Are lipid trajectory patterns over the life course associated with cardiovascular disease risk in midlife?In this cohort study of 1201 participants with multiple lipid measurements from childhood to adulthood, discrete lifetime lipid trajectories were associated with subclinical atherosclerosis in midlife.These results suggest that the longitudinal trajectory patterns of lipid levels could be used to predict future cardiovascular risk; maintaining optimal lipid levels across the lifespan may be warranted to reduce cardiovascular risk. This cohort study examines the associations of lipid levels at different ages and discrete lipid trajectory patterns from childhood to adulthood with subclinical atherosclerosis in midlife. Childhood lipid levels have been associated with adult subclinical atherosclerosis; however, life-course lipid trajectories and their associations with cardiovascular disease risk are poorly characterized.To examine the associations of lipid levels at different ages and discrete lipid trajectory patterns from childhood to adulthood with subclinical atherosclerosis in midlife.This cohort study used data from the Bogalusa Heart Study, a prospective, population-based cohort study conducted in a semirural, biracial community in Bogalusa, Louisiana, with follow-up from 1973 to 2016 . Participants had 4 to 16 repeated measurements of lipids, including total cholesterol (TC), non–high-density lipoprotein cholesterol (non-HDL-C), low-density lipoprotein cholesterol (LDL-C), high-density lipoprotein cholesterol (HDL-C), and triglycerides (TG), from childhood to midlife and adult measurement of carotid intima-media thickness (IMT). Statistical analyses were conducted from July 1 to December 31, 2021.Age-specific lipid levels were estimated, and lipid trajectory patterns were identified using latent mixture modeling.Subclinical atherosclerosis measured by carotid IMT.The study evaluated 1201 adults . Levels of all lipids at each age from 5 to 45 years were significantly associated with adult IMT. The magnitude of associations generally increased with age, and non-HDL-C  and LDL-C  showed the strongest associations. After adjusting for race, sex, and other cardiovascular risk factors, mean IMT values were significantly higher in the low–slow increase, low–rapid increase, and high-stable trajectory groups for TC , the low–slow increase, low–rapid increase, moderate-stable, and high–stable trajectory groups for non-HDL-C  and LDL-C  and the low–rapid increase and moderate-stable trajectory groups for TG  vs the corresponding low-stable trajectory groups. These associations were slightly attenuated after further adjustment for lipid levels at baseline or follow-up. There were no significant differences in mean IMT among HDL-C trajectory groups.In this cohort study, discrete life-course lipid trajectories were associated with the development of atherosclerosis in midlife. The findings emphasize the importance of maintaining optimal lipid levels across the lifespan. Childhood lipid disorders have been shown to persist or track into adulthood8 and thus contribute to the increased risk of cardiovascular disease. Current pediatric guidelines for cardiovascular health and risk reduction by the National Heart, Lung, and Blood Institute recommend universal lipid screening in childhood.9 Therefore, identification and intervention of dyslipidemia in early life may prevent and reduce future cardiovascular events.Dyslipidemia, characterized by abnormal levels of total cholesterol (TC), non–high-density lipoprotein cholesterol (non-HDL-C), low-density lipoprotein cholesterol (LDL-C), high-density lipoprotein cholesterol (HDL-C), and triglycerides (TG), is a well-established risk factor for atherosclerotic cardiovascular disease.14 However, most of these studies have relied on single lipid measurements in childhood14 and thus could not investigate the associations of lipid levels at different ages and changes in lipid levels over time with future atherosclerosis development. Several studies of adults have found that distinct lipid trajectory patterns are associated with risks of cardiovascular disease16 and kidney disease.17 To our knowledge, no prior studies to date have characterized lipid trajectory patterns across the lifespan from young childhood to midlife and evaluated their associations with cardiovascular risk in later life. Using data from the Bogalusa Heart Study with repeated lipid measurements from childhood, we aimed to investigate the associations of lipid levels at different ages as well as discrete lipid trajectory patterns during follow-up with adult subclinical atherosclerosis measured by carotid artery intima-media thickness (IMT).Previous lifelong studies have demonstrated the association between childhood lipid levels and adult subclinical atherosclerosis.18 In the community of Bogalusa, Louisiana, 9 cross-sectional surveys of children aged 4 to 19 years and 12 cross-sectional surveys of adults aged 20 to 58 years who were previously examined as children were conducted between 1973 and 2016. The Bogalusa Heart Study is not a balanced longitudinal study design. It consisted of multiple cross-sectional surveys over 43 years in Bogalusa, with a population ranging from 18 400 in 1973 to 11 300 in 2016. The sample size varied from 1084 to 4222 in the 9 children surveys and from 238 to 1930 in the 12 adult surveys. The total number of different individuals recruited in these surveys was 8385 children and 3780 adults. These adults participated at least once in the children surveys. The exclusion criteria in the current study were nonfasting blood and missing values of lipids in each of the cross-sectional surveys. Linking these repeated cross-sectional examinations conducted every 2 to 3 years has resulted in serial observations from childhood to adulthood in the same individuals. In the longitudinal cohort, 1736 participants had been examined 4 to 16 times for lipids . Of these 1736 individuals, 1201 had B-mode ultrasound examination for carotid artery examination during 2004 to 2016. The median follow-up period was 36.8 years, ranging from 19.8 to 39.3 years. The study cohort selection is described in eFigure 1 in the The Bogalusa Heart Study is a series of long-term epidemiologic studies in a semirural biracial  community in Bogalusa, Louisiana, beginning in 1973. This study focuses on the early natural history of cardiovascular disease since childhood.STROBE) reporting guideline.Written informed consent was obtained from each study participant or parent or guardian when appropriate. Study protocols were approved by the institutional review board of the Tulane University Health Sciences Center. This study followed the Strengthening the Reporting of Observational Studies in Epidemiology  was calculated as weight in kilograms divided by height in meters squared. Systolic blood pressure (SBP) and diastolic blood pressure (DBP) were recorded using a mercury sphygmomanometer on right arms in a relaxed sitting position by 2 trained observers (3 times each). The mean values of the 6 readings were used for analysis. Adult hypertension was defined as SBP of 140 mm Hg or greater, DBP of 90 mm Hg or greater, or taking antihypertensive medications. Information on smoking and alcohol use was obtained using a staff-administered standardized questionnaire. Current smoking and alcohol drinking were defined as smoking at least 1 cigarette per day and consuming alcohol at least 3 times/wk, respectively, during the prior 12 months.19Venous blood samples were collected after fasting for at least 12 hours. During 1973 to 1986, cholesterol and TG levels were measured with a Technicon AutoAnalyzer II (Technicon Instrument Corp). Since 1987, lipid variables were determined by using an Abbott VP instrument (Abbott Laboratories) by enzymatic procedures. Both chemical and enzymatic procedures met the performance requirements of the Lipid Standardization Program of the US Centers for Disease Control and Prevention. Measurements on Centers for Disease Control and Prevention–assigned quality control samples showed no consistent bias over time within or between surveys. Lipoprotein cholesterols were analyzed by using a combination of heparin-calcium precipitation and agar-agarose gel electrophoresis procedures. Non-HDL-C was calculated as TC − HDL-C. Between 1978 and 1991, blood glucose was determined with a glucose oxidase method using a Beckman Glucose Analyzer (Beckman Instruments). Since 1992, glucose was measured enzymatically as part of a multichemistry profile. Diabetes was defined as fasting blood glucose of 126.13 mg/dL  or taking glucose-lowering medication.20 The mean values of the maximum carotid IMT readings of 3 right and 3 left far walls for common, bulb, and internal segments were used for analysis.Carotid artery examination was conducted in adult surveys during 2004 to 2016. If there were 2 or more examinations, the last examination was used in the analysis. Trained sonographers performed ultrasonography examinations with a Sonolayer SSH160A  and a 7.5-MHz linear array transducer on participants in the supine position with the head slightly extended and turned to the opposite direction of the carotid artery being studied. Images were recorded at the common carotid, carotid bulb (bifurcation), and internal carotid arteries bilaterally according to previously developed protocols for the Atherosclerosis Risk in Communities Study.22 Cubic curves were fitted for all lipid variables in race-sex groups. The lipid levels at each age from 5 to 45 years were estimated based on growth curve parameters for each individual.Log-transformed TG levels (log-TG) were used for analysis. To estimate age-specific lipid levels, random-effects mixed models were used to build lipid growth curves for each individual using multiple lipid measurements during follow-up. This individual-based trajectory model was fitted by SAS Proc Mixed, as previously described.25 Briefly, multiple lipid data were fitted by the maximum likelihood method as a mixture of multiple latent trajectories in a censored normal model with a polynomial function of age. We tested trajectory models of lipids with the number of trajectory groups ranging from 2 to 5 and the polynomial function of age being the same as that in individual-based models. The optimal model was selected by the maximum bayesian information criterion. We named the trajectories based on baseline lipid levels and the visual change patterns of lipids over time. We then calculated each participant’s posterior predicted probability of belonging to each lipid trajectory group and assigned participants into the trajectory to which their posterior probability of membership was greatest.We used latent class mixture models to identify distinct subgroups that shared similar underlying trajectories of lipids. This group-based trajectory model was fitted by SAS Proc Traj, as previously described.Multivariable linear regression analyses were performed to examine the associations of lipid levels at different ages with adult IMT, adjusted for race and sex and age, BMI, lipid-lowering medication, hypertension, diabetes, smoking, and alcohol drinking in the last adult survey. We examined the associations between lipid trajectory patterns and adult IMT using multivariable linear regression analyses adjusted for the covariates mentioned previously. We performed additional analyses with further adjustment for baseline or follow-up lipid levels to investigate how they changed associations. To assess the stability of findings, we did a sensitivity analysis after excluding participants taking lipid-lowering medications.P < .05 