District-Level Universal Masking Policies and COVID-19 Incidence During the First 8 Weeks of School in Texas.

Texas discontinued state-sponsored business restrictions and mask mandates on March 10, 2021, and mandated that no government officials, including public school officials, may implement mask requirements even in areas where COVID-19 hospitalizations comprised more than 15% of hospitalizations. Nonetheless, some public school districts began the 2021-2022 school year with mask mandates in place. We used quasi-experimental methods to analyze the impact of school mask mandates, which appear to have resulted in approximately 40 fewer student cases per week in the first eight weeks of school. (Am J Public Health. 2022;112(6):871-875. https://doi.org/10.2105/AJPH.2022.306769).

Development and validation of optimal phenomapping methods to estimate long-term atherosclerotic cardiovascular disease risk in patients with type 2 diabetes.

AIMS/HYPOTHESIS: Type 2 diabetes is a heterogeneous disease process with variable trajectories of CVD risk. We aimed to evaluate four phenomapping strategies and their ability to stratify CVD risk in individuals with type 2 diabetes and to identify subgroups who may benefit from specific therapies. METHODS: Participants with type 2 diabetes and free of baseline CVD in the Action to Control Cardiovascular Risk in Diabetes (ACCORD) trial were included in this study (N = 6466). Clustering using Gaussian mixture models, latent class analysis, finite mixture models (FMMs) and principal component analysis was compared. Clustering variables included demographics, medical and social history, laboratory values and diabetes complications. The interaction between the phenogroup and intensive glycaemic, combination lipid and intensive BP therapy for the risk of the primary outcome (composite of fatal myocardial infarction, non-fatal myocardial infarction or unstable angina) was evaluated using adjusted Cox models. The phenomapping strategies were independently assessed in an external validation cohort (Look Action for Health in Diabetes [Look AHEAD] trial: n = 4211; and Bypass Angioplasty Revascularisation Investigation 2 Diabetes [BARI 2D] trial: n = 1495). RESULTS: Over 9.1 years of follow-up, 789 (12.2%) participants had a primary outcome event. FMM phenomapping with three phenogroups was the best-performing clustering strategy in both the derivation and validation cohorts as determined by Bayesian information criterion, Dunn index and improvement in model discrimination. Phenogroup 1 (n = 663, 10.3%) had the highest burden of comorbidities and diabetes complications, phenogroup 2 (n = 2388, 36.9%) had an intermediate comorbidity burden and lowest diabetes complications, and phenogroup 3 (n = 3415, 52.8%) had the fewest comorbidities and intermediate burden of diabetes complications. Significant interactions were observed between phenogroups and treatment interventions including intensive glycaemic control (p-interaction = 0.042) and combination lipid therapy (p-interaction < 0.001) in the ACCORD, intensive lifestyle intervention (p-interaction = 0.002) in the Look AHEAD and early coronary revascularisation (p-interaction = 0.003) in the BARI 2D trial cohorts for the risk of the primary composite outcome. Favourable reduction in the risk of the primary composite outcome with these interventions was noted in low-risk participants of phenogroup 3 but not in other phenogroups. Compared with phenogroup 3, phenogroup 1 participants were more likely to have severe/symptomatic hypoglycaemic events and medication non-adherence on follow-up in the ACCORD and Look AHEAD trial cohorts. CONCLUSIONS/INTERPRETATION: Clustering using FMMs was the optimal phenomapping strategy to identify replicable subgroups of patients with type 2 diabetes with distinct clinical characteristics, CVD risk and response to therapies.

Toward standardization, harmonization, and integration of social determinants of health data: A Texas Clinical and Translational Science Award institutions collaboration.

