Kidney Volume Growth and Kidney Function of En Bloc Pediatric Kidney Transplants: A Case Series.

Rationale & Objective: Kidney transplant is a mainstay of kidney replacement therapy. Given a continued shortage of organs, pediatric en bloc kidney transplants may have substantial utility. We present our long-term experience with en bloc transplants from donors aged 3 to 60 months, including changes in kidney function and kidney volume over time as well as biopsy findings. Study Design: Case series. Setting & Participants: Medical records from a single academic medical center were reviewed. Aggregate serial volumes of 22 en bloc kidney allografts from 2010 to 2017 were assessed at the time of transplant and during follow-up. Estimated glomerular filtration rates (eGFR) were described at 3 months after transplant (baseline) as well as over the ensuing 3 years. Interstitial fibrosis, a finding determined by histopathologic review, which results from an accumulation of collagen that is produced from mediators produced from complex interaction of multiple inflammatory cells, was assessed on 20 protocol biopsies obtained from 6 patients, of which 4 patients had 4 biopsies and 2 patients had 1 biopsy. Results: Kidney volume was obtained from 51 ultrasound studies performed up to 74 months after transplant. Kidney volume generally increased and eGFR rose over time after the transplant, with 23% patients achieving an eGFR of >75 mL/min/1.73 m2 at 3 months posttransplant. The remainder achieved an eGFR >75 mL/min/1.73 m2 over the ensuing 3 years. Interstitial fibrosis noted on biopsies appeared to foreshadow an eventual reduction in kidney volume. Limitations: Retrospective study, possible selection bias, single-center experience. Conclusions: The kidney en bloc allografts increased in size after transplantation, with associated improved kidney function. Chronic damage to the graft, from interstitial fibrosis and tubular atrophy, resulted in long-term reduction in kidney volume.

Lessons learned: Development of COVID-19 clinical staging models at a large urban research institution.

Background/Objective: The University of Illinois at Chicago (UIC), along with many academic institutions worldwide, made significant efforts to address the many challenges presented during the COVID-19 pandemic by developing clinical staging and predictive models. Data from patients with a clinical encounter at UIC from July 1, 2019 to March 30, 2022 were abstracted from the electronic health record and stored in the UIC Center for Clinical and Translational Science Clinical Research Data Warehouse, prior to data analysis. While we saw some success, there were many failures along the way. For this paper, we wanted to discuss some of these obstacles and many of the lessons learned from the journey. Methods: Principle investigators, research staff, and other project team members were invited to complete an anonymous Qualtrics survey to reflect on the project. The survey included open-ended questions centering on participants' opinions about the project, including whether project goals were met, project successes, project failures, and areas that could have been improved. We then identified themes among the results. Results: Nine project team members (out of 30 members contacted) completed the survey. The responders were anonymous. The survey responses were grouped into four key themes: Collaboration, Infrastructure, Data Acquisition/Validation, and Model Building. Conclusion: Through our COVID-19 research efforts, the team learned about our strengths and deficiencies. We continue to work to improve our research and data translation capabilities.

A Quick Reference Guide for Incidental Findings on Lung Cancer Screening CT Examinations.

PURPOSE: The US Preventive Services Task Force has recommended lung cancer screening (LCS) with low-dose CT (LDCT) in high-risk individuals since 2013. Because LDCT encompasses the lower neck, chest, and upper abdomen, many incidental findings (IFs) are detected. The authors created a quick reference guide to describe common IFs in LCS to assist LCS program navigators and ordering providers in managing the care continuum in LCS. METHODS: The ACR IF white papers were reviewed for findings on LDCT that were age appropriate for LCS. A draft guide was created on the basis of recommendations in the IF white papers, the medical literature, and input from subspecialty content experts. The draft was piloted with LCS program navigators recruited through contacts by the ACR LCS Steering Committee. The navigators completed a survey on overall usefulness, clarity, adequacy of content, and user experience with the guide. RESULTS: Seven anatomic regions including 15 discrete organs with 45 management recommendations were identified as relevant to the age of individuals eligible for LCS. The draft was piloted by 49 LCS program navigators from 32 facilities. The guide was rated as useful and clear by 95% of users. No unexpected or adverse experiences were reported in using the guide. On the basis of feedback, relevant sections were reviewed and edited. CONCLUSIONS: The ACR Lung Cancer Screening CT Incidental Findings Quick Reference Guide outlines the common IFs in LCS and can serve as an easy-to-use resource for ordering providers and LCS program navigators to help guide management.

