Coding long COVID: characterizing a new disease through an ICD-10 lens.

BACKGROUND: Naming a newly discovered disease is a difficult process; in the context of the COVID-19 pandemic and the existence of post-acute sequelae of SARS-CoV-2 infection (PASC), which includes long COVID, it has proven especially challenging. Disease definitions and assignment of a diagnosis code are often asynchronous and iterative. The clinical definition and our understanding of the underlying mechanisms of long COVID are still in flux, and the deployment of an ICD-10-CM code for long COVID in the USA took nearly 2 years after patients had begun to describe their condition. Here, we leverage the largest publicly available HIPAA-limited dataset about patients with COVID-19 in the US to examine the heterogeneity of adoption and use of U09.9, the ICD-10-CM code for "Post COVID-19 condition, unspecified." METHODS: We undertook a number of analyses to characterize the N3C population with a U09.9 diagnosis code (n = 33,782), including assessing person-level demographics and a number of area-level social determinants of health; diagnoses commonly co-occurring with U09.9, clustered using the Louvain algorithm; and quantifying medications and procedures recorded within 60 days of U09.9 diagnosis. We stratified all analyses by age group in order to discern differing patterns of care across the lifespan. RESULTS: We established the diagnoses most commonly co-occurring with U09.9 and algorithmically clustered them into four major categories: cardiopulmonary, neurological, gastrointestinal, and comorbid conditions. Importantly, we discovered that the population of patients diagnosed with U09.9 is demographically skewed toward female, White, non-Hispanic individuals, as well as individuals living in areas with low poverty and low unemployment. Our results also include a characterization of common procedures and medications associated with U09.9-coded patients. CONCLUSIONS: This work offers insight into potential subtypes and current practice patterns around long COVID and speaks to the existence of disparities in the diagnosis of patients with long COVID. This latter finding in particular requires further research and urgent remediation.

PLCOjs, a FAIR GWAS web SDK for the NCI Prostate, Lung, Colorectal and Ovarian Cancer Genetic Atlas project.

MOTIVATION: The Division of Cancer Epidemiology and Genetics (DCEG) and the Division of Cancer Prevention (DCP) at the National Cancer Institute (NCI) have recently generated genome-wide association study (GWAS) data for multiple traits in the Prostate, Lung, Colorectal, and Ovarian (PLCO) Genomic Atlas project. The GWAS included 110 000 participants. The dissemination of the genetic association data through a data portal called GWAS Explorer, in a manner that addresses the modern expectations of FAIR reusability by data scientists and engineers, is the main motivation for the development of the open-source JavaScript software development kit (SDK) reported here. RESULTS: The PLCO GWAS Explorer resource relies on a public stateless HTTP application programming interface (API) deployed as the sole backend service for both the landing page's web application and third-party analytical workflows. The core PLCOjs SDK is mapped to each of the API methods, and also to each of the reference graphic visualizations in the GWAS Explorer. A few additional visualization methods extend it. As is the norm with web SDKs, no download or installation is needed and modularization supports targeted code injection for web applications, reactive notebooks (Observable) and node-based web services. AVAILABILITY AND IMPLEMENTATION: code at https://github.com/episphere/plco; project page at https://episphere.github.io/plco.

A method for comparing multiple imputation techniques: A case study on the U.S. national COVID cohort collaborative.

Healthcare datasets obtained from Electronic Health Records have proven to be extremely useful for assessing associations between patients' predictors and outcomes of interest. However, these datasets often suffer from missing values in a high proportion of cases, whose removal may introduce severe bias. Several multiple imputation algorithms have been proposed to attempt to recover the missing information under an assumed missingness mechanism. Each algorithm presents strengths and weaknesses, and there is currently no consensus on which multiple imputation algorithm works best in a given scenario. Furthermore, the selection of each algorithm's parameters and data-related modeling choices are also both crucial and challenging. In this paper we propose a novel framework to numerically evaluate strategies for handling missing data in the context of statistical analysis, with a particular focus on multiple imputation techniques. We demonstrate the feasibility of our approach on a large cohort of type-2 diabetes patients provided by the National COVID Cohort Collaborative (N3C) Enclave, where we explored the influence of various patient characteristics on outcomes related to COVID-19. Our analysis included classic multiple imputation techniques as well as simple complete-case Inverse Probability Weighted models. Extensive experiments show that our approach can effectively highlight the most promising and performant missing-data handling strategy for our case study. Moreover, our methodology allowed a better understanding of the behavior of the different models and of how it changed as we modified their parameters. Our method is general and can be applied to different research fields and on datasets containing heterogeneous types.

