Artificial Intelligence-Enhanced Risk Stratification of Cancer Therapeutics-Related Cardiac Dysfunction Using Electrocardiographic Images.

Background: Risk stratification strategies for cancer therapeutics-related cardiac dysfunction (CTRCD) rely on serial monitoring by specialized imaging, limiting their scalability. We aimed to examine an application of artificial intelligence (AI) to electrocardiographic (ECG) images as a surrogate for imaging risk biomarkers, and its association with early CTRCD. Methods: Across a U.S.-based health system (2013-2023), we identified 1,550 patients (age 60 [IQR:51-69] years, 1223 [78.9%] women) without cardiomyopathy who received anthracyclines and/or trastuzumab for breast cancer or non-Hodgkin lymphoma and had ECG performed ≤12 months before treatment. We deployed a validated AI model of left ventricular systolic dysfunction (LVSD) to baseline ECG images and defined low, intermediate, and high-risk groups based on AI-ECG LVSD probabilities of <0.01, 0.01 to 0.1, and ≥0.1 (positive screen), respectively. We explored the association with early CTRCD (new cardiomyopathy, heart failure, or left ventricular ejection fraction [LVEF]<50%), or LVEF<40%, up to 12 months post-treatment. In a mechanistic analysis, we assessed the association between global longitudinal strain (GLS) and AI-ECG LVSD probabilities in studies performed within 15 days of each other. Results: Among 1,550 patients without known cardiomyopathy (median follow-up: 14.1 [IQR:13.4-17.1] months), 83 (5.4%), 562 (36.3%) and 905 (58.4%) were classified as high, intermediate, and low risk by baseline AI-ECG. A high- vs low-risk AI-ECG screen (≥0.1 vs <0.01) was associated with a 3.4-fold and 13.5-fold higher incidence of CTRCD (adj.HR 3.35 [95%CI:2.25-4.99]) and LVEF<40% (adj.HR 13.52 [95%CI:5.06-36.10]), respectively. Post-hoc analyses supported longitudinal increases in AI-ECG probabilities within 6-to-12 months of a CTRCD event. Among 1,428 temporally-linked echocardiograms and ECGs, AI-ECG LVSD probabilities were associated with worse GLS (GLS -19% [IQR:-21 to -17%] for probabilities <0.1, to -15% [IQR:-15 to -9%] for ≥0.5 [p<0.001]). Conclusions: AI applied to baseline ECG images can stratify the risk of early CTRCD associated with anthracycline or trastuzumab exposure in the setting of breast cancer or non-Hodgkin lymphoma therapy.

Tracking the pre-clinical progression of transthyretin amyloid cardiomyopathy using artificial intelligence-enabled electrocardiography and echocardiography.

Background and Aims: Diagnosing transthyretin amyloid cardiomyopathy (ATTR-CM) requires advanced imaging, precluding large-scale testing for pre-clinical disease. We examined the application of artificial intelligence (AI) to echocardiography (TTE) and electrocardiography (ECG) as a scalable strategy to quantify pre-clinical trends in ATTR-CM. Methods: Across age/sex-matched case-control datasets in the Yale-New Haven Health System (YNHHS) we trained deep learning models to identify ATTR-CM-specific signatures on TTE videos and ECG images (area under the curve of 0.93 and 0.91, respectively). We deployed these across all studies of individuals referred for cardiac nuclear amyloid imaging in an independent population at YNHHS and an external population from the Houston Methodist Hospitals (HMH) to define longitudinal trends in AI-defined probabilities for ATTR-CM using age/sex-adjusted linear mixed models, and describe discrimination metrics during the early pre-clinical stage. Results: Among 984 participants referred for cardiac nuclear amyloid imaging at YNHHS (median age 74 years, 44.3% female) and 806 at HMH (69 years, 34.5% female), 112 (11.4%) and 174 (21.6%) tested positive for ATTR-CM, respectively. Across both cohorts and modalities, AI-defined ATTR-CM probabilities derived from 7,423 TTEs and 32,205 ECGs showed significantly faster progression rates in the years before clinical diagnosis in cases versus controls (p time × group interaction ≤0.004). In the one-to-three-year window before cardiac nuclear amyloid imaging sensitivity/specificity metrics were estimated at 86.2%/44.2% [YNHHS] vs 65.7%/65.5% [HMH] for AI-Echo, and 89.8%/40.6% [YNHHS] vs 88.5%/35.1% [HMH] for AI-ECG. Conclusions: We demonstrate that AI tools for echocardiographic videos and ECG images can enable scalable identification of pre-clinical ATTR-CM, flagging individuals who may benefit from risk-modifying therapies.