Transfontanelle photoacoustic imaging of intraventricular brain hemorrhages in live sheep.

Intraventricular (IVH) and periventricular (PVH) hemorrhages in preterm neonates are common because the periventricular blood vessels are still developing up to 36 weeks and are fragile. Currently, transfontanelle ultrasound (US) imaging is utilized for screening for IVH and PVH, largely through the anterior fontanelle. However for mild hemorrhages, inconclusive diagnoses are common, leading to failure to detect IVH/PVH or, when other clinical symptoms are present, use of second stage neuroimaging modalities requiring transport of vulnerable patients. Yet even mild IVH/PVH increases the risk of moderate-severe neurodevelopmental impairment. Here, we demonstrate the capability of transfontanelle photoacoustic imaging (TFPAI) to detect IVH and PVH in-vivo in a large animal model. TFPAI was able to detect IVH/PVH as small as 0.3 mL in volume in the brain (p < 0.05). By contrast, US was able to detect hemorrhages as small as 0.5 mL. These preliminary results suggest TFPAI could be translated into a portable bedside imaging probe for improved diagnosis of clinically relevant brain hemorrhages in neonates.

Myo1e overexpression in lung adenocarcinoma is associated with increased risk of mortality.

This study aims to perform a comprehensive genomic analysis to assess the influence of overexpression of MYO1E in non-small cell lung carcinoma (NSCLC) and whether there are differences in survival and mortality risk in NSCLC patients depending on both DNA methylation and RNA expression of MYO1E. The DNA methylation probe cg13887966 was inversely correlated with MYO1E RNA expression in both LUAD and LUSC subpopulations showing that lower MYO1E RNA expression was associated with higher MYO1E DNA methylation. Late stages of lung cancer showed significantly lower MYO1E DNA methylation and significantly higher MYO1E RNA expression for LUAD but not for LUSC. Low DNA methylation as well as high RNA expression of MYO1E are associated with a shorter median survival time and an increased risk of mortality for LUAD, but not for LUSC. This study suggests that changes in MYO1E methylation and expression in LUAD patients may have an essential role in lung cancer's pathogenesis. It shows the utility of MYO1E DNA methylation and RNA expression in predicting survival for LUAD patients. Also, given the low normal expression of MYO1E in blood cells MYO1E DNA methylation has the potential to be used as circulating tumor marker in liquid biopsies.

Predicting severe chronic obstructive pulmonary disease exacerbations using quantitative CT: a retrospective model development and external validation study.