Introduction: The focus on social determinants of health (SDOH) and their impact on health outcomes is evident in U.S. federal actions by Centers for Medicare & Medicaid Services and Office of National Coordinator for Health Information Technology. The disproportionate impact of COVID-19 on minorities and communities of color heightened awareness of health inequities and the need for more robust SDOH data collection. Four Clinical and Translational Science Award (CTSA) hubs comprising the Texas Regional CTSA Consortium (TRCC) undertook an inventory to understand what contextual-level SDOH datasets are offered centrally and which individual-level SDOH are collected in structured fields in each electronic health record (EHR) system potentially for all patients. Methods: Hub teams identified American Community Survey (ACS) datasets available via their enterprise data warehouses for research. Each hub's EHR analyst team identified structured fields available in their EHR for SDOH using a collection instrument based on a 2021 PCORnet survey and conducted an SDOH field completion rate analysis. Results: One hub offered ACS datasets centrally. All hubs collected eleven SDOH elements in structured EHR fields. Two collected Homeless and Veteran statuses. Completeness at four hubs was 80%-98%: Ethnicity, Race; < 10%: Education, Financial Strain, Food Insecurity, Housing Security/Stability, Interpersonal Violence, Social Isolation, Stress, Transportation. Conclusion: Completeness levels for SDOH data in EHR at TRCC hubs varied and were low for most measures. Multiple system-level discussions may be necessary to increase standardized SDOH EHR-based data collection and harmonization to drive effective value-based care, health disparities research, translational interventions, and evidence-based policy.

Erratum: Toward standardization, harmonization, and integration of social determinants of health data: A Texas Clinical and Translational Science Award institutions collaboration - CORRIGENDUM.

[This corrects the article DOI: 10.1017/cts.2024.2.].

Preventing cognitive decline by reducing BP target (PCOT): A randomized, pragmatic, multi-health systems clinical trial.

BACKGROUND: Growing evidence suggests that intensive lowering of systolic blood pressure (BP) may prevent mild cognitive impairment (MCI) and dementia. However, current guidelines provide inconsistent recommendations regarding optimal BP targets, citing safety concerns of excessive BP lowering in the diverse population of older adults. We are conducting a pragmatic trial to determine if an implementation strategy to reduce systolic BP to <130 and diastolic BP to <80 mmHg will safely slow cognitive decline in older adults with hypertension when compared to patients receiving usual care. METHODS: The Preventing Cognitive Decline by Reducing BP Target Trial (PCOT) is an embedded randomized pragmatic clinical trial in 4000 patients from two diverse health-systems who are age ≥ 70 years with BP >130/80 mmHg. Participants are randomized to the intervention arm or usual care using a permuted block randomization within each health system. The intervention is a combination of team-based care with clinical decision support to lower home BP to <130/80 mmHg. The primary outcome is cognitive decline as determined by the change in the modified Telephone Interview for Cognitive Status (TICS-m) scores from baseline. As a secondary outcome, patients who decline ≥3 points on the TICS-m will complete additional cognitive assessments and this information will be reviewed by an expert panel to determine if they meet criteria for MCI or dementia. CONCLUSION: The PCOT trial will address the effectiveness and safety of hypertension treatment in two large health systems to lower BP targets to reduce risk of cognitive decline in real-world settings.

Early immune factors associated with the development of post-acute sequelae of SARS-CoV-2 infection in hospitalized and non-hospitalized individuals.

Background: Infection by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) can lead to post-acute sequelae of SARS-CoV-2 (PASC) that can persist for weeks to years following initial viral infection. Clinical manifestations of PASC are heterogeneous and often involve multiple organs. While many hypotheses have been made on the mechanisms of PASC and its associated symptoms, the acute biological drivers of PASC are still unknown. Methods: We enrolled 494 patients with COVID-19 at their initial presentation to a hospital or clinic and followed them longitudinally to determine their development of PASC. From 341 patients, we conducted multi-omic profiling on peripheral blood samples collected shortly after study enrollment to investigate early immune signatures associated with the development of PASC. Results: During the first week of COVID-19, we observed a large number of differences in the immune profile of individuals who were hospitalized for COVID-19 compared to those individuals with COVID-19 who were not hospitalized. Differences between individuals who did or did not later develop PASC were, in comparison, more limited, but included significant differences in autoantibodies and in epigenetic and transcriptional signatures in double-negative 1 B cells, in particular. Conclusions: We found that early immune indicators of incident PASC were nuanced, with significant molecular signals manifesting predominantly in double-negative B cells, compared with the robust differences associated with hospitalization during acute COVID-19. The emerging acute differences in B cell phenotypes, especially in double-negative 1 B cells, in PASC patients highlight a potentially important role of these cells in the development of PASC.

Public perception of COVID-19 vaccines through analysis of Twitter content and users.

