Using the EPIS Framework to Implement a Physical Activity Promotion Intervention at a Large: Academic Healthcare System.

Objectives: To describe the implementation of a workplace health promotion to address low levels of physical activity (PA).

Methods: Using the Exploration, Preparation, Implementation, Sustainment (EPIS) framework we implemented and evaluated a 10-week workplace step-count challenge to promote PA. All health system employees invited to participate.

Social Isolation Changes and Long-Term Outcomes Among Older Adults.

Importance: While the association between cross-sectional measures of social isolation and adverse health outcomes is well established, less is known about the association between changes in social isolation and health outcomes.

Objective: To assess changes of social isolation and mortality, physical function, cognitive function, cardiovascular disease (CVD), and stroke.

Design, Setting, And Participants: In a cohort design, social isolation changes in 4 years and subsequent risk of mortality and other outcomes were assessed using the 13 649 eligible Health and Retirement Study (HRS) respondents from the 2006 to 2020 waves.

Model-based estimation of individual-level social determinants of health and its applications in All of Us.

Objectives: We introduce a widely applicable model-based approach for estimating individual-level Social Determinants of Health (SDoH) and evaluate its effectiveness using the All of Us Research Program.

Materials And Methods: Our approach utilizes aggregated SDoH datasets to estimate individual-level SDoH, demonstrated with examples of no high school diploma (NOHSDP) and no health insurance (UNINSUR) variables. Models are estimated using American Community Survey data and applied to derive individual-level estimates for All of Us participants.

Fair Survival Time Prediction via Mutual Information Minimization.

Survival analysis is a general framework for predicting the time until a specific event occurs, often in the presence of censoring. Although this framework is widely used in practice, few studies to date have considered fairness for time-to-event outcomes, despite recent significant advances in the algorithmic fairness literature more broadly. In this paper, we propose a framework to achieve demographic parity in survival analysis models by minimizing the mutual information between predicted time-to-event and sensitive attributes.

A randomized, placebo-controlled crossover trial to assess the influence of body weight on aspirin-triggered specialized pro-resolving mediators: Protocol for the DISCOVER Study.

Background: Low-dose aspirin is ineffective for primary prevention of cardiovascular events in people with body weight greater than 70kg. While the prevalent explanation for this is reduced platelet cyclooxygenase-1 (COX-1) inhibition at higher body weights, supporting data are limited, thereby demanding further investigation of the reason(s) underlying this observation. We propose that aspirin-mediated cyclooxygenase-2 (COX-2) acetylation and the resulting synthesis of 15-epi-lipoxin A, a specialized pro-resolving mediator, is suboptimal in higher weight individuals, which may contribute to the clinical trial findings.

Bias correction models for electronic health records data in the presence of non-random sampling.

Electronic health records (EHRs) contain rich clinical information for millions of patients and are increasingly used for public health research. However, non-random inclusion of subjects in EHRs can result in selection bias, with factors such as demographics, socioeconomic status, healthcare referral patterns, and underlying health status playing a role. While this issue has been well documented, little work has been done to develop or apply bias-correction methods, often due to the fact that most of these factors are unavailable in EHRs.

Kinase Insert Domain Receptor Q472H Pathogenic Germline Variant Impacts Melanoma Tumor Growth and Patient Treatment Outcomes.

Background: We previously reported a higher incidence of a pathogenic germline variant in the kinase insert domain receptor (KDR) in melanoma patients compared to the general population. Here, we dissect the impact of this genotype on melanoma tumor growth kinetics, tumor phenotype, and response to treatment with immune checkpoint inhibitors (ICIs) or targeted therapy.

Methods: The KDR genotype was determined and the associations between the KDR Q472H variant (KDR-Var), angiogenesis, tumor immunophenotype, and response to MAPK inhibition or ICI treatment were examined.

Adapting the Diabetes Prevention Program for Older Adults: Descriptive Study.

Background: Prediabetes affects 26.4 million people aged 65 years or older (48.8%) in the United States.