Development and multinational validation of an algorithmic strategy for high Lp(a) screening.

Elevated lipoprotein (a) (Lp(a)) is associated with premature atherosclerotic cardiovascular disease. However, fewer than 0.5% of individuals undergo Lp(a) testing, limiting the evaluation and use of novel targeted therapeutics currently under development. Here we describe the development of a machine learning model for targeted screening for elevated Lp(a) (≥150 nmol l-1) in the UK Biobank (N = 456,815), the largest cohort with protocolized Lp(a) testing. We externally validated the model in 3 large cohort studies, ARIC (N = 14,484), CARDIA (N = 4,124) and MESA (N = 4,672). The model, Algorithmic Risk Inspection for Screening Elevated Lp(a) (ARISE), reduced the number needed to test to find one individual with elevated Lp(a) by up to 67.3%, based on the probability threshold, with consistent performance across external validation cohorts. ARISE could be used to optimize screening for elevated Lp(a) using commonly available clinical features, with the potential for its deployment in electronic health records to enhance the yield of Lp(a) testing in real-world settings.

Artificial intelligence-enhanced risk stratification of cancer therapeutics-related cardiac dysfunction using electrocardiographic images.

Background: Risk stratification strategies for cancer therapeutics-related cardiac dysfunction (CTRCD) rely on serial monitoring by specialized imaging, limiting their scalability. Objectives: To examine an artificial intelligence (AI)-enhanced electrocardiographic (AI-ECG) surrogate for imaging risk biomarkers, and its association with CTRCD. Methods: Across a five-hospital U.S.-based health system (2013-2023), we identified patients with breast cancer or non-Hodgkin lymphoma (NHL) who received anthracyclines (AC) and/or trastuzumab (TZM), and a control cohort receiving immune checkpoint inhibitors (ICI). We deployed a validated AI model of left ventricular systolic dysfunction (LVSD) to ECG images (≥0.1, positive screen) and explored its association with i) global longitudinal strain (GLS) measured within 15 days (n=7,271 pairs); ii) future CTRCD (new cardiomyopathy, heart failure, or left ventricular ejection fraction [LVEF]<50%), and LVEF<40%. In the ICI cohort we correlated baseline AI-ECG-LVSD predictions with downstream myocarditis. Results: Higher AI-ECG LVSD predictions were associated with worse GLS (-18% [IQR:-20 to -17%] for predictions<0.1, to -12% [IQR:-15 to -9%] for ≥0.5 (p<0.001)). In 1,308 patients receiving AC/TZM (age 59 [IQR:49-67] years, 999 [76.4%] women, 80 [IQR:42-115] follow-up months) a positive baseline AI-ECG LVSD screen was associated with ~2-fold and ~4.8-fold increase in the incidence of the composite CTRCD endpoint (adj.HR 2.22 [95%CI:1.63-3.02]), and LVEF<40% (adj.HR 4.76 [95%CI:2.62-8.66]), respectively. Among 2,056 patients receiving ICI (age 65 [IQR:57-73] years, 913 [44.4%] women, follow-up 63 [IQR:28-99] months) AI-ECG predictions were not associated with ICI myocarditis (adj.HR 1.36 [95%CI:0.47-3.93]). Conclusion: AI applied to baseline ECG images can stratify the risk of CTRCD associated with anthracycline or trastuzumab exposure.

Use of Smart Devices to Track Cardiovascular Health Goals in the United States.

BACKGROUND: Smartphone-based health applications are increasingly popular, but their real-world use for cardiovascular risk management remains poorly understood. OBJECTIVES: The purpose of this study was to investigate the patterns of tracking health goals using smart devices, including smartphones and/or tablets, in the United States. METHODS: Using the nationally representative Health Information National Trends Survey for 2017 to 2020, we examined self-reported tracking of health-related goals (optimizing body weight, increasing physical activity, and/or quitting smoking) using smart devices among those with cardiovascular disease (CVD) or cardiovascular risk factors of hypertension, diabetes, obesity, and/or smoking. Survey analyses were used to obtain national estimates of use patterns and identify features associated with the use of these devices for tracking health goals. RESULTS: Of 16,092 Health Information National Trends Survey participants, 10,660 had CVD or cardiovascular risk factors, representing 154.2 million (95% CI: 149.2-159.3 million) U.S. adults. Among the general U.S. adult population, 46% (95% CI: 44%-47%) tracked their health goals using their smart devices, compared with 42% (95% CI: 40%-43%) of those with or at risk of CVD. Younger age, female, Black race, higher educational attainment, and greater income were independently associated with tracking of health goals using smart devices. CONCLUSIONS: Two in 5 U.S. adults with or at risk of CVD use their smart devices to track health goals. While representing a potential avenue to improve care, the lower use of smart devices among older and low-income individuals, who are at higher risk of adverse cardiovascular outcomes, requires that digital health interventions are designed so as not to exacerbate existing disparities.