BACKGROUND: With the global continuation of the COVID-19 pandemic, the large-scale administration of a SARS-CoV-2 vaccine is crucial to achieve herd immunity and curtail further spread of the virus, but success is contingent on public understanding and vaccine uptake. We aim to understand public perception about vaccines for COVID-19 through the wide-scale, organic discussion on Twitter. METHODS: This cross-sectional observational study included Twitter posts matching the search criteria (('covid*' OR 'coronavirus') AND 'vaccine') posted during vaccine development from February 1st through December 11th, 2020. These COVID-19 vaccine related posts were analyzed with topic modeling, sentiment and emotion analysis, and demographic inference of users to provide insight into the evolution of public attitudes throughout the study period. FINDINGS: We evaluated 2,287,344 English tweets from 948,666 user accounts. Individuals represented 87.9 % (n = 834,224) of user accounts. Of individuals, men (n = 560,824) outnumbered women (n = 273,400) by 2:1 and 39.5 % (n = 329,776) of individuals were ≥40 years old. Daily mean sentiment fluctuated congruent with news events, but overall trended positively. Trust, anticipation, and fear were the three most predominant emotions; while fear was the most predominant emotion early in the study period, trust outpaced fear from April 2020 onward. Fear was more prevalent in tweets by individuals (26.3 % vs. organizations 19.4 %; p < 0.001), specifically among women (28.4 % vs. males 25.4 %; p < 0.001). Multiple topics had a monthly trend towards more positive sentiment. Tweets comparing COVID-19 to the influenza vaccine had strongly negative early sentiment but improved over time. INTERPRETATION: This study successfully explores sentiment, emotion, topics, and user demographics to elucidate important trends in public perception about COVID-19 vaccines. While public perception trended positively over the study period, some trends, especially within certain topic and demographic clusters, are concerning for COVID-19 vaccine hesitancy. These insights can provide targets for educational interventions and opportunity for continued real-time monitoring.

Electronic phenotypes to distinguish clinician attention to high body mass index, hypertension, lipid disorders, fatty liver and diabetes in pediatric primary care: Diagnostic accuracy of electronic phenotypes compared to masked comprehensive chart review.

BACKGROUND/OBJECTIVES: Electronic phenotyping is a method of using electronic-health-record (EHR) data to automate identifying a patient/population with a characteristic of interest. This study determines validity of using EHR data of children with overweight/obesity to electronically phenotype evidence of clinician 'attention' to high body mass index (BMI) and each of four distinct comorbidities. METHODS: We built five electronic phenotypes classifying 2-18-year-old children with overweight/obesity (n = 17,397) by electronic/health-record evidence of distinct attention to high body mass index, hypertension, lipid disorders, fatty liver, and prediabetes/diabetes. We reviewed, selected and cross-checked random charts to define items clinicians select in EHRs to build problem lists, and to order medications, laboratory tests and referrals to electronically classify attention to overweight/obesity and each comorbidity. Operating characteristics of each clinician-attention phenotype were determined by comparing comprehensive chart review by reviewers masked to electronic classification who adjudicated evidence of clinician attention to high BMI and each comorbidity. RESULTS: In a random sample of 817 visit-records reviewed/coded, specificity of each electronic phenotype is 99%-100% (with PPVs ranging from 96.8% for prediabetes/diabetes to 100% for dyslipidemia and hypertension). Sensitivities of the attention classifications range from 69% for hypertension (NPV, 98.9%) to 84.7% for high-BMI attention (NPV, 92.3%). CONCLUSIONS: Electronic phenotypes for clinician attention to overweight/obesity and distinct comorbidities are highly specific, with moderate (BMI) to modest (each comorbidity) sensitivity. The high specificity supports using phenotypes to identify children with prior high-BMI/comorbidity attention.

Distinct temporal trajectories and risk factors for Post-acute sequelae of SARS-CoV-2 infection.

