Assessing Heuristic Bias During Care for Patients Hospitalized for Heart Failure: Get With The Guidelines-Heart Failure.

BACKGROUND: Heuristic biases are increasingly recognized, and potentially modifiable, contributors to patient care and outcomes. Left digit bias is a cognitive bias where continuous variables are categorized by their left-most digit. The impact of this heuristic bias applied to patient age on quality of care in heart failure has not been explored. METHODS: We examined participants admitted from 2005 to 2021 in the Get With The Guidelines-Heart Failure registry. To create 2 naturally randomized groups, isolating the effect of left digit bias, we dichotomized patients into those discharged within 60 days prior to their 80th birthday (N=4238) and those discharged within 60 days after their 80th birthday (N=4329). We performed multivariable logistic regression to assess the association between discharge date relative to 80th birthday and several in-hospital quality metrics and in-hospital outcomes. Among Medicare participants (N=2759), we performed adjusted Cox regression to analyze the relationship between discharge date and risk of 1-year mortality or readmission. RESULTS: Among 8567 patients, 50.4% were female, 73% were non-Hispanic White, and 42.9% had an ejection fraction ≤40%. Discharge date relative to 80th birthday was not associated with numerous in-hospital quality metrics or in-hospital outcomes on unadjusted or adjusted logistic regression. Among Medicare beneficiaries, there was no association between discharge date and risk of mortality or readmission at 1-year postdischarge (hazard ratio, 1.03 [95% CI, 0.95-1.12]; P=0.52). CONCLUSIONS: In a large registry of patients hospitalized for heart failure, we did not detect a left digit bias' with respect to age at discharge, which resulted in differential quality of care or outcomes.

The Role of Behavioral Economics in Improving Cardiovascular Health Behaviors and Outcomes.

PURPOSE OF REVIEW: Behavioral economics represents a promising set of principles to inform the design of health-promoting interventions. Techniques from the field have the potential to increase quality of cardiovascular care given suboptimal rates of guideline-directed care delivery and patient adherence to optimal health behaviors across the spectrum of cardiovascular care delivery. RECENT FINDINGS: Cardiovascular health-promoting interventions have demonstrated success in using a wide array of principles from behavioral economics, including loss framing, social norms, and gamification. Such approaches are becoming increasingly sophisticated and focused on clinical cardiovascular outcomes in addition to health behaviors as a primary endpoint. Many approaches can be used to improve patient decisions remotely, which is particularly useful given the shift to virtual care in the context of the COVID-19 pandemic. Numerous applications for behavioral economics exist in the cardiovascular care delivery space, though more work is needed before we will have a full understanding of ways to best leverage such applications in each clinical context.

Patient Characteristics Associated With Telemedicine Access for Primary and Specialty Ambulatory Care During the COVID-19 Pandemic.

Importance: The coronavirus disease 2019 (COVID-19) pandemic has required a shift in health care delivery platforms, necessitating a new reliance on telemedicine. Objective: To evaluate whether inequities are present in telemedicine use and video visit use for telemedicine visits during the COVID-19 pandemic. Design, Setting, and Participants: In this cohort study, a retrospective medical record review was conducted from March 16 to May 11, 2020, of all patients scheduled for telemedicine visits in primary care and specialty ambulatory clinics at a large academic health system. Age, race/ethnicity, sex, language, median household income, and insurance type were all identified from the electronic medical record. Main Outcomes and Measures: A successfully completed telemedicine visit and video (vs telephone) visit for a telemedicine encounter. Multivariable models were used to assess the association between sociodemographic factors, including sex, race/ethnicity, socioeconomic status, and language, and the use of telemedicine visits, as well as video use specifically. Results: A total of 148 402 unique patients (86 055 women [58.0%]; mean [SD] age, 56.5 [17.7] years) had scheduled telemedicine visits during the study period; 80 780 patients (54.4%) completed visits. Of 78 539 patients with completed visits in which visit modality was specified, 35 824 (45.6%) were conducted via video, whereas 24 025 (56.9%) had a telephone visit. In multivariable models, older age (adjusted odds ratio [aOR], 0.85 [95% CI, 0.83-0.88] for those aged 55-64 years; aOR, 0.75 [95% CI, 0.72-0.78] for those aged 65-74 years; aOR, 0.67 [95% CI, 0.64-0.70] for those aged ≥75 years), Asian race (aOR, 0.69 [95% CI, 0.66-0.73]), non-English language as the patient's preferred language (aOR, 0.84 [95% CI, 0.78-0.90]), and Medicaid insurance (aOR, 0.93 [95% CI, 0.89-0.97]) were independently associated with fewer completed telemedicine visits. Older age (aOR, 0.79 [95% CI, 0.76-0.82] for those aged 55-64 years; aOR, 0.78 [95% CI, 0.74-0.83] for those aged 65-74 years; aOR, 0.49 [95% CI, 0.46-0.53] for those aged ≥75 years), female sex (aOR, 0.92 [95% CI, 0.90-0.95]), Black race (aOR, 0.65 [95% CI, 0.62-0.68]), Latinx ethnicity (aOR, 0.90 [95% CI, 0.83-0.97]), and lower household income (aOR, 0.57 [95% CI, 0.54-0.60] for income <$50 000; aOR, 0.89 [95% CI, 0.85-0.92], for $50 000-$100 000) were associated with less video use for telemedicine visits. These results were similar across medical specialties. Conclusions and Relevance: In this cohort study of patients scheduled for primary care and medical specialty ambulatory telemedicine visits at a large academic health system during the early phase of the COVID-19 pandemic, older patients, Asian patients, and non-English-speaking patients had lower rates of telemedicine use, while older patients, female patients, Black, Latinx, and poorer patients had less video use. Inequities in accessing telemedicine care are present, which warrant further attention.

