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Objective: Sodium–glucose cotransporter 2 inhibitors (SGLT2i) have been associated with diabetic ketoacidosis at the time of colonoscopy. This study aimed to identify factors associated with ketone concentrations in SGLT2i‐treated type 2 diabetes compared with non‐SGLT2i‐treated diabetes, and those with impaired fasting glycaemia (IFG) and normoglycaemia. Design: Cross‐sectional, multicentre, observational study June–December 2020 in four Australian tertiary hospitals. Participants: Capillary glucose and ketones were measured in people undergoing colonoscopy: 37 SGLT2i‐treated and 105 non‐SGLT2i‐treated type 2 diabetes, 65 IFG and 151 normoglycaemia. Measurements: Body mass index (BMI), age, glucose, fasting duration and where relevant, HbA1c and time since last SGLT2i dose. Results: In SGLT2i‐treated diabetes, BMI (ρ = −0.43 [95% confidence interval: −0.67, −0.11]) and duration since last SGLT2i dose (ρ = −0.33 [−0.60, 0.00]) correlated negatively with increasing ketones, but there was no correlation with fasting duration. In non‐SGLT2i‐treated diabetes, BMI correlated negatively (ρ = −0.24 [−0.42, −0.05]) and fasting duration positively (ρ = 0.26 [0.07, 0.43]) with ketones. In IFG participants, only fasting duration correlated with ketones (ρ = 0.28 [0.03, 0.49]). In normoglycaemic participants, there were negative correlations with BMI (ρ = −0.20 [−0.35, −0.04]) and fasting glucose (ρ = −0.31 [−0.45, −0.15]) and positive correlations with fasting duration (ρ = 0.20 [0.04, 0.35]) and age (ρ = 0.19 [0.03, 0.34]). Multiple regression analysis of the entire cohort showed BMI, age and fasting glucose remained independently associated with ketones, but in SGLT2i‐treated participants only BMI remained independently associated. Conclusions: In SGLT2i‐treated diabetes, lower BMI was a novel risk factor for higher ketones precolonoscopy. Pending larger confirmatory studies, extra vigilance for ketoacidosis is warranted in these people. [ABSTRACT FROM AUTHOR]

This study reports a comprehensive environmental scan of the generative AI (GenAI) infrastructure in the national network for clinical and translational science across 36 institutions supported by the CTSA Program led by the National Center for Advancing Translational Sciences (NCATS) of the National Institutes of Health (NIH) at the United States. Key findings indicate a diverse range of institutional strategies, with most organizations in the experimental phase of GenAI deployment. The results underscore the need for a more coordinated approach to GenAI governance, emphasizing collaboration among senior leaders, clinicians, information technology staff, and researchers. Our analysis reveals that 53% of institutions identified data security as a primary concern, followed by lack of clinician trust (50%) and AI bias (44%), which must be addressed to ensure the ethical and effective implementation of GenAI technologies., Competing Interests: Competing interestsI.G.C. is a member of the Bayer Bioethics Council, the Chair of the ethics advisory board for Illumina, and an advisor for World Class Health. He was also compensated for speaking at events organized by Philips with the Washington Post, by the Doctors Company and Danaher, attending the Transformational Therapeutics Leadership Forum organized by Galen Atlantica. He has been retained as an expert in health privacy, gender-affirming care, and reproductive technology lawsuits. Y.Z. serves as the editor but is not involved in the decision-making process for this paper., (© The Author(s) 2025.)

Guideline concordant opioid therapy is a key part of the concerted effort to address the opioid crisis in the United States. The study aimed to compare the rates of guideline concordant care between veterans who solely used VA services (mono users) and veterans who used both VA services and community care (dual-system users). We used electronic health record data from the Washington DC and Baltimore VA Medical Centers from 2015 to 2019. We provided descriptive statistics as well as generalized estimating equations models to find associations between mono vs. dual-system users and each guideline outcome, controlling for demographic factors and comorbid conditions. The study found that overall rates of guideline concordant care were high in both mono and dual-system users with over 90% adherence rates for the majority of recommendations. However, there were variations in adherence to specific guidelines, with urine drug screening at initiation being the least commonly followed recommendation (8.9% of mono-user opioid initiators and 11.2% of dual-user initiators). This study also found that there was no consistent pattern of higher guideline adherence in mono vs. dual-system users but did show that through the course of this study (2015-2019) overall rates of guideline concordance increased. Future research will explore additional guideline recommendations and potential coordination issues among dual-system users., (© 2024. This is a U.S. Government work and not under copyright protection in the US; foreign copyright protection may apply.)

