A Randomized Controlled Trial of the Implementation of BREASTChoice, a Multilevel Breast Reconstruction Decision Support Tool With Personalized Risk Predictiona.

OBJECTIVE: To implement the BREASTChoice decision tool into the electronic health record and evaluate its effectiveness. BACKGROUND: BREASTChoice, is a multilevel decision tool that: 1) educates patients about breast reconstruction; 2) estimates personalized risk of complications; 3) clarifies patient preferences; and 4) informs clinicians about patients' risk and preferences. METHODS: A multisite randomized controlled trial enrolled adult women with stage 0-III breast malignancy undergoing mastectomy. Participants were randomized to BREASTChoice or a control website. A survey assessed knowledge, preferences, decisional conflict, shared decision-making, preferred treatment, and usability. We conducted intent-to-treat (ITT), per-protocol (PP) analyses (those randomized to BREASTChoice who accessed the tool), and stratified analyses. RESULTS: 23/25 eligible clinicians enrolled. 369/761 (48%) contacted patients enrolled and were randomized. Patients' average age was 51 years; 15% were older than 65. BREASTChoice participants had higher knowledge than control participants (ITT: mean 70.6 vs. 67.4, P=0.08; PP: mean 71.4 vs. 67.4, P=0.03), especially when stratified by site (ITT: P=0.04, PP: P=0.01), age (ITT: P=0.04, PP: P=0.02), and race (ITT: P=0.04, PP: P=0.01). BREASTChoice did not improve decisional conflict, match between preferences and treatment, or shared decision-making. In PP analyses, fewer high-risk patients using BREASTChoice chose reconstruction. BREASTChoice had high usability. CONCLUSIONS: BREASTChoice is a novel decision tool incorporating risk prediction, patient education, and clinician engagement. Patients using BREASTChoice had higher knowledge; older adults and those from racially minoritized backgrounds especially benefitted. There was no impact on other decision outcomes. Future studies should overcome implementation barriers and specifically examine decision outcomes among high-risk patients.

Cardiovascular health assessment in routine cancer follow-up in community settings: survivor risk awareness and perspectives.

BACKGROUND: Guidelines recommend cardiovascular risk assessment and counseling for cancer survivors. For effective implementation, it is critical to understand survivor cardiovascular health (CVH) profiles and perspectives in community settings. We aimed to (1) Assess survivor CVH profiles, (2) compare self-reported and EHR-based categorization of CVH factors, and (3) describe perceptions regarding addressing CVH during oncology encounters. METHODS: This cross-sectional analysis utilized data from an ongoing NCI Community Oncology Research Program trial of an EHR heart health tool for cancer survivors (WF-1804CD). Survivors presenting for routine care after potentially curative treatment recruited from 8 oncology practices completed a pre-visit survey, including American Heart Association Simple 7 CVH factors (classified as ideal, intermediate, or poor). Medical record abstraction ascertained CVD risk factors and cancer characteristics. Likert-type questions assessed desired discussion during oncology care. RESULTS: Of 502 enrolled survivors (95.6% female; mean time since diagnosis = 4.2 years), most had breast cancer (79.7%). Many survivors had common cardiovascular comorbidities, including high cholesterol (48.3%), hypertension or high BP (47.8%) obesity (33.1%), and diabetes (20.5%); 30.5% of survivors received high cardiotoxicity potential cancer treatment. Less than half had ideal/non-missing levels for physical activity (48.0%), BMI (18.9%), cholesterol (17.9%), blood pressure (14.1%), healthy diet (11.0%), and glucose/ HbA1c (6.0%). While > 50% of survivors had concordant EHR-self-report categorization for smoking, BMI, and blood pressure; cholesterol, glucose, and A1C were unknown by survivors and/or missing in the EHR for most. Most survivors agreed oncology providers should talk about heart health (78.9%). CONCLUSIONS: Tools to promote CVH discussion can fill gaps in CVH knowledge and are likely to be well-received by survivors in community settings. TRIAL REGISTRATION: NCT03935282, Registered 10/01/2020.