Risk factors associated with post-acute sequelae of SARS-CoV-2: an N3C and NIH RECOVER study.

BACKGROUND: More than one-third of individuals experience post-acute sequelae of SARS-CoV-2 infection (PASC, which includes long-COVID). The objective is to identify risk factors associated with PASC/long-COVID diagnosis. METHODS: This was a retrospective case-control study including 31 health systems in the United States from the National COVID Cohort Collaborative (N3C). 8,325 individuals with PASC (defined by the presence of the International Classification of Diseases, version 10 code U09.9 or a long-COVID clinic visit) matched to 41,625 controls within the same health system and COVID index date within ± 45 days of the corresponding case's earliest COVID index date. Measurements of risk factors included demographics, comorbidities, treatment and acute characteristics related to COVID-19. Multivariable logistic regression, random forest, and XGBoost were used to determine the associations between risk factors and PASC. RESULTS: Among 8,325 individuals with PASC, the majority were > 50 years of age (56.6%), female (62.8%), and non-Hispanic White (68.6%). In logistic regression, middle-age categories (40 to 69 years; OR ranging from 2.32 to 2.58), female sex (OR 1.4, 95% CI 1.33-1.48), hospitalization associated with COVID-19 (OR 3.8, 95% CI 3.05-4.73), long (8-30 days, OR 1.69, 95% CI 1.31-2.17) or extended hospital stay (30 + days, OR 3.38, 95% CI 2.45-4.67), receipt of mechanical ventilation (OR 1.44, 95% CI 1.18-1.74), and several comorbidities including depression (OR 1.50, 95% CI 1.40-1.60), chronic lung disease (OR 1.63, 95% CI 1.53-1.74), and obesity (OR 1.23, 95% CI 1.16-1.3) were associated with increased likelihood of PASC diagnosis or care at a long-COVID clinic. Characteristics associated with a lower likelihood of PASC diagnosis or care at a long-COVID clinic included younger age (18 to 29 years), male sex, non-Hispanic Black race, and comorbidities such as substance abuse, cardiomyopathy, psychosis, and dementia. More doctors per capita in the county of residence was associated with an increased likelihood of PASC diagnosis or care at a long-COVID clinic. Our findings were consistent in sensitivity analyses using a variety of analytic techniques and approaches to select controls. CONCLUSIONS: This national study identified important risk factors for PASC diagnosis such as middle age, severe COVID-19 disease, and specific comorbidities. Further clinical and epidemiological research is needed to better understand underlying mechanisms and the potential role of vaccines and therapeutics in altering PASC course.

Mortality Tracker: the COVID-19 case for real time web APIs as epidemiology commons.

MOTIVATION: Mortality Tracker is an in-browser application for data wrangling, analysis, dissemination and visualization of public time series of mortality in the United States. It was developed in response to requests by epidemiologists for portable real time assessment of the effect of COVID-19 on other causes of death and all-cause mortality. This is performed by comparing 2020 real time values with observations from the same week in the previous 5 years, and by enabling the extraction of temporal snapshots of mortality series that facilitate modeling the interdependence between its causes. RESULTS: Our solution employs a scalable 'Data Commons at Web Scale' approach that abstracts all stages of the data cycle as in-browser components. Specifically, the data wrangling computation, not just the orchestration of data retrieval, takes place in the browser, without any requirement to download or install software. This approach, where operations that would normally be computed server-side are mapped to in-browser SDKs, is sometimes loosely described as Web APIs, a designation adopted here. AVAILABILITYAND IMPLEMENTATION: https://episphere.github.io/mortalitytracker; webcast demo: youtu.be/ZsvCe7cZzLo. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

NSAID use and clinical outcomes in COVID-19 patients: a 38-center retrospective cohort study.