Background: The understanding of Post-acute sequelae of SARS-CoV-2 infection (PASC) can be improved by longitudinal assessment of symptoms encompassing the acute illness period. To gain insight into the various disease trajectories of PASC, we assessed symptom evolution and clinical factors associated with the development of PASC over 3 months, starting with the acute illness period. Methods: We conducted a prospective cohort study to identify parameters associated with PASC. We performed cluster and case control analyses of clinical data, including symptomatology collected over 3 months following infection. Results: We identified three phenotypic clusters associated with PASC that could be characterized as remittent, persistent, or incident based on the 3-month change in symptom number compared to study entry: remittent (median; min, max: -4; -17, 3), persistent (-2; -14, 7), or incident (4.5; -5, 17) (p = 0.041 remittent vs. persistent, p < 0.001 remittent vs. incident, p < 0.001 persistent vs. incident). Despite younger age and lower hospitalization rates, the incident phenotype had a greater number of symptoms (15; 8, 24) and a higher proportion of participants with PASC (63.2%) than the persistent (6; 2, 9 and 52.2%) or remittent clusters (1; 0, 6 and 18.7%). Systemic corticosteroid administration during acute infection was also associated with PASC at 3 months [OR (95% CI): 2.23 (1.14, 4.36)]. Conclusion: An incident disease phenotype characterized by symptoms that were absent during acute illness and the observed association with high dose steroids during acute illness have potential critical implications for preventing PASC.

A Large-Scale Retrospective Study on Thrombocytopenia Associated with Beta-Lactam Antibiotics.

While the ability for beta-lactams (BL) to induce thrombocytopenia (TCP) is well understood, their association is not well quantified in the general population. Despite this, when platelets drop in the clinical setting, BL are frequently substituted for alternative antibiotics, leading to suboptimal outcomes. Here, we present a large-scale, retrospective study on the association of TCP and BL when compared to alternative non beta-lactam (nBL) therapy. All adult inpatients who received at least one antibiotic between 2008 and 2021 were included. Incidence of TCP in the 30 days following antibiotic administration was compared across patients receiving exclusively BLs vs nBLs as well as with each antibiotic subclass permutation following propensity score matching. There is a mild, though statistically significant increase in TCP risk for BL when compared to alternative nBL therapy. Risks and benefits should be considered prior to switching off BL therapy if clinically indicated.

Applying an LDL-C threshold-based approach to identify individuals with familial hypercholesterolemia.

BACKGROUND: Familial hypercholesterolemia (FH) remains underdiagnosed and undertreated. The optimal electronic health record (EHR) screening strategy for FH is unclear. OBJECTIVE: To evaluate an LDL-C threshold-based approach of identifying patients with FH from the EHR to determine the optimal LDL-C range for FH consideration. METHODS: Individuals from UT Southwestern Medical Center with an LDL-C level ≥190mg/dL at any time were enrolled in an FH registry. These 5,786 patients were divided into four categories of LDL-C (190- 219, 220 - 249, 250 - 299, and ≥ 300mg/dL) with 100 individuals randomly selected for manual chart review in each category. Chart review included 1) the presence of secondary causes of dyslipidemia, 2) diagnosis of possible/definite FH by modified Simon Broome criteria, and 3) probable/definite FH by modified Dutch Lipid Clinic Network (DLCN) criteria. RESULTS: Of the 400 individuals with an LDL-C level ≥190mg/dL (mean age 52 years ± 14), the presence of secondary causes increased across each LDL-C category (p < 0.001) with the greatest prevalence in those ≥ 300mg/dL (52%). The prevalence of possible/probable or definite FH also varied by LDL-C category, with the highest prevalence of FH by Simon Broome criteria in the 220 - 249mg/dL category (52%) and by DLCN criteria in the 250 - 299mg/dL category (46%). CONCLUSIONS: Among those with LDL-C ≥ 190mg/dL, the prevalence of secondary causes increased markedly with higher LDL-C, while the diagnosis of FH has a parabolic relationship. Patients with intermediate LDL-C (220 - 299mg/dL) may be the optimal group to prioritize for FH screening.

Managing Pandemics with Health Informatics: Successes and Challenges.

INTRODUCTION: The novel COVID-19 pandemic struck the world unprepared. This keynote outlines challenges and successes using data to inform providers, government officials, hospitals, and patients in a pandemic. METHODS: The authors outline the data required to manage a novel pandemic including their potential uses by governments, public health organizations, and individuals. RESULTS: An extensive discussion on data quality and on obstacles to collecting data is followed by examples of successes in clinical care, contact tracing, and forecasting. Generic local forecast model development is reviewed followed by ethical consideration around pandemic data. We leave the reader with thoughts on the next inevitable outbreak and lessons learned from the COVID-19 pandemic. CONCLUSION: COVID-19 must be a lesson for the future to direct us to better planning and preparing to manage the next pandemic with health informatics.