Using Behavioral Economics and Technology to Improve Outcomes in Cardio-Oncology.

Patients with cancer are often at elevated risk for cardiovascular disease due to overlapping risk factors and cardiotoxic anticancer treatments. Their cancer diagnoses may be the predominant focus of clinical care, with less of an emphasis on concurrent cardiovascular risk management. Widely adopted technology platforms, including electronic health records and mobile devices, can be leveraged to improve the cardiovascular outcomes of these patients. These technologies alone may be insufficient to change behavior and may have greater impact if combined with behavior change strategies. Behavioral economics is a scientific field that uses insights from economics and psychology to help explain why individuals are often predictably irrational. Combining insights from behavioral economics with these scalable technology platforms can positively impact medical decision-making and sustained healthy behaviors. This review focuses on the principles of behavioral economics and how "nudges" and scalable technology can be used to positively impact clinician and patient behaviors.

Comprehensive Assessment of Changes in Left Ventricular Diastolic Function With Contemporary Breast Cancer Therapy.

OBJECTIVES: This study determined the effects of doxorubicin and/or trastuzumab on diastolic function and the relationship between diastolic function and systolic dysfunction. BACKGROUND: Doxorubicin and trastuzumab can result in left ventricular ejection fraction (LVEF) declines. However, the effects of these therapies on diastolic function remain incompletely defined. METHODS: In a rigorously phenotyped, longitudinal cohort study of 362 breast cancer participants treated with doxorubicin, doxorubicin followed by trastuzumab, or trastuzumab alone, changes in diastolic function were evaluated using linear models estimated via generalized estimating equations. Associations between baseline and changes in diastolic function with LVEF and longitudinal strain were also determined using generalized estimating equations. The Kaplan-Meier estimator derived the proportion of participants who experienced incident diastolic dysfunction. Cox proportional hazards models estimated the associations between participant characteristics and diastolic dysfunction risk, and between diastolic function and cancer therapy-related cardiac dysfunction risk, defined by an LVEF decline of ≥10% to <50%. RESULTS: Over a median of 2.1 years (interquartile range [IQR]: 1.3 to 4.2 years), participants treated with doxorubicin or doxorubicin followed by trastuzumab demonstrated a persistent worsening in diastolic function, with reductions in the E/A ratio, lateral and septal e' velocities, and increases in E/e' (p < 0.01). These changes were not observed with trastuzumab alone. Incident abnormal diastolic function grade occurred in 60% at 1 year, 70% by 2 years, and 80% by 3 years. Abnormal diastolic function grade was associated with a subsequent decrease in LVEF (-2.1%; 95% confidence intervals [CI]: -3.1 to -1.2; p < 0.001) and worsening in longitudinal strain (0.6%; 95% CI: 0.1 to 1.1; p = 0.013) over time. Changes in E/e' ratio were modestly associated with worsening longitudinal strain (0.1%; 95% CI: 0.0 to 0.2; p = 0.022). CONCLUSIONS: A modest, persistent worsening of diastolic function is observed with contemporary breast cancer therapy. Abnormal and worsening diastolic dysfunction is associated with a small risk of subsequent systolic dysfunction. (Cardiotoxicity of Cancer Therapy [CCT]; NCT01173341).

