Faculty Bibliography

Acute kidney injury (AKI) and chronic kidney disease (CKD) are common conditions among patients with cirrhosis whose development is associated with increased morbidity and mortality. Recurrent episodes of AKI within this population can ultimately lead to CKD, while patients with underlying CKD are in turn more likely to experience AKI. Determining the etiology of kidney dysfunction in the setting of cirrhosis is often challenging given the considerable limitations of currently available diagnostic tools. Ongoing research into the role of novel serum and urine biomarkers may allow for more timely diagnosis and treatment of kidney injury among patients with cirrhosis. This review provides an overview of kidney dysfunction in cirrhosis with a focus on the role of these emerging biomarkers for diagnosis of AKI and CKD.

BACKGROUND/UNASSIGNED:Coronary CT angiography (CCTA) enables early noninvasive detection of coronary atherosclerosis, but its use in younger adults is limited by low pretest probability. Coronary artery disease polygenic risk score (CAD-PRS) may help identify individuals with increased likelihood of coronary plaque. METHODS/UNASSIGNED:= 1991), with follow-up through 2024. CAD-PRS was analyzed continuously and by risk categories (high [top decile], intermediate, low [bottom decile]). Plaque was classified as absent (CAD-RADS 0), non-obstructive (CAD-RADS 1-2), or obstructive (CAD-RADS 3-5). Extensive plaque was defined as ≥3-vessel involvement. Relative risk regression evaluated associations with plaque stratified by age. Kaplan-Meier and Cox proportional hazards models evaluated associations with adverse cardiovascular outcomes. RESULTS/UNASSIGNED:< .001). CONCLUSIONS/UNASSIGNED:High CAD-PRS was independently associated with greater presence, extent, and severity of CAD on CCTA. The impact of genetic risk was strongest in younger adults-a group for whom identifying early atherosclerotic plaque may have the greatest impact.

BACKGROUND/UNASSIGNED:Nontuberculous mycobacteria pulmonary disease (NTM PD) has been increasing in the United States, and New York City is an important hotspot, with a high burden of disease. RESEARCH QUESTION/UNASSIGNED:We assessed how neighborhood-level risk factors influenced NTM PD prevalence in New York City. STUDY DESIGN AND METHODS/UNASSIGNED:We used outpatient claims data from hospitals participating in the Patient-Centered Outcomes Research Institute network, compiled by the INSIGHT Clinical Research Network at Weill Cornell Medicine. NTM PD period prevalence (referred to here as 'prevalence') was estimated by New York City neighborhood for the study period 2012 through 2022. A case was defined as someone with at least one claim for NTM PD. De-identified NTM PD case data and demographic data were analyzed by neighborhood of residence at diagnosis. Using prevalence estimates, we detected high- and low-prevalence regions within New York City and associated these estimates with demographic, clinical, socioeconomic, and environmental neighborhood-level factors using Poisson regression and backward elimination of covariates. RESULTS/UNASSIGNED:Overall, 6,169 NTM PD cases were identified among persons receiving care across 17 private New York City hospitals. The mean age was 67.5 years, 68.7% were female, and 57.3% were White. Over the study period, NTM PD prevalence increased throughout New York City, and median year built of housing units, median income, and median age of residents were significant neighborhood-level risk factors. The highest prevalence neighborhoods were in Manhattan, while the lowest prevalence neighborhoods were in Brooklyn and Staten Island. INTERPRETATION/UNASSIGNED:Our findings indicate that neighborhood-level access to care may explain the heterogeneity in NTM PD prevalence among New York City neighborhoods, as higher income, newer neighborhoods exhibited the highest NTM PD prevalences. Future studies should examine the extent of undetected NTM PD in New York City, particularly in low-income areas.

SUMMARY/CONCLUSIONS:Association measurements, such as mutual information (MI), are fundamental in the analysis of cancer multi-omics data for identifying cancer-related genes, gene signatures, and gene regulatory networks, thereby shedding light on tumor development, progression, and treatment. Confounding factors, including tumor purity and mutation burden, can bias association measurements in MI, potentially leading to the misclassification of passenger events as drivers. Conditional mutual information (CMI) provides a robust framework for assessing both linear and nonlinear associations while effectively accounting for different confounding factors. An R package called conMItion is introduced to estimate CMI and its statistical significance for multi-omics data, with the flexibility to adjust for one or two confounding factors. We demonstrated the utilization of conMItion through two use cases. First, we identified interchromosomal somatic copy number alteration-expression associations in bladder cancer. Second, we identified associated cell types within the lung cancer tumor microenvironment using single-cell RNA sequencing datasets. AVAILABILITY AND IMPLEMENTATION/METHODS:The conMItion package is freely available on CRAN at https://CRAN.R-project.org/package=conMItion. The two use cases described in the paper can be accessed at https://github.com/GJYWang/conMItion. A supplementary document is available online.