was considered significant.All analyses were performed using SAS version 9.4 (SAS Institute). A 2-tailed Of 1201 participants, age ranged between 26 and 57 years . Participants comprised 510 (42.5%) men and 691 (57.5%) women, 392 (32.6%) Black and 809 (67.4%) White residents, and 150 (12.5%) participants who reported taking lipid-lowering medications .Growth curves of lipids from childhood to adulthood by race and sex are shown in eFigure 2 in the Using group-based trajectory modeling, we identified 5 discrete trajectories from age 5 to 45 years separately for all lipid variables . The traAs shown in R2 values after incorporating childhood lipid levels . In contrast, the changes in R2 values were significant after including adult lipid levels into the trajectory-IMT association models for TC , non-HDL-C , and LDL-C  but not for TG  and HDL-C .After further adjustment for baseline lipid levels, the associations between lipid trajectory groups and IMT were slightly attenuated. The adjusted mean values of adult IMT remained significantly higher in the high-stable group of TC, all 4 adverse groups of non-HDL-C, and low–slow increase and low–rapid increase groups of LDL-C. There were no significant differences in the adjusted mean values of adult IMT among TG and HDL-C trajectory groups . SimilarMost associations between lipid trajectory and adult IMT slightly changed after excluding 150 participants taking lipid-lowering medications. Details appear in eTable 8 in the This longitudinal cohort study with follow up from childhood into middle adulthood found that lipid levels at each age during follow-up were significantly associated with adult subclinical atherosclerosis, and the strength of these associations generally increased with age. The 5 distinct life-course lipid trajectories we identified, particularly trajectories of non-HDL-C and LDL-C, were associated with subclinical atherosclerosis development later in midlife beyond other cardiovascular risk factors. Notably, most of these associations were independent of lipid levels at baseline or follow-up. These findings underscore the importance of maintaining optimal lipid levels from early life for long-term cardiovascular health benefits.26 Previous studies, including our own, have found that individual lipid levels in childhood or time-averaged lipid levels during follow-up are significantly and independently associated with adult IMT.28 In the current study, we extended the previous studies by estimating age-specific lipid levels during ages 5 to 45 years for each individual based on serial lipid measurements and found that the levels of all lipids at each age were significantly associated with adult IMT. Moreover, the strength of these associations generally increased with age. These observations are consistent with previous findings that childhood TC and non-HDL-C levels were significantly associated with adult subclinical atherosclerosis.29 Additionally, we found that LDL-C and non-HDL-C levels had stronger associations with adult IMT than other lipid measures. In line with these findings, many studies in adults have shown LDL-C and non-HDL-C levels are more strongly associated with risks of cardiovascular mortality31 than other lipid parameters. Indeed, the adult guidelines for dyslipidemia management recommend LDL-C as the primary treatment target and non-HDL-C as the secondary therapy target.32 Our findings provide strong evidence that universal screening for lipid disorders should be conducted from early life, particularly for non-HDL-C and LDL-C.It is well documented that the atherosclerotic process begins in childhood.8 heterogeneous life-course trajectories may exist. The Cardiovascular Risk in Young Finns Study has identified several trajectories of lipids across childhood and early adulthood that are associated with the risk of later-life depressive symptoms in 824 participants,33 but the lipid trajectories may be less accurate because only 4 measurements of lipids were available. In contrast, we have collected up to 16 lipid measurements during 36.8 years of follow-up and identified 5 distinct trajectory groups for lipids using latent class mixture modeling. These group-based trajectories account for baseline values, variations, and long-term trends over time; distinguish heterogeneity within individuals; and provide a more realistic understanding of longitudinal changes in lipid levels during lifetime.23 Nevertheless, the determinants of these trajectories are not fully understood, and future studies are required to examine the influences of genetic, lifestyle, and biological factors on the heterogeneous development of lipid disorders.Despite strong child-to-adult tracking of lipids,17 Our previous publications have demonstrated that changes in dyslipidemia status of LDL-C and HDL-C between adolescence and adulthood were associated with adult IMT.29 A recent study found that individuals who resolve their non-HDL-C dyslipidemia by adulthood have normalized risk of developing high carotid IMT in adulthood.28 In these studies, lipid changes were calculated as differences between first childhood and last adult measurements and thus did not reflect the age-related lipid trajectory patterns.Previous studies have shown that lipid trajectories during adulthood are associated with risks of cardiovascular and kidney outcomes.37 All these findings together suggest that longitudinal measurements of lipids and other cardiovascular risk factors starting from early life may be important for identifying individuals at high risk for further cardiovascular disease.In the current study, we found that life-course trajectories of lipids (except for HDL-C) were associated with adult preclinical atherosclerosis independent of race, sex, and other cardiovascular risk factors. Compared with lipid trajectory groups with consistently low lipid levels, the mean values of adult IMT were significantly higher in most trajectory groups that had moderate or high levels of TC, non-HDL, LDL-C and TG. Importantly, participants who had low lipid levels in early life but showed an increase during follow-up also had higher adult IMT, suggesting a critical deleterious effect of adverse lipid changes on adult atherosclerosis development. Most of these associations between lipid trajectory and adult IMT slightly changed after further adjustment for lipid levels at baseline or follow-up, suggesting that life-course lipid trajectories characterized by serially repeated measurements provide additional information beyond single lipid measurements. Previous extensive evidence has shown that life-course trajectory patterns of BMI and blood pressure are also significantly associated with adult cardiovascular risk.This study has strengths. The community-based longitudinal study cohort with repeated lipid measurements from childhood to adulthood provides a unique opportunity to examine the association of lipid levels at specific age points as well as lipid trajectory patterns during follow-up with adult subclinical atherosclerosis.This study also has several limitations. First, our study is a biracial cohort of Black and White participants. The identified trajectory groups may not be generalizable to other populations. Second, individuals under pharmacological treatments targeting dyslipidemia represent a subgroup who, without treatment, would be expected to have the most adverse lipid levels. This may result in bias in trajectory and association analyses to some extent. For that reason, we performed a sensitivity analysis by excluding participants with pharmacological treatments, and the results remained nearly unchanged. Third, the sample size was not sufficiently large for analyses by subgroups, especially Black men, and thus race- and sex-specific trajectory analyses were not performed. Large-scale longitudinal studies with multiple ethnic groups are needed to confirm the findings of the current study.This study found that lipid levels beginning in childhood as well as distinct lipid trajectories over the life course are associated with subclinical atherosclerosis in midlife. The findings suggest that screening for dynamic changes in lipid profiles from early life may improve identification of high-risk individuals and prevention of cardiovascular disease.


Enzalutamide attenuated angiogenic tubule formation and induced cytotoxicity in HVECs in vitro, and rapidly induced sustained hypoxia in LNCaP xenografts. Subsequent reoxygenation, following prolonged enzalutamide treatment, was associated with increased tumor vessel density and accelerated tumor growth. Hypoxia increased AR expression and transcriptional activity in prostate cells in vitro. Coinhibition of IL8 and VEGF-A restored tumor response in the presence of enzalutamide, confirming the functional importance of their elevated expression in enzalutamide-resistant models. Moreover, coinhibition of IL8 and VEGF-A resulted in a durable, effective resolution of enzalutamide-sensitive prostate tumors. We conclude that concurrent inhibition of two hypoxia-induced factors, IL8 and VEGF-A, prolongs tumor sensitivity to enzalutamide in preclinical models and may delay the onset of enzalutamide resistance.Inhibiting androgen signaling using androgen signaling inhibitors (ASI) remains the primary treatment for castrate-resistant prostate cancer. Acquired resistance to androgen receptor (AR)-targeted therapy represents a major impediment to durable clinical response. Understanding resistance mechanisms, including the role of AR expressed in other cell types within the tumor microenvironment, will extend the clinical benefit of AR-targeted therapy. Here, we show the ASI enzalutamide induces vascular catastrophe and promotes hypoxia and microenvironment adaptation. We characterize treatment-induced hypoxia, and subsequent induction of angiogenesis, as novel mechanisms of relapse to enzalutamide, highlighting the importance of two hypoxia-regulated cytokines in underpinning relapse. We confirmed AR expression in CD34Targeting hypoxia-induced signaling may extend the therapeutic benefit of enzalutamide, providing an improved treatment strategy for patients with resistant disease. The inhibition of androgen signaling, through use of androgen ablating agents and androgen signaling inhibitors (ASI), remains the therapeutic mainstay in the treatment of primary and metastatic prostate cancer, respectively . The ASIin vivo  in vivo  in vivo . Rapid vin vivo . Similar(in vivo . TranscrMycoplasma by MycAlert (Lonza) according to the manufacturer's instructions.PC3  and LNCaP  were cultured in standard culture media as recommended by ATCC . Human uCell viability was assessed by MTT assay as described . Briefly18S, BCL2, CAIX, CXCL8, VEGFA have been reported  and VEGF-A  were detected by ELISA according to the manufacturer's instructions. Briefly, cells were plated and treated as indicated and the media was collected and the cell number was recorded using a Coulter Counter (Beckman Coulter). Standards and samples were added to a 96-well plate that had been coated with the appropriate capture antibody and incubated as required. Colorimetric detection was achieved using streptavidin-HRP and absorbance at 450 nm (reference 570 nm). A standard curve was generated and the quantity of IL8 or VEGFA in each sample was calculated and correlated to cell number.Cells were seeded on coverslips, fixed, permeabilized, blocked, and visualized  and Alexa Fluor 568  antibodies. Nuclei were visualized using ProLong Gold Antifade with DAPI . Cells were viewed under a Nikon Eclipse Ti-S fluorescent microscope and images captured using NIS-Elements software.RNA sequencing analysis was performed using established protocols as described . Array dIHC was performed for AR as described . BrieflyV2A angiogenesis assay  was carried out as described . Briefly5cells/embryo). 