal/external rotation hops. It was hypothesized that knee abduction angle and ACL relative elongation would be greater in women than in men during all activities. Bilateral knee kinematics and ACL relative elongation were determined in 19 collegiate athletes using dynamic biplane radiography. Sex differences in kinematics and ACL relative elongation waveforms were identified using statistical parametric mapping. Average absolute side-to-side differences (SSDA ) in kinematics and ACL relative elongation waveforms were determined for each activity. Women had up to 2.3° (all p < 0.05) less knee adduction angle and had greater ACL relative elongation (max. 4.8%-9.2%; all p < 0.01) than men during all activities, in support of the hypotheses. SSDA in kinematics were 1.4 mm and 5.5° or less in all components of translation and rotation, respectively, while SSDA in ACL relative elongation was 3.6% or less across all activities. Greater ACL relative elongation across a variety of activities may make women more susceptible to ACL injury than men. This study provides valuable reference data for identifying abnormal asymmetry in knee kinematics and ACL elongation in athletes after the ACL injury. These novel results improve our understanding of ACL elongation during demanding athletic activities and may help guide the development of sex-specific risk screening metrics, return to play assessments, and rehabilitation protocols after the ACL injury.

Coronary Artery And Aortic Arch Calcification In Ungated Lung Ct Scans As Predictors Of Ascvd In The Multi-ethnic Study Of Atherosclerosis: Methods And Reproducibility

Introduction: Coronary Calcium (CAC) is a validated marker of subclinical atherosclerosis on both ECG-gated/cardiac and non-gated/lung CT, and it offers a robust assessment of CVD risk. Aortic arch calcium (AAC), frequently detected on ungated lung CT scans, can serve as a marker of subclinical atherosclerotic burden. Yet, the prognostic value of AAC remains to be established. Our study thus sought to validate novel cardiac screening metrics of subclinical atherosclerosis, by evaluating inter- and intra- observer reproducibility of AAC measurements obtained with ungated Lung CT scans.

The Significance of EEG Alpha Oscillation Spectral Power and Beta Oscillation Phase Synchronization for Diagnosing Probable Alzheimer Disease.

Alzheimer disease (AD) is the most common cause of dementia in geriatric population. At present, no effective treatments exist to reverse the progress of AD, however, early diagnosis and intervention might delay its progression. The search for biomarkers with good safety, repeatable detection, reliable sensitivity and community application is necessary for AD screening and early diagnosis and timely intervention. Electroencephalogram (EEG) examination is a non-invasive, quantitative, reproducible, and cost-effective technique which is suitable for screening large population for possible AD. The power spectrum, complexity and synchronization characteristics of EEG waveforms in AD patients have distinct deviation from normal elderly, indicating these EEG features can be a promising candidate biomarker of AD. However, current reported deviation results are inconsistent, possibly due to multiple factors such as diagnostic criteria, sample sizes and the use of different computational measures. In this study, we collected two neurological tests scores (MMSE and MoCA) and the resting-state EEG of 30 normal control elderly subjects (NC group) and 30 probable AD patients confirmed by Pittsburgh compound B positron emission tomography (PiB-PET) inspection (AD group). We calculated the power spectrum, spectral entropy and phase synchronization index features of these two groups’ EEG at left/right frontal, temporal, central and occipital brain regions in 4 frequency bands: δ oscillation (1–4 Hz), θ oscillation (4–8 Hz), α oscillation (8–13 Hz), and β oscillation (13–30 Hz). In most brain areas, we found that the AD group had significant differences compared to NC group: (1) decreased α oscillation power and increased θ oscillation power; (2) decreased spectral entropy in α oscillation and elevated spectral entropy in β oscillation; and (3) decrease phase synchronization index in δ, θ, and β oscillation. We also found that α oscillation spectral power and β oscillation phase synchronization index correlated well with the MMSE/MoCA test scores in AD groups. Our study suggests that these two EEG features might be useful metrics for population screening of probable AD patients.