Lifetime healthcare expenses across demographic and cardiovascular risk groups: The application of a novel modeling strategy in a large multiethnic cohort study.

Objective: To understand the burden of healthcare expenses over the lifetime of individuals and evaluate differences among those with cardiovascular risk factors and among disadvantaged groups based on race/ethnicity and sex. Methods: We linked data from the longitudinal multiethnic Dallas Heart Study, which recruited participants between 2000 and 2002, with inpatient and outpatient claims from all hospitals in the Dallas-Fort Worth metroplex through December 2018, capturing encounter expenses. Race/ethnicity and sex, as well as five risk factors, hypertension, diabetes, hyperlipidemia, smoking, and overweight/obesity, were defined at cohort enrollment. For each individual, expenses were indexed to age and cumulated between 40 and 80 years of age. Lifetime expenses across exposures were evaluated as interactions in generalized additive models. Results: A total of 2184 individuals (mean age, 45±10 years; 61% women, 53% Black) were followed between 2000 and 2018. The mean modeled lifetime cumulative healthcare expenses were $442,629 (IQR, $423,850 to $461,408). In models that included 5 risk factors, Black individuals had $21,306 higher lifetime healthcare spending compared with non-Black individuals (P < .001), and men had modestly higher expenses than women ($5987, P < .001). Across demographic groups, the presence of risk factors was associated with progressively higher lifetime expenses, with significant independent association of diabetes ($28,075, P < .001), overweight/obesity ($8816, P < .001), smoking ($3980, P = .009), and hypertension ($528, P = .02) with excess spending. Conclusion: Our study suggests Black individuals have higher lifetime healthcare expenses, exaggerated by the substantially higher prevalence of risk factors, with differences emerging in older age.

Use of Wearable Devices in Individuals With or at Risk for Cardiovascular Disease in the US, 2019 to 2020.

Importance: Wearable devices may be able to improve cardiovascular health, but the current adoption of these devices could be skewed in ways that could exacerbate disparities. Objective: To assess sociodemographic patterns of use of wearable devices among adults with or at risk for cardiovascular disease (CVD) in the US population in 2019 to 2020. Design, Setting, and Participants: This population-based cross-sectional study included a nationally representative sample of the US adults from the Health Information National Trends Survey (HINTS). Data were analyzed from June 1 to November 15, 2022. Exposures: Self-reported CVD (history of heart attack, angina, or congestive heart failure) and CVD risk factors (≥1 risk factor among hypertension, diabetes, obesity, or cigarette smoking). Main Outcomes and Measures: Self-reported access to wearable devices, frequency of use, and willingness to share health data with clinicians (referred to as health care providers in the survey). Results: Of the overall 9303 HINTS participants representing 247.3 million US adults (mean [SD] age, 48.8 [17.9] years; 51% [95% CI, 49%-53%] women), 933 (10.0%) representing 20.3 million US adults had CVD (mean [SD] age, 62.2 [17.0] years; 43% [95% CI, 37%-49%] women), and 5185 (55.7%) representing 134.9 million US adults were at risk for CVD (mean [SD] age, 51.4 [16.9] years; 43% [95% CI, 37%-49%] women). In nationally weighted assessments, an estimated 3.6 million US adults with CVD (18% [95% CI, 14%-23%]) and 34.5 million at risk for CVD (26% [95% CI, 24%-28%]) used wearable devices compared with an estimated 29% (95% CI, 27%-30%) of the overall US adult population. After accounting for differences in demographic characteristics, cardiovascular risk factor profile, and socioeconomic features, older age (odds ratio [OR], 0.35 [95% CI, 0.26-0.48]), lower educational attainment (OR, 0.35 [95% CI, 0.24-0.52]), and lower household income (OR, 0.42 [95% CI, 0.29-0.60]) were independently associated with lower use of wearable devices in US adults at risk for CVD. Among wearable device users, a smaller proportion of adults with CVD reported using wearable devices every day (38% [95% CI, 26%-50%]) compared with overall (49% [95% CI, 45%-53%]) and at-risk (48% [95% CI, 43%-53%]) populations. Among wearable device users, an estimated 83% (95% CI, 70%-92%) of US adults with CVD and 81% (95% CI, 76%-85%) at risk for CVD favored sharing wearable device data with their clinicians to improve care. Conclusions and Relevance: Among individuals with or at risk for CVD, fewer than 1 in 4 use wearable devices, with only half of those reporting consistent daily use. As wearable devices emerge as tools that can improve cardiovascular health, the current use patterns could exacerbate disparities unless there are strategies to ensure equitable adoption.

