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.

The Association of Frailty and Neighborhood Disadvantage with Emergency Department Visits and Hospitalizations in Older Adults.

BACKGROUND: Risk stratification and population management strategies are critical for providing effective and equitable care for the growing population of older adults in the USA. Both frailty and neighborhood disadvantage are constructs that independently identify populations with higher healthcare utilization and risk of adverse outcomes. OBJECTIVE: To examine the joint association of these factors on acute healthcare utilization using two pragmatic measures based on structured data available in the electronic health record (EHR). DESIGN: In this retrospective observational study, we used EHR data to identify patients aged ≥ 65 years at Atrium Health Wake Forest Baptist on January 1, 2019, who were attributed to affiliated Accountable Care Organizations. Frailty was categorized through an EHR-derived electronic Frailty Index (eFI), while neighborhood disadvantage was quantified through linkage to the area deprivation index (ADI). We used a recurrent time-to-event model within a Cox proportional hazards framework to examine the joint association of eFI and ADI categories with healthcare utilization comprising emergency visits, observation stays, and inpatient hospitalizations over one year of follow-up. KEY RESULTS: We identified a cohort of 47,566 older adults (median age = 73, 60% female, 12% Black). There was an interaction between frailty and area disadvantage (P = 0.023). Each factor was associated with utilization across categories of the other. The magnitude of frailty's association was larger than living in a disadvantaged area. The highest-risk group comprised frail adults living in areas of high disadvantage (HR 3.23, 95% CI 2.99-3.49; P < 0.001). We observed additive effects between frailty and living in areas of mid- (RERI 0.29; 95% CI 0.13-0.45; P < 0.001) and high (RERI 0.62, 95% CI 0.41-0.83; P < 0.001) neighborhood disadvantage. CONCLUSIONS: Considering both frailty and neighborhood disadvantage may assist healthcare organizations in effectively risk-stratifying vulnerable older adults and informing population management strategies. These constructs can be readily assessed at-scale using routinely collected structured EHR data.

Characteristics of Adult Primary Care Patients Who Use the Patient Portal: A Cross-Sectional Analysis.

BACKGROUND: The patient portal allows patients to engage with their health care team beyond the clinical encounter. While portals can improve patient outcomes, there may be disparities in which patients access the portal by sociodemographic factors. Understanding the characteristics of patients who use the portal could help design future interventions to expand portal adoption. OBJECTIVES: This study aimed to (1) examine the socioeconomic factors, comorbid conditions, and health care utilization among patients of a large academic primary care network who are users and non-users of the patient portal; and (2) describe the portal functions most frequently utilized. METHODS: We included all adult patients at Atrium Health Wake Forest Baptist who had at least two primary care visits between 2018 and 2019. Patients' demographics, comorbidities, health care utilization, and portal function usage were extracted from the electronic health record and merged with census data (income, education, and unemployment) from the American Community Survey. A myWakeHealth portal user was defined as a patient who used a bidirectional portal function at least once during the study period. We used multivariable logistic regression to determine which patient characteristics were independently associated with being a portal user. RESULTS: Of the 178,720 patients who met inclusion criteria, 32% (N = 57,122) were users of myWakeHealth. Compared to non-users, users were more likely to be 18 to 64 years of age, female, non-Hispanic White, married, commercially insured, have higher disease burden, and have lower health care utilization. Patients residing in areas with the highest educational attainment had 51% higher odds of being a portal user than the lowest (p <0.001). Among portal users, the most commonly used function was messaging clinic providers. CONCLUSION: We found that patient demographics and area socioeconomic factors were associated with patient portal adoption. These findings suggest that efforts to improve portal adoption should be targeted at vulnerable patients.

Addressing cancer survivors' cardiovascular health using the automated heart health assessment (AH-HA) EHR tool: Initial protocol and modifications to address COVID-19 challenges.