Online recruitment of youth for mHealth studies.

Social networking sites and apps have emerged as an opportunity to engage in research young men who have sex with men (YMSM) at risk of HIV infection who may not be otherwise reached by in-person recruitment efforts. This paper highlights lessons learned, best practices and on-going recruitment challenges in the iTech network of the NIH Adolescent Trials Network for HIV Interventions. Recruitment was conducted for four randomized controlled trials (RCTs) of mHealth HIV prevention interventions for YMSM living in 10 US cities. Advertising was purchased on Facebook, Instagram, Snapchat, Twitter and Grindr. Users who clicked on banner ads were taken directly to a study-specific eligibility screener and if eligible, were asked to provide contact information for follow-up by respective study site staff. Ad and screening metrics (impressions, clicks, cost per click (CPC), click-through rate (CTR), number screened, number eligible, number who provided contact information and cost per eligible contact) were compared across platforms, studies and geographic areas (where available). Screening metrics were also calculated for in-person recruitment efforts. Grindr and Snapchat ads produced the highest CTRs as compared to Facebook ads. However, these ads had the lowest proportions of users who initiated eligibility screeners and ultimately Facebook ads yielded the lowest cost per eligible contact across studies. Instagram ads yielded the highest proportions of eligible contacts who were racial/ethnic minorities and under the age of 18. Geographic variability in cost per eligible contact was observed for studies with identical eligibility criteria running concurrently in different regions, driven by both advertising costs and the screening and eligibility rates. Despite lower eligibility rates, the total numbers of eligible contacts were higher for online advertising campaigns, as compared to other recruitment efforts, for all studies except P3. Ads recruiting for P3 had the highest cost per eligible contact, likely due to this study having the most stringent eligibility criteria of the studies described. We implemented a successful online advertising strategy to recruit YMSM at high risk for HIV infection into four RCTs of mHealth interventions. This report provides a framework for evaluation of data from future online recruitment efforts across platforms and geographic areas, regardless of inevitable changes in the digital marketing space.

A systematic framework for using membrane metrics for strain engineering

The cell membrane plays a central role in the fitness and performance of microbial cell factories and therefore it is an attractive engineering target. The goal of this work is to develop a systematic framework for identifying membrane features for use as engineering targets. The metrics that describe the composition of the membrane can be visualized as “knobs” that modulate various “outcomes”, such as physical properties of the membrane and metabolic activity in the form of growth and productivity, with these relationships varying depending on the condition. We generated a set of strains with altered membrane lipid composition via expression of des, fabA and fabB and performed a rigorous characterization of these knobs and outcomes across several individual inhibitory conditions. Here, the knobs are the relative abundance of unsaturated lipids and lipids containing cyclic rings; the average lipid length, and the ratio of linear and non-linear lipids (L/nL ratio). The outcomes are membrane permeability, hydrophobicity, fluidity, and specific growth rate. This characterization identified significant correlations between knobs and outcomes that were specific to individual inhibitors, but also were significant across all tested conditions. For example, across all conditions, the L/nL ratio is positively correlated with the cell surface hydrophobicity, and the average lipid length is positively correlated with specific growth rate. A subsequent analysis of the data with the individual inhibitors identified pairs of lipid metrics and membrane properties that were predicted to impact cell growth in seven modeled scenarios with two or more inhibitors. The L/nL ratio and the membrane hydrophobicity were predicted to impact cell growth with the highest frequency. We experimentally validated this prediction in the combined condition of 42 °C, 2.5 mM furfural and 2% v/v ethanol in minimal media. Membrane hydrophobicity was confirmed to be a significant predictor of ethanol production. This work demonstrates that membrane physical properties can be used to predict the performance of biocatalysts in single and multiple inhibitory conditions, and possibly as an engineering target. In this manner, membrane properties can possibly be used as screening or selection metrics for library- or evolution-based strain engineering.

