Incident mental disorders after breast cancer: A matched population-based cohort.

Objective: To assess the risk of mental disorders and contributing factors of depression in breast cancer survivors, as well as to estimate the risk of death.

Methods: A total of 20,440 women diagnosed with breast cancer between 1997 and 2017 were identified in the Utah Cancer Registry and matched to a general population of women without cancer. To assess the risk of mental disorders and death following breast cancer, hazard ratios (HRs) and 99 % confidence intervals (CI) were estimated using Cox proportional-hazards models overall, within 2, > 2 to 5, and > 5-years of follow-up.

Comparative effectiveness of insulin glargine, glucagon-like peptide-1 receptor agonists and sodium-glucose cotransporter-2 inhibitors in veterans with type 2 diabetes.

Aims: To compare the risk of all-cause death and cardiovascular events in new users of insulin glargine, glucagon-like peptide-1 receptor agonists (GLP-1RA) and sodium-glucose cotransporter-2 inhibitors (SGLT2i), particularly in subgroups defined by baseline haemoglobin A1C (HbA1C), body mass index (BMI) and estimated glomerular filtration rate (eGFR).

Materials And Methods: We conducted an active comparator, new user design study in a national cohort of 161 405 veterans with type 2 diabetes (T2D) on metformin and initiated insulin glargine (n = 54 375), GLP-1RA (n = 22 145) or SGLT2i (n = 84 885) between 1 January 2018 and 31 December 2021. Patients were followed until 31 March 2023.

Capturing Chemotherapy and Radiotherapy Dose Among Breast Cancer Patients With the Utah All-Payer Claims Database Compared With Gold-Standard Abstraction.

Objective: To evaluate the validity of the Utah statewide All-Payer Claims Database (APCD), we compared breast cancer-specific treatments and dosages with gold-standard abstraction of medical records.

Study Design: In this pilot study, breast cancer treatments were abstracted by a certified tumor registrar at the Utah Cancer Registry (UCR) for patients diagnosed in 2013 with breast cancer. The abstraction of medical records was the gold standard for comparison with treatments identified in the APCD.

Oral health-related quality of life among oropharyngeal cancer survivors.

Objectives: The incidence of oropharyngeal cancer continues to rise in the United States, yet studies on the quality of life (QoL) of oropharyngeal cancer patients are limited. The objective of this pilot study was to assess the impact of oral health on the QoL in oropharyngeal cancer survivors.

Materials And Methods: Oropharyngeal cancer survivors with a confirmed cancer diagnosis from 1996 to 2016 were sampled from the Utah Cancer Registry.

Adverse Health Outcomes among Rural and Urban Breast Cancer Survivors: A Population-Based Cohort Study.

Background: Limited population-based studies have focused on breast cancer survivors in rural populations. We sought to evaluate the risk of adverse health outcomes among rural and urban breast cancer survivors and to evaluate potential predictors for the highest risk outcomes.

Methods: A population-based cohort of rural and urban breast cancer survivors diagnosed between 1997 and 2017 was identified in the Utah Cancer Registry (UCR).

Medication for Opioid Use Disorder During Pregnancy - Maternal and Infant Network to Understand Outcomes Associated with Use of Medication for Opioid Use Disorder During Pregnancy (MAT-LINK), 2014-2021.

Problem: Medication for opioid use disorder (MOUD) is recommended for persons with opioid use disorder (OUD) during pregnancy. However, knowledge gaps exist about best practices for management of OUD during pregnancy and these data are needed to guide clinical care.

Period Covered: 2014-2021.

Relations of Current and Past Cancer with Severe Outcomes among 104,590 Hospitalized COVID-19 Patients: The COVID EHR Cohort at the University of Wisconsin.

Background: There is mixed evidence about the relations of current versus past cancer with severe COVID-19 outcomes and how they vary by patient and cancer characteristics.

Methods: Electronic health record data of 104,590 adult hospitalized patients with COVID-19 were obtained from 21 United States health systems from February 2020 through September 2021. In-hospital mortality and ICU admission were predicted from current and past cancer diagnoses.

Cardiovascular disease risk in long-term breast cancer survivors: A population-based cohort study.

Background: Breast cancer survival is increasing, making late effects such as cardiovascular disease (CVD) more relevant. The purpose of this study was to evaluate incident CVD following breast cancer diagnosis among long-term survivors and to investigate possible risk factors for CVD.

Methods: A population-based cohort of 6641 breast cancer survivors diagnosed between 1997 and 2009 who survived at least 10 years was identified within the Utah Cancer Registry.

An explainable artificial intelligence approach for predicting cardiovascular outcomes using electronic health records.

Understanding the conditionally-dependent clinical variables that drive cardiovascular health outcomes is a major challenge for precision medicine. Here, we deploy a recently developed massively scalable comorbidity discovery method called Poisson Binomial based Comorbidity discovery (PBC), to analyze Electronic Health Records (EHRs) from the University of Utah and Primary Children's Hospital (over 1.6 million patients and 77 million visits) for comorbid diagnoses, procedures, and medications.

Age-Related Disease Risks in Younger versus Older B-Cell Non-Hodgkin's Lymphoma Survivors.