Background: The COVID-19 pandemic accelerated telehealth adoption across disease cohorts of patients. For many patients, routine medical care was no longer an option, and others chose not to visit medical offices in order to minimize COVID-19 exposure. In this study, we take a comprehensive multidisease approach in studying the impact of the COVID-19 pandemic on health care usage and the adoption of telemedicine through the first 12 months of the COVID-19 pandemic., Objective: We studied the impact of the COVID-19 pandemic on in-person health care usage and telehealth adoption across chronic diseases to understand differences in telehealth adoption across disease cohorts and patient demographics (such as the Social Vulnerability Index [SVI])., Methods: We conducted a retrospective cohort study of 6 different disease cohorts (anxiety: n=67,578; depression: n=45,570; diabetes: n=81,885; kidney failure: n=29,284; heart failure: n=21,152; and cancer: n=35,460). We used summary statistics to characterize changes in usage and regression analysis to study how patient characteristics relate to in-person health care and telehealth adoption and usage during the first 12 months of the pandemic., Results: We observed a reduction in in-person health care usage across disease cohorts (ranging from 10% to 24%). For most diseases we study, telehealth appointments offset the reduction in in-person visits. Furthermore, for anxiety and depression, the increase in telehealth usage exceeds the reduction in in-person visits (by up to 5%). We observed that younger patients and men have higher telehealth usage after accounting for other covariates. Patients from higher SVI areas are less likely to use telehealth; however, if they do, they have a higher number of telehealth visits, after accounting for other covariates., Conclusions: The COVID-19 pandemic affected health care usage across diseases, and the role of telehealth in replacing in-person visits varies by disease cohort. Understanding these differences can inform current practices and provides opportunities to further guide modalities of in-person and telehealth visits. Critically, further study is needed to understand barriers to telehealth service usage for patients in higher SVI areas. A better understanding of the role of social determinants of health may lead to more support for patients and help individual health care providers improve access to care for patients with chronic conditions., (©Margrét Vilborg Bjarnadóttir, David Anderson, Kelley M Anderson, Omar Aljwfi, Alina Peluso, Adam Ghannoum, Gayle Balba, Nawar Shara. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 03.10.2024.)

Background: Cardiovascular conditions (eg, cardiac and coronary conditions, hypertensive disorders of pregnancy, and cardiomyopathies) were the leading cause of maternal mortality between 2017 and 2019. The United States has the highest maternal mortality rate of any high-income nation, disproportionately impacting those who identify as non-Hispanic Black or Hispanic. Novel clinical approaches to the detection and diagnosis of cardiovascular conditions are therefore imperative. Emerging research is demonstrating that machine learning (ML) is a promising tool for detecting patients at increased risk for hypertensive disorders during pregnancy. However, additional studies are required to determine how integrating ML and big data, such as electronic health records (EHRs), can improve the identification of obstetric patients at higher risk of cardiovascular conditions., Objective: This study aimed to evaluate the capability and timing of a proprietary ML algorithm, Healthy Outcomes for all Pregnancy Experiences-Cardiovascular-Risk Assessment Technology (HOPE-CAT), to detect maternal-related cardiovascular conditions and outcomes., Methods: Retrospective data from the EHRs of a large health care system were investigated by HOPE-CAT in a virtual server environment. Deidentification of EHR data and standardization enabled HOPE-CAT to analyze data without pre-existing biases. The ML algorithm assessed risk factors selected by clinical experts in cardio-obstetrics, and the algorithm was iteratively trained using relevant literature and current standards of risk identification. After refinement of the algorithm's learned risk factors, risk profiles were generated for every patient including a designation of standard versus high risk. The profiles were individually paired with clinical outcomes pertaining to cardiovascular pregnancy conditions and complications, wherein a delta was calculated between the date of the risk profile and the actual diagnosis or intervention in the EHR., Results: In total, 604 pregnancies resulting in birth had records or diagnoses that could be compared against the risk profile; the majority of patients identified as Black (n=482, 79.8%) and aged between 21 and 34 years (n=509, 84.4%). Preeclampsia (n=547, 90.6%) was the most common condition, followed by thromboembolism (n=16, 2.7%) and acute kidney disease or failure (n=13, 2.2%). The average delta was 56.8 (SD 69.7) days between the identification of risk factors by HOPE-CAT and the first date of diagnosis or intervention of a related condition reported in the EHR. HOPE-CAT showed the strongest performance in early risk detection of myocardial infarction at a delta of 65.7 (SD 81.4) days., Conclusions: This study provides additional evidence to support ML in obstetrical patients to enhance the early detection of cardiovascular conditions during pregnancy. ML can synthesize multiday patient presentations to enhance provider decision-making and potentially reduce maternal health disparities., (©Nawar Shara, Roxanne Mirabal-Beltran, Bethany Talmadge, Noor Falah, Maryam Ahmad, Ramon Dempers, Samantha Crovatt, Steven Eisenberg, Kelley Anderson. Originally published in JMIR Cardio (https://cardio.jmir.org), 22.04.2024.)