10-μmol/L enzalutamide was delivered intravenously on days 1 and 2 post-tumor cell inoculation. On day 5, the lower cam was excised and analyzed with human-specific Alu-qPCR as described  1:105 SYBR Green dye  and 0.4 μmol/L of each Alu primers: ense (5′-ACGCCTGTAATCCCAGCACTT-3′) and Alu antisense (5′-TCGCCCAGGCTGGGTGCA-3′). The actual number of tumor cells present in the chorioallantoic membrane (CAM) was determined using a standard curve generated by serial dilution of human tumor cells as previously described  Act 1986 and the United Kingdom Co-ordinating Committee on Cancer Research (UKCCCR) Guidelines (2010) for the welfare of animals in experimental models. Drug administration and study protocols were as described in Supplementary Methods.t test, Mann–Whitney U test or 2-way ANOVA with Bonferroni post-tests, as appropriate using GraphPad Prism software. All experiments were done three times and the total number of experiments and samples are indicated in the appropriate figure legends. Data are presented as the mean ± SEM.Statistical significance was determined using a two-tailed Student All animal experiments complied with the Animal (Scientific Procedures) Act 1986 and the UKCCCR Guidelines (2010) for the welfare of animals in experimental models and were approved by the Queen's University Belfast's Animal Welfare Ethical Review Body.The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.P < 0.001; Experiments were conducted to characterize the effect of enzalutamide (MDV3100) on tumor oxygenation and MVD. Using LNCaP xenografts, we observed that enzalutamide administration induced a rapid drop in tumor oxygenation reaching a nadir at 14 days, after which oxygen levels were shown to gradually increase towards pretreatment levels . To deteP = 0.001) and 14 . This trend was reversed at days 21 and 28 , where increasing vessel density is supportive of an angiogenic response and consistent with prior oxygen tension recordings  and was further suppressed in response to the anti-VEGF/anti-IL8+enzalutamide treatment .The tumor area covered by vessels was assessed as part of the study. Coverage was 21% ± 3% at treatment initiation and increased by 10% in vehicle-treated tumors by day 28 . Consistcordings . In contin vivo  in these experiments to place emphasis on the effects of enzalutamide on the endothelium as opposed to tumor cells. As an initial control experiment, CM harvested from enzalutamide-treated PC3 cells had no destructive effect on tubule formation in vitro suggesting the absence of tumor-derived factors that negatively affected HVEC viability . However, enzalutamide administration to the CAM model significantly enhanced PC3 metastatic colonization in the model  but was ineffective in hypoxic environments  gene expression . Our observations are consistent with increased or sustained high AR activity under hypoxic conditions.Further experiments were conducted to determine whether the reduced sensitivity to enzalutamide was associated with the modulation of AR target expression or pathway activation. The effect of hypoxia on AR expression and activity was explored in LNCaP, 22Rv1, and CWR-R1 cell lines. Hypoxia induced a time-dependent increase in the expression of AR protein in all three cell lines over a 24-hour time course, with further evidence of increased AR-V7 expression detected in the 22Rv1 and CWRR-1 lines . The effpression ; early oin vitro. Concurrent administration of the anti-VEGF/IL8 nAbs abrogated  the acute, hypoxia-induced increases in FLAR mRNA and KLK3 (PSA) mRNA  mRNA  and ARFLpression . Inhibitin vivo  or combined IL8/VEGF nAb's (P < 0.01); image analysis confirmed a significant reduction in the number of vascular junctions observed under these conditions. The direct addition of these nAbs to the endothelial cells did not affect tubule formation , suggesting that the hypoxic tumor cells are the predominant source of these pro-angiogenic factors. Furthermore, we confirmed that the addition of each neutralizing Ab abrogated the detectable levels of the targeted cytokine in the CM of hypoxic LNCaP cells , however, we also observed that the anti-IL8 nAb partially reversed VEGF secretion, and vice versa, demonstrating the potential interaction between these cytokines.Our next experiments sought to characterize the extrinsic effect of tumor-derived IL8 and VEGF signaling on the vascular endothelial cells. Although VEGF and IL8 are considered to be pro-angiogenic in vivo , only thin vivo. Enzalutamide treatment caused 99% tumor control compared with vehicle , while IL8 neutralization alone did not attenuate the response the enzalutamide. However, dual neutralization of IL8 and VEGF from day 14 reduced tumor volume by 98.8% (P < 0.0001). Average endpoint tumor weights demonstrated significant reduction following enzalutamide/anti-VEGF  compared with vehicle (P = 0.011), highlighting the importance of coinhibition of IL8 and VEGF to enhance tumor reduction.Finally, we focused on examining the combined impacts of inhibiting the intrinsic and extrinsic effects of IL8 and VEGF signaling on LNCaP tumor growth  vehicle . Dual neFL and AR-V7 expression and resistance to enzalutamide was validated . Consistent with our hypothesis, VEGF and IL8 mRNA expression  and -resistant (EnzR) models . ARFL anpression  and protpression  were incin vitro; enzalutamide or neutralization of either IL8 or VEGF alone did not affect the viability of LNCaP-EnzR cells relative to control, but dual neutralization of IL8/VEGF in combination with enzalutamide significantly decreased cell viability by 28% ± 6% . All further experiments explored the impact of combined VEGF/IL8 neutralization. Under normoxic conditions, dual neutralization of IL8/VEGF potentiated enzalutamide response in both LNCaP-Par and LNCaP-EnzR cells (P < 0.05) and 45% ± 13% in CWR-R1-EnzR cells (P < 0.01) relative to corresponding enzalutamide/IgG controls . In contrast, the coadministration of anti-VEGF/anti-IL8 nAbs concurrent with the enzalutamide treatment resulted in the regression of tumors, with 25% being unmeasurable after 20 days, and with no significant change in toxicity compared with enzalutamide alone , highlighting a novel opportunity for efficacious therapy in an EnzR model.We further characterized the impact of dual inhibition of IL8/VEGF in the paired LNCaP-EnzR model. As expected, enzalutamide failed to reduce tumor volume compared with controls . ConsistThe effect of enzalutamide on tumor oxygenation was also analyzed for both LNCaP-Par and LNCaP-EnzR xenograft models  and F. Ein vitro. These effects on vascular endothelial cells are consistent with our in vivo observations of a rapid onset and sustained tumor hypoxia following enzalutamide administration to prostate tumor xenografts. The loss of tumor control afforded by enzalutamide, observed at approximately 14 days of therapy, was concurrent with an increased tumor MVD and a rise in oxygen tension, indicative of an environment-promoted angiogenic response. Our results with enzalutamide are consistent with prior imaging of vascular collapse in Bicalutamide-treated tumors, and similar loss of tumor oxygen tension observed in response to finasteride, a 5-alpha-reducatase inhibitor, suggesting that treatment-observed hypoxia is not a drug-specific effect in tumor models, but rather an effect arising from perturbation of androgen signaling in the endothelium  resulted in a marked delay of LNCaP tumor reoxygenation, increase in MVD and regrowth. Latterly, we observed that administering the anti-VEGF/IL8 nAbs during the period of hypoxia development  effected a resolution of drug-sensitive LNCaP tumors to below detectable thresholds within 14 days, with sustained tumor control observed for a period extending greater than 30 days post-cessation of treatment, consistent with a durable response. Furthermore, combined inhibition of these hypoxia-regulated factors had pronounced effects even in enzalutamide-resistant LNCaP-derived tumors, either in the absence or presence of enzalutamide, suggesting a potential clinical utility of an anti-VEGF/IL8 therapeutic strategy in tumors that have progressed on enzalutamide therapy. The very significant effects observed with the antibody cocktail alone in this drug-resistant model suggest a potential addiction of these tumors to these cytokines which is essential to tumor adaptation to the hypoxia-conditioned microenvironment.To our knowledge, this is the first study to demonstrate the importance of their functional cooperativity in prostate cancer in vivo experiments quantifying MVD clearly demonstrate the importance of promoting angiogenesis within the tumors as a major contributing mechanism, consistent with the pro-angiogenic functions of VEGF and IL8. Furthermore, our in vitro data demonstrate the association of IL8 and VEGF signaling with regulation of AR expression in hypoxic prostate cancer cells, consistent with these factors contributing to a target-associated resistance. We speculate that further contributing and overlapping mechanisms are probable, including the ability of cooperative IL8 and VEGF signaling to stimulate key transcriptional programs including that of the AR, NF-κB, and HIF-1, resulting in a broad and sustained intrinsic modulation of pro-tumorigenic, resistance-associated, and proliferation-driving genes in tumor and vascular endothelial cells resident within the hypoxic zone  originating from murine stromal and vascular endothelial cells which remains intact and therefore may provide some degree of functional compensation. As such, administration of pharmacologic agents that provide total disruption of hypoxia-induced ELR+ chemokine signaling within the prostate TME, and in the presence of a fully competent and humanized immune system may yet provide even more impressive results.Clinical investigation of angiogenesis inhibitors has been studied in advanced prostate cancer. Phase III trials of anti-angiogenic compounds including Bevacizumab, Sunitinib, and Lenalidomide were discontinued due to poor efficacy in metastatic CRPC . The cliin vivo and restores sensitivity of drug-resistant models encourages future evaluation in further experimental models, and potential translation of this discovery to proof-of-concept trials. Moreover, our results provide further insights into the complex nature and multi-targeted therapeutic regimens that may be required to prolong the clinical benefit of next-generation AR antagonists in men with advanced, life-threatening prostate cancer.In conclusion, our study defines a further mode of microenvironment-mediated resistance, which arises as a consequence of treatment-induced collapse of the vascular supply to prostate tumors. This mode of resistance is distinct from the numerous modes of intrinsic resistance mechanisms and confirms that the impact of inhibiting androgen signaling extends beyond that of CAFs within the TME. Our demonstration that combined VEGF/IL8 inhibition extends the duration of sensitivity of prostate tumors to enzalutamide We show treatment-promoted hypoxia and microenvironment adaptation facilitates resistance to enzalutamide. Concurrent inhibition of two hypoxia-induced factors, IL8 and VEGF-A, prolongs tumor sensitivity to enzalutamide in preclinical models. Targeting hypoxia-induced signaling may extend the therapeutic benefit of enzalutamide and provide an improved treatment strategy for patients with resistant disease.Supplementary FigureClick here for additional data file.