BACKGROUND: Non-steroidal anti-inflammatory drugs (NSAIDs) are commonly used to reduce pain, fever, and inflammation but have been associated with complications in community-acquired pneumonia. Observations shortly after the start of the COVID-19 pandemic in 2020 suggested that ibuprofen was associated with an increased risk of adverse events in COVID-19 patients, but subsequent observational studies failed to demonstrate increased risk and in one case showed reduced risk associated with NSAID use. METHODS: A 38-center retrospective cohort study was performed that leveraged the harmonized, high-granularity electronic health record data of the National COVID Cohort Collaborative. A propensity-matched cohort of 19,746 COVID-19 inpatients was constructed by matching cases (treated with NSAIDs at the time of admission) and 19,746 controls (not treated) from 857,061 patients with COVID-19 available for analysis. The primary outcome of interest was COVID-19 severity in hospitalized patients, which was classified as: moderate, severe, or mortality/hospice. Secondary outcomes were acute kidney injury (AKI), extracorporeal membrane oxygenation (ECMO), invasive ventilation, and all-cause mortality at any time following COVID-19 diagnosis. RESULTS: Logistic regression showed that NSAID use was not associated with increased COVID-19 severity (OR: 0.57 95% CI: 0.53-0.61). Analysis of secondary outcomes using logistic regression showed that NSAID use was not associated with increased risk of all-cause mortality (OR 0.51 95% CI: 0.47-0.56), invasive ventilation (OR: 0.59 95% CI: 0.55-0.64), AKI (OR: 0.67 95% CI: 0.63-0.72), or ECMO (OR: 0.51 95% CI: 0.36-0.7). In contrast, the odds ratios indicate reduced risk of these outcomes, but our quantitative bias analysis showed E-values of between 1.9 and 3.3 for these associations, indicating that comparatively weak or moderate confounder associations could explain away the observed associations. CONCLUSIONS: Study interpretation is limited by the observational design. Recording of NSAID use may have been incomplete. Our study demonstrates that NSAID use is not associated with increased COVID-19 severity, all-cause mortality, invasive ventilation, AKI, or ECMO in COVID-19 inpatients. A conservative interpretation in light of the quantitative bias analysis is that there is no evidence that NSAID use is associated with risk of increased severity or the other measured outcomes. Our results confirm and extend analogous findings in previous observational studies using a large cohort of patients drawn from 38 centers in a nationally representative multicenter database.

Use of Hydroxychloroquine, Remdesivir, and Dexamethasone Among Adults Hospitalized With COVID-19 in the United States : A Retrospective Cohort Study.

BACKGROUND: Relatively little is known about the use patterns of potential pharmacologic treatments of COVID-19 in the United States. OBJECTIVE: To use the National COVID Cohort Collaborative (N3C), a large, multicenter, longitudinal cohort, to characterize the use of hydroxychloroquine, remdesivir, and dexamethasone, overall as well as across individuals, health systems, and time. DESIGN: Retrospective cohort study. SETTING: 43 health systems in the United States. PARTICIPANTS: 137 870 adults hospitalized with COVID-19 between 1 February 2020 and 28 February 2021. MEASUREMENTS: Inpatient use of hydroxychloroquine, remdesivir, or dexamethasone. RESULTS: Among 137 870 persons hospitalized with confirmed or suspected COVID-19, 8754 (6.3%) received hydroxychloroquine, 29 272 (21.2%) remdesivir, and 53 909 (39.1%) dexamethasone during the study period. Since the release of results from the RECOVERY (Randomised Evaluation of COVID-19 Therapy) trial in mid-June, approximately 78% to 84% of people who have had invasive mechanical ventilation have received dexamethasone or other glucocorticoids. The use of hydroxychloroquine increased during March 2020, peaking at 42%, and started declining by April 2020. By contrast, remdesivir and dexamethasone use gradually increased over the study period. Dexamethasone and remdesivir use varied substantially across health centers (intraclass correlation coefficient, 14.2% for dexamethasone and 84.6% for remdesivir). LIMITATION: Because most N3C data contributors are academic medical centers, findings may not reflect the experience of community hospitals. CONCLUSION: Dexamethasone, an evidence-based treatment of COVID-19, may be underused among persons who are mechanically ventilated. The use of remdesivir and dexamethasone varied across health systems, suggesting variation in patient case mix, drug access, treatment protocols, and quality of care. PRIMARY FUNDING SOURCE: National Center for Advancing Translational Sciences; National Heart, Lung, and Blood Institute; and National Institute on Aging.