Introduction: Efforts to achieve opioid guideline concordant care may be undermined when patients access multiple opioid prescription sources. Limited data are available on the impact of dual-system sources of care on receipt of opioid medications., Objective: We examined whether dual-system use was associated with increased rates of new opioid prescriptions, continued opioid prescriptions and diagnoses of opioid use disorder (OUD). We hypothesized that dual-system use would be associated with increased odds for each outcome., Methods: This retrospective cohort study was conducted using Veterans Administration (VA) data from two facilities from 2015 to 2019, and included active patients, defined as Veterans who had at least one encounter in a calendar year (2015-2019). Dual-system use was defined as receipt of VA care as well as VA payment for community care (non-VA) services. Mono users were defined as those who only received VA services. There were 77,225 dual-system users, and 442,824 mono users. Outcomes were three binary measures: new opioid prescription, continued opioid prescription (i.e., received an additional opioid prescription), and OUD diagnosis (during the calendar year). We conducted a multivariate logistic regression accounting for the repeated observations on patient and intra-class correlations within patients., Results: Dual-system users were significantly younger than mono users, more likely to be women, and less likely to report white race. In adjusted models, dual-system users were significantly more likely to receive a new opioid prescription during the observation period [Odds ratio (OR) = 1.85, 95% confidence interval (CI) 1.76-1.93], continue prescriptions (OR = 1.24, CI 1.22-1.27), and to receive an OUD diagnosis (OR = 1.20, CI 1.14-1.27)., Discussion: The prevalence of opioid prescriptions has been declining in the US healthcare systems including VA, yet the prevalence of OUD has not been declining at the same rate. One potential problem is that detailed notes from non-VA visits are not immediately available to VA clinicians, and information about VA care is not readily available to non-VA sources. One implication of our findings is that better health system coordination is needed. Even though care was paid for by the VA and presumably closely monitored, dual-system users were more likely to have new and continued opioid prescriptions., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2023 Goulet, Cheng, Becker, Brandt, Sandbrink, Workman, Ma, Libin, Shara, Spevak, Kupersmith and Zeng-Treitler.)

Competing Interests: Declaration of Conflicting Interests: The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

We examined relationships between thyroid hormone (TH) metabolites in humans by measuring 3,5-diiodothyronine (3,5-T2) and 3-iodothyronamine (3-T1AM) levels after liothyronine administration. In secondary analyses, we measured 3,5-T2 and 3-T1AM concentrations in stored samples from two clinical trials. In 12 healthy volunteers, THs and metabolites were documented for 96 h after a single dose of 50 mcg liothyronine. In 18 patients treated for hypothyroidism, levothyroxine therapy was replaced by daily dosing of 30-45 mcg liothyronine. Analytes were measured prior to the administration of liothyronine weekly for 6 weeks, and then hourly for 8 h after the last liothyronine dose of the study. In the weekly samples from the hypothyroid patients, 3,5-T2 was higher by 0.033 nmol/L with each mcg/dL increase in T4 and 0.24 nmol/L higher with each ng/dL increase in FT4 ( p -values = 0.007, 0.0365). In hourly samples after the last study dose of liothyronine, patients with T3 values higher by one ng/dL had 3-T1AM values that were lower by 0.004 nmol/L ( p -value = 0.0473); patients with 3,5-T2 higher by one nmol/L had 3-T1AM values higher by 2.45 nmol/L ( p -value = 0.0044). The positive correlations between weekly trough levels of 3,5-T2 and T4/FT4 during liothyronine therapy may provide insight into 3,5-T2 production, possibly supporting some production of 3,5-T2 from endogenous T4, but not from exogenous liothyronine. In hourly sampling after liothyronine administration, the negative correlation between T3 levels and 3-T1AM, but positive correlation between 3,5-T2 levels and 3-T1AM could support the hypothesis that 3-T1AM production occurs via 3,5-T2 with negative regulation by T3.