Background: Younger cancer survivors may develop age-related diseases due to the cancer treatment that they undergo. The aim of this population-based study is to estimate incidence of age-related diseases besides cardiovascular disease among younger versus older B-cell non-Hodgkin's lymphoma (B-NHL) survivors compared with their respective general population cohorts.

Methods: Survivors of B-NHL were diagnosed between 1997 and 2015 from the Utah Cancer Registry.

Understanding the Prevalence of Prediabetes and Diabetes in Patients With Cancer in Clinical Practice: A Real-World Cohort Study.

Background: This study aimed to understand the prevalence of prediabetes (preDM) and diabetes mellitus (DM) in patients with cancer overall and by tumor site, cancer treatment, and time point in the cancer continuum.

Methods: This cohort study was conducted at Huntsman Cancer Institute at the University of Utah. Patients with a first primary invasive cancer enrolled in the Total Cancer Care protocol between July 2016 and July 2018 were eligible.

Long-term diabetes risk among endometrial cancer survivors in a population-based cohort study.

Objective: The majority of endometrial cancer patients are overweight or obese at cancer diagnosis. Obesity is a shared risk factor for both endometrial cancer and diabetes, but it is unknown whether endometrial cancer patients have increased diabetes risks. The aim of our study was to investigate diabetes risk among endometrial cancer patients.

Endocrine and Metabolic Diseases Among Colorectal Cancer Survivors in a Population-Based Cohort.

Background: There are an estimated 1.4 million colorectal cancer (CRC) survivors in the United States. Research on endocrine and metabolic diseases over the long term in CRC survivors is limited.

Impact of computerized provider order entry (CPOE) on length of stay and mortality.

Objective: To examine changes in patient outcome variables, length of stay (LOS), and mortality after implementation of computerized provider order entry (CPOE).

Materials And Methods: A 5-year retrospective pre-post study evaluated 66 186 patients and 104 153 admissions (49 683 pre-CPOE, 54 470 post-CPOE) at an academic medical center. Generalized linear mixed statistical tests controlled for 17 potential confounders with 2 models per outcome.

Value Driven Outcomes (VDO): a pragmatic, modular, and extensible software framework for understanding and improving health care costs and outcomes.

Objective: To develop expeditiously a pragmatic, modular, and extensible software framework for understanding and improving healthcare value (costs relative to outcomes).

Materials And Methods: In 2012, a multidisciplinary team was assembled by the leadership of the University of Utah Health Sciences Center and charged with rapidly developing a pragmatic and actionable analytics framework for understanding and enhancing healthcare value. Based on an analysis of relevant prior work, a value analytics framework known as Value Driven Outcomes (VDO) was developed using an agile methodology.

Integrating historical clinical and financial data for pharmacological research.

Background: Retrospective research requires longitudinal data, and repositories derived from electronic health records (EHR) can be sources of such data. With Health Information Technology for Economic and Clinical Health (HITECH) Act meaningful use provisions, many institutions are expected to adopt EHRs, but may be left with large amounts of financial and historical clinical data, which can differ significantly from data obtained from newer systems, due to lack or inconsistent use of controlled medical terminologies (CMT) in older systems. We examined different approaches for semantic enrichment of financial data with CMT, and integration of clinical data from disparate historical and current sources for research.

Evaluating the informatics for integrating biology and the bedside system for clinical research.

Background: Selecting patient cohorts is a critical, iterative, and often time-consuming aspect of studies involving human subjects; informatics tools for helping streamline the process have been identified as important infrastructure components for enabling clinical and translational research. We describe the evaluation of a free and open source cohort selection tool from the Informatics for Integrating Biology and the Bedside (i2b2) group: the i2b2 hive.

Methods: Our evaluation included the usability and functionality of the i2b2 hive using several real world examples of research data requests received electronically at the University of Utah Health Sciences Center between 2006 - 2008.

Efficiency of CYP2C9 genetic test representation for automated pharmacogenetic decision support.

Objectives: We investigated the suitability of representing discrete genetic test results in the electronic health record (EHR) as individual single nucleotide polymorphisms (SNPs) and as alleles, using the CYP2C9 gene and its polymorphic states, as part of a pilot study. The purpose of our investigation was to determine the appropriate level of data abstraction when reporting genetic test results in the EHR that would allow meaningful interpretation and clinical decision support based on current knowledge, while retaining sufficient information in order to enable reinterpretation of the results in the context of future discoveries.

Methods: Based on the SNP & allele models, we designed two separate lab panels within the laboratory information system, one containing SNPs and the other containing alleles, built separate rules in the clinical decision support system based on each model, and evaluated the performance of these rules in an EHR simulation environment using real-world scenarios.

A clinical use case to evaluate the i2b2 Hive: predicting asthma exacerbations.

To evaluate the i2b2 Hive as a tool to query, visualize, and extract clinical data, we selected a use case from the i2b2 airways diseases driving biology project: asthma exacerbations prediction. We analyzed the cohort selection and the extraction of the clinical data used by this asthma exacerbations prediction study. The structured data included the asthma diagnosis, birthdate, age, race, sex, height, weight, and BMI.