BACKGROUND: The purpose of this paper is to describe the Automated Heart-Health Assessment (AH-HA) study protocol, which demonstrates an agile approach to cancer care delivery research. This study aims to assess the effect of a clinical decision support tool for cancer survivors on cardiovascular health (CVH) discussions, referrals, completed visits with primary care providers and cardiologists, and control of modifiable CVH factors and behaviors. The COVID-19 pandemic has caused widespread disruption to clinical trial accrual and operations. Studies conducted with potentially vulnerable populations, including cancer survivors, must shift towards virtual consent, data collection, and study visits to reduce risk for participants and study staff. Studies examining cancer care delivery innovations may also need to accommodate the increased use of virtual visits. METHODS/DESIGN: This group-randomized, mixed methods study will recruit 600 cancer survivors from 12 National Cancer Institute Community Oncology Research Program (NCORP) practices. Survivors at intervention sites will use the AH-HA tool with their oncology provider; survivors at usual care sites will complete routine survivorship visits. Outcomes will be measured immediately after the study visit, with follow-up at 6 and 12 months. The study was amended during the COVID-19 pandemic to allow for virtual consent, data collection, and intervention options, with the goal of minimizing participant-staff in-person contact and accommodating virtual survivorship visits. CONCLUSIONS: Changes to the study protocol and procedures allow important cancer care delivery research to continue safely during the COVID-19 pandemic and give sites and survivors flexibility to conduct study activities in-person or remotely.

Cardiovascular Assessment Tool for Breast Cancer Survivors and Oncology Providers: Usability Study.

BACKGROUND: Cardiovascular health is of increasing concern to breast cancer survivors and their health care providers, as many survivors are more likely to die from cardiovascular disease than cancer. Implementing clinical decision support tools to address cardiovascular risk factor awareness in the oncology setting may enhance survivors' attainment or maintenance of cardiovascular health. OBJECTIVE: We sought to evaluate survivors' awareness of cardiovascular risk factors and examine the usability of a novel electronic health record enabled cardiovascular health tool from the perspective of both breast cancer survivors and oncology providers. METHODS: Breast cancer survivors (n=49) recruited from a survivorship clinic interacted with the cardiovascular health tool and completed pre and posttool assessments about cardiovascular health knowledge and perceptions of the tool. Oncologists, physician assistants, and nurse practitioners (n=20) who provide care to survivors also viewed the cardiovascular health tool and completed assessments of perceived usability and acceptability. RESULTS: Enrolled breast cancer survivors (84% White race, 4% Hispanic ethnicity) had been diagnosed 10.8 years ago (SD 6.0) with American Joint Committee on Cancer stage 0, I, or II (45/49, 92%). Prior to viewing the tool, 65% of survivors (32/49) reported not knowing their level for one or more cardiovascular health factors (range 0-4). On average, only 45% (range 0%-86%) of survivors' known cardiovascular health factors were at an ideal level. More than 50% of survivors had ideal smoking status (45/48, 94%) or blood glucose level (29/45, 64%); meanwhile, less than 50% had ideal blood pressure (12/49, 24%), body mass index (12/49, 24%), cholesterol level (17/35, 49%), diet (7/49, 14%), and physical activity (10/49. 20%). More than 90% of survivors thought the tool was easy to understand (46/47, 98%), improved their understanding (43/47, 91%), and was helpful (45/47, 96%); overall, 94% (44/47 survivors) liked the tool. A majority of survivors (44/47, 94%) thought oncologists should discuss cardiovascular health during survivorship care. Most (12/20, 60%) oncology providers (female: 12/20, 60%; physicians: 14/20, 70%) had been practicing for more than 5 years. Most providers agreed the tool provided useful information (18/20, 90%), would help their effectiveness (18/20, 90%), was easy to use (20/20, 100%), and presented information in a useful format (19/20, 95%); and 85% of providers (17/20) reported they would use the tool most or all of the time when providing survivorship care. CONCLUSIONS: These usability data demonstrate acceptability of a cardiovascular health clinical decision support tool in oncology practices. Oncology providers and breast cancer survivors would likely value the integration of such apps in survivorship care. By increasing awareness and communication regarding cardiovascular health, electronic health record-enabled tools may improve survivorship care delivery for breast cancer and ultimately patient outcomes.

Developing a Data Quality Standard Primer for Cardiovascular Risk Assessment from Electronic Health Record Data Using the DataGauge Process.

The learning health systems aim to support the needs of patients with chronic diseases, which require methods that account for electronic health recorded (EHR) data limitations. EHR data is often used to calculate cardiovascular risk scores. However, it is unclear whether EHR data presents high enough quality to provide accurate estimates. Still, there is currently no open standard available to assess data quality for such applications. We applied the DataGauge process to develop a data quality standard based on expert clinical, analytical and informatics knowledge by conducting four interviews and one focus group that produced 61 individual data quality requirements. These requirements covered all standard data quality dimensions and uncovered 705 quality issues in EHR data for 456 patients. These requirements will be expanded and further validated in future work. Our work initiates the development of open and explicit data quality standards for specific secondary uses of clinical data.

