Socioeconomic status and the progression of heart failure

This dissertation explores the relationship between socioeconomic status and the progression of heart failure following an incident heart failure hospitalization, defined in three domains: rehospitalization, mortality and self-rated health. Hospital admissions for heart failure are on the rise in the United States, and mortality remains high among heart failure patients. Meanwhile, self-rated health is a potent predictor of future health, and its trajectory among heart failure patients is unknown. The first aim was to estimate the effect of neighborhood socioeconomic and Medicaid status on the time to first rehospitalization and the rehospitalization rate. Participants who lived in low neighborhood socioeconomic areas at baseline who had multiple comorbidities during the incident heart failure hospitalization were rehospitalized faster and more often compared to participants living in high socioeconomic neighborhoods at baseline with multiple comorbidities. Meanwhile, Medicaid recipients with a low level of comorbidity were rehospitalized faster and more often compared to non-Medicaid recipients. The second aim was to estimate the effect of neighborhood socioeconomic and Medicaid status on the time to and risk of mortality. Participants who lived in low neighborhood socioeconomic areas at baseline who had multiple comorbidities during the index heartfailure hospitalization experienced a shorter time to death compared to participants living in high socioeconomic neighborhoods at baseline with multiple comorbidities. A comparison of the trajectory of self-rated health across time was examined among participants as part of the third aim. Predictors of a decline in self-rated health across time were assessed, and factors shown to contribute to poorer self-rated health regardless of incident disease status included advanced age, low educational attainment, current smoking and obesity. This dissertation brings to attention several areas for future research in cardiovascular disease epidemiology. The first is a need to better understand the relationship of socioeconomic status and the progression of heart failure in terms of its out-of-hospital management. The second is to explore the mechanisms underlying the relationship between poor socioeconomic status and increased mortality. Lastly, interventions can be tested to help understand how to improve self-rated health, and the resulting health outcomes, among aging adults

Lipid-lowering pharmacotherapy and socioeconomic status: atherosclerosis risk in communities (ARIC) surveillance study

BACKGROUND: Lipid-reduction pharmacotherapy is often employed to reduce morbidity and mortality risk for patients with dyslipidemia or established cardiovascular disease. Associations between socioeconomic factors and the prescribing and use of lipid-lowering agents have been reported in several developed countries. METHODS: We evaluated the association of census tract-level neighborhood household income (nINC) and lipid-lowering medications received during hospitalization or at discharge among 3,546 (5,335 weighted) myocardial infarction (MI) events in the United States (US) Atherosclerosis Risk In Communities (ARIC) surveillance study (1999–2002). Models included neighborhood household income, race, gender, age, study community, year of MI, hospital type (teaching vs. nonteaching), current or past history of hypertension, diabetes or heart failure, and presence of cardiac pain. RESULTS: About fifty-nine percent of patients received lipid-lowering pharmacotherapy during hospitalization or at discharge. Low nINC was associated with a lower likelihood (prevalence ratio 0.89, 95% confidence interval: 0.79, 1.01) of receiving lipid-lowering pharmacotherapy compared to high neighborhood household income, and no significant change in this association resulted when adjusted for the above-mentioned covariates. CONCLUSION: Patient’s socioeconomic status appeared to influence whether they were prescribed a lipid-lowering pharmacotherapy after hospitalization for myocardial infarction in the US ARIC surveillance study (1999–2002)