COVID-19 Mass Vaccination Resource Calculator.

BACKGROUND: Despite the recent emergency use authorization of two vaccines for the prevention of the 2019 novel coronavirus (COVID-19) disease, vaccination rates are lower than expected. Vaccination efforts may be hampered by supply, delivery, storage, patient prioritization, administration infrastructure or logistics problems. To address the last issue, our institution is sharing publically a calculator to optimize the management of staffing and facility resources in an outpatient mass vaccination effort. OBJECTIVE: By sharing our calculator locally and through this paper, we aim to help health organizations administering vaccines optimize resource allocation while maximizing efficiency. METHODS: Our calculator determines the maximum number of vaccinations that can be administered per hour, the number of check-in staff (clerks) needed, the number of vaccination staff (nurses) needed, and the required room capacity needed for the vaccination and the mandatory 15-minute observation period after inoculation. RESULTS: We provide a functional version of the calculator, allowing users to replicate the calculation for their own vaccine events. CONCLUSION: An efficient and organized vaccination program is critical to halting the spread of COVID-19. By sharing this calculator, it is our hope that other organizations may use it to facilitate rapid and efficient vaccination.

Understanding public perception of coronavirus disease 2019 (COVID-19) social distancing on Twitter.

OBJECTIVE: Social distancing policies are key in curtailing severe acute respiratory coronavirus virus 2 (SARS-CoV-2) spread, but their effectiveness is heavily contingent on public understanding and collective adherence. We studied public perception of social distancing through organic, large-scale discussion on Twitter. DESIGN: Retrospective cross-sectional study. METHODS: Between March 27 and April 10, 2020, we retrieved English-only tweets matching two trending social distancing hashtags, #socialdistancing and #stayathome. We analyzed the tweets using natural language processing and machine-learning models, and we conducted a sentiment analysis to identify emotions and polarity. We evaluated the subjectivity of tweets and estimated the frequency of discussion of social distancing rules. We then identified clusters of discussion using topic modeling and associated sentiments. RESULTS: We studied a sample of 574,903 tweets. For both hashtags, polarity was positive (mean, 0.148; SD, 0.290); only 15% of tweets had negative polarity. Tweets were more likely to be objective (median, 0.40; IQR, 0-0.6) with ~30% of tweets labeled as completely objective (labeled as 0 in range from 0 to 1). Approximately half of tweets (50.4%) primarily expressed joy and one-fifth expressed fear and surprise. Each correlated well with topic clusters identified by frequency including leisure and community support (ie, joy), concerns about food insecurity and quarantine effects (ie, fear), and unpredictability of coronavirus disease 2019 (COVID-19) and its implications (ie, surprise). CONCLUSIONS: Considering the positive sentiment, preponderance of objective tweets, and topics supporting coping mechanisms, we concluded that Twitter users generally supported social distancing in the early stages of their implementation.

Survey of Hospital Chargemaster Transparency.

BACKGROUND: In January 2019, the Centers for Medicare & Medicaid Services (CMS) required hospitals to list their standard charges (chargemasters) publicly in an effort to increase price transparency in health care. Surveying hospital chargemasters may be informative to assess the implementation of this rule and its utility to consumers. OBJECTIVE: We aimed to compare hospital chargemaster data within a local hospital market where patients would reasonably try to shop or compare services. METHODS: We identified and aggregated Dallas County hospital chargemasters available in a database compatible format in May 2019. We manually examined a convenience sampling of 10 common laboratory tests, medications, and procedures. RESULTS: Thirteen hospital chargemasters were identified. Eleven hospitals had chargemasters available in a database compatible format (xlsx or csv). These 11 chargemasters were aggregated into a single file containing 155,576 chargeable items, prices, and descriptions. We observed heterogeneous names and descriptions of synonymous items across institutions, preventing automated comparisons. The examined items revealed a high variation in charges. The largest charge variation for laboratory tests examined included a 2,606% difference (partial thromboplastin time: $18.70-506.00), for medications an 18,617% difference (5-mg tablet of amlodipine: $0.23-43.05), and for procedures a 2,889% difference (circumcision: $252.00-7,532.10). One institution accounted for 27% of the lowest prices and another accounted for 60% of the highest prices. CONCLUSION: Chargemaster data presentation varied among the hospitals surveyed, making automatic comparison impossible. Chargemaster data are difficult to interpret for health care decisions. Refining the minimum requirements for publishing chargemaster data could increase their utility.