Role of Sex in the Association of Socioeconomic Status With Cardiovascular Health in Black Americans: The Jackson Heart Study.

BACKGROUND: Socioeconomic status (SES) is associated with cardiovascular health (CVH). Potential differences by sex in this association remain incompletely understood in Black Americans, where SES disparities are posited to be partially responsible for cardiovascular inequities. The association of SES measures (income, education, occupation, and insurance) with CVH scores was examined in the Jackson Heart Study. METHODS AND RESULTS: American Heart Association CVH components (non-high-density-lipoprotein cholesterol, blood pressure, diet, tobacco use, physical activity, sleep, glycemia, and body mass index) were scored cross-sectionally at baseline (scale: 0-100). Differences in CVH and 95% CIs (Estimate, 95% CI) were calculated using linear regression, adjusting for age, sex, and discrimination. Heterogeneity by sex was assessed. Participants had a mean age of 54.8 years (SD 12.6 years), and 65% were women. Lower income, education, occupation (non-management/professional versus management/professional occupations), and insurance status (uninsured, Medicaid, Veterans Affairs, or Medicare versus private insurance) were associated with lower CVH scores (all P<0.01). There was heterogeneity by sex, with greater magnitude of associations of SES measures with CVH in women versus men. The lowest education level (high school) was associated with 8.8-point lower (95% CI: -10.2 to -7.3) and 5.4-point lower (95% CI: -7.2 to -3.6) CVH scores in women and men, respectively (interaction P=0.003). The lowest (<25 000) versus highest level of income (≥$75 000) was associated with a greater reduction in CVH scores in women than men (interaction P=0.1142). CONCLUSIONS: Among Black Americans, measures of SES were associated with CVH, with a greater magnitude in women compared with men for education and income. Interventions aimed to address CVH through SES should consider the role of sex.

Using the sociotechnical model to conduct a focused usability assessment of a breast reconstruction decision tool.

INTRODUCTION: BREASTChoice is a web-based breast reconstruction decision aid. The previous clinical trial-prior to the adaptation of this refined tool in which we explored usability-measured decision quality, quality of life, patient activation, shared decision making, and treatment choice. The current usability study was designed to elicit patients' and clinicians' perspectives on barriers and facilitators for implementing BREASTChoice into the clinical workflow. METHODS: We conducted qualitative interviews with patients and clinicians from two Midwestern medical specialty centers from August 2020 to April 2021. Interviews were first double coded until coders achieved a kappa > 0.8 and percent agreement > 95%, then were coded independently. We used a sociotechnical framework to evaluate BREASTChoice's implementation and sustainability potential according to end-users, human-computer interaction, and contextual factors. RESULTS: Twelve clinicians and ten patients completed interviews. Using the sociotechnical framework we determined the following. People Using the Tool: Patients and clinicians agreed that BREASTChoice could help patients make more informed decisions about their reconstruction and prepare better for their first plastic surgery appointment. Workflow and Communications: They felt that BREASTChoice could improve communication and process if the patient could view the tool at home and/or in the waiting room. Clinicians suggested the information from BREASTChoice about patients' risks and preferences be included in the patient's chart or the clinician electronic health record (EHR) inbox for accessibility during the consultation. Human Computer Interface: Patients and clinicians stated that the tool contains helpful information, does not require much time for the patient to use, and efficiently fills gaps in knowledge. Although patients found the risk profile information helpful, they reported needing time to read and digest. CONCLUSION: BREASTChoice was perceived as highly usable by patients and clinicians and has the potential for sustainability. Future research will implement and test the tool after integrating the stakeholder-suggested changes to its delivery process and content. It is critical to conduct usability assessments such as these prior to decision aid implementation to ensure success of the tool to improve risk communication.

