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Background The digital transformation of medical data enables health systems to leverage real‐world data from electronic health records to gain actionable insights for improving hypertension care. Methods and Results We performed a serial cross‐sectional analysis of outpatients of a large regional health system from 2010 to 2021. Hypertension was defined by systolic blood pressure ≥140 mm Hg, diastolic blood pressure ≥90 mm Hg, or recorded treatment with antihypertension medications. We evaluated 4 methods of using blood pressure measurements in the electronic health record to define hypertension. The primary outcomes were age‐adjusted prevalence rates and age‐adjusted control rates. Hypertension prevalence varied depending on the definition used, ranging from 36.5% to 50.9% initially and increasing over time by ≈5%, regardless of the definition used. Control rates ranged from 61.2% to 71.3% initially, increased during 2018 to 2019, and decreased during 2020 to 2021. The proportion of patients with a hypertension diagnosis ranged from 45.5% to 60.2% initially and improved during the study period. Non‐Hispanic Black patients represented 25% of our regional population and consistently had higher prevalence rates, higher mean systolic and diastolic blood pressure, and lower control rates compared with other racial and ethnic groups. Conclusions In a large regional health system, we leveraged the electronic health record to provide real‐world insights. The findings largely reflected national trends but showed distinctive regional demographics and findings, with prevalence increasing, one‐quarter of the patients not controlled, and marked disparities. This approach could be emulated by regional health systems seeking to improve hypertension care.

Abstract Background Symptomatic COVID-19 and Long COVID, also referred to as post-acute sequelae of SARS-CoV-2 (PASC) or post-COVID conditions, have been widely reported in young, healthy people, but their prevalence has not yet been determined in student athletes. We sought to estimate the prevalence of reported COVID-19, symptomatic COVID-19, and Long COVID in college athletes in the United States attending 18 schools from spring 2020 to fall 2021. Methods We developed an online survey to measure the prevalence of student athletes who tested positive for COVID-19, developed Long COVID, and did not return to their sport during the relevant time period. We surveyed a convenience sample of 18 collegiate school administrators, representing about 7,000 student athletes. Of those schools surveyed, 16 responded regarding the spring 2020 semester, and 18 responded regarding the full academic year of fall 2020 to spring 2021 (both semesters). Results According to the survey responses, there were 9.8% of student athletes who tested positive for COVID-19 in spring 2020 and 25.4% who tested positive in the academic year of fall 2020 to spring 2021. About 4% of student athletes who tested positive from spring 2020 to spring 2021 developed Long COVID, defined as new, recurring, or ongoing physical or mental health consequences occurring 4 or more weeks after SARS-CoV-2 infection. Conclusions This study highlights that Long COVID occurs among young, healthy athletes and is a real consequence of COVID-19. Understanding the prevalence of Long COVID in this population requires longer follow-up and further study.

Abstract Randomized clinical trials (RCT) represent the cornerstone of evidence-based medicine but are resource-intensive. We propose and evaluate a machine learning (ML) strategy of adaptive predictive enrichment through computational trial phenomaps to optimize RCT enrollment. In simulated group sequential analyses of two large cardiovascular outcomes RCTs of (1) a therapeutic drug (pioglitazone versus placebo; Insulin Resistance Intervention after Stroke (IRIS) trial), and (2) a disease management strategy (intensive versus standard systolic blood pressure reduction in the Systolic Blood Pressure Intervention Trial (SPRINT)), we constructed dynamic phenotypic representations to infer response profiles during interim analyses and examined their association with study outcomes. Across three interim timepoints, our strategy learned dynamic phenotypic signatures predictive of individualized cardiovascular benefit. By conditioning a prospective candidate’s probability of enrollment on their predicted benefit, we estimate that our approach would have enabled a reduction in the final trial size across ten simulations (IRIS: −14.8% ± 3.1%, p one-sample t-test = 0.001; SPRINT: −17.6% ± 3.6%, p one-sample t-test

