Cardiovascular disease (CVD) is the leading cause of death in the United States (US). Better cardiovascular health (CVH) is associated with CVD prevention. Predicting future CVH levels may help ...providers better manage patients' CVH. We hypothesized that CVH measures can be predicted based on previous measurements from longitudinal electronic health record (EHR) data.
The Guideline Advantage (TGA) dataset was used and contained EHR data from 70 outpatient clinics across the United States (US). We studied predictions of 5 CVH submetrics: smoking status (SMK), body mass index (BMI), blood pressure (BP), hemoglobin A1c (A1C), and low-density lipoprotein (LDL). We applied embedding techniques and long short-term memory (LSTM) networks - to predict future CVH category levels from all the previous CVH measurements of 216,445 unique patients for each CVH submetric.
The LSTM model performance was evaluated by the area under the receiver operator curve (AUROC): the micro-average AUROC was 0.99 for SMK prediction; 0.97 for BMI; 0.84 for BP; 0.91 for A1C; and 0.93 for LDL prediction. Model performance was not improved by using all 5 submetric measures compared with using single submetric measures.
We suggest that future CVH levels can be predicted using previous CVH measurements for each submetric, which has implications for population cardiovascular health management. Predicting patients' future CVH levels might directly increase patient CVH health and thus quality of life, while also indirectly decreasing the burden and cost for clinical health system caused by CVD and cancers.
Background Cancer is the second leading cause of death in the United States. Cancer screenings can detect precancerous cells and allow for earlier diagnosis and treatment. Our purpose was to better ...understand risk factors for cancer screenings and assess the effect of cancer screenings on changes of Cardiovascular health (CVH) measures before and after cancer screenings among patients. Methods We used The Guideline Advantage (TGA)-American Heart Association ambulatory quality clinical data registry of electronic health record data (n = 362,533 patients) to investigate associations between time-series CVH measures and receipt of breast, cervical, and colon cancer screenings. Long short-term memory (LSTM) neural networks was employed to predict receipt of cancer screenings. We also compared the distributions of CVH factors between patients who received cancer screenings and those who did not. Finally, we examined and quantified changes in CVH measures among the screened and non-screened groups. Results Model performance was evaluated by the area under the receiver operator curve (AUROC): the average AUROC of 10 curves was 0.63 for breast, 0.70 for cervical, and 0.61 for colon cancer screening. Distribution comparison found that screened patients had a higher prevalence of poor CVH categories. CVH submetrics were improved for patients after cancer screenings. Conclusion Deep learning algorithm could be used to investigate the associations between time-series CVH measures and cancer screenings in an ambulatory population. Patients with more adverse CVH profiles tend to be screened for cancers, and cancer screening may also prompt favorable changes in CVH. Cancer screenings may increase patient CVH health, thus potentially decreasing burden of disease and costs for the health system (e.g., cardiovascular diseases and cancers).
Antiplatelets and statins are efficacious for preventing future cardiovascular events in patients with coronary heart disease. Disparity in cardiovascular outcomes exists by race/ethnicity and ...gender; however, few studies have explored potential disparities in long-term antiplatelet and statin use by race/ethnicity and gender. We conducted a repeated cross-sectional analysis using the nationally representative Medical Expenditure Panel Survey from 2003 to 2012. The sample consisted of 14,334 men and women >29 years with coronary heart disease. We identified antiplatelet and statin use, medical conditions, and sociodemographic characteristics. Rates of use did not change for statins or the combination of statins and antiplatelets from 2003 to 2012 but decreased for antiplatelets (p = 0.015). Of the total sample, 70.9% (95% confidence interval CI 69.7 to 72.1) reported use of antiplatelets, 52.5% (95% CI 51.1 to 53.8) reported statin use, and 43.1% (95% CI 41.8 to 44.4) reported the combination. Use of antiplatelets and statins were associated with one another (odds ratio 3.22; 95% CI 2.87 to 3.62). From 2009 to 2012, black and Hispanic men along with all race/ethnicities of women were less likely to report use of statins, antiplatelets, and the combination of the 2 compared with white men, even after controlling for sociodemographics. Changing the definition of a medication use, inclusion of cardiovascular risk factors, or the inclusion of warfarin in the antiplatelet category did not substantially change the results. Future practice and policy goals should focus on increasing the number of high-risk patients on appropriate preventative medications while focusing particular attention on decreasing the identified disparity.