Cardiovascular toxicities after anthracycline and VEGF-targeted therapies in adolescent and young adult cancer survivors.

BACKGROUND: Cancer survival rates have been steadily improving in the adolescent and young adult (AYA) population, but survivors are at increased risk for cardiovascular disease (CVD). The cardiotoxic effects of anthracycline therapy have been well studied. However, the cardiovascular toxicity associated with newer therapies, such as the vascular endothelial growth factor (VEGF) inhibitors, is less well understood. OBJECTIVE: This retrospective study of AYA cancer survivors sought to gain insight into their burden of cardiovascular toxicities (CT) following initiation of anthracycline and/or VEGF inhibitor therapy. METHODS: Data were extracted from electronic medical records over a fourteen-year period at a single institution. Cox proportional hazards regression modeling was used to examine risk factors for CT within each treatment group. Cumulative incidence was calculated with death as a competing risk. RESULTS: Of the 1,165 AYA cancer survivors examined, 32%, 22%, and 34% of patients treated with anthracycline, VEGF inhibitor, or both, developed CT. Hypertension was the most common outcome reported. Males were at increased risk for CT following anthracycline therapy (HR: 1.34, 95% CI 1.04-1.73). The cumulative incidence of CT was highest in patients who received both anthracycline and VEGF inhibitor (50% at ten years of follow up). CONCLUSIONS: CT was common among AYA cancer survivors who received anthracycline and/or VEGF inhibitor therapy. Male sex was an independent risk factor for CT following anthracycline treatment. Further screening and surveillance are warranted to continue understanding the burden of CVD following VEGF inhibitor therapy.

Healthy Lifestyle Index and Risk of Cardiovascular Disease Among Postmenopausal Women With Normal Body Mass Index.

Background A lifestyle comprising a healthy diet, light alcohol consumption, no smoking, and moderate or intense physical activity has been associated with reduced risk of cardiovascular disease (CVD). We examined the association of a healthy lifestyle index (HLI), derived from scores for each of these components plus waist circumference, with the risk of incident CVD and CVD subtypes in postmenopausal women with normal body mass index (18.5-<25.0 kg/m2). Methods and Results We studied 40 118 participants in the Women's Health Initiative, aged 50 to 79 years at enrollment, with a normal body mass index and no history of CVD. The HLI score was categorized into quintiles. We estimated multivariable adjusted hazard ratios (HR) and 95% CIs for the association of HLI with risk of CVD and CVD subtypes using Cox regression models. A total of 3821 cases of incident CVD were ascertained during a median follow-up of 20.1 years. Compared with the lowest quintile (unhealthiest lifestyle), higher HLI quintiles showed inverse associations with the risk of CVD (HRquintile-2=0.74 [95% CI, 0.67-0.81]; HRquintile-3=0.66 [95% CI, 0.60-0.72]; HRquintile-4=0.57 [95% CI, 0.51-0.63]; and HRquintile-5=0.48 [95% CI, 0.43-0.54], P-trend=<0.001). HLI was also inversely associated with risks of stroke, coronary heart disease, myocardial infarction, angina, and coronary revascularization. Subgroup analyses, stratified by age (≤63 years vs >63 years), body mass index (

Endometrial cancer survivors' perceptions of their cardiovascular disease risk (results from WF-1804CD AH-HA).