Coronary heart disease and mortality following a breast cancer diagnosis

BACKGROUND: Coronary heart disease (CHD) is a leading cause of morbidity and mortality for breast cancer survivors, yet the joint effect of adverse cardiovascular health (CVH) and cardiotoxic cancer treatments on post-treatment CHD and death has not been quantified. METHODS: We conducted statistical and machine learning approaches to evaluate 10-year risk of these outcomes among 1934 women diagnosed with breast cancer during 2006 and 2007. Overall CVH scores were classified as poor, intermediate, or ideal for 5 factors, smoking, body mass index, blood pressure, glucose/hemoglobin A1c, and cholesterol from clinical data within 5 years prior to the breast cancer diagnosis. The receipt of potentially cardiotoxic breast cancer treatments was indicated if the patient received anthracyclines or hormone therapies. We modeled the outcomes of post-cancer diagnosis CHD and death, respectively. RESULTS: Results of these approaches indicated that the joint effect of poor CVH and receipt of cardiotoxic treatments on CHD (75.9%) and death (39.5%) was significantly higher than their independent effects [poor CVH (55.9%) and cardiotoxic treatments (43.6%) for CHD, and poor CVH (29.4%) and cardiotoxic treatments (35.8%) for death]. CONCLUSIONS: Better CVH appears to be protective against the development of CHD even among women who had received potentially cardiotoxic treatments. This study determined the extent to which attainment of ideal CVH is important not only for CHD and mortality outcomes among women diagnosed with breast cancer

Discovering disease-disease associations using electronic health records in The Guideline Advantage (TGA) dataset

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

Predicting mortality among patients with liver cirrhosis in electronic health records with machine learning

OBJECTIVE: Liver cirrhosis is a leading cause of death and effects millions of people in the United States. Early mortality prediction among patients with cirrhosis might give healthcare providers more opportunity to effectively treat the condition. We hypothesized that laboratory test results and other related diagnoses would be associated with mortality in this population. Our another assumption was that a deep learning model could outperform the current Model for End Stage Liver disease (MELD) score in predicting mortality. MATERIALS AND METHODS: We utilized electronic health record data from 34,575 patients with a diagnosis of cirrhosis from a large medical center to study associations with mortality. Three time-windows of mortality (365 days, 180 days and 90 days) and two cases with different number of variables (all 41 available variables and 4 variables in MELD-NA) were studied. Missing values were imputed using multiple imputation for continuous variables and mode for categorical variables. Deep learning and machine learning algorithms, i.e., deep neural networks (DNN), random forest (RF) and logistic regression (LR) were employed to study the associations between baseline features such as laboratory measurements and diagnoses for each time window by 5-fold cross validation method. Metrics such as area under the receiver operating curve (AUC), overall accuracy, sensitivity, and specificity were used to evaluate models. RESULTS: Performance of models comprising all variables outperformed those with 4 MELD-NA variables for all prediction cases and the DNN model outperformed the LR and RF models. For example, the DNN model achieved an AUC of 0.88, 0.86, and 0.85 for 90, 180, and 365-day mortality respectively as compared to the MELD score, which resulted in corresponding AUCs of 0.81, 0.79, and 0.76 for the same instances. The DNN and LR models had a significantly better f1 score compared to MELD at all time points examined. CONCLUSION: Other variables such as alkaline phosphatase, alanine aminotransferase, and hemoglobin were also top informative features besides the 4 MELD-Na variables. Machine learning and deep learning models outperformed the current standard of risk prediction among patients with cirrhosis. Advanced informatics techniques showed promise for risk prediction in patients with cirrhosis

Development and structure of an accurate machine learning algorithm to predict inpatient mortality and hospice outcomes in the coronavirus disease 2019 era

BACKGROUND: The coronavirus disease 2019 (COVID-19) pandemic has challenged the accuracy and racial biases present in traditional mortality scores. An accurate prognostic model that can be applied to hospitalized patients irrespective of race or COVID-19 status may benefit patient care. RESEARCH DESIGN: This cohort study utilized historical and ongoing electronic health record features to develop and validate a deep-learning model applied on the second day of admission predicting a composite outcome of in-hospital mortality, discharge to hospice, or death within 30 days of admission. Model features included patient demographics, diagnoses, procedures, inpatient medications, laboratory values, vital signs, and substance use history. Conventional performance metrics were assessed, and subgroup analysis was performed based on race, COVID-19 status, and intensive care unit admission. SUBJECTS: A total of 35,521 patients hospitalized between April 2020 and October 2020 at a single health care system including a tertiary academic referral center and 9 community hospitals. RESULTS: Of 35,521 patients, including 9831 non-White patients and 2020 COVID-19 patients, 2838 (8.0%) met the composite outcome. Patients who experienced the composite outcome were older (73 vs. 61 y old) with similar sex and race distributions between groups. The model achieved an area under the receiver operating characteristic curve of 0.89 (95% confidence interval: 0.88, 0.91) and an average positive predictive value of 0.46 (0.40, 0.52). Model performance did not differ significantly in White (0.89) and non-White (0.90) subgroups or when grouping by COVID-19 status and intensive care unit admission. CONCLUSION: A deep-learning model using large-volume, structured electronic health record data can effectively predict short-term mortality or hospice outcomes on the second day of admission in the general inpatient population without significant racial bias