Breast cancer mortality after eight years of an improved screening program using digital breast tomosynthesis.

ObjectivesTo assess screening quality metrics and to describe mortality rates eight years after redesign of breast cancer screening and diagnosis pathways, and the introduction of digital breast tomosynthesis.SettingBreast Unit of the Toledo Health Area in the region of Castilla-La Mancha (Spain).MethodsWe recorded screening metrics and mortality data following the introduction of digital breast tomosynthesis in 2011 for screening and diagnosis pathways. We then compared the mortality between Toledo Health Area and the rest of Castilla-La Mancha, where digital breast tomosynthesis is not available.ResultsAll screening quality metrics improved following the introduction of digital breast tomosynthesis. The cancer detection rate significantly increased from 2.3 (95% confidence interval (CI): 1.9–3.6) to 4.5 per 1000 women (95% CI: 3.2–5.2) on average between the periods 2005–2009 and 2015–2018, while the recall rate significantly decreased from 7.0% (95% CI: 6.8%–8.2%) to 2.6% (95% CI: 2.0%–3.6%). Comparing breast cancer mortality rates for 2014–2018 in the Toledo Health Area with the rest of Castilla-La Mancha, which had similar cancer treatment access and management protocols but without digital breast tomosynthesis, the crude mortality rate was 17.79 (95% CI: 15.38 -20.19) vs. 24.76 per 100,000 (95% CI: 26.12–23.39), respectively. The cumulative risk of death was also significantly lower for the Toledo Health Area than for Castilla-La Mancha.ConclusionThe introduction of digital breast tomosynthesis improved screening quality indicators. Breast cancer mortality simultaneously decreased with respect to the rest of Castilla-La Mancha. Further research is needed to assess the long-term results, and the role that the redesign may have played in reducing mortality.

Application Fever: Reviewing the Causes, Costs, and Cures for Residency Application Inflation.

Over the past decade, the number of residency applications submitted per applicant has nearly doubled. This epidemic of “Application Fever” is expensive for applicants, burdensome for programs, and ultimately does not improve overall Match outcomes. In this review, we discuss the phenomenon of Application Fever, with a focus on contributing factors and costs of this behavior. Application Fever has its origins in the early 1990s. At that time, the number of residency applicants began to outpace the number of available positions. Because an applicant who applies to more residency programs has a greater probability of securing a residency position than an otherwise equivalent applicant who applies to fewer, "overapplication" became a dominant strategy and residency applicants began to apply to more residency programs each year. This trend was enhanced and enabled by the introduction of the Electronic Residency Application Service (ERAS). Although Application Fever is a rational decision for applicants, it imposes a substantial evaluative burden on program directors and necessitates the use of convenience screening metrics. We then briefly review potential solutions, including informational strategies, application limits, and marketplace incentives to reduce application numbers. Although a fixed cap on applications would reduce application numbers and facilitate a holistic selection process, greater transparency from residency programs regarding their selection criteria would be required to help applicants choose where to apply. To improve the residency application process for programs and applicants alike, we call upon the medical community to further study Application Fever and carefully consider solutions, including fixed application caps.

Use of Imputation and Decision Modeling to Improve Diagnosis and Management of Patients at Risk for New-Onset Diabetes After Transplantation.