Individualising intensive systolic blood pressure reduction in hypertension using computational trial phenomaps and machine learning: a post-hoc analysis of randomised clinical trials.

BACKGROUND: The cardiovascular benefits of intensive systolic blood pressure control vary across clinical populations tested in large randomised clinical trials. We aimed to evaluate the application of machine learning to clinical trials of patients without and with type 2 diabetes to define the personalised cardiovascular benefit of intensive control of systolic blood pressure. METHODS: In SPRINT, a trial of intensive (systolic blood pressure <120 mm Hg) versus standard (systolic blood pressure <140 mm Hg) systolic blood pressure control in patients without type 2 diabetes, we defined a phenotypic representation of the study population using 59 baseline variables. We extracted personalised treatment effect estimates for the primary outcome, time-to-first major adverse cardiovascular event (MACE; cardiovascular death, myocardial infarction or acute coronary syndrome, stroke, and acute decompensated heart failure), through iterative Cox regression analyses providing average hazard ratio (HR) estimates weighted for the phenotypic distance of each participant from the index patient of each iteration. Next, we trained an extreme gradient boosting algorithm (known as XGBoost) to predict the personalised effect of intensive systolic blood pressure control using features most consistently linked to increased personalised benefit, before evaluating its performance in the ACCORD BP trial of patients with type 2 diabetes randomly assigned to receive intensive versus standard systolic blood pressure control. We stratified patients based on their predicted treatment effect, and key demographic groups (age, sex, cardiovascular disease, and smoking). We assessed the presence of heterogeneity with an interaction test, and assessed the performance of the algorithm in a simulation analysis of SPRINT in the presence or absence of an artificially introduced heterogeneous treatment effect. FINDINGS: From SPRINT, we included all 9361 study participants (mean age 67·9 years [SD 9·4], 3332 [35·6%] female) who underwent randomisation to either intensive (n=4678) or standard (n=4683) treatment. The median individualised HR for MACE was 0·63 (IQR 0·53-0·78). An eight-feature tool built for this analysis to predict personalised benefit in SPRINT was externally tested in ACCORD BP (4733 participants (mean age 62·7 years [SD 6·7], 2258 [47·7%] female), wherein it successfully identified individuals with differential benefit from intensive versus standard systolic blood pressure control (adjusted HR for MACE of 0·70 [95% CI 0·55-0·90] in individuals with above-median MACE benefit versus 1·05 [95% CI 0·84-1·32] for below-median predicted benefit; pinteraction=0·0184). Subgroup analysis based on age (<65 years: HR 0·89 [95% CI 0·71-1·12]; ≥65 years: 0·85 [0·67-1·09]), sex (male: 0·89 [0·72-1·10]; female: 0·85 [0·65-1·10]), established cardiovascular disease (no: 0·89 [0·70-1·14]; yes: 0·84 [0·67-1·06]), or active smoking (no: 0·85 [0·71-1·02]; yes: 1·01 [0·64-1·60]) did not identify groups with heterogeneity of treatment effect. In a simulation analysis of SPRINT, the proposed algorithm detected groups with heterogeneous treatment effects in the presence, but not absence, of simulated subgroup differences. INTERPRETATION: By use of machine learning to define an individual's personalised benefit through phenotypic representations of clinical trials, we created a practical tool for individualising the selection of intensive versus standard systolic blood pressure control in patients without and with type 2 diabetes. FUNDING: National Heart, Lung, and Blood Institute of the US National Institutes of Health.

Identifying quality of life outcome patterns to inform treatment choices in ischemic cardiomyopathy.