Derivation With Internal Validation of a Multivariable Predictive Model to Predict COVID-19 Test Results in Emergency Department Patients.

OBJECTIVES: The COVID-19 pandemic has placed acute care providers in demanding situations in predicting disease given the clinical variability, desire to cohort patients, and high variance in testing availability. An approach to stratifying patients by likelihood of disease based on rapidly available emergency department (ED) clinical data would offer significant operational and clinical value. The purpose of this study was to develop and internally validate a predictive model to aid in the discrimination of patients undergoing investigation for COVID-19. METHODS: All patients greater than 18 years presenting to a single academic ED who were tested for COVID-19 during this index ED evaluation were included. Outcome was defined as the result of COVID-19 polymerase chain reaction (PCR) testing during the index visit or any positive result within the following 7 days. Variables included chest radiograph interpretation, disease-specific screening questions, and laboratory data. Three models were developed with a split-sample approach to predict outcome of the PCR test utilizing logistic regression, random forest, and gradient-boosted decision tree methods. Model discrimination was evaluated comparing area under the receiver operator curve (AUC) and point statistics at a predefined threshold. RESULTS: A total of 1,026 patients were included in the study collected between March and April 2020. Overall, there was disease prevalence of 9.6% in the population under study during this time frame. The logistic regression model was found to have an AUC of 0.89 (95% confidence interval [CI] = 0.84 to 0.94) when including four features: exposure history, temperature, white blood cell count (WBC), and chest radiograph result. Random forest method resulted in AUC of 0.86 (95% CI = 0.79 to 0.92) and gradient boosting had an AUC of 0.85 (95% CI = 0.79 to 0.91). With a consistently held negative predictive value, the logistic regression model had a positive predictive value of 0.29 (0.2-0.39) compared to 0.2 (0.14-0.28) for random forest and 0.22 (0.15-0.3) for the gradient-boosted method. CONCLUSION: The derived predictive models offer good discriminating capacity for COVID-19 disease and provide interpretable and usable methods for those providers caring for these patients at the important crossroads of the community and the health system. We found utilization of the logistic regression model utilizing exposure history, temperature, WBC, and chest X-ray result had the greatest discriminatory capacity with the most interpretable model. Integrating a predictive model-based approach to COVID-19 testing decisions and patient care pathways and locations could add efficiency and accuracy to decrease uncertainty.

Rolling Up the Sleeve: Equitable, Efficient, and Safe COVID-19 Mass Immunization for Academic Medical Center Employees.

BACKGROUND: Novel coronavirus disease 2019 (COVID-19) vaccine administration has faced distribution barriers across the United States. We sought to delineate our vaccine delivery experience in the first week of vaccine availability, and our effort to prioritize employees based on risk with a goal of providing an efficient infrastructure to optimize speed and efficiency of vaccine delivery while minimizing risk of infection during the immunization process. OBJECTIVE: This article aims to evaluate an employee prioritization/invitation/scheduling system, leveraging an integrated electronic health record patient portal framework for employee COVID-19 immunizations at an academic medical center. METHODS: We conducted an observational cross-sectional study during January 2021 at a single urban academic center. All employees who met COVID-19 allocation vaccine criteria for phase 1a.1 to 1a.4 were included. We implemented a prioritization/invitation/scheduling framework and evaluated time from invitation to scheduling as a proxy for vaccine interest and arrival to vaccine administration to measure operational throughput. RESULTS: We allotted vaccines for 13,753 employees but only 10,662 employees with an active patient portal account received an invitation. Of those with an active account, 6,483 (61%) scheduled an appointment and 6,251 (59%) were immunized in the first 7 days. About 66% of invited providers were vaccinated in the first 7 days. In contrast, only 41% of invited facility/food service employees received the first dose of the vaccine in the first 7 days (p < 0.001). At the vaccination site, employees waited 5.6 minutes (interquartile range [IQR]: 3.9-8.3) from arrival to vaccination. CONCLUSION: We developed a system of early COVID-19 vaccine prioritization and administration in our health care system. We saw strong early acceptance in those with proximal exposure to COVID-19 but noticed significant difference in the willingness of different employee groups to receive the vaccine.