BackgroundNew-onset diabetes after transplantation (NODAT) is a complication of solid organ transplantation. We sought to determine the extent to which NODAT goes undiagnosed over the course of 1 year following transplantation, analyze missed or later-diagnosed cases of NODAT due to poor hemoglobin A1c (HbA1c) and fasting blood glucose (FBG) collection, and to estimate the impact that improved NODAT screening metrics may have on long-term outcomes.Material/MethodsThis was a retrospective study utilizing 3 datasets from a single center on kidney, liver, and heart transplantation patients. Retrospective analysis was supplemented with an imputation procedure to account for missing data and project outcomes under perfect information. In addition, the data were used to inform a simulation model used to estimate life expectancy and cost-effectiveness of a hypothetical intervention.ResultsEstimates of NODAT incidence increased from 27% to 31% in kidney transplantation patients, from 31% to 40% in liver transplantation patients, and from 45% to 67% in heart transplantation patients, when HbA1c and FBG were assumed to be collected perfectly at all points. Perfect screening for kidney transplantation patients was cost-saving, while perfect screening for liver and heart transplantation patients was cost-effective at a willingness-to-pay threshold of $100 000 per life-year.ConclusionsImproved collection of HbA1c and FBG is a cost-effective method for detecting many additional cases of NODAT within the first year alone. Additional research into both improved glucometric monitoring as well as effective strategies for mitigating NODAT risk will become increasingly important to improve health in this population.

Abstract PO-247: Use of prediction models to reduce racial/ethnic disparities in eligibility for lung-cancer screening

Abstract Background: For the same age and smoking history as whites, minorities have substantially different lung-cancer risk. However, current US Preventive Services Task Force (USPSTF) lung-cancer screening recommendations make no allowance for race/ethnicity and may induce health disparities. Incorporating individualized prediction-models into USPSTF guidelines may reduce racial/ethnic disparities in lung-cancer screening eligibility. We examine whether expanding current USPSTF lung cancer screening eligibility to include ever-smokers whose risk (calculated by an individualized prediction model) exceeded a threshold would reduce racial/ethnic disparities induced by current USPSTF guidelines. Methods: We used the US- representative 2015 National Health Interview Survey to examine screening eligibility. We identified the thresholds for each of 5 models: lung-cancer risk (Bach, PLCOM2012 and LCRAT models), lung-cancer death risk (LCDRAT model), and life- years gained by attending screening (LYFS-CT model), which select the same number of ever-smokers aged 50-80yrs as USPSTF guidelines. We defined 5 cohorts of ever- smokers as eligible for screening if they were eligible by each screening model or USPSTF guidelines. Among each race/ethnicity, we calculated the number eligible for screening, proportion of preventable lung-cancer deaths prevented (LCD sensitivity), proportion of gainable life-years gained (LYG sensitivity) and screening effectiveness (the number needed to screen to prevent one lung-cancer death). Results: USPSTF criteria performed best for whites (20% eligible, preventing 55% of preventable lung- cancer deaths). Asian-Americans had the least effective screening (NNS=419), only 13% of African-Americans were eligible despite having the most effective screening (NNS=135), and Hispanic-Americans had the lowest percentages eligible (9%) and deaths preventable (30%). Augmenting USPSTF criteria with LCDRAT or LYFS-CT prediction-models nearly equalized the performance of screening for African- Americans with that of whites, doubling the number of African-Americans eligible and increasing the number of preventable deaths and life-years gained by nearly 80%, although at a 25% loss in effectiveness. Prediction-models improved all screening metrics for Asian-Americans and Hispanic-Americans. However models estimated risk more accurately for whites than minorities. Conclusions: Augmenting USPSTF criteria with the LCDRAT or LYFS-CT prediction-models nearly eliminated the white/African-American disparity. All screening metrics were substantially improved for Asian/Hispanic-Americans. Citation Format: Rebecca Landy, Corey D. Young, Martin Skarzynski, Li C. Cheung, Christine D. Berg, M. Patricia Rivera, Hilary A. Robbins, Anil K. Chaturvedi, Hormuzd A. Katki. Use of prediction models to reduce racial/ethnic disparities in eligibility for lung-cancer screening [abstract]. In: Proceedings of the AACR Virtual Conference: Thirteenth AACR Conference on the Science of Cancer Health Disparities in Racial/Ethnic Minorities and the Medically Underserved; 2020 Oct 2-4. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2020;29(12 Suppl):Abstract nr PO-247.