OBJECTIVE: Despite considerable burden of cardiovascular disease (CVD), data on endometrial cancer survivors' CVD perceptions are lacking. We assessed survivors' perspectives on addressing CVD risk during oncology care. METHODS: This cross-sectional analysis utilized data from an ongoing trial of an EHR heart health tool (R01CA226078 & UG1CA189824) conducted through the NCI Community Oncology Research Program (NCORP, WF-1804CD). Endometrial cancer survivors post-potentially curative treatment were recruited from community practices and completed a pre-visit baseline survey, including American Heart Association Simple 7 CVD factors. Likert-type questions assessed confidence in understanding CVD risk, CVD risk perception, and desired discussion during oncology care. Medical record abstraction ascertained data on CVD and cancer characteristics. RESULTS: Survivors (N = 55, median age = 62; 62% 0-2 years post-diagnosis) were predominately white, non-Hispanic (87%). Most agreed/strongly agreed heart disease poses a risk to their health (87%) and oncology providers should talk to patients about heart health (76%). Few survivors reported smoking (12%) but many had poor/intermediate values for blood pressure (95%), body mass index (93%), fasting glucose/A1c (60%), diet (60%), exercise (47%) and total cholesterol (53%). 16% had not seen a PCP in the last year; these survivors were more likely to report financial hardship (22% vs 0%; p = 0.02). Most reported readiness to take steps to maintain or improve heart health (84%). CONCLUSIONS: Discussions of CVD risk during routine oncology care are likely to be well received by endometrial cancer survivors. Strategies are needed to implement CVD risk assessment guidelines and to enhance communication and referrals with primary care. Clinical Trials #: NCT03935282.

Leveraging Artificial Intelligence and Synthetic Data Derivatives for Spine Surgery Research.

STUDY DESIGN: Retrospective cohort study. OBJECTIVES: Leveraging electronic health records (EHRs) for spine surgery research is impeded by concerns regarding patient privacy and data ownership. Synthetic data derivatives may help overcome these limitations. This study's objective was to validate the use of synthetic data for spine surgery research. METHODS: Data came from the EHR from 15 hospitals. Patients that underwent anterior cervical or posterior lumbar fusion (2010-2020) were included. Real data were obtained from the EHR. Synthetic data was generated to simulate the properties of the real data, without maintaining a one-to-one correspondence with real patients. Within each cohort, ability to predict 30-day readmissions and 30-day complications was evaluated using logistic regression and extreme gradient boosting machines (XGBoost). RESULTS: We identified 9,072 real and 9,088 synthetic cervical fusion patients. Descriptive characteristics were nearly identical between the 2 datasets. When predicting readmission, models built using real and synthetic data both had c-statistics of .69-.71 using logistic regression and XGBoost. Among 12,111 real and 12,126 synthetic lumbar fusion patients, descriptive characteristics were nearly the same for most variables. Using logistic regression and XGBoost to predict readmission, discrimination was similar with models built using real and synthetic data (c-statistics .66-.69). When predicting complications, models derived using real and synthetic data showed similar discrimination in both cohorts. Despite some differences, the most influential predictors were similar in the real and synthetic datasets. CONCLUSION: Synthetic data replicate most descriptive and predictive properties of real data, and therefore may expand EHR research in spine surgery.

Integrating a Patient Decision Aid into the Electronic Health Record: A Case Report on the Implementation of BREASTChoice at 2 Sites.

Patient decision aids can support shared decision making and improve decision quality. However, decision aids are not widely used in clinical practice due to multiple barriers. Integrating patient decision aids into the electronic health record (EHR) can increase their use by making them more clinically relevant, personalized, and actionable. In this article, we describe the procedures and considerations for integrating a patient decision aid into the EHR, based on the example of BREASTChoice, a decision aid for breast reconstruction after mastectomy. BREASTChoice's unique features include 1) personalized risk prediction using clinical data from the EHR, 2) clinician- and patient-facing components, and 3) an interactive format. Integrating a decision aid with patient- and clinician-facing components plus interactive sections presents unique deployment issues. Based on this experience, we outline 5 key implementation recommendations: 1) engage all relevant stakeholders, including patients, clinicians, and informatics experts; 2) explicitly and continually map all persons and processes; 3) actively seek out pertinent institutional policies and procedures; 4) plan for integration to take longer than development of a stand-alone decision aid or one with static components; and 5) transfer knowledge about the software programming from one institution to another but expect local and context-specific changes. Integration of patient decision aids into the EHR is feasible and scalable but requires preparation for specific challenges and a flexible mindset focused on implementation. Highlights: Integrating an interactive decision aid with patient- and clinician-facing components into the electronic health record could advance shared decision making but presents unique implementation challenges.We successfully integrated a decision aid for breast reconstruction after mastectomy called BREASTChoice into the electronic health record.Based on this experience, we offer these implementation recommendations: 1) engage relevant stakeholders, 2) explicitly and continually map persons and processes, 3) seek out institutional policies and procedures, 4) plan for it to take longer than for a stand-alone decision aid, and 5) transfer software programming from one site to another but expect local changes.