Reciprocal regulation of pancreatic ductal adenocarcinoma growth and molecular subtype by HNF4α and SIX1/4.

OBJECTIVE: Pancreatic ductal adenocarcinoma (PDAC) is an aggressive malignancy with a 5-year survival of less than 5%. Transcriptomic analysis has identified two clinically relevant molecular subtypes of PDAC: classical and basal-like. The classical subtype is characterised by a more favourable prognosis and better response to chemotherapy than the basal-like subtype. The classical subtype also expresses higher levels of lineage specifiers that regulate endodermal differentiation, including the nuclear receptor hepatocyte nuclear factor 4 α (HNF4α). The objective of this study is to evaluate the role of HNF4α, SIX4 and SIX1 in regulating the growth and molecular subtype of PDAC. DESIGN: We manipulate the expression of HNF4α, SIX4 and SIX1 in multiple in vitro and in vivo PDAC models. We determine the consequences of manipulating these genes on PDAC growth, differentiation and molecular subtype using functional assays, gene expression analysis and cross-species comparisons with human datasets. RESULTS: We show that HNF4α restrains tumour growth and drives tumour cells toward an epithelial identity. Gene expression analysis of murine models and human tumours shows that HNF4α activates expression of genes associated with the classical subtype. HNF4α also directly represses SIX4 and SIX1, two mesodermal/neuronal lineage specifiers expressed in the basal-like subtype. Finally, SIX4 and SIX1 drive proliferation and regulate differentiation in HNF4α-negative PDAC. CONCLUSION: Our data show that HNF4α regulates the growth and molecular subtype of PDAC by multiple mechanisms, including activation of the classical gene expression programme and repression of SIX4 and SIX1, which may represent novel dependencies of the basal-like subtype.

A distinct microglial subset at the tumor-stroma interface of glioma.

The characterization of the tumor microenvironment (TME) in high grade gliomas (HGG) has generated significant interest in an effort to understand how neoplastic lesions in the central nervous system (CNS) are supported and to devise novel therapeutic targets. The TME of the CNS contains unique and specialized cells, including the resident myeloid cells, microglia. Myeloid involvement in HGG, such as glioblastoma, is associated with poor outcomes. Glioma-associated microglia and infiltrating monocytes/macrophages (GAM) accumulate within the neoplastic lesion where they facilitate tumor growth and drive immunosuppression. However, it has been difficult to differentiate whether microglia and macrophages have similar or distinct roles in pathology, and if the spatial organization of these cells informs outcomes. Here, we characterize the tumor-stroma border and identify peritumoral GAM (PGAM) as a unique subpopulation of GAM. Using data mining and analyses of samples derived from both murine and human sources we show that PGAM exhibit a pro-inflammatory and chemotactic phenotype that is associated with peripheral monocyte recruitment, and decreased overall survival. PGAM act as a unique subset of GAM at the tumor-stroma interface. We define a novel gene signature to identify these cells and suggest that PGAM constitute a cellular target of the TME.

Utilizing Automated Breast Cancer Detection to Identify Spatial Distributions of Tumor-Infiltrating Lymphocytes in Invasive Breast Cancer.