Objectives: We assessed short-, medium-, and long-term all-cause mortality risks after a primary SARS-CoV-2 infection. Methods: A national, matched, retrospective cohort study was conducted in Qatar to assess risk of all-cause mortality in the national SARS-CoV-2 primary infection cohort compared with the national infection-naïve cohort. Associations were estimated using Cox proportional-hazards regression models. Analyses were stratified by vaccination status and clinical vulnerability status. Results: Among unvaccinated persons, within 90 days after primary infection, the adjusted hazard ratio (aHR) comparing mortality incidence in the primary-infection cohort with the infection-naïve cohort was 1.19 (95% confidence interval 1.02-1.39). aHR was 1.34 (1.11-1.63) in persons more clinically vulnerable to severe COVID-19 and 0.94 (0.72-1.24) in those less clinically vulnerable. Beyond 90 days after primary infection, aHR was 0.50 (0.37-0.68); aHR was 0.41 (0.28-0.58) at 3-7 months and 0.76 (0.46-1.26) at ≥8 months. The aHR was 0.37 (0.25-0.54) in more clinically vulnerable persons and 0.77 (0.48-1.24) in less clinically vulnerable persons. Among vaccinated persons, mortality incidence was comparable in the primary-infection versus infection-naïve cohorts, regardless of clinical vulnerability status. Conclusions: COVID-19 mortality was primarily driven by an accelerated onset of death among individuals who were already vulnerable to all-cause mortality, but vaccination prevented these accelerated deaths.

Abstract The impact and effectiveness of clinical trial data sharing initiatives may differ depending on the data sharing model used. We characterized outcomes associated with models previously used by the U.S. National Institutes of Health (NIH): National Heart, Lung, and Blood Institute’s (NHLBI) centralized model and National Cancer Institute’s (NCI) decentralized model. We identified trials completed in 2010–2013 that met NIH data sharing criteria and matched studies based on cost and/or size, determining whether trial data were shared, and for those that were, the frequency of secondary internal publications (authored by at least one author from the original research team) and shared data publications (authored by a team external to the original research team). We matched 77 NHLBI-funded trials to 77 NCI-funded trials; among these, 20 NHLBI-sponsored trials (26%) and 4 NCI-sponsored trials (5%) shared data (OR 6.4, 95% CI: 2.1, 19.8). From the 4 NCI-sponsored trials sharing data, we identified 65 secondary internal and 2 shared data publications. From the 20 NHLBI-sponsored trials sharing data, we identified 188 secondary internal and 53 shared data publications. The NHLBI’s centralized data sharing model was associated with more trials sharing data and more shared data publications when compared with the NCI’s decentralized model.

Abstract Artificial intelligence (AI) can detect left ventricular systolic dysfunction (LVSD) from electrocardiograms (ECGs). Wearable devices could allow for broad AI-based screening but frequently obtain noisy ECGs. We report a novel strategy that automates the detection of hidden cardiovascular diseases, such as LVSD, adapted for noisy single-lead ECGs obtained on wearable and portable devices. We use 385,601 ECGs for development of a standard and noise-adapted model. For the noise-adapted model, ECGs are augmented during training with random gaussian noise within four distinct frequency ranges, each emulating real-world noise sources. Both models perform comparably on standard ECGs with an AUROC of 0.90. The noise-adapted model performs significantly better on the same test set augmented with four distinct real-world noise recordings at multiple signal-to-noise ratios (SNRs), including noise isolated from a portable device ECG. The standard and noise-adapted models have an AUROC of 0.72 and 0.87, respectively, when evaluated on ECGs augmented with portable ECG device noise at an SNR of 0.5. This approach represents a novel strategy for the development of wearable-adapted tools from clinical ECG repositories.

Abstract The management and follow-up of moderate aortic stenosis (AS) lacks consensus as the progression patterns are not well understood. This study aimed to identify the hemodynamic progression of AS, and associated risk factors and outcomes. We included patients with moderate AS with at least three transthoracic echocardiography (TTE) studies performed between 2010 and 2021. Latent class trajectory modeling was used to classify AS groups with distinctive hemodynamic trajectories, which were determined by serial systolic mean pressure gradient (MPG) measurements. Outcomes were defined as all-cause mortality and aortic valve replacement (AVR). A total of 686 patients with 3093 TTE studies were included in the analysis. Latent class model identified two distinct AS trajectory groups based on their MPG: a slow progression group (44.6%) and a rapid progression group (55.4%). Initial MPG was significantly higher in the rapid progression group (28.2 ± 5.6 mmHg vs. 22.9 ± 2.8 mmHg, P