The Use of the Consolidated Framework for Implementation Research to Understand Facilitators and Barriers to Sexually Transmitted Infection Screening in Primary Care.

BACKGROUND: Adolescents bear a disproportionate burden of sexually transmitted infections (STIs) and the sequelae of delayed treatment, yet STI screening is infrequently performed in pediatric primary care clinics with many of those at-risk not administered testing. This study aims to understand contextual factors influencing STI screening and testing among adolescents in pediatric primary care. METHODS: We used the Consolidated Framework for Implementation Research (CFIR) as part of a stepwise approach to facilitate a deep understanding the pediatric primary care environment. We conducted semistructured interviews of physicians, nurses, and patient-parent dyads from 4 pediatric primary care practices in the St. Louis metropolitan area about STI screening practices and common concerns regarding STI screening. Qualitative analysis was conducted using a categorical coding technique informed by the CFIR followed by a thematic coding technique. RESULTS: We interviewed 23 physicians/nurses and 12 patient-parent dyads. Individual-level barriers to STI screening and testing included wide variability in clinicians' practice patterns and their perception of STI risk in the patient population. Structural barriers included a lack of capacity to perform testing in clinic and time constraints during patient visits. Confidentiality issues also created significant barriers to screening and testing on both individual and structural levels. Adopting confidential methods for testing and educating providers on patients' recommendations for STI testing were discussed as ways to potentially improve STI care in pediatric patients. CONCLUSIONS: Our use of the CFIR facilitated a systematic approach to identify gaps in STI care for adolescents and identified opportunities to close those gaps. An integrated, systematic approach that enhances patient confidentiality and improves clinicians' knowledge could address gaps in STI care in pediatric primary care settings.

Estrogen hormone is an essential sex factor inhibiting inflammation and immune response in COVID-19.

Although vaccines have been evaluated and approved for SARS-CoV-2 infection prevention, there remains a lack of effective treatments to reduce the mortality of COVID-19 patients already infected with SARS-CoV-2. The global data on COVID-19 showed that men have a higher mortality rate than women. We further observed that the proportion of mortality of females increases starting from around the age of 55 significantly. Thus, sex is an essential factor associated with COVID-19 mortality, and sex related genetic factors could be interesting mechanisms and targets for COVID-19 treatment. However, the associated sex factors and signaling pathways remain unclear. Here, we propose to uncover the potential sex associated factors using systematic and integrative network analysis. The unique results indicated that estrogens, e.g., estrone and estriol, (1) interacting with ESR1/2 receptors, (2) can inhibit SARS-CoV-2 caused inflammation and immune response signaling in host cells; and (3) estrogens are associated with the distinct fatality rates between male and female COVID-19 patients. Specifically, a high level of estradiol protects young female COVID-19 patients, and estrogens drop to an extremely low level in females after about 55 years of age causing the increased fatality rate of women. In conclusion, estrogen, interacting with ESR1/2 receptors, is an essential sex factor that protects COVID-19 patients from death by inhibiting inflammation and immune response caused by SARS-CoV-2 infection. Moreover, medications boosting the down-stream signaling of ESR1/ESR2, or inhibiting the inflammation and immune-associated targets on the signaling network can be potentially effective or synergistic combined with other existing drugs for COVID-19 treatment.