Electronic Health Records-Based Cardio-Oncology Registry for Care Gap Identification and Pragmatic Research: Procedure and Observational Study.

BACKGROUND: Professional society guidelines are emerging for cardiovascular care in cancer patients. However, it is not yet clear how effectively the cancer survivor population is screened and treated for cardiomyopathy in contemporary clinical practice. As electronic health records (EHRs) are now widely used in clinical practice, we tested the hypothesis that an EHR-based cardio-oncology registry can address these questions. OBJECTIVE: The aim of this study was to develop an EHR-based pragmatic cardio-oncology registry and, as proof of principle, to investigate care gaps in the cardiovascular care of cancer patients. METHODS: We generated a programmatically deidentified, real-time EHR-based cardio-oncology registry from all patients in our institutional Cancer Population Registry (N=8275, 2011-2017). We investigated: (1) left ventricular ejection fraction (LVEF) assessment before and after treatment with potentially cardiotoxic agents; and (2) guideline-directed medical therapy (GDMT) for left ventricular dysfunction (LVD), defined as LVEF<50%, and symptomatic heart failure with reduced LVEF (HFrEF), defined as LVEF<50% and Problem List documentation of systolic congestive heart failure or dilated cardiomyopathy. RESULTS: Rapid development of an EHR-based cardio-oncology registry was feasible. Identification of tests and outcomes was similar using the EHR-based cardio-oncology registry and manual chart abstraction (100% sensitivity and 83% specificity for LVD). LVEF was documented prior to initiation of cancer therapy in 19.8% of patients. Prevalence of postchemotherapy LVD and HFrEF was relatively low (9.4% and 2.5%, respectively). Among patients with postchemotherapy LVD or HFrEF, those referred to cardiology had a significantly higher prescription rate of a GDMT. CONCLUSIONS: EHR data can efficiently populate a real-time, pragmatic cardio-oncology registry as a byproduct of clinical care for health care delivery investigations.

Phenomapping of patients with heart failure with preserved ejection fraction using machine learning-based unsupervised cluster analysis.

AIM: To identify distinct phenotypic subgroups in a highly-dimensional, mixed-data cohort of individuals with heart failure (HF) with preserved ejection fraction (HFpEF) using unsupervised clustering analysis. METHODS AND RESULTS: The study included all Treatment of Preserved Cardiac Function Heart Failure with an Aldosterone Antagonist (TOPCAT) participants from the Americas (n = 1767). In the subset of participants with available echocardiographic data (derivation cohort, n = 654), we characterized three mutually exclusive phenogroups of HFpEF participants using penalized finite mixture model-based clustering analysis on 61 mixed-data phenotypic variables. Phenogroup 1 had higher burden of co-morbidities, natriuretic peptides, and abnormalities in left ventricular structure and function; phenogroup 2 had lower prevalence of cardiovascular and non-cardiac co-morbidities but higher burden of diastolic dysfunction; and phenogroup 3 had lower natriuretic peptide levels, intermediate co-morbidity burden, and the most favourable diastolic function profile. In adjusted Cox models, participants in phenogroup 1 (vs. phenogroup 3) had significantly higher risk for all adverse clinical events including the primary composite endpoint, all-cause mortality, and HF hospitalization. Phenogroup 2 (vs. phenogroup 3) was significantly associated with higher risk of HF hospitalization but a lower risk of atherosclerotic event (myocardial infarction, stroke, or cardiovascular death), and comparable risk of mortality. Similar patterns of association were also observed in the non-echocardiographic TOPCAT cohort (internal validation cohort, n = 1113) and an external cohort of patients with HFpEF [Phosphodiesterase-5 Inhibition to Improve Clinical Status and Exercise Capacity in Heart Failure with Preserved Ejection Fraction (RELAX) trial cohort, n = 198], with the highest risk of adverse outcome noted in phenogroup 1 participants. CONCLUSIONS: Machine learning-based cluster analysis can identify phenogroups of patients with HFpEF with distinct clinical characteristics and long-term outcomes.