Physical activity is associated lower risk for a broad range of non-communicable diseases and early mortality, and even small changes in daily activity levels could have a profound effect on public health at the population level. The COVID-19 pandemic reshaped daily life for United States (US) adults resulting in reductions in physical activity early in the pandemic, but its longer-term effects on daily activities are unknown. To examine the longer-term impact of the pandemic on daily activity levels, we conducted a nationwide longitudinal study of 1,635 adults (20–75 years) in AmeriSpeak. Previous-day recalls of time-use, sedentary time, and physical activity were completed on randomly selected days in Fall 2019 (pre-pandemic) and Fall 2020. Overall, US adults reported less time in transportation (-0.47 hrs/d), more total discretionary time (0.40 hrs/d), but no changes in total sedentary time (0.10 hrs/d) or leisure-time physical activity (-0.06 hrs/d). Women reported significantly less total activity (-0.36 hrs/d) and participants with children < 13 yrs reported more sedentary time (0.60 to 0.82 hrs/d) and less moderate-to-vigorous intensity activity (-0.84 to -0.72 hrs/d). Adults without children reported no changes in sedentary time (0.02 hrs/d) or moderate-vigorous intensity activity (-0.06 hrs/d). Adults who started working from home reported no changes in physical activity, but they were among the most sedentary and least active population groups at both timepoints. Our findings describe the complex inter-play between competing behaviors as time-use demands have changed in response to the pandemic, particularly for adults with younger children. Many US adults are likely to continue working from home; therefore, implementation of evidence-based approaches to increase physical activity and reduce sedentary time in this growing population subgroup appears warranted. The COVID-19 pandemic dramatically reshaped daily life in the United States and worldwide –4, our uincreased participation in leisure-time activities —a potential benefit of the pandemic that could compensate for possible reductions in activity in other life domains. Additionally, rates of telecommuting increased dramatically [The initial United States lockdown spring 2020) was associated with a 12% reduction in daily step counts among Fitbit users 020 was a decreaseatically , 9 and ratically . For exaatically .To date, most studies examining changes in physical activity and related behaviors were conducted early in the pandemic using convenience samples , 7 with Participants were from AmeriSpeak, a probability-based panel designed to represent the US population , 20. ThePanelists were sent e-invitations to complete a short online-survey and unannounced 24-hour recalls on randomly selected days. In Fall 2019 participants were asked to complete two recalls in a 1–2-week period, and in 2020 they were asked to complete one recall. Recalls could only be completed on the randomly targeted recall day and no make-up recalls on later days were attempted. The online survey included questions about self-rated health, physical activity, height, weight, and current occupational status and location of work. Additional demographic information including self-reported race and ethnicity was collected at enrollment into the AmeriSpeak panel. Participants received $15/recall. Data from this study can be found in the The AmeriSpeak panel is based on a stratified two-stage sampling design. Primary sampling units in the first stage are National Frame Areas and the secondary sampling units are defined from Census tracts or block groups. To develop our sample weights, we started with the final weights calculated for each AmeriSpeak panelist in our longitudinal sample and for each recall we calculated study-specific weights that also adjusted for selection probabilities from the panel, non-response in our study, and population coverage. Final weights are further adjusted  to external population totals with respect to age, sex, education, race and Hispanic ethnicity, housing tenure, telephone status, and Census Division derived from the Current Population Survey . Samplinhttps://dceg.cancer.gov/research/how-we-study/exposure-assessment/physical-activities-completed-over-time-24-hours-act-24).Recalls were completed online using the self-administered ACT24 previous-day recall . To compWe described the characteristics for participants who completed recalls in 2019 and 2020. We tabulated the actual unweighted number of participants and the weighted population percentages in each demographic category. For 2019, we calculated the mean of each time variable  using observations from both 2019 recalls, and for 2020, we calculated the mean of each time variable using observations from the sole recall. Using survey methods and the weights described above, we obtained unbiased estimates of population averages and robust standard errors. For estimating the differences between 2019 and 2020, we regressed each time variable on study year using observations from available recalls. We repeated the regression in strata defined by gender , age , family structure at home , and current work from home status . All analyses were performed in SAS 9.4 using survey procedures  to account for the complex sampling design. Statistical significance of two-sided tests was defined as p-value < 0.05 and did not account for multiple testing.In 2019, 19% of panelists  completed the short survey and a provisionally valid recall. Participants from 2019 who were still active in AmeriSpeak in 2020 were invited to participate in 2020, and 69%  completed a second survey and an additional recall. After additional ACT24 quality control processing was completed, 1,635 of the 1,788 (91%) longitudinal participants remained eligible for analysis. Comparison with demographic characteristics of the US population in the Current Population Survey revealed few differences in our weighted demographic data from the longitudinal sample with provisionally valid recalls, except for an overrepresentation of participants with an annual income of less than $50K and an underrepresentation of participants reporting $150K or more . The desWe observed small non-significant increases (p > 0.05) in time reported in-bed/sleeping (0.14 hrs/d) and sedentary time (0.10 hrs/d) in 2020 and corresponding non-significant reductions in total (-0.24 hrs/d) and moderate-to-vigorous intensity activity  to 2020 in men, but women reported significantly less total physical activity , but no changes in this metric in the other age-groups . Adults Among participants with younger children , we observed substantial increases in total sedentary time (0.60 to 0.82 hrs/d) and reductions in moderate-to-vigorous intensity physical activity , while those who did not work from home or were unemployed or retired/disabled did not. Adults who reported being unemployed in 2020 reported significantly less moderate-to-vigorous intensity physical activity , US adults overall reported spending 0.47 hrs/d less total time in transportation and 0.40 hrs/d more in overall leisure time, but they did not report spending this newfound discretionary time in physically active exercise or recreational pursuits. While we found no major changes in levels of physical activity or sedentary behavior in the overall population, suggesting no change from pre-pandemic levels, several population subgroups did report significant changes that warrant attention. Women reported significantly less total physical activity during the pandemic, and men and women with younger children reported substantial increases in sedentary time and reductions in moderate-to-vigorous intensity activity. In contrast, adults who started working from home reported no major changes in sedentary time or physical activity, but this group was among the most sedentary and least active segment of the population both before and after the pandemic. In the following sections we expand on each of these findings, consider their implications, and discuss the strengths and limitations of this research.To our knowledge this is the first detailed longitudinal analysis of changes in physical activity and sedentary behavior in a population-based sample of US adults well after the initial lockdowns arising from the COVID-19 pandemic. In the Fall of 2020 (November), the US was largely back “open” after the initial lockdowns, with the New York Times reporting that 31 states were “Open with some limits” or had “No restrictions”, 13 states were “Mostly open” and only one state was “Partly open” . Never-tSome, largely but not entirely, cross-sectional studies have indicated more dramatic changes in time use at specific times in the pandemic  and signThe time-use changes described above were the predominant patterns we observed in most population subgroups, although there were some exceptions. Adults aged 40–64 years reported spending less time in transportation, but also more time in-bed/sleeping (0.25 hrs/d) and more time working (0.43 hrs/d) rather than more leisure-time. Overall, women reported spending 0.36 hrs/d less in total physical activity later in the pandemic and this change appeared to be driven by the subgroup of women with likely demands associated with having children < 18 yrs) in the household. This finding is broadly consistent with results from Curtis and colleagues  who repo8 yrs in able to start working from home during the pandemic were among the most sedentary (11.0 hrs/d) and least active group  in the population, comparable to much older retired or disabled adults in our sample. The high levels of sedentary time and lower activity appear to be due to underlying differences in occupational sitting and physical activity in these two occupational groups. Thus, working from home may not lead to more sedentary time and less activity, but rather, adults who can work from home may be more likely to have these less healthy behavioral characteristics. Adults in this group were more educated and more likely to be employed in more sedentary white collar service jobs, such as business and finance, and the educational and health sectors [Changes in the location of the workplace, or working from home, may be one of the most enduring effects of the pandemic. Prior to the pandemic 2019), 22% of working adults in the US reported working from home, but during the pandemic (2020) this proportion nearly doubled, increasing to 42% , 22% of  and US aUltimately, it is unclear what proportion of working adults will continue to work from home after the pandemic resolves, but our finding that this group reported sitting about 1.5 hrs/d longer than the national average in 2019  raises cThis study has both strengths and limitations. An important limitation of our study is that we did not have data during the early period of the pandemic. While we cannot describe changes in each period of the pandemic the consistent strong reductions in the early days of the pandemic in the US –4 our fiDuring the Fall of 2020, US adults on average reported spending less time in overall transportation and more total time in leisure pursuits, with little or no increase in exercise and recreational activities compared to pre-pandemic levels. Both men and women appeared to use the extra discretionary leisure-time in sedentary rather than in physically active behaviors. Adults with younger children reported the highest increases in sedentary time and reductions in moderate-to-vigorous intensity activity. Furthermore, adults who started working from home did not report changes in their activity levels but were found to be among the most sedentary and least active subgroups in the population, both before and during the pandemic. These findings point to the need for enhanced support for adults with young families to help them maintain their activity levels and highlight the unique challenges for adults who continue to or start working from home because of the COVID-19 pandemic. The prevalence of working from home is unlikely to return to pre-pandemic levels; therefore, implementing evidence-based individual and community-based approaches to increase physical activity and reduce sedentary time  may be nS1 Fig(DOCX)Click here for additional data file.S1 Table(DOCX)Click here for additional data file.S2 Table1Major categories separated by semi-colon. 2Other category is composed of Categories or data occurrences with a low prevalence of reporting.(DOCX)Click here for additional data file.S3 Table1Values are for participants prior to ACT24 quality control procedures. 2Difference = difference in proportions between Weighted and CPS 2020 estimates.CPS 2020 = US Census Current Population Survey, March 2020. (DOCX)Click here for additional data file.S4 Table1Additional groups include, individuals self-identifying as non-Hispanic Asian, Other, or being from more than one race or ethnic group.(DOCX)Click here for additional data file.S5 Table(DOCX)Click here for additional data file.S6 Table(DOCX)Click here for additional data file.S1 Data(SAS7BDAT)Click here for additional data file.