Background: Studies show that digoxin use is declining but is still prevalent. Recent data on digoxin prescription and characteristics of digoxin prescribers are unknown, which can help understand its contemporary use. Methods: Using Medicare Part D data from 2013 to 2019, we studied the change in number and proportion of digoxin prescriptions and digoxin prescribers, overall and by specialty. Using logistic regression, we identified prescriber characteristics associated with digoxin prescription. Results: From 2013 to 2019, total digoxin prescriptions (4.6 to 1.8 million) and proportion of digoxin prescribers decreased (9.1% to 4.3% overall; 26.6% to 11.8% among General Medicine prescribers and 65.4% to 48.9% among Cardiology). Of digoxin prescribers from 2013 practicing in 2019 (91.2% remained active), 59.1% did not prescribe digoxin at all, 31.7% reduced, and 9.2% maintained or increased prescriptions. The proportion of all digoxin prescriptions that were prescribed by General Medicine prescribers declined from 59.7% to 48.2% and increased for Cardiology (29% to 38.5%). Among new prescribers in 2019 (N = 85,508), only 1.9% prescribed digoxin. Digoxin prescribers when compared to non–digoxin prescribers were more likely male, graduated from medical school earlier, were located in the Midwest or South, and belonged to Cardiology (all P < .001). Conclusions: Digoxin prescriptions continue to decline with over half of 2013 prescribers no longer prescribing digoxin in 2019. This may be a result of the increasing availability of newer heart failure therapies. The decline in digoxin prescription was greater among general medicine physicians than cardiologists, suggesting a change in digoxin use to a medication prescribed increasingly by specialists.

Background Though associations between obstructive sleep apnea (OSA) and cardiovascular outcomes are well described, limited data exist regarding the impact of OSA on sex‐specific outcomes after acute myocardial infarction (AMI). Methods and Results The VIRGO (Variation in Recovery: Role of Gender on Outcomes of Young AMI Patients) study enrolled 3572 adults aged 18 to 55 years with AMI from the United States and Spain during 2008 to 2012. We included patients for whom the Berlin Questionnaire for OSA was scored at the time of AMI admission (3141; 2105 women, 1036 men). We examined the sex‐specific association between baseline OSA risk with functional outcomes including health status and depressive symptoms at 1 and 12 months after AMI. Among both groups, 49% of patients were at high risk for OSA (1040 women; 509 men), but only 4.7% (148) of patients had a diagnosed history of OSA. Though patients with a high OSA risk reported worse physical and mental health status and depression than low‐risk patients in both sexes, the difference in these functional outcomes was wider in women than men. Moreover, women with a high OSA risk had worse health status, depression, and quality of life than high‐risk men, both at baseline and at 1 and 12 months after AMI. Conclusions Young women with a high OSA risk have poorer health status and more depressive symptoms than men at the time of AMI, which may place them at higher risk of poorer health outcomes over the year following the AMI. Further, the majority of patients at high risk of OSA are undiagnosed at the time of presentation of AMI.

Background Stress experienced in a marriage or committed relationship may be associated with worse patient‐reported outcomes after acute myocardial infarction (AMI), but little is known about this association in young adults (≤55 years) with AMI. Methods and Results We used data from VIRGO (Variation in Recovery: Role of Gender on Outcomes of Young AMI Patients), an observational cohort study that enrolled individuals aged 18 to 55 years with AMI (2008–2012). Marital stress was self‐reported 1 month after AMI using the Stockholm Marital Stress Scale (categorized as absent/mild, moderate, and severe). Outcomes were physical/mental health (Short Form‐12), generic health status (EuroQol‐5 Dimensions), cardiac‐specific quality of life and angina (Seattle Angina Questionnaire), depressive symptoms (Patient Health Questionnaire‐9), and all‐cause readmission 1 year after AMI. Regression models were sequentially adjusted for baseline health, demographics (sex, age, race or ethnicity), and socioeconomic factors (education, income, employment, and insurance). Sex and marital stress interaction was also tested. Among 1593 married/partnered participants, 576 (36.2%) reported severe marital stress, which was more common in female than male participants (39.4% versus 30.4%, P=0.001). Severe marital stress was significantly associated with worse mental health (beta=−2.13, SE=0.75, P=0.004), generic health status (beta=−3.87, SE=1.46, P=0.008), cardiac‐specific quality of life (beta=−6.41, SE=1.65, P0.05). Conclusions The association between marital stress and worse 1‐year health outcomes was statistically significant in young patients with AMI, suggesting a need for routine screening and the creation of interventions to support patients with stress recovering from an AMI.