Community violence, particularly gun violence, is a leading cause of morbidity and mortality in young people in the United States. Because persons experiencing violence‐related injuries are likely to ...receive medical care through emergency departments, hospitals are increasingly seen as primary locations for violence intervention services. Currently, there is little research on how best to implement hospital‐based violence intervention programs (HVIPs) across large hospital systems. This study explored the factors influencing the implementation of a multi‐site HVIP using qualitative interviews with a purposive sample of 20 multidisciplinary stakeholders. Thematic analysis was used to generate several themes that included: (1) reframing gun violence as a public health issue; (2) developing networks of community–hospital–university partners; (3) demonstrating effectiveness and community benefit; and (4) establishing patient engagement pathways. Effective implementation and sustainment of HVIPs requires robust and sustained multidisciplinary partnerships within and across hospital systems and the establishment of HVIPs as a standard of care.
The impact of a healthy lifestyle on risk of heart failure (HF) is not well known.
The objectives of this study were to evaluate the effect of a combination of lifestyle factors on incident HF and to ...further investigate whether weighting each lifestyle factor has additional impact.
Participants were 84,537 post-menopausal women from the WHI (Women's Health Initiative) observational study, free of self-reported HF at baseline. A healthy lifestyle score (HL score) was created wherein women received 1 point for each healthy criterion met: high-scoring Alternative Healthy Eating Index, physically active, healthy body mass index, and currently not smoking. A weighted score (wHL score) was also created in which each lifestyle factor was weighted according to its independent magnitude of effect on HF. The incidence of hospitalized HF was determined by trained adjudicators using standardized methodology.
There were 1,826 HF cases over a mean follow-up of 11 years. HL score was strongly associated with risk of HF (multivariable-adjusted hazard ratio HR 95% confidence interval (CI) 0.49 95% CI: 0.38 to 0.62, 0.36 95% CI: 0.28 to 0.46, 0.24 95% CI: 0.19 to 0.31, and 0.23 95% CI: 0.17 to 0.30 for HL score of 1, 2, 3, and 4 vs. 0, respectively). The HL score and wHL score were similarly associated with HF risk (HR: 0.46 95% CI: 0.41 to 0.52 for HL score; HR: 0.48 95% CI: 0.42 to 0.55 for wHL score, comparing the highest tertile to the lowest). The HL score was also strongly associated with HF risk among women without antecedent coronary heart disease, diabetes, or hypertension.
An increasingly healthy lifestyle was associated with decreasing HF risk among post-menopausal women, even in the absence of antecedent coronary heart disease, hypertension, and diabetes. Weighting the lifestyle factors had minimal impact.
Certain diseases have strong comorbidity and co-occurrence with others. Understanding disease-disease associations can potentially increase awareness among healthcare providers of co-occurring ...conditions and facilitate earlier diagnosis, prevention and treatment of patients. In this study, we utilized the valuable and large The Guideline Advantage (TGA) longitudinal electronic health record dataset from 70 outpatient clinics across the United States to investigate potential disease-disease associations. Specifically, the most prevalent 50 disease diagnoses were manually identified from 165,732 unique patients. To investigate the co-occurrence or dependency associations among the 50 diseases, the categorical disease terms were first mapped into numerical vectors based on disease co-occurrence frequency in individual patients using the Word2Vec approach. Then the novel and interesting disease association clusters were identified using correlation and clustering analyses in the numerical space. Moreover, the distribution of time delay (Δt) between pair-wise strongly associated diseases (correlation coefficients ≥ 0.5) were calculated to show the dependency among the diseases. The results can indicate the risk of disease comorbidity and complications, and facilitate disease prevention and optimal treatment decision-making.
Sex hormones have been hypothesized to explain the strong male predominance in esophageal adenocarcinoma, but evidence is needed. This study examined how circulating sex hormone levels influence ...future risk of esophageal adenocarcinoma.
This case-control study was nested in a prospective Norwegian cohort (Janus Serum Bank Cohort), including 244 male patients with esophageal adenocarcinoma and 244 male age-matched control participants. Associations between prediagnostic circulating levels of 12 sex hormones and risk of esophageal adenocarcinoma were assessed using conditional logistic regression. In addition, a random-effect meta-analysis combined these data with a similar prospective study for 5 sex hormones.