BACKGROUND: The Surgical Treatment for Ischemic Heart Failure (STICH) trial found that routine use of coronary artery bypass surgery (CABG) improved mean quality of life (QoL) scores relative to guideline-directed medical therapy (GDMT) in patients with ischemic cardiomyopathy. However, mean differences in QoL scores do not provide what patients want to know when facing a high-risk/high-benefit treatment choice. METHODS: We analyzed Kansas City Cardiomyopathy Questionnaire (KCCQ) Overall Summary scores in CABG and GDMT patients over 36 months using a combination of statistical methods to group QoL data into clinically relevant outcome patterns (phenotype trajectories) and to then identify the main baseline predictors of each phenotype. QoL outcome phenotypes were developed using mixture models to define the dominant phenotype trajectories present in STICH QoL data. Logistic regression models were used to predict each patient's probability of achieving each outcome pattern with each treatment. RESULTS: In STICH, 592 patients underwent CABG and 607 were managed with GDMT. Our analyses identified 3 phenotype trajectory patterns in both treatment groups. Two of the 3 trajectories showed improving patterns, and were classified as "good QoL trajectories," seen in 498 (84.1%) CABG and 449 (73.9%) GDMT patients. Defining a consequential CABG-GDMT treatment difference as a >10% higher absolute predicted probability of belonging to good QoL trajectories, 277 (23.5%) patients were more likely to have good outcome with CABG while 45 (3.8%) patients were more likely to have a good outcome with GDMT. For 644 (54.7%) patients, CABG and GDMT probabilities of a good outcome were within 5% of each other. CONCLUSIONS: The pattern of QoL outcomes after CABG compared with GDMT in STICH followed 3 main phenotypic trajectories, which could be predicted based on individual baseline features. Patient-specific predictions about expected QoL outcomes with different treatment choices provide an intuitive framework for personalizing patient decision making.

Sex-Specific Risk Factors Associated With First Acute Myocardial Infarction in Young Adults.

Importance: An increasing proportion of people in the US hospitalized for acute myocardial infarction (AMI) are younger than 55 years, with the largest increase in young women. Effective prevention requires an understanding of risk factors associated with risk of AMI in young women compared with men. Objectives: To assess the sex-specific associations of demographic, clinical, and psychosocial risk factors with first AMI among adults younger than 55 years, overall, and by AMI subtype. Design, Setting, and Participants: This study used a case-control design with 2264 patients with AMI, aged 18 to 55 years, from the VIRGO (Variation in Recovery: Role of Gender on Outcomes of Young AMI Patients) study and 2264 population-based controls matched for age, sex, and race and ethnicity from the National Health and Nutrition Examination Survey from 2008 to 2012. Data were analyzed from April 2020 to November 2021. Exposures: A wide range of demographic, clinical, and psychosocial risk factors. Main Outcomes and Measures: Odds ratios (ORs) and population attributable fractions (PAF) for first AMI associated with demographic, clinical, and psychosocial risk factors. Results: Of the 4528 case patients and matched controls, 3122 (68.9%) were women, and the median (IQR) age was 48 (44-52) years. Seven risk factors (diabetes [OR, 3.59 (95% CI, 2.72-4.74) in women vs 1.76 (1.19-2.60) in men], depression [OR, 3.09 (95% CI, 2.37-4.04) in women vs 1.77 (1.15-2.73) in men], hypertension [OR, 2.87 (95% CI, 2.31-3.57) in women vs 2.19 (1.65-2.90) in men], current smoking [OR, 3.28 (95% CI, 2.65-4.07) in women vs 3.28 (2.65-4.07) in men], family history of premature myocardial infarction [OR, 1.48 (95% CI, 1.17-1.88) in women vs 2.42 (1.71-3.41) in men], low household income [OR, 1.79 (95% CI, 1.28-2.50) in women vs 1.35 (0.82-2.23) in men], hypercholesterolemia [OR, 1.02 (95% CI, 0.81-1.29) in women vs 2.16 (1.49-3.15) in men]) collectively accounted for the majority of the total risk of AMI in women (83.9%) and men (85.1%). There were significant sex differences in risk factor associations: hypertension, depression, diabetes, current smoking, and family history of diabetes had stronger associations with AMI in young women, whereas hypercholesterolemia had a stronger association in young men. Risk factor profiles varied by AMI subtype, and traditional cardiovascular risk factors had higher prevalence and stronger ORs for type 1 AMI compared with other AMI subtypes. Conclusions and Relevance: In this case-control study, 7 risk factors, many potentially modifiable, accounted for 85% of the risk of first AMI in young women and men. Significant differences in risk factor profiles and risk factor associations existed by sex and by AMI subtype. These findings suggest the need for sex-specific strategies in risk factor modification and prevention of AMI in young adults. Further research is needed to improve risk assessment of AMI subtypes.

Cost-Related Medication Nonadherence in Adults With Diabetes in the United States: The National Health Interview Survey 2013-2018.