Frailty Screening Using the Electronic Health Record Within a Medicare Accountable Care Organization.

BACKGROUND: The accumulation of deficits model for frailty has been used to develop an electronic health record (EHR) frailty index (eFI) that has been incorporated into British guidelines for frailty management. However, there have been limited applications of EHR-based approaches in the United States. METHODS: We constructed an adapted eFI for patients in our Medicare Accountable Care Organization (ACO, N = 12,798) using encounter, diagnosis code, laboratory, medication, and Medicare Annual Wellness Visit (AWV) data from the EHR. We examined the association of the eFI with mortality, health care utilization, and injurious falls. RESULTS: The overall cohort was 55.7% female, 85.7% white, with a mean age of 74.9 (SD = 7.3) years. In the prior 2 years, 32.1% had AWV data. The eFI could be calculated for 9,013 (70.4%) ACO patients. Of these, 46.5% were classified as prefrail (0.10 < eFI ≤ 0.21) and 40.1% frail (eFI > 0.21). Accounting for age, comorbidity, and prior health care utilization, the eFI independently predicted all-cause mortality, inpatient hospitalizations, emergency department visits, and injurious falls (all p < .001). Having at least one functional deficit captured from the AWV was independently associated with an increased risk of hospitalizations and injurious falls, controlling for other components of the eFI. CONCLUSIONS: Construction of an eFI from the EHR, within the context of a managed care population, is feasible and can help to identify vulnerable older adults. Future work is needed to integrate the eFI with claims-based approaches and test whether it can be used to effectively target interventions tailored to the health needs of frail patients.

Opportunistic Measurement of Skeletal Muscle Size and Muscle Attenuation on Computed Tomography Predicts 1-Year Mortality in Medicare Patients.

BACKGROUND: Opportunistic assessment of sarcopenia on CT examinations is becoming increasingly common. This study aimed to determine relationships between CT-measured skeletal muscle size and attenuation with 1-year risk of mortality in older adults enrolled in a Medicare Shared Savings Program (MSSP). METHODS: Relationships between skeletal muscle metrics and all-cause mortality were determined in 436 participants (52% women, mean age 75 years) who had abdominopelvic CT examinations. On CT images, skeletal muscles were segmented at the level of L3 using two methods: (a) all muscles with a threshold of -29 to +150 Hounsfield units (HU), using a dedicated segmentation software, (b) left psoas muscle using a free-hand region of interest tool on a clinical workstation. Muscle cross-sectional area (CSA) and muscle attenuation were measured. Cox regression models were fit to determine the associations between muscle metrics and mortality, adjusting for age, sex, race, smoking status, cancer diagnosis, and Charlson comorbidity index. RESULTS: Within 1 year of follow-up, 20.6% (90/436) participants died. In the fully-adjusted model, higher muscle index and muscle attenuation were associated with lower risk of mortality. A one-unit standard deviation (SD) increase was associated with a HR = 0.69 (95% CI = 0.49, 0.96; p = .03) for total muscle index, HR = 0.67 (95% CI = 0.49, 0.90; p < .01) for psoas muscle index, HR = 0.54 (95% CI = 0.40, 0.74; p < .01) for total muscle attenuation, and HR = 0.79 (95% CI = 0.66, 0.95; p = .01) for psoas muscle attenuation. CONCLUSION: In older adults, higher skeletal muscle index and muscle attenuation on abdominopelvic CT examinations were associated with better survival, after adjusting for multiple risk factors.

The Impact of Socioeconomic Status on the Utilization of Spinal Imaging.