Racial, Ethnic, and Socioeconomic Inequities in the Prescription of Direct Oral Anticoagulants in Patients With Venous Thromboembolism in the United States.

BACKGROUND: Beginning in 2012, direct oral anticoagulants (DOACs) were approved for treatment and prevention of venous thromboembolism. Prior investigations have demonstrated slow rates of adoption of novel therapeutics for black patients. We assessed the association of racial/ethnic and socioeconomic factors with DOAC use among commercially insured venous thromboembolism patients. METHODS AND RESULTS: We performed a retrospective cohort analysis of adult patients with an incident diagnosis of venous thromboembolism between January 2010 and December 2016 using OptumInsight's Clinformatics Data Mart. We identified the first filled oral anticoagulant prescription within 30 days of discharge of an inpatient admission. We performed a multivariable logistic regression, adjusting for age, sex, race/ethnicity, region, zip code-linked household income, and clinical covariates to identify factors associated with the use of DOACs. Race and ethnicity were determined in this database through a combination of public records, self-report, and proprietary ethnicity code tables. There were 14 140 patients included in the analysis. Treatment with DOACs increased from <0.1% in 2010 to 65.6% in 2016. In multivariable analyses, black patients were less likely to receive a DOAC compared with white patients (odds ratio, 0.86; 95% CI, 0.77-0.97; P=0.02). There were no differences in DOAC utilization among Asian (odds ratio, 1.06; 95% CI, 0.75-1.49; P=0.74) or Hispanic patients (odds ratio, 1.04; 95% CI, 0.88-1.22; P=0.66) compared with whites. Patients with a household income over $100 000 per year were more likely to receive DOAC therapy compared with patients with a household income of <$40 000 per year (odds ratio, 1.50; 95% CI, 1.33-1.69; P<0.0001). CONCLUSIONS: Although DOAC adoption has increased steadily since 2012, among a commercially insured population, black race and low household income were associated with lower use of DOACs for incident venous thromboembolism despite controlling for other clinical and socioeconomic factors. These findings suggest the possibility of both racial and socioeconomic inequity in access to this novel pharmacotherapy.

Loss-Framed Financial Incentives and Personalized Goal-Setting to Increase Physical Activity Among Ischemic Heart Disease Patients Using Wearable Devices: The ACTIVE REWARD Randomized Trial.

BACKGROUND: Regular physical activity reduces the risk of cardiovascular events, but most ischemic heart disease (IHD) patients do not obtain enough. METHODS AND RESULTS: ACTIVE REWARD (A Clinical Trial Investigating Effects of a Randomized Evaluation of Wearable Activity Trackers with Financial Rewards) was a 24-week home-based, remotely monitored, randomized trial with a 16-week intervention (8-week ramp-up incentive phase and 8-week maintenance incentive phase) and an 8-week follow-up. Patients used wearable devices to track step counts and establish a baseline. Patients in control received no other interventions. Patients in the incentive arm received personalized step goals and daily feedback for all 24 weeks. In the ramp-up incentive phase, daily step goals increased weekly by 15% from baseline with a maximum of 10 000 steps and then remained fixed. Each week, $14 was allocated to a virtual account; $2 could be lost per day for not achieving step goals. The primary outcome was change in mean daily steps from baseline to the maintenance incentive phase. Ischemic heart disease patients had a mean (SD) age of 60 (11) years and 70% were male. Compared with control, patients in the incentive arm had a significantly greater increase in mean daily steps from baseline during ramp-up (1388 versus 385; adjusted difference, 1061 [95% confidence interval, 386-1736]; P<0.01), maintenance (1501 versus 264; adjusted difference, 1368 [95% confidence interval, 571-2164]; P<0.001), and follow-up (1066 versus 92; adjusted difference, 1154 [95% confidence interval, 282-2027]; P<0.01). CONCLUSIONS: Loss-framed financial incentives with personalized goal setting significantly increased physical activity among ischemic heart disease patients using wearable devices during the 16-week intervention, and effects were sustained during the 8-week follow-up. CLINICAL TRIAL REGISTRATION: URL: https://www.clinicaltrials.gov. Unique identifier: NCT02531022.