Decreased odds ratios (ORs) of esophageal adenocarcinoma were found comparing the highest with lowest quartiles of testosterone (OR = 0.44, 95% confidence interval CI 0.22-0.88), testosterone:estradiol ratio (OR = 0.37, 95% CI 0.19-0.72), and luteinizing hormone (OR = 0.50, 95% CI 0.30-0.98), after adjustment for tobacco smoking and physical activity. These associations were attenuated after further adjustment for body mass index (OR = 0.56, 95% CI 0.27-1.13 for testosterone; OR = 0.46, 95% CI 0.23-0.91 for testosterone:estradiol ratio; OR = 0.55, 95% CI 0.29-1.08 for luteinizing hormone). No associations were observed for sex hormone-binding globulin, dehydroepiandrosterone sulfate, follicle-stimulating hormone, prolactin, 17-OH progesterone, progesterone, androstenedione, or free testosterone index. The meta-analysis showed an inverse association between testosterone levels and risk of esophageal adenocarcinoma (pooled OR for the highest vs lowest quartile = 0.60, 95% CI 0.38-0.97), whereas no associations were identified for androstenedione, sex hormone-binding globulin, estradiol, or testosterone:estradiol ratio.
Higher circulating testosterone levels may decrease the risk of esophageal adenocarcinoma in men.
Population cardiovascular health, or improving cardiovascular health among patients and the population at large, requires a redoubling of primordial and primary prevention efforts as declines in ...cardiovascular disease mortality have decelerated over the past decade. Great potential exists for healthcare systems-based approaches to aid in reversing these trends. A learning healthcare system, in which population cardiovascular health metrics are measured, evaluated, intervened on, and re-evaluated, can serve as a model for developing the evidence base for developing, deploying, and disseminating interventions. This scientific statement on optimizing population cardiovascular health summarizes the current evidence for such an approach; reviews contemporary sources for relevant performance and clinical metrics; highlights the role of implementation science strategies; and advocates for an interdisciplinary team approach to enhance the impact of this work.
Self-rated health is a simple measure that may identify individuals who are at a higher risk for hospitalization or death.
To quantify the association between a single measure of self-rated health ...and future risk of recurrent hospitalizations or death.
Atherosclerosis Risk in Communities (ARIC) study, a community-based prospective cohort study of middle-aged men and women with follow-up beginning from 1987 to 1989.
We quantified the associations between initial self-rated health with risk of recurrent hospitalizations and of death using a recurrent events survival model that allowed for dependency between the rates of hospitalization and hazards of death, adjusted for demographic and clinical factors.
Of the 14,937 ARIC cohort individuals with available self-rated health and covariate information, 34% of individuals reported "excellent" health, 47% "good," 16% "fair," and 3% "poor" at study baseline. After a median follow-up of 27.7 years, 1955 (39%), 3569 (51%), 1626 (67%), and 402 (83%) individuals with "excellent," "good," "fair," and "poor" health, respectively, had died. After adjusting for demographic factors and medical history, a less favorable self-rated health status was associated with increased rates of hospitalization and death. As compared to those reporting "excellent" health, adults with "good," "fair," and "poor" health had 1.22 (1.07 to 1.40), 2.01 (1.63 to 2.47), and 3.13 (2.39 to 4.09) times the rate of hospitalizations, respectively. The hazards of death also increased with worsening categories of self-rated health, with "good," "fair," and "poor" health individuals experiencing 1.30 (1.12 to 1.51), 2.15 (1.71 to 2.69), and 3.40 (2.54 to 4.56) times the hazard of death compared to "excellent," respectively.
Even after adjusting for demographic and clinical factors, having a less favorable response on a single measure of self-rated health taken in middle age is a potent marker of future hospitalizations and death.
Adults with clinical anxiety have significant symptom overlap and above average rates of attention-deficit/hyperactivity disorder (ADHD). Despite this, ADHD remains a vastly under-detected disorder ...among this population, indicating the need for a screener with well-understood symptom dimensions and good discriminant validity. The current study compared competing models of ADHD as well as discriminant properties of self-reported ADHD symptoms as measured by the Adult ADHD Self-Report Scale (ASRS-v1.1) in 618 adults with clinical anxiety. A three-factor correlated model of Inattention, Impulsivity, and Hyperactivity, with the movement of one item,
, to a factor of Impulsivity from Hyperactivity fit better than the one-factor, two-factor, and traditional three-factor models of ADHD. Discriminant properties of the screener were fair to good against measures of clinical anxiety and distress; however, some items within the Hyperactivity factor (e.g., difficulty relaxing; feeling driven by a motor) loaded more strongly onto factors of clinical anxiety than ADHD when measures were pooled together. These results suggest that clinicians making differential diagnoses between adult ADHD and anxiety or related disorders should look for evidence of ADHD beyond the overlapping symptoms, particularly for those within the Hyperactivity factor.
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