BACKGROUND: Quantitative CT is becoming increasingly common for the characterisation of lung disease; however, its added potential as a clinical tool for predicting severe exacerbations remains understudied. We aimed to develop and validate quantitative CT-based models for predicting severe chronic obstructive pulmonary disease (COPD) exacerbations. METHODS: We analysed the Subpopulations and Intermediate Outcome Measures In COPD Study (SPIROMICS) cohort, a multicentre study done at 12 clinical sites across the USA, of individuals aged 40-80 years from four strata: individuals who never smoked, individuals who smoked but had normal spirometry, individuals who smoked and had mild to moderate COPD, and individuals who smoked and had severe COPD. We used 3-year follow-up data to develop logistic regression classifiers for predicting severe exacerbations. Predictors included age, sex, race, BMI, pulmonary function, exacerbation history, smoking status, respiratory quality of life, and CT-based measures of density gradient texture and airway structure. We externally validated our models in a subset from the Genetic Epidemiology of COPD (COPDGene) cohort. Discriminative model performance was assessed using the area under the receiver operating characteristic curve (AUC), which was also compared with other predictors, including exacerbation history and the BMI, airflow obstruction, dyspnoea, and exercise capacity (BODE) index. We evaluated model calibration using calibration plots and Brier scores. FINDINGS: Participants in SPIROMICS were enrolled between Nov 12, 2010, and July 31, 2015. Participants in COPDGene were enrolled between Jan 10, 2008, and April 15, 2011. We included 1956 participants from the SPIROMICS cohort who had complete 3-year follow-up data: the mean age of the cohort was 63·1 years (SD 9·2) and 1017 (52%) were men and 939 (48%) were women. Among the 1956 participants, 434 (22%) had a history of at least one severe exacerbation. For the CT-based models, the AUC was 0·854 (95% CI 0·852-0·855) for at least one severe exacerbation within 3 years and 0·931 (0·930-0·933) for consistent exacerbations (defined as ≥1 acute episode in each of the 3 years). Models were well calibrated with low Brier scores (0·121 for at least one severe exacerbation; 0·039 for consistent exacerbations). For the prediction of at least one severe event during 3-year follow-up, AUCs were significantly higher with CT biomarkers (0·854 [0·852-0·855]) than exacerbation history (0·823 [0·822-0·825]) and BODE index 0·812 [0·811-0·814]). 6965 participants were included in the external validation cohort, with a mean age of 60·5 years (SD 8·9). In this cohort, AUC for at least one severe exacerbation was 0·768 (0·767-0·769; Brier score 0·088). INTERPRETATION: CT-based prediction models can be used for identification of patients with COPD who are at high risk of severe exacerbations. The newly identified CT biomarkers could potentially enable investigation into underlying disease mechanisms responsible for exacerbations. FUNDING: National Institutes of Health and the National Heart, Lung, and Blood Institute.

Validation of a deep learning, value-based care model to predict mortality and comorbidities from chest radiographs in COVID-19.

We validate a deep learning model predicting comorbidities from frontal chest radiographs (CXRs) in patients with coronavirus disease 2019 (COVID-19) and compare the model's performance with hierarchical condition category (HCC) and mortality outcomes in COVID-19. The model was trained and tested on 14,121 ambulatory frontal CXRs from 2010 to 2019 at a single institution, modeling select comorbidities using the value-based Medicare Advantage HCC Risk Adjustment Model. Sex, age, HCC codes, and risk adjustment factor (RAF) score were used. The model was validated on frontal CXRs from 413 ambulatory patients with COVID-19 (internal cohort) and on initial frontal CXRs from 487 COVID-19 hospitalized patients (external cohort). The discriminatory ability of the model was assessed using receiver operating characteristic (ROC) curves compared to the HCC data from electronic health records, and predicted age and RAF score were compared using correlation coefficient and absolute mean error. The model predictions were used as covariables in logistic regression models to evaluate the prediction of mortality in the external cohort. Predicted comorbidities from frontal CXRs, including diabetes with chronic complications, obesity, congestive heart failure, arrhythmias, vascular disease, and chronic obstructive pulmonary disease, had a total area under ROC curve (AUC) of 0.85 (95% CI: 0.85-0.86). The ROC AUC of predicted mortality for the model was 0.84 (95% CI,0.79-0.88) for the combined cohorts. This model using only frontal CXRs predicted select comorbidities and RAF score in both internal ambulatory and external hospitalized COVID-19 cohorts and was discriminatory of mortality, supporting its potential use in clinical decision making.

Comparison of positive SARS-CoV-2 incidence rate with environmental and socioeconomic factors in northern Illinois.

Early studies showed positive associations fine particulate matter (PM2.5), course particulate matter PM10, nitrogen dioxide (NO2) and Ozone (O3) concentrations with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) confirmed cases in the United States. One study showed that a1 µg/m3 increase in PM2.5 is associated with an 8% increase in the COVID-19 death rate. Specifically, Chicago and surrounding suburbs have been labeled hot spots in the United States and correlation with air pollutants concentration will help identify specific communities most at risk. A number of studies have identified demographic variables associated with increased positive SARS-CoV-2 and the importance of air quality and socioeconomic factors must be further understood for more targeted public health responses. The results of this analysis noted positive relationships between zip code SARS-CoV-2 incidence rate and environmental and demographic EJ indicators. Evaluation of race and SARS-CoV-2 incidence rate at the zip code level found positive moderate correlations for ethnic minority individuals.