Quantitative assessment of spatial relations between tumor and tumor-infiltrating lymphocytes (TIL) is increasingly important in both basic science and clinical aspects of breast cancer research. We have developed and evaluated convolutional neural network analysis pipelines to generate combined maps of cancer regions and TILs in routine diagnostic breast cancer whole slide tissue images. The combined maps provide insight about the structural patterns and spatial distribution of lymphocytic infiltrates and facilitate improved quantification of TILs. Both tumor and TIL analyses were evaluated by using three convolutional neural network networks (34-layer ResNet, 16-layer VGG, and Inception v4); the results compared favorably with those obtained by using the best published methods. We have produced open-source tools and a public data set consisting of tumor/TIL maps for 1090 invasive breast cancer images from The Cancer Genome Atlas. The maps can be downloaded for further downstream analyses.

Correction: Direct therapeutic targeting of immune checkpoint PD-1 in pancreatic cancer.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

Association of Proteinuria and Hematuria with Acute Kidney Injury and Mortality in Hospitalized Patients with COVID-19.

INTRODUCTION: Acute kidney injury (AKI) is strongly associated with poor outcomes in hospitalized patients with coronavirus disease 2019 (COVID-19), but data on the association of proteinuria and hematuria are limited to non-US populations. In addition, admission and in-hospital measures for kidney abnormalities have not been studied separately. METHODS: This retrospective cohort study aimed to analyze these associations in 321 patients sequentially admitted between March 7, 2020 and April 1, 2020 at Stony Brook University Medical Center, New York. We investigated the association of proteinuria, hematuria, and AKI with outcomes of inflammation, intensive care unit (ICU) admission, invasive mechanical ventilation (IMV), and in-hospital death. We used ANOVA, t test, χ2 test, and Fisher's exact test for bivariate analyses and logistic regression for multivariable analysis. RESULTS: Three hundred patients met the inclusion criteria for the study cohort. Multivariable analysis demonstrated that admission proteinuria was significantly associated with risk of in-hospital AKI (OR 4.71, 95% CI 1.28-17.38), while admission hematuria was associated with ICU admission (OR 4.56, 95% CI 1.12-18.64), IMV (OR 8.79, 95% CI 2.08-37.00), and death (OR 18.03, 95% CI 2.84-114.57). During hospitalization, de novo proteinuria was significantly associated with increased risk of death (OR 8.94, 95% CI 1.19-114.4, p = 0.04). In-hospital AKI increased (OR 27.14, 95% CI 4.44-240.17) while recovery from in-hospital AKI decreased the risk of death (OR 0.001, 95% CI 0.001-0.06). CONCLUSION: Proteinuria and hematuria both at the time of admission and during hospitalization are associated with adverse clinical outcomes in hospitalized patients with COVID-19.

Direct therapeutic targeting of immune checkpoint PD-1 in pancreatic cancer.

BACKGROUND: Pancreatic cancer (PC) hijacks innate cellular processes to promote cancer growth. We hypothesized that PC exploits PD-1/PD-L1 not only to avoid immune responses, but to directly enhance growth. We also hypothesized that immune checkpoint inhibitors (ICIs) have direct cytotoxicity in PC. We sought to elucidate therapeutic targeting of PD-1/PD-L1. METHODS: PD-1 was assessed in PC cells, patient-derived organoids (PDOs), and clinical tissues. Then, PC cells were exposed to PD-L1 to evaluate proliferation. To test PD-1/PD-L1 signaling, cells were exposed to PD-L1 and MAPK was examined. Radio-immunoconjugates with anti-PD-1 drugs were developed to test uptake in patient-derived tumor xenografts (PDTXs). Next, PD-1 function was assessed by xenografting PD-1-knockdown cells. Finally, PC models were exposed to ICIs. RESULTS: PD-1 expression was demonstrated in PCs. PD-L1 exposure increased proliferation and activated MAPK. Imaging PDTXs revealed uptake of radio-immunoconjugates. PD-1 knockdown in vivo revealed 67% smaller volumes than controls. Finally, ICI treatment of both PDOs/PDTXs demonstrated cytotoxicity and anti-MEK1/2 combined with anti-PD-1 drugs produced highest cytotoxicity in PDOs/PDTXs. CONCLUSIONS: Our data reveal PCs innately express PD-1 and activate druggable oncogenic pathways supporting PDAC growth. Strategies directly targeting PC with novel ICI regimens may work with adaptive immune responses for optimal cytotoxicity.