Background Gender‐related factors are psycho‐socio‐cultural characteristics and are associated with adverse clinical outcomes in acute myocardial infarction, independent of sex. Whether sex‐ and gender‐related factors contribute to the substantial heterogeneity in hospital length of stay (LOS) among patients with non–ST‐segment–elevation myocardial infarction remains unknown. Methods and Results This observational cohort study combined and analyzed data from the GENESIS‐PRAXY (Gender and Sex Determinants of Cardiovascular Disease: From Bench to Beyond Premature Acute Coronary Syndrome study), EVA (Endocrine Vascular Disease Approach study), and VIRGO (Variation in Recovery: Role of Gender on Outcomes of Young AMI [Acute Myocardial Infarction] Patients study) cohorts of adults hospitalized across Canada, the United States, Switzerland, Italy, Spain, and Australia for non–ST‐segment–elevation myocardial infarction. In total, 5219 participants were assessed for eligibility. Sixty‐three patients were excluded for missing LOS, and 2938 were excluded because of no non–ST‐segment–elevation myocardial infarction diagnosis. In total, 2218 participants were analyzed (66% women; mean±SD age, 48.5±7.9 years; 67.8% in the United States). Individuals with longer LOS (51%) were more likely to be White race, were more likely to have diabetes, hypertension, and a lower income, and were less likely to be employed and have completed secondary education. No univariate association between sex and LOS was observed. In the adjusted multivariable model, age (0.62 d/10 y; P

Abstract Understanding postoperative recovery is critical for guiding efforts to improve post-acute phase care. How recovery evolves during the first 30 days after cardiac surgery is not well-understood. A digital platform may enable granular quantification of recovery by frequently capturing patient-reported outcome measures (PROM) that can be clinically implemented to support recovery. We conduct a prospective cohort study using a digital platform to measure recovery after cardiac surgery using a PROM sent every 3 days for 30 days after surgery to characterize recovery in multiple domains (e.g., pain, sleep, activities of daily living, anxiety) and to identify factors related to the patient’s perception of overall recovery. We enroll patients who underwent cardiac surgery at a tertiary center between January 2019 and March 2020 and automatically deliver PROMs and reminders electronically. Of the 10 surveys delivered per patient, 8 (IQR 6–10) are completed. Patients who experienced postoperative complications more commonly belong to the worst overall recovery trajectory. Of the 12 domains modeled, only the worst anxiety trajectory is associated with the worse overall recovery trajectory membership, suggesting that even when patients struggle in the recovery of other domains, the patient may still feel progress in their recovery. We demonstrate that using a digital platform, automated PROM data collection, and characterization of multi-domain recovery trajectories is feasible and likely implementable in clinical practice. Overall recovery may be impacted by complications, while slow progress in constituent domains may still allow for the perception of overall recovery progression.

Abstract The strategies of academic medical centers arise from core values and missions that aim to provide unmatched clinical care, patient experience, research, education, and training. These missions drive nearly all activities. They should also drive digital health activities – and particularly now given the rapid adoption of digital health, marking one of the great transformations of healthcare; increasing pressures on health systems to provide more cost-effective care; the pandemic-accelerated funding and rise of well-funded new entrants and technology giants that provide more convenient forms of care; and a more favorable regulatory and reimbursement landscape to incorporate digital health approaches. As academic medical centers emerge from a pandemic-related reactionary digital health posture, where pressures to adopt more digital health technologies mount, a broad digital health realignment that leverages the strengths of such centers is required to accomplish their missions.