Despite many studies, optimal treatment sequences or intervals are still questionable in retinal vein occlusion (RVO) macular edema. The aim of this study was to examine the real-world treatment patterns of RVO macular edema. A retrospective analysis of the Observational Medical Outcomes Partnership Common Data Model, a distributed research network, of four large tertiary referral centers  identified 3286 eligible. We visualized treatment pathways (prescription volume and treatment sequence) with sunburst and Sankey diagrams. We calculated the average number of intravitreal injections per patient in the first and second years to evaluate the treatment intensities. Bevacizumab was the most popular first-line drug (80.9%), followed by triamcinolone (15.1%) and dexamethasone (2.28%). Triamcinolone was the most popular drug (8.88%), followed by dexamethasone (6.08%) in patients who began treatment with anti-vascular endothelial growth factor (VEGF) agents. The average number of all intravitreal injections per person decreased in the second year compared with the first year. The average number of injections per person in the first year increased throughout the study. Bevacizumab was the most popular first-line drug and steroids were considered the most common as second-line drugs in patients first treated with anti-VEGF agents. Intensive treatment patterns may cause an increase in intravitreal injections. Macular edema is the major cause of visual deterioration in patients with RVO4. Advances in imaging and therapeutic modalities such as optical coherence tomography (OCT) and anti-vascular endothelial growth factor (VEGF) therapy have revolutionized the diagnosis and treatment of RVO5. The proangiogenic protein, VEGF, and inflammatory cytokines play key roles in the pathogenesis of macular edema6. There are five major agents used for treatment of RVO-related macular edema: three anti-VEGF agents including bevacizumab, ranibizumab, and aflibercept, and two steroid agents including triamcinolone and dexamethasone. Representative studies including pivotal studies demonstrated the effectiveness of these treatments14. Nevertheless, treatment patterns still contain unmet clinical needs such as optimal treatment sequence or the interval of intravitreal injections, an important factor that determines treatment burden16. In addition, a low number of studies on second or third-line drugs means that clinicians must depend on their own judgement when the first-line drug is ineffective. Randomized controlled trials (RCTs) are still the gold standard. However, RCTs cannot reflect the real-world patient population, which limits their generalizability and external validity17. Real-world evidence influences not only clinical practice but also healthcare policies and medical resource utilization. Therefore, an evaluation of the real-world treatment patterns on macular edema following RVO helps the development of healthcare policies and allocation of healthcare resources. In this respect, the Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM) could be useful in providing large-scale real-world data for the analysis of drug utilization patterns. The OMOP CDM, a distributed research network adopted by Observational Health Data Sciences and Informatics (OHDSI), presents healthcare data from diverse sources in a consistent and standardized way18. OHDSI is an international collaborative aiming at creating open-source solutions that emphasize the value of observational health data through large-scale analytics19. The OMOP CDM adopts standardized structures, contents, and semantics of observational data. This feature permits researchers to share their analysis codes across disparate observational healthcare databases when their healthcare data are properly extracted, transformed, and loaded in accordance with the OMOP CDM20. A number of large-scale collaborative studies across different data sources have been conducted, and real-world evidence has been generated using OMOP CDM22. Recently, a study has been conducted using data from the OMOP CDM to analyze the real-world incidence of endophthalmitis following anti-VEGF therapy20.Retinal vein occlusion (RVO) is a vision-threatening disorder with an incidence rate of approximately 48 per 100,000 person-yearsIn this study, we conducted a characterization study to examine the real-world treatment intensities and pathways of macular edema secondary to RVO during the first two years of treatment from multiple tertiary referral centers using OMOP CDM.23. The characteristics of each OMOP CDM are shown in Supplementary Table This was a retrospective, observational study. Our study used four OMOP CDM databases (version 5.3.1) derived from the electronic health records (EHR) of four large tertiary referral hospitals, Seoul National University Bundang Hospital (SNUBH), Ajou University Hospital (AUH), Yeoeuido Saint Mary’s Hospital (YSMH), and Saint Vincent’s Hospital (SVH). These EHR-based OMOP CDM databases included a total of 9,202,032 patients . Each analysis was performed independently and only the aggregated results were shared and analyzed. No patient-level data were pooled across sites23. We created a retrospective, observational cohort, that included all patients diagnosed with RVO and related with five intravitreal drugs . The diagnosis was coded according to the Systematized Nomenclature of Medicine Clinical Terms (SNOMED CT)24. We defined the index date for each case as the time of the first treatment with one of the intravitreal drugs. We only included patients with at least one diagnosis code corresponding to RVO  since the off-label bevacizumab first started being used in 2006. The third period was after January 2012, since ranibizumab, first on-label anti-VEGF drugs for RVO macular edema, was approved in 2012 (January 2012–December 2018). The second period was from January 2006 to December 2011.We analyzed the prescription patterns of intravitreal drugs. First, we evaluated the prescription sequences of the five target intravitreal drugs. We defined these prescription sequences as the treatment pathways23. Privacy was maintained by retaining protected information of data nodes within their firewalls25. In this study, we used the open-source R version 3.6.3 , sunburstR package version 2.1.6 (https://cran.r-project.org/web/packages/sunburstR/index.html), and networkD3 package version 0.4 (https://cran.r-project.org/web/packages/networkD3/index.html) for treatment pathway26. We also used the standard query language for treatment intensity. The source codes we had modified or developed were distributed and run locally, and only aggregate results were returned centrally 25.OHDSI develops validated methodologies to aggregate results from multiple sites into a single answer to produce real-world evidenceThis study was conducted in accordance with the tenets of the Declaration of Helsinki. Each center obtained approval from the Seoul National University Institutional Review Board (IRB) (IRB# X-1907-555-904), Ajou University Hospital IRB (IRB# RB-MED-MDB-20-271), and Catholic Medical Center (CMC) IRB . The waiver of informed consent was also approved as the OMOP CDM contains only de-identified data and this study was retrospective.Figure We identified 39 unique treatment pathways for RVO macular edema throughout the study period. Figures Figures Table 4. Intravitreal steroids were generally considered as a second-line drug for macular edema patients treated with anti-VEGF drugs first. Intravitreal triamcinolone and dexamethasone implants had almost equal proportions as second-line drugs for patients who received bevacizumab as a first-line drug  reported that the mean number of injections decreased from 2.5 (between weeks 24 and 52) to 1.3 (between weeks 52 and 76)6. Long term results of pivotal studies for ranibizumab in branch retinal vein occlusion (BRVO) and CRVO found that the mean number of injections decreased from 3.1 (between months 6 and 11) to 1.3 (second year) and from 4.0 (between months 6 and 11) to 2.25 (second year), respectively36. Other real-world studies about RVO macular edema represented similar tendencies. A study of 5,661 patients with BRVO macular edema reported that treatment visits per patient were 5.1 and 1.3 in the first and second year, respectively 37. Another study of 8,876 patients with BRVO macular edema showed that the mean number of anti-VEGF injections decreased from 4.5 to 2.9 from the first 6 months to the second 6 months38. This study also revealed that the mean number of anti-VEGF injections decreased from 4.6 to 3.0 in 6737 patients with CRVO macular edema38.A decrease in the average number of injections in the second year suggests that macular edema could, in many cases, be successfully resolved with only the first injection. Poor response to the treatment, missed visits, or use of other treatment modalities rather than intravitreal injection could also contribute the reduced number of injections. However, the results of previous studies support the former conclusion. A large retrospective study of pro-re-nata treatment reported that patients with RVO received a median of six anti-VEGF injections in the first year and fewer injections in subsequent years10. Furthermore, the effectiveness of monthly consecutive injections for at least 3 months has been widely accepted among physicians in neovascular age-related macular degeneration40. A multicenter, retrospective study revealed that this dosing regimen was also maintained in macular edema following RVO41. Therefore, intensive practice patterns might cause an increase in intravitreal injections.The mean number of injections in the first year increased over time, especially for anti-VEGF drugs, whose popularity also increased over the course of the year. Pivotal studies for ranibizumab and aflibercept, and studies on the efficacy of bevacizumab have been linked to the increased use of anti-VEGF drugs41. In our country, all medical services are provided under the National Healthcare Insurance Act and “covered services” allow patients to pay only part of the cost. Although covered services include treatments with on-label drugs for macular edema following RVO, their use is regulated by several conditions, such as minimum macular thickness, visual acuity, the limitation of the number of injections, or response to treatment. This makes physicians hesitant to use on-label drugs and provide an incentive to choose bevacizumab, which is free from these constraints. The noninferiority of bevacizumab might also ensures that it remains a valid choice of treatment31. In addition, the lower cost of bevacizumab compared with other agents is likely one of the most important factors for physicians and patients to prefer it. A