Background: Heart failure (HF) is a serious health condition, associated with high health care costs, and poor outcomes. Patient empowerment and self-care are a key component of successful HF management. The emergence of telehealth may enable providers to remotely monitor patients' statuses, support adherence to medical guidelines, improve patient wellbeing, and promote daily awareness of overall patients' health., Objective: To assess the feasibility of a voice activated technology for monitoring of HF patients, and its impact on HF clinical outcomes and health care utilization., Methods: We conducted a randomized clinical trial; ambulatory HF patients were randomized to voice activated technology or standard of care (SOC) for 90 days. The system developed for this study monitored patient symptoms using a daily survey and alerted healthcare providers of pre-determined reported symptoms of worsening HF. We used summary statistics and descriptive visualizations to study the alerts generated by the technology and to healthcare utilization outcomes., Results: The average age of patients was 54 years, the majority were Black and 45% were women. Almost all participants had an annual income below $50,000. Baseline characteristics were not statistically significantly different between the two arms. The technical infrastructure was successfully set up and two thirds of the invited study participants interacted with the technology. Patients reported favorable perception and high comfort level with the use of voice activated technology. The responses from the participants varied widely and higher perceived symptom burden was not associated with hospitalization on qualitative assessment of the data visualization plot. Among patients randomized to the voice activated technology arm, there was one HF emergency department (ED) visit and 2 HF hospitalizations; there were no events in the SOC arm., Conclusions: This study demonstrates the feasibility of remote symptom monitoring of HF patients using voice activated technology. The varying HF severity and the wide range of patient responses to the technology indicate that personalized technological approaches are needed to capture the full benefit of the technology. The differences in health care utilization between the two arms call for further study into the impact of remote monitoring on health care utilization and patients' wellbeing., Competing Interests: The authors have declared that no competing interests exist.

Aim: Wild-type transthyretin amyloid cardiomyopathy (ATTRwt-CM) is frequently misdiagnosed, and delayed diagnosis is associated with substantial morbidity and mortality. At three large academic medical centers, combinations of phenotypic features were implemented in electronic health record (EHR) systems to identify patients with heart failure at risk for ATTRwt-CM. Methods: Phenotypes/phenotype combinations were selected based on strength of correlation with ATTRwt-CM versus non-amyloid heart failure; different clinical decision support and reporting approaches and data sources were evaluated on Cerner and Epic EHR platforms. Results: Multiple approaches/sources showed potential usefulness for incorporating predictive analytics into the EHR to identify at-risk patients. Conclusion: These preliminary findings may guide other medical centers in building and implementing similar systems to improve recognition of ATTRwt-CM in patients with heart failure.