OBJECTIVE: Health-related expenditures resulting from diabetes are rising in the U.S. Medication nonadherence is associated with worse health outcomes among adults with diabetes. We sought to examine the extent of reported cost-related medication nonadherence (CRN) in individuals with diabetes in the U.S. RESEARCH DESIGN AND METHODS: We studied adults age ≥18 years with self-reported diabetes from the National Health Interview Survey (NHIS) (2013-2018), a U.S. nationally representative survey. Adults reporting skipping doses, taking less medication, or delaying filling a prescription to save money in the past year were considered to have experienced CRN. The weighted prevalence of CRN was estimated overall and by age subgroups (<65 and ≥65 years). Logistic regression was used to identify sociodemographic characteristics independently associated with CRN. RESULTS: Of the 20,326 NHIS participants with diabetes, 17.6% (weighted 2.3 million) of those age <65 years reported CRN, compared with 6.9% (weighted 0.7 million) among those age ≥65 years. Financial hardship from medical bills, lack of insurance, low income, high comorbidity burden, and female sex were independently associated with CRN across age groups. Lack of insurance, duration of diabetes, current smoking, hypertension, and hypercholesterolemia were associated with higher odds of reporting CRN among the nonelderly but not among the elderly. Among the elderly, insulin use significantly increased the odds of reporting CRN (odds ratio 1.51; 95% CI 1.18, 1.92). CONCLUSIONS: In the U.S., one in six nonelderly and one in 14 elderly adults with diabetes reported CRN. Removing financial barriers to accessing medications may improve medication adherence among these patients, with the potential to improve their outcomes.

Atherosclerotic Cardiovascular Disease, Cancer, and Financial Toxicity Among Adults in the United States.

BACKGROUND: Financial toxicity (FT) is a well-established side-effect of the high costs associated with cancer care. In recent years, studies have suggested that a significant proportion of those with atherosclerotic cardiovascular disease (ASCVD) experience FT and its consequences. OBJECTIVES: This study aimed to compare FT for individuals with neither ASCVD nor cancer, ASCVD only, cancer only, and both ASCVD and cancer. METHODS: From the National Health Interview Survey, we identified adults with self-reported ASCVD and/or cancer between 2013 and 2018, stratifying results by nonelderly (age <65 years) and elderly (age ≥65 years). We defined FT if any of the following were present: any difficulty paying medical bills, high financial distress, cost-related medication nonadherence, food insecurity, and/or foregone/delayed care due to cost. RESULTS: The prevalence of FT was higher among those with ASCVD when compared with cancer (54% vs. 41%; p < 0.001). When studying the individual components of FT, in adjusted analyses, those with ASCVD had higher odds of any difficulty paying medical bills (odds ratio [OR]: 1.22; 95% confidence interval [CI]: 1.09 to 1.36), inability to pay bills (OR: 1.25; 95% CI: 1.04 to 1.50), cost-related medication nonadherence (OR: 1.28; 95% CI: 1.08 to 1.51), food insecurity (OR: 1.39; 95% CI: 1.17 to 1.64), and foregone/delayed care due to cost (OR: 1.17; 95% CI: 1.01 to 1.36). The presence of ≥3 of these factors was significantly higher among those with ASCVD and those with both ASCVD and cancer when compared with those with cancer (23% vs. 30% vs. 13%, respectively; p < 0.001). These results remained similar in the elderly population. CONCLUSIONS: Our study highlights that FT is greater among patients with ASCVD compared with those with cancer, with the highest burden among those with both conditions.

Prevalence of Missing Data in the National Cancer Database and Association With Overall Survival.