sharp increase in the use of dexamethasone implants was in line with the influence of national insurance policies, because of the deregulation in the number of dexamethasone implant injections in 2019.It should be noted that the popularity of the off-label drug bevacizumab in our study, far exceeded that of ranibizumab, aflibercept, or dexamethasone . The efficacy and safety of on-label drugs has been already been proven by level 1 evidence research: prospective, randomized, and controlled studies. Nevertheless, for the patients in this study, the most popular first-line drug for macular edema secondary to RVO was bevacizumab. Another real-world report on RVO macular edema revealed that a majority of patients received ranibizumab (51.3%), with bevacizumab (44.1%) and aflibercept (4.6%) as the next most popular choices42. Our study, which examined real-world patient data, provides valuable real-world evidence and practice patterns for the treatment of RVO-related macular edema.Real-world practice patterns are influenced not only by the introduction of drugs and their efficacies, but also by the real-world environment, such as government policies, cost, resources, or burden related to the treatment. A public health policy maker should consider these aspects. If gaps between pivotal studies and real-world practice patterns persist, iterative studies are necessary to determine the best course of action in a given real-world environment. Recently, many real-world studies, including registry studies, have been conducted to provide real-world evidence. The results of these studies are reflected in clinical practice guidelines43. Many subsequent studies using these data will be conducted and reported soon. Lack of information on special situations, such as a patient switching to another drug for nonmedical reasons, is another limitation because the CDM restricts the review of individual records. In addition, a history of focal, grid laser, or vitrectomy cannot be collected because not all hospitals have uploaded records related to previous procedures into the CDM. Further studies including these procedures will be conducted in the near future.This study is a multicenter, large-scale, and real-world study that can be expanded further at any time owing to the scalability of CDM. Furthermore, it identifies real-world practice patterns and visualizes the treatment pathways and intensities of macular edema secondary to RVO. However, there are several limitations to this study because of its retrospective nature. First, we did not distinguish between CRVO and BRVO because information loss occurred during mapping of the original diagnostic data to SNOMED CT. Moreover, it is also difficult to distinguish whether the macular edema is associated with retinal vein occlusion or diabetes (i.e. diabetic macular edema) when retinal vein occlusion and macular edema exist together in patients with diabetes. We considered them as patients with macular edema following retinal vein occlusion inevitably. Second, the parameters of visual outcomes  could not be collected because the results of ophthalmic examination are still being extracted, transformed and loaded into the CDMIn conclusion, bevacizumab was the most popular prescription as a first-line drug, and its volume has increased over time. Triamcinolone and dexamethasone were most popularly chosen as second-line drugs, and the preference for dexamethasone implants rapidly increased over time. The CDM can reflect real-world practice patterns better than claim databases because it includes drugs not covered by health insurance. Furthermore, it is a valuable tool owing to its scalability for large-scale multicenter studies without patient-level pooled analysis; it only allows an analysis with aggregated results.Supplementary Information.


Admissions are generally classified as COVID-19 hospitalizations if the patient has a positive SARS-CoV-2 polymerase chain reaction (PCR) test. However, because 35% of SARS-CoV-2 infections are asymptomatic, patients admitted for unrelated indications with an incidentally positive test could be misclassified as a COVID-19 hospitalization. Electronic health record (EHR)–based studies have been unable to distinguish between a hospitalization specifically for COVID-19 versus an incidental SARS-CoV-2 hospitalization. Although the need to improve classification of COVID-19 versus incidental SARS-CoV-2 is well understood, the magnitude of the problems has only been characterized in small, single-center studies. Furthermore, there have been no peer-reviewed studies evaluating methods for improving classification.The aims of this study are to, first, quantify the frequency of incidental hospitalizations over the first 15 months of the pandemic in multiple hospital systems in the United States and, second, to apply electronic phenotyping techniques to automatically improve COVID-19 hospitalization classification.From a retrospective EHR-based cohort in 4 US health care systems in Massachusetts, Pennsylvania, and Illinois, a random sample of 1123 SARS-CoV-2 PCR-positive patients hospitalized from March 2020 to August 2021 was manually chart-reviewed and classified as “admitted with COVID-19”  versus specifically admitted for COVID-19 (“for COVID-19”). EHR-based phenotyping was used to find feature sets to filter out incidental admissions.EHR-based phenotyped feature sets filtered out incidental admissions, which occurred in an average of 26% of hospitalizations . The top site-specific feature sets had 79%-99% specificity with 62%-75% sensitivity, while the best-performing across-site feature sets had 71%-94% specificity with 69%-81% sensitivity.A large proportion of SARS-CoV-2 PCR-positive admissions were incidental. Straightforward EHR-based phenotypes differentiated admissions, which is important to assure accurate public health reporting and research. Despite the ongoing COVID-19 pandemic and the dozens of research groups and consortia worldwide that continue to utilize clinical data available in electronic health records (EHRs), critical gaps remain in both our understanding of COVID-19 and how to accurately predict poor outcomes, including hospitalization and mortality .One of the most prominent gaps in the field is how to distinguish hospital admissions specifically for COVID-19-related indications  from an incidentally positive SARS-CoV-2 polymerase chain reaction (PCR) test in admissions for an unrelated reason . Approximately 800,000 new SARS-CoV-2 cases are being reported daily, and approximately 150,000 patients are hospitalized with a positive SARS-CoV-2 PCR test [International Classification of Diseases, Tenth Revision (ICD-10) diagnosis code U07, which, according to guidelines, is equivalent to a positive test [Misclassification in research studies occurs because patients are usually considered COVID-19 patients if they have a recent positive SARS-CoV-2 PCR test or the ive test . This apive test  and is ision ICD- diagnosiive test . The magA potential solution is EHR-based phenotyping, which identifies patient populations of interest based on proxies derived from EHR observations. EHR phenotypes are developed by first performing manual chart review to classify cases and then applying a machine learning or statistical reasoning method to the EHR data to create an explainable predictive model ,16. For The Massachusetts Department of Public Health has recently begun using a simple phenotype to report COVID-19 hospitalizations ,23. AlthIn this study, we utilized EHR data from 60 hospitals across 4 US health care systems in 4CE, combined with clinical expertise, data analytics, and manual EHR chart review, to determine whether patients admitted to the hospital and who had a positive SARS-CoV-2 PCR test were hospitalized for COVID-19 (for-COVID-19 group) or were admitted for a different indication and simply had an incidental positive test (admitted-with-COVID-19 group).clinical expert  and 1 data analytics expert . Eligible patients for this study were those included in the 4CE COVID-19 cohort: all hospitalized patients (pediatric and adult) with their first positive SARS-CoV-2 PCR test 7 days before to 14 days after hospitalization [We selected a sample of 4 4CE sites across the United States to participate in the development of our for-COVID-19 hospitalization phenotype. These sites included the Beth Israel Deaconess Medical Center (BIDMC), Mass General Brigham (MGB), Northwestern University (NWU), and the University of Pittsburgh/University of Pittsburgh Medical Center (UPITT). Each site involved at least 1 Each development site randomly sampled an equal number of admissions in each quarter  or month  from their cohort of SARS-CoV-2 PCR-positive patients over the period of March 2020 until at least March 2021 (N=1123). Clinical experts reviewed the charts in the EHRs and recorded whether these patients were admitted for COVID-19-related reasons, as defined later. The total number of chart reviews per site was somewhat variable and determined by availability of the clinical experts. Participating sites and the number of chart reviews are listed in To develop chart review criteria, a 4CE subgroup met during March-July 2021. The group consists of about 20 researchers in 4CE, with a mixture of physicians, medical informaticians, and data scientists. In the process, dozens of real patient charts were considered, and edge cases were discussed until consensus was reached on the minimal chart review necessary to determine the reason a patient was hospitalized.Based on the developed criteria, chart reviewers  classified the patients based on review of primarily the admission note, discharge summary (or death note), and laboratory values for the hospitalization. Each site had Institutional Review Board (IRB) approval to view the charts locally, and only deidentified aggregate summaries were presented to the subgroup. Each site summarized the chart reviews in a spreadsheet that was then linked to the site’s 4CE EHR data, wherein medical record numbers were replaced with 4CE’s patient pseudoidentifiers, and criteria classifications were coded as an integer. The 4CE EHR data set is a COVID-19-related subset of raw EHR data consisting of selected laboratory test, medication, and procedure categories and all available ICD-10 diagnosis codes. The data dictionary is explained in more detail in We developed an R script (R Core Team) at the MGB to perform basic data summarization. This did the following: calculated chart review summary statistics, aggregated data on ICD-10 diagnosis codes used during the hospitalization to compare to the chart review classification, and generated a bubble plot that visualizes the change in proportion of hospitalizations, specifically for COVID-19, among all chart reviews over the course of the pandemic, by month. A trendline was fitted with locally estimated scatterplot smoothing (loess) regression using ggplot2 and was weighted by the number of chart reviews performed that month. Each participating health care site ran the R script on its chart-reviewed patient cohort.We developed an algorithm as an R script to choose phenotypes of admissions specifically for COVID-19, using established hospital dynamics measures of ordering/charting patterns in the EHRs  ,24. The We applied our algorithm to find patterns in 4CE EHR data at each site using the presence of medications, laboratory tests, and diagnoses to select the best phenotypes.  