BACKGROUND: Few studies have examined the general correlation between socioeconomic status and imaging. This study is the first to analyze this relationship in the spine patient population. OBJECTIVE: To assess the effect of socioeconomic status on the frequency with which imaging studies of the lumbar spine are ordered and completed. METHODS: Patients that were diagnosed with lumbar radiculopathy and/or myelopathy and had at least 1 subsequent lumbar magnetic resonance imaging (MRI), computed tomography (CT), or X-ray ordered were retrospectively identified. Demographic information and the number of ordered and completed imaging studies were among the data collected. Patient insurance status and income level (estimated based on zip code) served as representations of socioeconomic status. RESULTS: A total of 24,105 patients met the inclusion criteria for this study. Regression analyses demonstrated that uninsured patients were significantly less likely to have an MRI, CT, or X-ray study ordered (P < .001 for all modalities) and completed (P < .001 for MRI and X-ray, P = .03 for CT). Patients with lower income had higher rates of MRI, CT, and X-ray (P < .001 for all) imaging ordered but were less likely to have an ordered X-ray be completed (P = .009). There was no significant difference in the completion rate of ordered MRIs or CTs. CONCLUSION: Disparities in image utilization based on socioeconomic characteristics such as insurance status and income level highlight a critical gap in access to health care. Physicians should work to mitigate the influence of such factors when deciding whether to order imaging studies, especially in light of the ongoing shift in health policy in the United States.

Using the landmark method for creating prediction models in large datasets derived from electronic health records.

With the integration of electronic health records (EHRs), health data has become easily accessible and abounded. The EHR has the potential to provide important healthcare information to researchers by creating study cohorts. However, accessing this information comes with three major issues: 1) Predictor variables often change over time, 2) Patients have various lengths of follow up within the EHR, and 3) the size of the EHR data can be computationally challenging. Landmark analyses provide a perfect complement to EHR data and help to alleviate these three issues. We present two examples that utilize patient birthdays as landmark times for creating dynamic datasets for predicting clinical outcomes. The use of landmark times help to solve these three issues by incorporating information that changes over time, by creating unbiased reference points that are not related to a patient's exposure within the EHR, and reducing the size of a dataset compared to true time-varying analysis. These techniques are shown using two example cohort studies from the Cleveland Clinic that utilized 4.5 million and 17,787 unique patients, respectively.

The risk of overall mortality in patients with type 2 diabetes receiving glipizide, glyburide, or glimepiride monotherapy: a retrospective analysis.

OBJECTIVE: Sulfonylureas have historically been analyzed as a medication class, which may be inappropriate given the differences in properties inherent to the individual sulfonylureas (hypoglycemic risk, sulfonylurea receptor selectivity, and effects on myocardial ischemic preconditioning). The purpose of this study was to assess the relationship of individual sulfonylureas and the risk of overall mortality in a large cohort of patients with type 2 diabetes. RESEARCH DESIGN AND METHODS: A retrospective cohort study was conducted using an academic health center enterprise-wide electronic health record (EHR) system to identify 11,141 patients with type 2 diabetes (4,279 initiators of monotherapy with glyburide, 4,325 initiators of monotherapy with glipizide, and 2,537 initiators of monotherapy with glimepiride), >or=18 years of age with and without a history of coronary artery disease (CAD) and not on insulin or a noninsulin injectable at baseline. The patients were followed for mortality by documentation in the EHR and Social Security Death Index. Multivariable Cox models were used to compare cohorts. RESULTS: No statistically significant difference in the risk of overall mortality was observed among these agents in the entire cohort, but we did find evidence of a trend toward an increased overall mortality risk with glyburide versus glimepiride (hazard ratio 1.36 [95% CI 0.96-1.91]) and glipizide versus glimepiride (1.39 [0.99-1.96]) in those with documented CAD. CONCLUSIONS: Our results did not identify an increased mortality risk among the individual sulfonylureas but did suggest that glimepiride may be the preferred sulfonylurea in those with underlying CAD.

Defining the optimal treatment for clinical stage I nonseminomatous germ cell testicular cancer using decision analysis.

PURPOSE There is equipoise regarding the optimal treatment of clinical stage (CS) I nonseminomatous germ cell testicular cancer (NSGCT). Formal mechanisms that enable patients to consider cancer outcomes, treatment-related morbidity, and personal preferences are needed to facilitate decision making between retroperitoneal lymph node dissection (RPLND), primary chemotherapy, and surveillance. METHODS Decision analysis was performed using a Markov model that incorporated likelihoods of survival, treatment-related morbidity, and utilities for seven undesired post-treatment health states to estimate the quality-adjusted survival (QAS) for each treatment option. Utilities were obtained from 24 hypothetical NSGCT patients using a visual analog (rating) scale and standard gamble. Results Overall, QAS associated with each treatment was high and differences in QAS were small. Surveillance was the preferred intervention for patients with a risk of relapse less than 33% and 37% using the rating scale and standard-gamble method of utility assessment, respectively. Active treatment was favored over surveillance for patients with relapse risk on surveillance greater than 33% and 37% by the rating scale (RPLND preferred) and standard-gamble methods (primary chemotherapy preferred), respectively. Substantial differences in average utilities were seen depending on the method used. By the rating scale, patients substantially devalued life in six of seven undesired health states but they were surprisingly tolerant of treatment-related morbidity using standard gamble. CONCLUSION A decision model has been developed for CS I NSGCT that estimates QAS for RPLND, primary chemotherapy, and surveillance by considering cancer outcomes, morbidity, and patient preferences. Surveillance was the preferred intervention for all except those patients at high risk for relapse.