Status of Cardiovascular Health in US Adults and Children Using the American Heart Association's New "Life's Essential 8" Metrics: Prevalence Estimates From the National Health and Nutrition Examination Survey (NHANES), 2013 Through 2018.

BACKGROUND: The American Heart Association recently published an updated algorithm for quantifying cardiovascular health (CVH)-the Life's Essential 8 score. We quantified US levels of CVH using the new score. METHODS: We included individuals ages 2 through 79 years (not pregnant or institutionalized) who were free of cardiovascular disease from the National Health and Nutrition Examination Surveys in 2013 through 2018. For all participants, we calculated the overall CVH score (range, 0 [lowest] to 100 [highest]), as well as the score for each component of diet, physical activity, nicotine exposure, sleep duration, body mass index, blood lipids, blood glucose, and blood pressure, using published American Heart Association definitions. Sample weights and design were incorporated in calculating prevalence estimates and standard errors using standard survey procedures. CVH scores were assessed across strata of age, sex, race and ethnicity, family income, and depression. RESULTS: There were 23 409 participants, representing 201 728 000 adults and 74 435 000 children. The overall mean CVH score was 64.7 (95% CI, 63.9-65.6) among adults using all 8 metrics and 65.5 (95% CI, 64.4-66.6) for the 3 metrics available (diet, physical activity, and body mass index) among children and adolescents ages 2 through 19 years. For adults, there were significant differences in mean overall CVH scores by sex (women, 67.0; men, 62.5), age (range of mean values, 62.2-68.7), and racial and ethnic group (range, 59.7-68.5). Mean scores were lowest for diet, physical activity, and body mass index metrics. There were large differences in mean scores across demographic groups for diet (range, 23.8-47.7), nicotine exposure (range, 63.1-85.0), blood glucose (range, 65.7-88.1), and blood pressure (range, 49.5-84.0). In children, diet scores were low (mean 40.6) and were progressively lower in higher age groups (from 61.1 at ages 2 through 5 to 28.5 at ages 12 through 19); large differences were also noted in mean physical activity (range, 63.1-88.3) and body mass index (range, 74.4-89.4) scores by sociodemographic group. CONCLUSIONS: The new Life's Essential 8 score helps identify large group and individual differences in CVH. Overall CVH in the US population remains well below optimal levels and there are both broad and targeted opportunities to monitor, preserve, and improve CVH across the life course in individuals and the population.

Association of STS database variables with repair durability in ischemic mitral regurgitation using machine learning.

BACKGROUND: Machine learning (ML) can identify nonintuitive clinical variable combinations that predict clinical outcomes. To assess the potential predictive contribution of standardized Society of Thoracic Surgeons (STS) Database clinical variables, we used ML to detect their association with repair durability in ischemic mitral regurgitation (IMR) patients in a single institution study. METHODS: STS Database variables (n = 53) served as predictors of repair durability in ML modeling of 224 patients who underwent surgical revascularization and mitral valve repair for IMR. Follow-up mortality and echocardiography data allowed 1-year outcome analysis in 173 patients. Supervised ML analyses were performed using recurrence (≥3+ IMR) or death versus nonrecurrence (<3+ IMR) as the binary outcome classification. RESULTS: We tested standard ML and deep learning algorithms, including support vector machines, logistic regression, and deep neural networks. Following training, final models were utilized to predict class labels for the patients in the test set, producing receiver operating characteristic (ROC) curves. The three models produced similar area under the curve (AUC), and predicted class labels with promising accuracy (AUC = 0.72-0.75). CONCLUSIONS: Readily-available STS Database variables have potential to play a significant role in the development of ML models to direct durable surgical therapy in IMR patients.

Translating Data Analytics Into Improved Spine Surgery Outcomes: A Roadmap for Biomedical Informatics Research in 2021.