Keratin 17 is a sensitive and specific biomarker of urothelial neoplasia.

There is a clinical need to identify novel biomarkers to improve diagnostic accuracy for the detection of urothelial tumors. The current study aimed to evaluate keratin 17 (K17), an oncoprotein that drives cell cycle progression in cancers of multiple anatomic sites, as a diagnostic biomarker of urothelial neoplasia in bladder biopsies and in urine cytology specimens. We evaluated K17 expression by immunohistochemistry in formalin-fixed, paraffin embedded tissue specimens of non-papillary invasive urothelial carcinoma (UC) (classical histological cases), high grade papillary UC (PUC-LG), low grade papillary UC (PUC-HG), papillary urothelial neoplasia of low malignant potential (PUNLMP), and normal bladder mucosa. A threshold was established to dichotomize K17 status in tissue specimens as positive vs. negative, based on the proportion of cells that showed strong staining. In addition, K17 immunocytochemistry was performed on urine cytology slides, scoring positive test results based on the detection of K17 in any urothelial cells. Mann-Whitney and receiver operating characteristic analyses were used to compare K17 expression between histologic diagnostic categories. The median proportion of K17 positive tumor cells was 70% (range 20-90%) in PUNLMP, 30% (range 5-100%) in PUC-LG, 20% (range 1-100%), in PUC-HG, 35% (range 5-100%) in UC but staining was rarely detected (range 0-10%) in normal urothelial mucosa. Defining cases in which K17 was detected in ≥10% of cells were considered positive, the sensitivity of K17 in biopsies was 89% (95% CI: 80-96%) and the specificity was 88% (95% CI: 70-95%) to distinguish malignant lesions (PUC-LG, PUC-HG, and UC) from normal urothelial mucosa. Furthermore, K17 immunocytochemistry had a sensitivity of 100% and a specificity of 96% for urothelial carcinoma in 112 selected urine specimens. Thus, K17 is a sensitive and specific biomarker of urothelial neoplasia in tissue specimens and should be further explored as a novel biomarker for the cytologic diagnosis of urine specimens.

Insights from an N3C RECOVER EHR-based cohort study characterizing SARS-CoV-2 reinfections and Long COVID.

BACKGROUND: Although the COVID-19 pandemic has persisted for over 3 years, reinfections with SARS-CoV-2 are not well understood. We aim to characterize reinfection, understand development of Long COVID after reinfection, and compare severity of reinfection with initial infection. METHODS: We use an electronic health record study cohort of over 3 million patients from the National COVID Cohort Collaborative as part of the NIH Researching COVID to Enhance Recovery Initiative. We calculate summary statistics, effect sizes, and Kaplan-Meier curves to better understand COVID-19 reinfections. RESULTS: Here we validate previous findings of reinfection incidence (6.9%), the occurrence of most reinfections during the Omicron epoch, and evidence of multiple reinfections. We present findings that the proportion of Long COVID diagnoses is higher following initial infection than reinfection for infections in the same epoch. We report lower albumin levels leading up to reinfection and a statistically significant association of severity between initial infection and reinfection (chi-squared value: 25,697, p-value: <0.0001) with a medium effect size (Cramer's V: 0.20, DoF = 3). Individuals who experienced severe initial and first reinfection were older in age and at a higher mortality risk than those who had mild initial infection and reinfection. CONCLUSIONS: In a large patient cohort, we find that the severity of reinfection appears to be associated with the severity of initial infection and that Long COVID diagnoses appear to occur more often following initial infection than reinfection in the same epoch. Future research may build on these findings to better understand COVID-19 reinfections.

More than three years after the start of the COVID-19 pandemic, individuals are frequently reporting multiple COVID-19 infections. However, these reinfections remain poorly understood. Here, we investigate COVID-19 reinfections in a large electronic health record cohort of over 3 million patients. We use data summary techniques and statistical tests to characterize reinfections and their relationships with disease severity, biomarkers, and Long COVID. We find that individuals with severe initial infection are more likely to experience severe reinfection, that some protein levels are lower, leading to reinfection, and that a lower proportion of individuals are diagnosed with Long COVID following reinfection than initial infection. Our work highlights the prevalence and impact of reinfections and suggests the need for further research.