Health care and productivity costs associated with diabetic patients with macrovascular comorbid conditions.

OBJECTIVE: To examine and quantify from the societal perspective the impact of macrovascular comorbid conditions (MVCCs) on health care and productivity costs in diabetic patients in the U.S. RESEARCH DESIGN AND METHODS: With use of the pooled Medical Expenditure Panel Survey (MEPS) 2004 and 2006 data, a nationally representative adult sample (aged >or=18 years) was included in the study. Health care cost was measured by the annual health care expenditure. Productivity cost was calculated from the lost productivity from missed work days and additional bed days due to illness/injury based on the 2006 average national hourly wage. Both 2004 and 2006 cost data were adjusted to 2006 dollars. Given the heavily right-skewed distribution of the cost data, the generalized linear model with log-link function and gamma variance was used to identify the relationship between MVCCs and costs after controlling for age, sex, race, ethnicity, education, income, employment status, smoking status, health insurance, diabetes severity, and comorbidities. Negative binomial models were applied to analyze the outcomes of missed work days and bed days. All statistics were adjusted using the proper sampling weight from MEPS. RESULTS: Compared with diabetic patients without MVCCs (n = 3,320), those with MVCCs (n = 913) had statistically significant higher annual health care costs (5,120 USD, P < 0.001), more missed work days (13.03 days, P < 0.001), and more bed days (7.60 days, P = 0.025) per patient after controlling for differences in sociodemographics, smoking, diabetes severity, and comorbidities. The marginal lost productivity cost was 2,388 USD annually per patient. CONCLUSIONS: From the U.S. societal perspective, MVCCs in diabetic patients are associated with increased health care and lost productivity costs.

Predicting 6-year mortality risk in patients with type 2 diabetes.

OBJECTIVE: The objective of this study was to create a tool that predicts the risk of mortality in patients with type 2 diabetes. RESEARCH DESIGN AND METHODS: This study was based on a cohort of 33,067 patients with type 2 diabetes identified in the Cleveland Clinic electronic health record (EHR) who were initially prescribed a single oral hypoglycemic agent between 1998 and 2006. Mortality was determined in the EHR and the Social Security Death Index. A Cox proportional hazards regression model was created using medication class and 20 other predictor variables chosen for their association with mortality. A prediction tool was created using the Cox model coefficients. The tool was internally validated using repeated, random subsets of the cohort, which were not used to create the prediction model. RESULTS: Follow-up in the cohort ranged from 1 day to 8.2 years (median 28.6 months), and 3,661 deaths were observed. The prediction tool had a concordance index (i.e., c statistic) of 0.752. CONCLUSIONS: We successfully created a tool that accurately predicts mortality risk in patients with type 2 diabetes. The incorporation of medications into mortality predictions in patients with type 2 diabetes should improve treatment decisions.

Using the electronic medical record to enhance the use of combination drugs.

The objective of this study was to increase combination drug prescriptions through the use of electronic point-of-care reminders, thereby maintaining quality while decreasing medication costs. The electronic medical record (EMR) was used to identify all patients who were potential candidates for one of the following 3 currently available combination drugs: fluticasone-salmeterol, amlodipine-benazepril, or glyburide-metformin. Point-of-care electronic reminders were attached to the medication record of the EMR for each patient, and providers were asked to consider using the available combination medication. Of the patients who had electronic reminders attached to their charts and were seen at the clinic during the study period, 47 of 175 were switched to a combination medication. A cost-savings analysis showed a total annual savings of dollars 6,159.30. Point-of-care reminders are a simple and effective tool for quality-improvement interventions. Combination drugs may play an important role in controlling medication costs.