PURPOSE/OBJECTIVE:Mobile health cardiac rehabilitation may improve access to care among older adults with ischemic heart disease, but engagement remains poorly understood. We analyzed weekly engagement data from the RESILIENT (Rehabilitation Using Mobile Health for Older Adults with Ischemic Heart Disease in the Home Setting) trial, a large, randomized trial of mobile health cardiac rehabilitation in older adults conducted in the United States. METHODS:Data from 298 intervention participants were analyzed. Weekly engagement was scored from 0 to 11 based on exercise entry (7 points), communication with exercise therapist (2 points), video viewing (1 point), and blood pressure measurement (1 point). Latent class analysis identified digital engagement phenotypes. Participant characteristics were compared, and multivariable logistic regression identified factors associated with phenotype membership. RESULTS:Median age was 71.0 years, 28% were women, 23% were non-White, and 62% were enrolled after elective percutaneous coronary intervention. Latent class analysis identified 3 phenotypes: persistently low (n = 81), intermediate declining (n = 93), and persistently high (n = 124). Participants with persistently low engagement were more likely to be non-White (48% vs 12% vs 15%, P < .001), Medicaid enrolled (22% vs 8% vs 7%, P = .001), have less than high school education (16% vs 4% vs 3%, P < .001), have frailty phenotype (28% vs 10% vs 7%, P < .001), and have a greater mean number of comorbidities (3.1 vs 3.0 vs 2.6; P = .012). After adjustment, non-White race and frailty remained independently associated with low engagement. Improvement in 6-minute walk test distance varied: 20.8 m (low), 29.7 m (intermediate), and 54.5 m (high) (P = .003). CONCLUSIONS:Three distinct digital engagement phenotypes emerged. Persistently low engagement was more common among non-White and frail participants, underscoring ongoing disparities despite efforts to overcome the digital divide.

BACKGROUND/UNASSIGNED:Racial and ethnic disparities are a concern in lung cancer screening. OBJECTIVE/UNASSIGNED:To investigate the performance of risk prediction models to define screening eligibility across 4 U.S. racial and ethnic groups. DESIGN/UNASSIGNED:Cohort study. SETTING/UNASSIGNED:United States, Lung Cancer Cohort Consortium. PARTICIPANTS/UNASSIGNED:641 830 participants aged 50 to 80 years with a smoking history from 12 U.S. cohorts, including 6390 Asian, 9781 Hispanic, 39 872 non-Hispanic Black, and 585 787 non-Hispanic White participants. MEASUREMENTS/UNASSIGNED:Calibration and discrimination were quantified for 16 lung cancer prediction models. Then, screening-related metrics were calculated after applying model thresholds to select the same number of eligible participants as the 2021 criteria from the U.S. Preventive Services Task Force (USPSTF-2021). These included eligibility, sensitivity, and efficiency measured as estimated number needed to screen (NNS; the ratio between participants and lung cancer cases) for each strategy or prediction model in each racial and ethnic group. RESULTS/UNASSIGNED:General patterns across the 16 models included substantial underestimation of lung cancer risk in non-Hispanic Black participants (expected-observed ratio < 0.75 for 11 of 16 models), lower discrimination in Asian participants than all other groups (13 of 16 models), and lower discrimination in non-Hispanic Black than non-Hispanic White participants (15 of 16 models). When a same-sized screening-eligible population as USPSTF-2021 (38.0%) was enforced, all risk-based strategies achieved better average estimated screening efficiency and reduced racial and ethnic differences in efficiency compared with USPSTF-2021. The Prostate, Lung, Colorectal, and Ovarian Cancer Screening Trial Model 2012 (PLCOm2012) and Life Years gained From Screening-Computed Tomography model (LYFS-CT) performed best (mean estimated NNS, 36.5 [SD, 8.8] and 40.1 [SD, 8.2], respectively). However, no strategy could simultaneously optimize eligibility, sensitivity, and efficiency while also reducing racial and ethnic differences. LIMITATION/UNASSIGNED:Smaller sample for Asian and Hispanic participants. CONCLUSION/UNASSIGNED:To optimize efficiency and minimize its variation across racial and ethnic groups, risk-based strategies were superior to USPSTF criteria. Further optimization of prediction models for the diverse U.S. population is needed. PRIMARY FUNDING SOURCE/UNASSIGNED:U.S. National Cancer Institute, Lung Cancer Research Foundation, and Cancer Research UK.