Association of Premorbid GLP-1RA and SGLT-2i Prescription Alone and in Combination with COVID-19 Severity.

INTRODUCTION: People with type 2 diabetes are at heightened risk for severe outcomes related to COVID-19 infection, including hospitalization, intensive care unit admission, and mortality. This study was designed to examine the impact of premorbid use of glucagon-like peptide-1 receptor agonist (GLP-1RA) monotherapy, sodium-glucose cotransporter-2 inhibitor (SGLT-2i) monotherapy, and concomitant GLP1-RA/SGLT-2i therapy on the severity of outcomes in individuals with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection. METHODS: Utilizing observational data from the National COVID Cohort Collaborative through September 2022, we compared outcomes in 78,806 individuals with a prescription of GLP-1RA and SGLT-2i versus a prescription of dipeptidyl peptidase 4 inhibitors (DPP-4i) within 24 months of a positive SARS-CoV-2 PCR test. We also compared concomitant GLP-1RA/SGLT-2i therapy to GLP-1RA and SGLT-2i monotherapy. The primary outcome was 60-day mortality, measured from the positive test date. Secondary outcomes included emergency room (ER) visits, hospitalization, and mechanical ventilation within 14 days. Using a super learner approach and accounting for baseline characteristics, associations were quantified with odds ratios (OR) estimated with targeted maximum likelihood estimation (TMLE). RESULTS: Use of GLP-1RA (OR 0.64, 95% confidence interval [CI] 0.56-0.72) and SGLT-2i (OR 0.62, 95% CI 0.57-0.68) were associated with lower odds of 60-day mortality compared to DPP-4i use. Additionally, the OR of ER visits and hospitalizations were similarly reduced with GLP1-RA and SGLT-2i use. Concomitant GLP-1RA/SGLT-2i use showed similar odds of 60-day mortality when compared to GLP-1RA or SGLT-2i use alone (OR 0.92, 95% CI 0.81-1.05 and OR 0.88, 95% CI 0.76-1.01, respectively). However, lower OR of all secondary outcomes were associated with concomitant GLP-1RA/SGLT-2i use when compared to SGLT-2i use alone. CONCLUSION: Among adults who tested positive for SARS-CoV-2, premorbid use of either GLP-1RA or SGLT-2i is associated with lower odds of mortality compared to DPP-4i. Furthermore, concomitant use of GLP-1RA and SGLT-2i is linked to lower odds of other severe COVID-19 outcomes, including ER visits, hospitalizations, and mechanical ventilation, compared to SGLT-2i use alone. Graphical abstract available for this article.

Clinically relevant orthotopic pancreatic cancer models for adoptive T cell transfer therapy.

BACKGROUND: Pancreatic ductal adenocarcinoma (PDAC) is an aggressive tumor. Prognosis is poor and survival is low in patients diagnosed with this disease, with a survival rate of ~12% at 5 years. Immunotherapy, including adoptive T cell transfer therapy, has not impacted the outcomes in patients with PDAC, due in part to the hostile tumor microenvironment (TME) which limits T cell trafficking and persistence. We posit that murine models serve as useful tools to study the fate of T cell therapy. Currently, genetically engineered mouse models (GEMMs) for PDAC are considered a "gold-standard" as they recapitulate many aspects of human disease. However, these models have limitations, including marked tumor variability across individual mice and the cost of colony maintenance. METHODS: Using flow cytometry and immunohistochemistry, we characterized the immunological features and trafficking patterns of adoptively transferred T cells in orthotopic PDAC (C57BL/6) models using two mouse cell lines, KPC-Luc and MT-5, isolated from C57BL/6 KPC-GEMM (KrasLSL-G12D/+p53-/- and KrasLSL-G12D/+p53LSL-R172H/+, respectively). RESULTS: The MT-5 orthotopic model best recapitulates the cellular and stromal features of the TME in the PDAC GEMM. In contrast, far more host immune cells infiltrate the KPC-Luc tumors, which have less stroma, although CD4+ and CD8+ T cells were similarly detected in the MT-5 tumors compared with KPC-GEMM in mice. Interestingly, we found that chimeric antigen receptor (CAR) T cells redirected to recognize mesothelin on these tumors that signal via CD3ζ and 41BB (Meso-41BBζ-CAR T cells) infiltrated the tumors of mice bearing stroma-devoid KPC-Luc orthotopic tumors, but not MT-5 tumors. CONCLUSIONS: Our data establish for the first time a reproducible and realistic clinical system useful for modeling stroma-rich and stroma-devoid PDAC tumors. These models shall serve an indepth study of how to overcome barriers that limit antitumor activity of adoptively transferred T cells.