Enabling hotspot detection and public health response to the COVID-19 pandemic

INTRODUCTION: Public-facing maps of COVID-19 cases, hospital admissions, and deaths are commonly displayed at the state, county, and zip code levels, and low case counts are suppressed to protect confidentiality. Public health authorities are tasked with case identification, contact tracing, and canvasing for educational purposes during a pandemic. Given limited resources, authorities would benefit from the ability to tailor their efforts to a particular neighborhood or congregate living facility. METHODS: We describe the methods of building a real-time visualization of patients with COVID-19-positive tests, which facilitates timely public health response to the pandemic. We developed an interactive street-level visualization that shows new cases developing over time and resolving after 14 days of infection. Our source data included patient demographics (ie, age, race and ethnicity, and sex), street address of residence, respiratory test results, and date of test. RESULTS: We used colored dots to represent infections. The resulting animation shows where new cases developed in the region and how patterns changed over the course of the pandemic. Users can enlarge specific areas of the map and see street-level detail on residential location of each case and can select from demographic overlays and contour mapping options to see high-level patterns and associations with demographics and chronic disease prevalence as they emerge. CONCLUSIONS: Before the development of this tool, local public health departments in our region did not have a means to map cases of disease to the street level and gain real-time insights into the underlying population where hotspots had developed. For privacy reasons, this tool is password-protected and not available to the public. We expect this tool to prove useful to public health departments as they navigate not only COVID-19 pandemic outcomes but also other public health threats, including chronic diseases and communicable disease outbreaks

Time-series cardiovascular risk factors and receipt of screening for breast, cervical, and colon cancer: The Guideline Advantage

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)

Diagnosis-Specific Readmission Risk Prediction Using Electronic Health Data: a Retrospective Cohort Study

Background: Readmissions after hospital discharge are a common occurrence and are costly for both hospitals and patients. Previous attempts to create universal risk prediction models for readmission have not met with success. In this study we leveraged a comprehensive electronic health record to create readmission-risk models that were institution- and patient- specific in an attempt to improve our ability to predict readmission. Methods: This is a retrospective cohort study performed at a large midwestern tertiary care medical center. All patients with a primary discharge diagnosis of congestive heart failure, acute myocardial infarction or pneumonia over a two-year time period were included in the analysis. The main outcome was 30-day readmission. Demographic, comorbidity, laboratory, and medication data were collected on all patients from a comprehensive information warehouse. Using multivariable analysis with stepwise removal we created three risk disease-specific risk prediction models and a combined model. These models were then validated on separate cohorts. Results: 3572 patients were included in the derivation cohort. Overall there was a 16.2% readmission rate. The acute myocardial infarction and pneumonia readmission-risk models performed well on a random sample validation cohort (AUC range 0.73 to 0.76) but less well on a historical validation cohort (AUC 0.66 for both). The congestive heart failure model performed poorly on both validation cohorts (AUC 0.63 and 0.64). Conclusions: The readmission-risk models for acute myocardial infarction and pneumonia validated well on a contemporary cohort, but not as well on a historical cohort, suggesting that models such as these need to be continuously trained and adjusted to respond to local trends. The poor performance of the congestive heart failure model may suggest that for chronic disease conditions social and behavioral variables are of greater importance and improved documentation of these variables within the electronic health record should be encouraged