STUDY DESIGN: Narrative review. OBJECTIVES: There is growing interest in the use of biomedical informatics and data analytics tools in spine surgery. Yet despite the rapid growth in research on these topics, few analytic tools have been implemented in routine spine practice. The purpose of this review is to provide a health information technology (HIT) roadmap to help translate data assets and analytics tools into measurable advances in spine surgical care. METHODS: We conducted a narrative review of PubMed and Google Scholar to identify publications discussing data assets, analytical approaches, and implementation strategies relevant to spine surgery practice. RESULTS: A variety of data assets are available for spine research, ranging from commonly used datasets, such as administrative billing data, to emerging resources, such as mobile health and biobanks. Both regression and machine learning techniques are valuable for analyzing these assets, and researchers should recognize the particular strengths and weaknesses of each approach. Few studies have focused on the implementation of HIT, and a variety of methods exist to help translate analytic tools into clinically useful interventions. Finally, a number of HIT-related challenges must be recognized and addressed, including stakeholder acceptance, regulatory oversight, and ethical considerations. CONCLUSIONS: Biomedical informatics has the potential to support the development of new HIT that can improve spine surgery quality and outcomes. By understanding the development life-cycle that includes identifying an appropriate data asset, selecting an analytic approach, and leveraging an effective implementation strategy, spine researchers can translate this potential into measurable advances in patient care.

Women and ethnoracial minorities with poor cardiovascular health measures associated with a higher risk of developing mood disorder.

BACKGROUND: Mood disorders (MDS) are a type of mental health illness that effects millions of people in the United States. Early prediction of MDS can give providers greater opportunity to treat these disorders. We hypothesized that longitudinal cardiovascular health (CVH) measurements would be informative for MDS prediction. METHODS: To test this hypothesis, the American Heart Association's Guideline Advantage (TGA) dataset was used, which contained longitudinal EHR from 70 outpatient clinics. The statistical analysis and machine learning models were employed to identify the associations of the MDS and the longitudinal CVH metrics and other confounding factors. RESULTS: Patients diagnosed with MDS consistently had a higher proportion of poor CVH compared to patients without MDS, with the largest difference between groups for Body mass index (BMI) and Smoking. Race and gender were associated with status of CVH metrics. Approximate 46% female patients with MDS had a poor hemoglobin A1C compared to 44% of those without MDS; 62% of those with MDS had poor BMI compared to 47% of those without MDS; 59% of those with MDS had poor blood pressure (BP) compared to 43% of those without MDS; and 43% of those with MDS were current smokers compared to 17% of those without MDS. CONCLUSIONS: Women and ethnoracial minorities with poor cardiovascular health measures were associated with a higher risk of development of MDS, which indicated the high utility for using routine medical records data collected in care to improve detection and treatment for MDS among patients with poor CVH.

Estrogen Hormone Is an Essential Sex Factor Inhibiting Inflammation and Immune Response in COVID-19.

Although vaccines have been evaluated and approved for SARS-CoV-2 infection prevention, there remains a lack of effective treatments to reduce the mortality of COVID-19 patients already infected with SARS-CoV-2. The global data of COVID-19 showed that men have a higher mortality rate than women. We further observed that the proportion of mortality of female increases starting from around the age of 55 significantly. Thus, sex is an essential factor associated with COVID-19 mortality, and sex related genetic factors could be interesting mechanisms and targets for COVID-19 treatment. However, the associated sex factors and signaling pathways remain unclear. Here, we propose to uncover the potential sex associated factors using systematic and integrative network analysis. The unique results indicated that estrogen hormones (ER), e.g., estrone and estriol, 1) interacting with ESR1/2 receptors, 2) can inhibit SARS-CoV-2 caused inflammation and immune response signaling in host cells; and 3) estrogen hormone is associated with the distinct fatality rates between male and female COVID-19 patients. Specifically, a high level of estradiol protecting young female COVID-19 patients, and estrogen loss to an extremely low level in females after about 55 years of age causing the increased fatality rate of women. In conclusion, estrogen hormone, interacting with ESR1/2 receptors, is an essential sex factor that protects COVID-19 patients by inhibiting inflammation and immune response caused by SARS-CoV-2 infection. Medications perturb the down-stream of ESR1/ESR2 to inhibit the inflammation and immune response can be effective or synergistic combined with other existing drugs for COVID-19 treatment.