Importance: Cancer registries are important real-world data sources consisting of data abstraction from the medical record; however, patients with unknown or missing data are underrepresented in studies that use such data sources. Objective: To assess the prevalence of missing data and its association with overall survival among patients with cancer. Design, Setting, and Participants: In this retrospective cohort study, all variables within the National Cancer Database were reviewed for missing or unknown values for patients with the 3 most common cancers in the US who received diagnoses from January 1, 2006, to December 31, 2015. The prevalence of patient records with missing data and the association with overall survival were assessed. Data analysis was performed from February to August 2020. Exposures: Any missing data field within a patient record among 63 variables of interest from more than 130 total variables in the National Cancer Database. Main Outcomes and Measures: Prevalence of missing data in the medical records of patients with cancer and associated 2-year overall survival. Results: A total of 1 198 749 patients with non-small cell lung cancer (mean [SD] age, 68.5 [10.9] years; 628 811 men [52.5%]), 2 120 775 patients with breast cancer (mean [SD] age, 61.0 [13.3] years; 2 101 758 women [99.1%]), and 1 158 635 patients with prostate cancer (mean [SD] age, 65.2 [9.0] years; 100% men) were included in the analysis. Among those with non-small cell lung cancer, 851 295 patients (71.0%) were missing data for variables of interest; 2-year overall survival was 33.2% for patients with missing data and 51.6% for patients with complete data (P < .001). Among those with breast cancer, 1 161 096 patients (54.7%) were missing data for variables of interest; 2-year overall survival was 93.2% for patients with missing data and 93.9% for patients with complete data (P < .001). Among those with prostate cancer, 460 167 patients (39.7%) were missing data for variables of interest; 2-year overall survival was 91.0% for patients with missing data and 95.6% for patients with complete data (P < .001). Conclusions and Relevance: This study found that within a large cancer registry-based real-world data source, there was a high prevalence of missing data that were unable to be ascertained from the medical record. The prevalence of missing data among patients with cancer was associated with heterogeneous differences in overall survival. Improvements in documentation and data quality are necessary to make optimal use of real-world data for clinical advancements.

Trends in Reoperative Coronary Artery Bypass Graft Surgery for Older Adults in the United States, 1998 to 2017.

Background The likelihood of undergoing reoperative coronary artery bypass graft surgery (CABG) is important for older patients who are considering first-time CABG. Trends in the reoperative CABG for these patients are unknown. Methods and Results We used the Medicare fee-for-service inpatient claims data of adults undergoing isolated first-time CABG between 1998 and 2017. The primary outcome was time to first reoperative CABG within 5 years of discharge from the index surgery, treating death as a competing risk. We fitted a Cox regression to model the likelihood of reoperative CABG as a function of patient baseline characteristics. There were 1 666 875 unique patients undergoing first-time isolated CABG and surviving to hospital discharge. The median (interquartile range) age of patients did not change significantly over time (from 74 [69-78] in 1998 to 73 [69-78] in 2017); the proportion of women decreased from 34.8% to 26.1%. The 5-year rate of reoperative CABG declined from 0.77% (95% CI, 0.72%-0.82%) in 1998 to 0.23% (95% CI, 0.19%-0.28%) in 2013. The annual proportional decline in the 5-year rate of reoperative CABG overall was 6.6% (95% CI, 6.0%-7.1%) nationwide, which did not differ across subgroups, except the non-white non-black race group that had an annual decline of 8.5% (95% CI, 6.2%-10.7%). Conclusions Over a recent 20-year period, the Medicare fee-for-service patients experienced a significant decline in the rate of reoperative CABG. In this cohort of older adults, the rate of declining differed across demographic subgroups.

The Upcoming Epidemic of Heart Failure in South Asia.

Currently, South Asia accounts for a quarter of the world population, yet it already claims ≈60% of the global burden of heart disease. Besides the epidemics of type 2 diabetes mellitus and coronary heart disease already faced by South Asian countries, recent studies suggest that South Asians may also be at an increased risk of heart failure (HF), and that it presents at earlier ages than in most other racial/ethnic groups. Although a frequently underrecognized threat, an eventual HF epidemic in the densely populated South Asian nations could have dramatic health, social and economic consequences, and urgent interventions are needed to flatten the curve of HF in South Asia. In this review, we discuss recent studies portraying these trends, and describe the mechanisms that may explain an increased risk of premature HF in South Asians compared with other groups, with a special focus on highly relevant features in South Asian populations including premature coronary heart disease, early type 2 diabetes mellitus, ubiquitous abdominal obesity, exposure to the world's highest levels of air pollution, highly prevalent pretransition forms of HF such as rheumatic heart disease, and underdevelopment of healthcare systems. Other rising lifestyle-related risk factors such as use of tobacco products, hypertension, and general obesity are also discussed. We evaluate the prognosis of HF in South Asian countries and the implications of an anticipated HF epidemic. Finally, we discuss proposed interventions aimed at curbing these adverse trends, management approaches that can improve the prognosis of prevalent HF in South Asian countries, and research gaps in this important field.

Cost-minimization study of the percutaneous approach to endovascular aortic aneurysm repair.