We further compared the output at each site to see whether there were similarities . We considered 2 cases: data that would be available in near-real time during a hospitalization (laboratory tests) and data that would be available for a retrospective analysis .Sites exported phenotypes with sensitivity of at least 0.60, ordered by specificity in descending order. (Site B applied a slightly lower sensitivity threshold because no phenotypes with sensitivity of at least 0.60 were available.) Specificity was chosen as the sorting variable because it measures the phenotype’s ability to detect and remove incidental SARS-CoV-2 admissions—a good measure of overall performance. Sensitivity, in contrast, measures the ability to select for-COVID-19 admissions, which can be easily maximized by simply selecting all patients. Groups of phenotypes were manually summarized into conjunctive normal form by combining AND and OR phenotypes at each site, when possible, and reporting a sensitivity and specificity range for the final combined phenotype. We excluded feature sets that were more complex but with the same performance as a simpler feature set.We also ran our phenotyping program to find the most predictive individual features at each site during every 6-month period of the pandemic, beginning January 2020. This analysis allowed us to examine the trend of HSD as the pandemic progressed.The final piece of analysis involved selecting multisite phenotypes and plotting their performance over time. First, we selected the features that appeared at multiple sites from the best phenotypes at each site. We used these to manually construct multisite phenotypes. We optimized these using MGB data by manually adding/removing OR components based on performance, because adding too many OR components degrades the specificity. We ran these constructed phenotypes at each site to ascertain their performance characteristics.This data-mining process can be seen visually in We also developed a temporal visualization used at each site  were preserved.IRB approval was obtained at the BIDMC (#2020P000565), the MGB (#2020P001483), the UPITT (STUDY20070095), and the NWU (STU00212845). Participant informed consent was waived by each IRB because the study involved only retrospective data and no individually identifiable data were share outside of each site’s local study team. Site names were anonymized  to comply with hospital privacy policies. At the MGB and the BIDMC, any counts of patients were blurred with a random number +/–3 before being shared centrally. Our previous work shows that for large counts, pooling blurred counts has minimal impact on the overall accuracy of the statistics . At all for COVID-19, 292 (26%) patients were admitted with incidental SARS-CoV-2, and 67 (6%) were uncertain  to comply with hospital privacy policies.The final chart review criteria are shown in ncertain . The 4 s19, 292 2% patientfor COVID-19 versus patients admitted incidentally with COVID-19. The input of the program includes the chart-reviewed classifications and patient-level EHR data on the presence of 22 selected laboratory test types, 11 selected medication categories, 12 procedure categories, and all ICD-10 diagnosis codes that are dated within 48 hours of admission. This resulted in 1880 distinct features across all sites.  was the only strong predictor of an admission specifically for COVID-19 across all 4 sites. In the second half of 2020, the phenotyping algorithm began selecting laboratory tests, including C-reactive protein (CRP), troponin, ferritin, and lactate dehydrogenase (LDH). In addition, the diagnosis “Other Coronavirus as Cause of Disease” (B97.29) began to be used at site B. By 2021, remdesivir and the diagnosis “Pneumonia due to COVID-19” (J12.82) additionally came into widespread use and became predictive of admissions specifically for COVID at site A.We manually constructed 5 multisite phenotypes from elements in The COVID-19 pandemic has lasted for over 2 years, with multiple waves worldwide. Although hospital systems have been cyclically overwhelmed by patients seeking care for COVID-19, as health care systems began to open up before the second wave, elective surgeries were again performed starting in the later part of 2020, and especially in the second quarter of 2021, many approached the health care system for health issues  while incidentally infected with SARS-CoV-2 . This, aWe applied an item set–mining approach and established HSD principles to phenotype SARS-CoV-2 PCR-positive patients who were admitted specifically for COVID-19 by using data on charting patterns  rather than results  ,37. HSD Our chart review protocol illustrates that patients who were admitted and had a positive SARS-CoV-2 PCR test were more likely to be admitted specifically for COVID-19 when disease prevalence was high (at least prior to Omicron). However, during periods in which health care systems were less restrictive , a secondary measure/phenotype was critical for accurately classifying admissions specifically for SARS-CoV-2 infection.As expected, we observed a lower proportion of hospitalizations specifically for COVID-19 in the summer months when disease prevalence was lower . One wourespiratory insufficiency in admissions specifically for COVID-19 and other for incidental and uncertain admissions with SARS-CoV-2. Surprisingly, 10%-20% of patients admitted with incidental SARS-CoV-2 were diagnosed with pneumonia, respiratory failure, or acute kidney injury . However, diagnoses are less prevalent in the population than laboratory tests and might not cover the entire population of admissions for COVID-19. Further, diagnoses early in hospitalization also do not always reflect the patient’s eventual diagnosis or hospital-related complications that are more accurately reflected in discharge diagnoses. There was also some heterogeneity in the diagnoses used at different sites . In addition, the presence of laboratory tests is useful for real-time detection systems because diagnosis codes usually are assigned after discharge. Clusters of tests for inflammatory markers  appeared across most sites as predictive of hospitalizations, specifically for COVID-19, which fits intuitively because an underlying systemic pathophysiological mechanism of SARS-CoV-2 is thought to be an inflammatory process ,40, and Our methods generated pairs of items using OR and groups of up to 4 using AND logical operators. Our feature sets were somewhat vulnerable to the problem that specificity decreases when multiple elements are combined with OR, although, in general, OR feature sets performed better across sites because they could be designed to choose the top-performing elements at each site.In addition to site differences, we also found changing disease management patterns over time. At the start of the pandemic, the only predictive phenotype was a pneumonia diagnosis. As standard COVID-19 order recommendations began to appear, laboratory orders became more consistent and predictive. Next, remdesivir began to be administered regularly. Finally, COVID-19-specific ICD-10 codes began to appear.Overall, we found that an informatics-informed phenotyping approach successfully improved classification of for-COVID-19 versus incidental SARS-CoV-2-positive admissions, although generalizability was a challenge. Although some transfer learning is apparent , local practice and charting patterns reduced generalizability. Specifically, phenotypes involving only laboratory tests did not perform well at site C, because the prevalence of these laboratory tests was low in the overall EHR data. This could be due to a data extraction or mapping issue in the underlying data warehouse. Site D had lower performance than other sites on the cross-site rules but not on the site-specific rules, perhaps highlighting less typical clinician treatment patterns.Any of the multisite phenotypes developed here could be implemented as a cohort enhancement tool in hospital systems or data research networks, and the laboratory-only phenotypes (“CRP and Ferritin”) could be used for real-time corrections in reporting. However, because of the changing nature of COVID-19 and practice and coding variation across sites, these phenotypes should be used primarily as a starting point. It is important to run the phenotyping algorithm on each individual site’s data to tweak the rules to optimize them for each implementation.Although the current data start at the beginning of the pandemic, they do not include the current Omicron wave nor much of the Delta wave. We believe that the techniques introduced here (if not the phenotypes themselves) will be applicable to these variants, and we are planning future studies to validate this.Our phenotypes demonstrated some transfer learning but not enough to create a single phenotype applicable to all sites. Technically, our system used machine learning at individual sites, but results were manually aggregated across sites. Emerging techniques for federated learning  might reFinally, an inherent weakness of EHR-based research is that EHR data do not directly represent the state of the patient, because some observations are not recorded in structured data and some entries in the EHR are made for nonclinical reasons . This is common to all EHR research efforts, and we mitigated this limitation by developing chart-verified phenotypes.At 4 health care systems around the United States over an 18-month period starting in March 2020, we developed and applied standardized chart review criteria to characterize the correct classification of hospitalization specifically for COVID-19 as compared to incidental hospitalization of a patient with a positive SARS-CoV-2 test or ICD-10 code. Then we applied HSD and frequent item set mining to electronic phenotyping to generate phenotypes specific to hospitalizations for COVID-19, and we showed how patterns changed over the course of the pandemic. Application of this approach could improve public health reporting, health care system resource disbursement, and research conclusions.