Implementing an Electronic Clinical Decision Support Tool Into Routine Care: A Qualitative Study of Stakeholders' Perceptions of a Post-Mastectomy Breast Reconstruction Tool.

Objective. To explore barriers and facilitators to implementing an evidence-based clinical decision support (CDS) tool (BREASTChoice) about post-mastectomy breast reconstruction into routine care. Materials and Methods. A stakeholder advisory group of cancer survivors, clinicians who discuss and/or perform breast reconstruction in women with cancer, and informatics professionals helped design and review the interview guide. Based on the Consolidated Framework for Implementation Research (CFIR), we conducted qualitative semistructured interviews with key stakeholders (patients, clinicians, informatics professionals) to explore intervention, setting characteristics, and process-level variables that can impact implementation. Interviews were transcribed, coded, and analyzed based on the CFIR framework using both inductive and deductive methods. Results. Fifty-seven potential participants were contacted; 49 (85.9%) were eligible, and 35 (71.4%) were enrolled, continuing until thematic saturation was reached. Participants consisted of 13 patients, 13 clinicians, and 9 informatics professionals. Stakeholders thought that BREASTChoice was useful and provided patients with an evidence-based source of information about post-mastectomy breast reconstruction, including their personalized risks. They felt that BREASTChoice could support shared decision making, improve workflow, and possibly save consultation time, but were uncertain about the best time to deliver BREASTChoice to patients. Some worried about cost, data availability, and security of integrating the tool into an electronic health record. Most acknowledged the importance of showing clinical utility to gain institutional buy-in and encourage routine adoption. Discussion and Conclusion. Stakeholders felt that BREASTChoice could support shared decision making, improve workflow, and reduce consultation time. Addressing key questions such as cost, data integration, and timing of delivering BREASTChoice could build institutional buy-in for CDS implementation. Results can guide future CDS implementation studies.

Predicting Incident Heart Failure in Women With Machine Learning: The Women's Health Initiative Cohort.

BACKGROUND: Heart failure (HF) is a leading cause of cardiac morbidity among women, whose risk factors differ from those in men. We used machine-learning approaches to develop risk- prediction models for incident HF in a cohort of postmenopausal women from the Women's Health Initiative (WHI). METHODS: We used 2 machine-learning methods-Least Absolute Shrinkage and Selection Operator (LASSO) and Classification and Regression Trees (CART)-to perform variable selection on 1227 baseline WHI variables for the primary outcome of incident HF. These variables were then used to construct separate Cox proportional hazard models, and we compared these results, using receiver-operating characteristic (ROC) curve analysis, against a comparator model built using variables from the Atherosclerosis Risk in Communities (ARIC) HF prediction model. We analyzed 43,709 women who had 2222 incident HF events; median follow-up was 14.3 years. RESULTS: LASSO selected 10 predictors, and CART selected 11 predictors. The highest correlation between selected variables was 0.46. In addition to selecting well-established predictors such as age, myocardial infarction, and smoking, novel predictors included physical function, number of pregnancies, number of previous live births and age at menopause. In ROC analysis, the CART-derived model had the highest C-statistic of 0.83 (95% confidence interval [CI], 0.81-0.85), followed by LASSO 0.82 (95% CI, 0.81-0.84) and ARIC 0.73 (95% CI, 0.70-0.76). CONCLUSIONS: Machine-learning approaches can be used to develop HF risk-prediction models that can have better discrimination compared with an established HF risk model and may provide a basis for investigating novel HF predictors.