OBJECTIVE: Percutaneous access for endovascular aortic aneurysm repair (P-EVAR) is less invasive compared with surgical access for endovascular aortic aneurysm repair (S-EVAR). P-EVAR has been associated with shorter recovery and fewer wound complications. However, vascular closure devices (VCDs) are costly, and the economic effects of P-EVAR have important implications for resource allocation. The objective of our study was to estimate the differences in the costs between P-EVAR and S-EVAR. METHODS: We used a decision tree to analyze the costs from a payer perspective throughout the course of the index hospitalization. The probabilities, relative risks, and mean difference summary measures were obtained from a systematic review and meta-analysis. We modelled differences in surgical site infection, lymphocele, and the length of hospitalization. Cost parameters were derived from the 2014 National Inpatient Sample using "International Classification of Diseases, 9th Revision, Clinical Modification" codes. Attributable costs were estimated using generalized linear models adjusted by age, sex, and comorbidities. A sensitivity analysis was performed to determine the robustness of the results. RESULTS: A total of 6876 abdominal and thoracic EVARs were identified. P-EVAR resulted in a mean cost savings of $751 per procedure. The mean costs for P-EVAR were $1287 (95% confidence interval [CI], $884-$1835) and for S-EVAR were $2038 (95% CI, $757-$4280). P-EVAR procedures were converted to open procedures in 4.3% of the cases. The P-EVAR patients had a difference of -1.4 days (95% CI, -0.12 to -2.68) in the length of hospitalization at a cost of $1190/d (standard error, $298). The cost savings of P-EVAR was primarily driven by the cost differences in the length of hospitalization. In the base case, four VCDs were used per P-EVAR at $200/device. In the two-way sensitivity analysis, P-EVAR resulted in cost savings, even when 1.5 times more VCDs had been used per procedure and the cost of each VCD was 1.5 times greater. In our probabilistic sensitivity analysis, P-EVAR was the cost savings strategy for 82.6% of 10,000 Monte Carlo simulations when simultaneously varying parameters across their uncertainty ranges. CONCLUSIONS: P-EVAR had lower costs compared with S-EVAR and could result in dramatic cost savings if extrapolated to the number of aortic aneurysms repaired. Our analysis was a conservative estimate that did not account for the improved quality of life after P-EVAR.

Managing Implantable Cardioverter-Defibrillators at End-of-Life: Practical Challenges and Care Considerations.

Implantable cardioverter-defibrillators (ICDs) monitor for and terminate malignant arrhythmias. Given their potential as a life-saving therapy, an increasing number of people receive an ICD every year, and a growing number are currently living with ICDs. However, cardiopulmonary arrest serves as the final common pathway of natural death, and the appropriate management of an ICD near the end-of-life is crucial to ensure that a patient's death is not marked by further suffering due to ICD shocks. The tenets of palliative care at the end-of-life include addressing any medical intervention that may preclude dying with dignity; thus, management of ICDs during this phase is necessary. Internists are at the forefront of discussions about end-of-life care, and are likely to find discussions about ICD care at the end-of-life particularly challenging. The present review addresses issues pertaining to ICDs near the end of a patient's life and their potential impact on dying patients and their families. A multidisciplinary, patient-centered approach can ensure that patients receive the maximum benefit from ICDs, without any unintended pain or suffering.

Effect of Neutropenic Diet on Infection Rates in Cancer Patients With Neutropenia: A Meta-analysis of Randomized Controlled Trials.

INTRODUCTION: Neutropenic diets are commonly prescribed to cancer patients with neutropenia with the intention of reducing rates of infection. These diets are restrictive and are associated with lower patient satisfaction and possibly malnutrition. Further, it is unclear if these restrictive diets are effective in reducing infection. We performed a meta-analysis on the rates of infection reported in trials comparing the neutropenic diet to unrestricted diets in cancer patients with neutropenia. METHODS AND MATERIALS: A comprehensive database search for all published randomized controlled trials comparing infection rates in cancer patients receiving a neutropenic diet versus an unrestricted diet was performed for all publications in English language from database's inception until September 12, 2017. The search strategy, study selection, and subsequent analysis adhered to PRISMA guidelines. Random effects modeling was used to obtain pooled relative risks. The primary outcome measure was the rate of infection. RESULTS: Five randomized controlled trials with a total of 388 patients were included in the final analysis. Patients mostly had acute myeloid leukemia (AML), acute lymphoblastic leukemia (ALL), or sarcoma. Infection was noted in 53.7% patients in the neutropenic diet group, as compared with 50% in the unrestricted diet group. No significant difference in infection rate was observed between the neutropenic diet versus unrestricted diet groups, pooled risk ratio (RR) 1.13 (95% CI, 0.98-1.30; P=0.10). CONCLUSIONS: This meta-analysis of randomized controlled trials suggests that the use of neutropenic diet was not associated with decreased risk of infection in neutropenic cancer patients. The continued use of neutropenic diets should be questioned.