Background: Per- and polyfluoroalkyl substances (PFAS) emissions from a plastic coating industrial source in southern New Hampshire (NH) have contaminated at least 65 square miles of drinking water. Prior research indicates that high levels of PFAS are associated with a variety of adverse health outcomes, including an increased risk of cancer. Reports indicate that mean blood serum levels of perfluorooctanoic acid (PFOA), one type of PFAS, in residents of the exposed community are more than 2 times greater than the mean blood serum level in the US. Merrimack public water supply customers also have higher average blood levels of perfluorooctane sulfonic acid (PFOS) and perfluorohexane sulfonic acid (PFHxS) than the time-matched US average. A 2018 report concludes that the incidence rate of cancer in Merrimack does not exceed the incidence rate of cancer in NH in general. However, prior reporting on the risk of cancer in Merrimack is compared only to a state-wide metric influenced by the Merrimack cancer incidence., Methods: Our ecological study compared the risk in Merrimack, NH residents for 24 types of cancer between 2005 and 2014, targeted in a previous study, and all-cause cancers, to US national cancer rates and cancer rates in demographically similar towns in New England. Four New England "unexposed towns" were chosen based on demographic similarity to Merrimack, with no documented PFAS exposure in water supplies. We utilized unadjusted logistical regression to approximate risk ratios (RR) and 95% confidence intervals (CI) assessing the risk of cancer in Merrimack NH to each of the 4 comparator communities, the pooled comparator variable, and national average incidence., Results: Residents of Merrimack, NH experienced a significantly higher risk of thyroid cancer (RR = 1.47, 95% CI 1.12-1.93), bladder cancer (RR = 1.45, 95% CI 1.17-1.81), esophageal cancer (RR = 1.71, 95% CI 1.1-2.65), and mesothelioma (RR = 2.41, 95% CI 1.09-5.34), compared to national averages. Our work also suggests that Merrimack residents experienced a significantly higher risk of all-cause cancer (RR = 1.34, 95% CI 1.25-1.43), thyroid cancer (RR = 1.69, 95% CI 1.19-2.39), colon cancer (RR = 1.27, 95% CI 1.02-1.57), and prostate cancer (RR = 1.36, 95% CI 1.15, 1.6) compared with similarly exposed New England communities. Our results indicate that residents of Merrimack may also have a significantly lower risk of some site-specific cancers compared to national averages, including lower risk of prostate cancer (RR = 0.57, 95% CI 0.5-0.66), female breast cancer (RR = 0.60, 95% CI 0.52-0.68), ovarian cancer (RR = 0.52, 95% CI 0.33-0.84) and cervical cancer (RR = 0.29, 95% CI 0.12-0.69)., Conclusion: Merrimack residents experienced a significantly higher risk of at least 4 types of cancer over 10 years between 2005 and 2014. Merrimack is a community with documented PFAS contamination of drinking water in public and private water sources. Results indicate that further research is warranted to elucidate if southern NH residents experience increased risk for various types of cancer due to exposure to PFAS contamination., Competing Interests: Declaration of conflicting interests: The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article., (© The Author(s) 2022.)

Background: Health care data are fragmenting as patients seek care from diverse sources. Consequently, patient care is negatively impacted by disparate health records. Machine learning (ML) offers a disruptive force in its ability to inform and improve patient care and outcomes. However, the differences that exist in each individual's health records, combined with the lack of health data standards, in addition to systemic issues that render the data unreliable and that fail to create a single view of each patient, create challenges for ML. Although these problems exist throughout health care, they are especially prevalent within maternal health and exacerbate the maternal morbidity and mortality crisis in the United States., Objective: This study aims to demonstrate that patient records extracted from the electronic health records (EHRs) of a large tertiary health care system can be made actionable for the goal of effectively using ML to identify maternal cardiovascular risk before evidence of diagnosis or intervention within the patient's record. Maternal patient records were extracted from the EHRs of a large tertiary health care system and made into patient-specific, complete data sets through a systematic method., Methods: We outline the effort that was required to define the specifications of the computational systems, the data set, and access to relevant systems, while ensuring that data security, privacy laws, and policies were met. Data acquisition included the concatenation, anonymization, and normalization of health data across multiple EHRs in preparation for their use by a proprietary risk stratification algorithm designed to establish patient-specific baselines to identify and establish cardiovascular risk based on deviations from the patient's baselines to inform early interventions., Results: Patient records can be made actionable for the goal of effectively using ML, specifically to identify cardiovascular risk in pregnant patients., Conclusions: Upon acquiring data, including their concatenation, anonymization, and normalization across multiple EHRs, the use of an ML-based tool can provide early identification of cardiovascular risk in pregnant patients., (©Nawar Shara, Kelley M Anderson, Noor Falah, Maryam F Ahmad, Darya Tavazoei, Justin M Hughes, Bethany Talmadge, Samantha Crovatt, Ramon Dempers. Originally published in JMIR Medical Informatics (https://medinform.jmir.org), 10.02.2022.)