INTRODUCTION/UNASSIGNED:Local COVID-19 policies are increasingly recognized as determinants of variation in disease transmission and other health outcomes. There is significant geographic variability in policy implementation, but little understanding of factors contributing to policy variation. DATA AND METHODS/UNASSIGNED: = 309 counties in 50 states across 2020-2021). Using multivariable regression, we examined the association of county characteristics with policy comprehensiveness overall and across three policy domains (containment/closure, economic support, public health). We focused on county characteristics capturing sociodemographic, political, and environmental factors that may represent predictors of policy adoption. RESULTS/UNASSIGNED:Urbanicity was positively associated with increased adoption of more public health policies, Democratic voter percentages were positively associated with increased adoption of all policies, and average temperature was negatively associated with adoption of all policies. CONCLUSION/UNASSIGNED:These findings provide insight into the possible predictors of the variation in local policy implementation during the COVID-19 pandemic, as well as informing future policy research and public health crisis management.

Given intensifying political polarization and sweeping legal actions targeting marginalized populations across the U.S., there are increasing calls to examine how these policy changes are shaping population health and health inequities. To meet these calls, researchers have developed and applied various approaches to support rigorous, theoretically-informed quantitative research on policies and health. To date, much of the literature has examined single policies; however, there is growing interest in examining alternative ways of conceptualizing policy exposures, namely as policy clusters or policy climates, to better capture how policies are enacted (and experienced) in "real-world" contexts. To advance this work, greater clarity is needed regarding how different approaches to policy conceptualization, measurement, and analysis align with distinct research questions and goals, ranging from identifying specific, manipulable policy levers to informing ways of extending the "policy space" beyond already existing laws to support broader social change. In this essay, we help fill this gap by outlining key issues related to policy exposures, including conceptualization, methods for measure development, and analytic approaches for understanding the relationship between policies and health. We end by discussing future directions for population health research on single policies, policy clusters, and policy climates, with an eye towards advancing health equity.

PURPOSE: Cardiovascular (CV) toxicity is prevalent in cancer survivors due to shared risk factors, direct CV injury from cancer therapy (CT), and development/exacerbation of CV risk factors during treatment. Succinct guidance on the minimum standard of CV care required of cancer care providers (CCPs) can improve outcomes for survivors. This clinical practice statement (CPS) is aimed at CCPs focused on identification, prevention, and management of CVD in individuals with cancer. METHODS: The International Cardio-Oncology Society and Multinational Association of Supportive Care in Cancer identified members with expertise across oncology, cardiology, and cancer survivorship. Key questions/areas of practice recommendations related to CV risk and CVD were identified across the cancer trajectory.Working groups performed targeted literature reviews and consensus was reached for all recommendations. RESULTS: 5 over-arching clinical practice recommendations for CCPs were identified: (1) perform a CV risk assessment prior to starting CT (2) identify and facilitate CV-related care during CT; (3) ensure measures are taken to mitigate the risk of cancer therapy related CV toxicity (CTR-CVT) prior to and during CT; (4) monitor CV status and risk factors during therapy and refer to the appropriate health care provider if CV risk factors are uncontrolled or new signs/symptoms develop; and (5) re-assess CV risk following completion of CT (periodically for those with advanced disease on life long treatment) and develop a CV surveillance plan. CONCLUSIONS: CCPs play a pivotal role in identifying and addressing CTR-CVT. This CPS provides specific, succinct, and actionable behaviours that can be readily implemented in cancer care globally.

BACKGROUND/UNASSIGNED:We conducted a head-to-head study comparing outcomes among commonly used implant brands, Allergan, Mentor, and Sientra, used in breast reconstruction. METHODS/UNASSIGNED:A multicenter retrospective review of patients undergoing acellular dermal matrix-assisted implant-based breast reconstruction (IBR) with Allergan, Mentor, or Sientra implants between January 2014 and July 2022 was conducted. Primary outcomes included rates of return to the operating room for implant removal. Secondary outcomes included complications occurring within 30 days, between 31 and 60 days, and after 60 days. RESULTS/UNASSIGNED:. One hundred ninety-nine (17.2%) breasts underwent adjuvant radiation and 168 (14.5%) underwent neoadjuvant chemotherapy. Implants were most often inserted in the prepectoral plane (n = 584, 50.6%). After 60 days, there were no significant differences in overall complication rates between brands. One hundred sixty-one (13.9%) breasts underwent eventual reoperation, including unplanned explantation (n = 95, 8.2%), elective revision (n = 94, 8.1%), oncological indications (n = 20, 1.7%), and infection (n = 15, 1.3%). Indications for unplanned explantation included infection (n = 26, 2.3%), capsular contracture (n = 15, 1.3%), and nonhealing wounds (n = 14, 1.2%). There were no differences in other complications or reoperations between cohorts. Body mass index, diabetes mellitus, smoking history, chemotherapy, radiation, skin-sparing mastectomy, type of incision, and tissue expander use were independent predictors of postoperative complications. CONCLUSIONS/UNASSIGNED:Low rates of complications among Allergan, Mentor, and Sientra implants in IBR support their continued use. No single brand was superior to others regarding complication profile in our study. Patient characteristics and operative approach seem more predictive of postoperative outcomes than the implant brand alone.