Is there evidence of in-class transmission of SARS-CoV-2 on a university campus that had mandated vaccination and masking?In this cohort study of 140 000 class meetings at a large US university, there were over 850 cases of SARS-CoV-2 infection identified through weekly surveillance testing of all students and faculty on campus during the fall 2021 semester. There were 9 instances of potential in-class transmission identified as identical lineages confirmed by SARS-CoV-2 genome sequencing, and none of these instances were confirmed to be in-class transmission.These results suggest that in-class transmission of SAR-CoV-2 in an urban university with masking and vaccine protocols in-place was negligible. This cohort study using surveillance testing data from a university located in a large US city examines incidence of in-classroom SARS-CoV-2 transmission on a college campus with contact tracing and vaccination and masking requirements. SARS-CoV-2, the causative agent of COVID-19, has displayed person-to-person transmission in a variety of indoor situations. This potential for robust transmission has posed significant challenges and concerns for day-to-day activities of colleges and universities where indoor learning is a focus for students, faculty, and staff.To assess whether in-class instruction without any physical distancing, but with other public health mitigation strategies, is a risk for driving SARS-CoV-2 transmission.This cohort study examined the evidence for SARS-CoV-2 transmission on a large urban US university campus using contact tracing, class attendance, and whole genome sequencing during the 2021 fall semester. Eligible participants were on-campus and off-campus individuals involved in campus activities. Data were analyzed between September and December 2021.Participation in class and work activities on a campus with mandated vaccination and indoor masking but that was otherwise fully open without physical distancing during a time of ongoing transmission of SARS-CoV-2, both at the university and in the surrounding counties.Likelihood of in-class infection was assessed by measuring the genetic distance between all potential in-class transmission pairings using polymerase chain reaction testing.More than 600 000 polymerase chain reaction tests were conducted throughout the semester, with 896 tests (0.1%) showing detectable SARS-CoV-2; there were over 850 cases of SARS-CoV-2 infection identified through weekly surveillance testing of all students and faculty on campus during the fall 2021 semester. The rolling mean average of positive tests ranged between 4 and 27 daily cases. Of more than 140 000 in-person class events and a total student population of 33 000 between graduate and undergraduate students, only 9 instances of potential in-class transmission were identified, accounting for 0.0045% of all classroom meetings.In this cohort study, the data suggested that under robust transmission abatement strategies, in-class instruction was not an appreciable source of disease transmission. To help limit SARS-CoV-2 transmission, some institutions of higher education implemented vaccination requirements for students and faculty, along with nonpharmacological interventions such as surveillance testing, on-campus isolation of infected individuals, quarantine and frequent testing of close contacts, indoor masking, and enhancements to air filtration and circulation. These multifaceted interventions allowed in-class instruction to fully resume and dormitories to reopen and repopulate. Despite these efforts, transmission has been continuously present in campus life.SARS-CoV-2, the causative agent of COVID-19, has posed significant challenges to day-to-day activities of colleges and universities. Many institutions of higher education faced immense challenges in the 2020-2021 and 2021-2022 school years from spiking infections.3 but evaluation of classroom transmission is minimal. Transmission in K-12 classrooms has shown to be minimal, but these studies lack comparability with lecture-size classrooms at the university level.6 The lack of such data has prompted uncertainty among students, faculty, and staff regarding the infection risk of in-person instruction. To address this evidence gap, we sought to unambiguously identify instances of SARS-CoV-2 transmission in the classroom environment at a large urban university. Using a blend of adaptive surveillance testing, traditional epidemiology, and viral genomics, we analyzed incidence of likely transmission over more than 140 000 class meetings.Understanding whether in-class instruction without any physical distancing is fueling transmission is an important question that has not yet been adequately addressed. Multiple theoretical calculations have suggested differing outcomes for vaccination or masking on in-class transmission,STROBE) reporting guidelines for cohort studies.We linked campus-wide surveillance testing, contact tracing, and class rosters from September 1, 2021, to December 1, 2021. September 2021 marked the full return to in-person instruction at BU. There were more than 140 000 in-person class meetings during this time period. Class sizes ranged from 2 to over 400 individuals . Potential instances of in-class transmission were defined as 2 or more SARS-CoV-2 positive individuals who shared an in-person class but did not identify one another as close contacts outside of the classroom environment. Outside classroom exposures included, but were not limited to, social gatherings in dormitory or off campus, dining hall interactions, study groups or group projects, and athletic events. The Boston University (BU) Charles River Campus institutional review board reviewed and approved the protocol for sequencing to determine variant distribution and emergence on the BU campus and waived informed consent requirements based on study design. This study followed Strengthening the Reporting of Observational Studies in Epidemiology . Adaptive testing with an increased testing cadence was requested under circumstances seen to be high risk . Anterior nares samples were self-collected in sterile saline, and following clinical testing the residual discarded positive sample material was stored at −80 °C. For students not considered in a high-contact group, surveillance testing was an observed nasal swab done at a collection site once weekly where staff were present to show individuals how to properly perform the test and ensure the tests were properly handled.8 Adherence to testing was in excess of 95% in a student population of approximately 30 000. Compliance for testing, vaccinations, and isolation or quarantine status was monitored using a color-based badge system, which was a centralized system that limited access to campus buildings during isolation and quarantine periods. Enhanced air filtration efforts were practiced, including increased air changes per hour. In areas where there were not HVACs, standing air filters were used.All BU buildings were inspected, received upgraded filtration when applicable, and had settings adjusted to increase frequency of fresh air exchanges. All mechanically ventilated classrooms had MERV-13  filter upgrades and settings maximized to allow for increased fresh air and a minimum of 2 to 4 air circulations per hour. Non–mechanically ventilated classrooms had windows open when outdoor temperatures allowed and commercial-grade HEPA (high-efficiency particulate absorbing) filters placed within the rooms. Faculty had the option to remove their mask while lecturing if they were more than 6 feet from students and had increased their surveillance testing frequency. COVID-19 vaccination was mandated as of September 2, 2021, allowing for religious or medical exemption. Vaccination compliance at BU was defined as either completion of a SARS-CoV-2 vaccination series or a documented medical or religious exemption. BU students, faculty, and staff performed routine testing up to twice a weekWe identified all individuals with detectable SARS-CoV-2 by reverse transcriptase real-time polymerase chain reaction (rRT-PCR) using the US Centers for Disease Control and Prevention (CDC) primers for N1, N2, and Rnase P. Students who were infected with SARS-CoV-2 were placed in isolation status, or red badge status, for 10 days. Students who were exposed by someone who was infected with SARS-CoV-2 were advised on a quarantine timeline based on vaccination status and placed in quarantine status, or orange badge status.7 In addition to traditional contact tracing, contact tracers utilized a Power BI (Microsoft) report to pull class rosters with classmate, faculty, and teaching assistant names to identify potential classroom crossover of confirmed cases. Contact tracing was used to identify potential at-work, in-class, or school-sponsored event transmission.Case investigation and contact tracing were conducted for each case by BU contact tracers following adapted CDC and Massachusetts Department of Public Health (MDPH) protocols.9 Full-length genomes for each amplified sample were then assembled through alignment to the Wuhan-Hu-1 reference sequence (NC_045512.2) using Bowtie 2.10 Nucleotide substitutions, insertions, and deletions were identified with LoFreq.11 Lineage assignment for each genome was carried out using Pangolin.12 Sequencing data was completed for all surveillance tests weekly and shared with the contact tracing team for further investigation or, in the reverse, individuals with suspicion of in-class transmission were flagged for sequencing.Residual discarded samples in sterile saline from SARS-CoV-2 positive swabs collected during the study window were used to amplify and sequence SARS-CoV-2 genomic RNA using the Illumina COVIDSeq Assay and sequenced on an Illumina NextSeq 500.Genome relatedness was quantified by counting the number of nucleotide differences between genomes using a custom script. Each genome was modeled as a binary single nucleotide variant (SNV) vector spanning all nucleotide changes found at greater than 50% in the data set. Presence of an SNV in more than 50% of the aligned reads was coded as a 1; otherwise, zero. After pairwise comparison identical (0 differences), different (1 to 2 differences), and unrelated (3 or more differences) sequence sets were defined.September 1, 2021, marked the full return to in-person instruction at BU. COVID-19 vaccination was mandated as of September 2, allowing for religious or medical exemption. Vaccination rates for BU faculty, staff, and students respectively were 98.5%, 93.5%, and 98.7%. Masking was mandatory during in-person instruction, but social distancing was not enforced. We analyzed SARS-CoV-2 transmission beginning September 1 to December 1, 2021, which was just prior to the end of term and the beginning of finals. During this time there were more than 140 000 in-person class meetings. Class sizes ranged from 2 to over 400 individuals .7 All members of the BU community with an on-campus presence were tested. In total more than 600 000 SARS-CoV-2 PCR tests were conducted; of these approximately 896 (0.1%) of these tests showed detectable SARS-CoV-2. The 7-day rolling mean average ranged between 4 and 27 daily cases for most of the semester with a dramatic increase the last week of November  at a Critical Laboratory Improvement Amendments–certified BU laboratory.November . Whole SSARS-CoV-2 transmission in the surrounding environment was also significant over the study period. The 7-day rolling mean of SARS-CoV-2 positive cases in Massachusetts was between 374 and 1346 cases per day, with a rise in the last week of November to more than 1200 per day . The oveAll 776 PCR positive samples were reported to the BU contact tracing team. The overwhelming majority of cases identified likely onto-campus (genome sequences not before identified on campus) transmission events associated with living arrangements , socialization, or extended contact-time events. Of all PCR positive cases, a small number were deemed potential in-class transmission events. There were 9 instances of potential classroom transmission, accounting for approximately 0.0045% of all classroom meetings. These instances of potential classroom transmission did not involve more than 3 potential linked individuals and were not clustered in time . They weFor each potential in-classroom transmission event, we carefully analyzed the SARS-CoV-2 genomes. Each successfully sequenced genome was compared with all other genomes identified through surveillance testing. Samples with zero nucleotide differences across the genome  were deemed to be part of a linked transmission event based on known SARS-CoV-2 mutation rates and our history of validation through contact tracing. Genomes with 1 nucleotide difference were deemed to be possibly related, and those with 2 nucleotide changes were considered highly unlikely to be transmission events unless there was clear epidemiological support. Genomes that differed by 3 or more nucleotides across the SARS-CoV-2 genome were not considered to be linked.Seven of 9 potential transmission event pairs were identified as coming from different Pango lineages and as having more than 3 nucleotide differences (between 14 and 45 nucleotide differences). Thus, the genomic analysis suggested that they are unlikely to be classroom transmission events . When coA second instance of potential transmission was from the middle of the study period . This poWith the ongoing concern of safety for students, faculty, and staff as universities return to fully normal functioning, the use of epidemiology and genome sequencing is an effective model for understanding overall disease transmission safety in the classroom setting. Understanding the risk of classroom transmission to assess against the benefits of in-person learning is important for university leadership decision-making. In this university setting that had mask mandates in the classroom and a vaccine mandate for the university community, our data showed that that in-person learning was not contributing to the overall spread of SARS-CoV-2 in the campus community.Limitations of our analysis included some inherent subjectivity in traditional epidemiologic investigations that center on an individual remembering all relevant interactions. This study occurred during a phase of the pandemic when the only circulating variants were associated with Delta sublineages, and the findings may not apply to other SARS-CoV-2 variants.With the ongoing concern of safety for students, faculty, and staff as universities return to fully functioning, an effective model for overall disease transmission safety in the classroom setting is necessary. Our data support the hypothesis that a combination of SARS-CoV-2 vaccination and risk mitigation measures including indoor masking, regular surveillance testing, and enhanced air filtration can be highly effective at limiting disease spread within a large university academic environment to the extent that classroom transmission risk is negligible.