Major Adverse Cardiovascular Events After Colchicine Administration Before Percutaneous Coronary Intervention: Follow-Up of the Colchicine-PCI Trial.

Periprocedural inflammation is associated with major adverse cardiovascular events in patients who undergo percutaneous coronary intervention (PCI). In the contemporary era, 5% to 10% of patients develop restenosis, and in the acute coronary syndrome cohort, there remains a 20% major adverse cardiovascular events rate at 3 years, half of which are culprit-lesion related. In patients at risk of restenosis, colchicine has been shown to reduce restenosis when started within 24 hours of PCI and continued for 6 months thereafter, compared with placebo.

Fair Generalized Linear Models with a Convex Penalty.

Despite recent advances in algorithmic fairness, methodologies for achieving fairness with generalized linear models (GLMs) have yet to be explored in general, despite GLMs being widely used in practice. In this paper we introduce two fairness criteria for GLMs based on equalizing expected outcomes or log-likelihoods. We prove that for GLMs both criteria can be achieved via a convex penalty term based solely on the linear components of the GLM, thus permitting efficient optimization.

Does Incident Cardiovascular Disease Lead to Greater Odds of Functional and Cognitive Impairment? Insights From the Health and Retirement Study.

Background: Although studies to date have broadly shown that cardiovascular disease (CVD) increases cognitive and physical impairment risk, there is still limited understanding of the magnitude of this risk among relevant CVD subtypes or age cohorts.

Methods: We analyzed longitudinal data from 16 679 U.S.

Associations of Age at Diagnosis and Duration of Diabetes With Morbidity and Mortality Among Older Adults.

Importance: Older adults vary widely in age at diagnosis and duration of type 2 diabetes, but treatment often ignores this heterogeneity.

Objectives: To investigate the associations of diabetes vs no diabetes, age at diagnosis, and diabetes duration with negative health outcomes in people 50 years and older.

Design, Setting, And Participants: This cohort study included participants in the 1995 through 2018 waves of the Health and Retirement Study (HRS), a population-based, biennial longitudinal health interview survey of older adults in the US.

Baseline Serum Autoantibody Signatures Predict Recurrence and Toxicity in Melanoma Patients Receiving Adjuvant Immune Checkpoint Blockade.

Purpose: Adjuvant immunotherapy produces durable benefit for patients with resected melanoma, but many develop recurrence and/or immune-related adverse events (irAE). We investigated whether baseline serum autoantibody (autoAb) signatures predicted recurrence and severe toxicity in patients treated with adjuvant nivolumab, ipilimumab, or ipilimumab plus nivolumab.

Experimental Design: This study included 950 patients: 565 from CheckMate 238 (408 ipilimumab versus 157 nivolumab) and 385 from CheckMate 915 (190 nivolumab versus 195 ipilimumab plus nivolumab).

Risk and tropism of central nervous system (CNS) metastases in patients with stage II and III cutaneous melanoma.

Background: Recent data suggest that patients with stage III melanoma are at high risk for developing central nervous system (CNS) metastases. Because a subset of patients with stage II melanoma experiences worse survival outcomes than some patients with stage III disease, the authors investigated the risk of CNS metastasis in stage II melanoma to inform surveillance guidelines for this population.

Methods: The authors examined clinicopathologic data prospectively collected from 1054 patients who had cutaneous melanoma.

Utilization of Palliative Care for Patients with COVID-19 and Acute Kidney Injury during a COVID-19 Surge.

Background And Objectives: AKI is a common complication of coronavirus disease 2019 (COVID-19) and is associated with high mortality. Palliative care, a specialty that supports patients with serious illness, is valuable for these patients but is historically underutilized in AKI. The objectives of this paper are to describe the use of palliative care in patients with AKI and COVID-19 and their subsequent health care utilization.

Factors associated with hospital admission and severe outcomes for older patients with COVID-19.

Background: Morbidity and death due to coronavirus disease 2019 (COVID-19) experienced by older adults in nursing homes have been well described, but COVID-19's impact on community-living older adults is less studied. Similarly, the previous ambulatory care experience of such patients has rarely been considered in studies of COVID-19 risks and outcomes.