Effect of Paxlovid Treatment During Acute COVID-19 on Long COVID Onset: An EHR-Based Target Trial Emulation from the N3C and RECOVER Consortia.

Preventing and treating post-acute sequelae of SARS-CoV-2 infection (PASC), commonly known as Long COVID, has become a public health priority. In this study, we examined whether treatment with Paxlovid in the acute phase of COVID-19 helps prevent the onset of PASC. We used electronic health records from the National Covid Cohort Collaborative (N3C) to define a cohort of 426,352 patients who had COVID-19 since April 1, 2022, and were eligible for Paxlovid treatment due to risk for progression to severe COVID-19. We used the target trial emulation (TTE) framework to estimate the effect of Paxlovid treatment on PASC incidence. We estimated overall PASC incidence using a computable phenotype. We also measured the onset of novel cognitive, fatigue, and respiratory symptoms in the post-acute period. Paxlovid treatment did not have a significant effect on overall PASC incidence (relative risk [RR] = 0.98, 95% confidence interval [CI] 0.95-1.01). However, it had a protective effect on cognitive (RR = 0.90, 95% CI 0.84-0.96) and fatigue (RR = 0.95, 95% CI 0.91-0.98) symptom clusters, which suggests that the etiology of these symptoms may be more closely related to viral load than that of respiratory symptoms.

Finding Long-COVID: Temporal Topic Modeling of Electronic Health Records from the N3C and RECOVER Programs.

Post-Acute Sequelae of SARS-CoV-2 infection (PASC), also known as Long-COVID, encompasses a variety of complex and varied outcomes following COVID-19 infection that are still poorly understood. We clustered over 600 million condition diagnoses from 14 million patients available through the National COVID Cohort Collaborative (N3C), generating hundreds of highly detailed clinical phenotypes. Assessing patient clinical trajectories using these clusters allowed us to identify individual conditions and phenotypes strongly increased after acute infection. We found many conditions increased in COVID-19 patients compared to controls, and using a novel method to associate patients with clusters over time, we additionally found phenotypes specific to patient sex, age, wave of infection, and PASC diagnosis status. While many of these results reflect known PASC symptoms, the resolution provided by this unprecedented data scale suggests avenues for improved diagnostics and mechanistic understanding of this multifaceted disease.

Finding Long-COVID: temporal topic modeling of electronic health records from the N3C and RECOVER programs.

Post-Acute Sequelae of SARS-CoV-2 infection (PASC), also known as Long-COVID, encompasses a variety of complex and varied outcomes following COVID-19 infection that are still poorly understood. We clustered over 600 million condition diagnoses from 14 million patients available through the National COVID Cohort Collaborative (N3C), generating hundreds of highly detailed clinical phenotypes. Assessing patient clinical trajectories using these clusters allowed us to identify individual conditions and phenotypes strongly increased after acute infection. We found many conditions increased in COVID-19 patients compared to controls, and using a novel method to associate patients with clusters over time, we additionally found phenotypes specific to patient sex, age, wave of infection, and PASC diagnosis status. While many of these results reflect known PASC symptoms, the resolution provided by this unprecedented data scale suggests avenues for improved diagnostics and mechanistic understanding of this multifaceted disease.