Methods: To investigate the relationship of advanced age (65+), on risk factors associated with COVID-19 outcomes in community-living elders, we identified an electronic health records cohort of older patients aged 65+ with laboratory-confirmed COVID-19 with and without an ambulatory care visit in the past 24 months (n = 47,219) in the New York City (NYC) academic medical institutions and the NYC public hospital system from January 2020 to February 2021.

A joint fairness model with applications to risk predictions for underrepresented populations.

In data collection for predictive modeling, underrepresentation of certain groups, based on gender, race/ethnicity, or age, may yield less accurate predictions for these groups. Recently, this issue of fairness in predictions has attracted significant attention, as data-driven models are increasingly utilized to perform crucial decision-making tasks. Existing methods to achieve fairness in the machine learning literature typically build a single prediction model in a manner that encourages fair prediction performance for all groups.

mTOR Inhibition with Sirolimus in Multiple System Atrophy: A Randomized, Double-Blind, Placebo-Controlled Futility Trial and 1-Year Biomarker Longitudinal Analysis.

Background: Multiple system atrophy (MSA) is a fatal neurodegenerative disease characterized by the aggregation of α-synuclein in glia and neurons. Sirolimus (rapamycin) is an mTOR inhibitor that promotes α-synuclein autophagy and reduces its associated neurotoxicity in preclinical models.

Objective: To investigate the efficacy and safety of sirolimus in patients with MSA using a futility design.

Reinforcement learning assisted oxygen therapy for COVID-19 patients under intensive care.

Background: Patients with severe Coronavirus disease 19 (COVID-19) typically require supplemental oxygen as an essential treatment. We developed a machine learning algorithm, based on deep Reinforcement Learning (RL), for continuous management of oxygen flow rate for critically ill patients under intensive care, which can identify the optimal personalized oxygen flow rate with strong potentials to reduce mortality rate relative to the current clinical practice.

Methods: We modeled the oxygen flow trajectory of COVID-19 patients and their health outcomes as a Markov decision process.

Preexisting immune-mediated inflammatory disease is associated with improved survival and increased toxicity in melanoma patients who receive immune checkpoint inhibitors.

Background: Immune-related adverse events (irAEs) are common, clinically significant autoinflammatory toxicities observed with immune checkpoint inhibitors (ICI). Preexisting immune-mediated inflammatory disease (pre-IMID) is considered a relative contraindication to ICI due to the risk of inciting flares. Improved understanding of the risks and benefits of treating pre-IMID patients with ICI is needed.

Clinical outcomes in cancer patients with COVID-19.

Background: Early reports on cancer patients with coronavirus disease 2019 (COVID-19) corroborated speculation that cancer patients are at increased risk for becoming infected with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and developing severe COVID-19. However, cancer patients are a heterogeneous population and their corresponding risk may be different.

Aim: To compare COVID-19 presentation in patients with active malignancy to those with a history of cancer to determine the impact of cancer status on COVID-19 outcomes in the two groups.

Reinforcement Learning Assisted Oxygen Therapy for COVID-19 Patients Under Intensive Care.

Background: Patients with severe Coronavirus disease 19 (COVID-19) typically require supplemental oxygen as an essential treatment. We developed a machine learning algorithm, based on deep Reinforcement Learning (RL), for continuous management of oxygen flow rate for critically ill patients under intensive care, which can identify the optimal personalized oxygen flow rate with strong potentials to reduce mortality rate relative to the current clinical practice.

Methods: We modeled the oxygen flow trajectory of COVID-19 patients and their health outcomes as a Markov decision process.

A Joint Fairness Model with Applications to Risk Predictions for Under-represented Populations.

In data collection for predictive modeling, under-representation of certain groups, based on gender, race/ethnicity, or age, may yield less-accurate predictions for these groups. Recently, this issue of fairness in predictions has attracted significant attention, as data-driven models are increasingly utilized to perform crucial decision-making tasks. Existing methods to achieve fairness in the machine learning literature typically build a single prediction model in a manner that encourages fair prediction performance for all groups.