Abstract Background Clinical decision support systems (CDSS) are low-cost, scalable tools with the potential to improve guideline-based antihypertensive treatment in primary care, but their effectiveness needs to be tested, especially in low- and middle-income countries such as China. Methods The Learning Implementation of Guideline-based decision support system for Hypertension Treatment (LIGHT) trial is a pragmatic, four-stage, cluster-randomized trial conducted in 94 primary care sites in China. For each city-based stage, sites are randomly assigned to either implementation of the CDSS for hypertension management (which guides doctors’ treatment recommendations based on measured blood pressure and patient characteristics), or usual care. Patients are enrolled during the first 3 months after site randomization and followed for 9 months. The primary outcome is the proportion of hypertension management visits at which guideline-based treatment is provided. In a nested trial conducted within the CDSS, with the patient as the unit of randomization, the LIGHT-ACD trial, patients are randomized to receive different initial mono- or dual-antihypertensive therapy. The primary outcome of the LIGHT-ACD trial is the changes in blood pressure. Discussion The LIGHT trial will provide evidence on the effectiveness of a CDSS for improving guideline adherence for hypertension management in primary care in China. The nested trial, the LIGHT-ACD trial, will provide data on the effect of different initial antihypertensive regimens for blood pressure management in this setting. Trial registration ClinicalTrials.gov, identifier: LIGHT (NCT03636334) and LIGHT-ACD (NCT03587103). Registered on 3 July 2018.

Abstract Diagnosis codes are used to study SARS-CoV2 infections and COVID-19 hospitalizations in administrative and electronic health record (EHR) data. Using EHR data (April 2020–March 2021) at the Yale-New Haven Health System and the three hospital systems of the Mayo Clinic, computable phenotype definitions based on ICD-10 diagnosis of COVID-19 (U07.1) were evaluated against positive SARS-CoV-2 PCR or antigen tests. We included 69,423 patients at Yale and 75,748 at Mayo Clinic with either a diagnosis code or a positive SARS-CoV-2 test. The precision and recall of a COVID-19 diagnosis for a positive test were 68.8% and 83.3%, respectively, at Yale, with higher precision (95%) and lower recall (63.5%) at Mayo Clinic, varying between 59.2% in Rochester to 97.3% in Arizona. For hospitalizations with a principal COVID-19 diagnosis, 94.8% at Yale and 80.5% at Mayo Clinic had an associated positive laboratory test, with secondary diagnosis of COVID-19 identifying additional patients. These patients had a twofold higher inhospital mortality than based on principal diagnosis. Standardization of coding practices is needed before the use of diagnosis codes in clinical research and epidemiological surveillance of COVID-19.

The application of artificial intelligence for automated diagnosis of electrocardiograms can improve care in remote settings but is limited by the reliance on infrequently available signal-based data. Here, the authors report the development of a multi-label automated diagnosis model for electrocardiographic images.

Abstract Background People who have been incarcerated have high rates of cardiovascular risk factors, such as hypertension and smoking, and cardiovascular disease (CVD) is a leading cause of hospitalizations and mortality in this population. Despite this, little is known regarding what pathways mediate the association between incarceration exposure and increased rates of CVD morbidity and especially what incarceration specific factors are associated with this risk. The objective of this study is to better understand CVD risk in people exposed to incarceration and the pathways by which accumulate cardiovascular risk over time. Methods and Analysis The Justice-Involved Individuals Cardiovascular Disease Epidemiology (JUSTICE) study is a prospective cohort study of individuals released from incarceration with known cardiovascular risk factors. We are recruiting 500 individuals within three months after release from jail/prison. At baseline we are assessing traditional risk factors for CVD, including diet, exercise, and smoking, and exposure to incarceration-related policies, psychosocial stress, and self-efficacy. Cardiovascular risk factors are measured at baseline through point of care testing. We are following these individuals for the 12 months following the index release from incarceration with re-evaluation of psychosocial factors and clinical risk factors every 6 months. Using these data, we will estimate the direct and indirect latent effects of incarceration on cardiovascular risk factors and the paths via which these effects are mediated. We will also model the anticipated 10-year burden of CVD incidence, health care use, and mortality associated with incarceration. Discussion Our study will identify factors associated with CVD risk factor control among people released from incarceration. Our measurement of incarceration-related exposures, psychosocial factors, and clinical measures of cardiovascular risk will allow for identification of unique targets for intervention to modify CVD risk in this vulnerable population.

Background Although aldosterone antagonists improve outcomes in select individuals with heart failure and reduced ejection fraction, studies in the United States have raised concerns about underuse and overuse. Variations in the prescription of aldosterone antagonist in China are unknown. Methods and Results In the multicenter, hospital‐based, retrospective China PEACE (China Patient‐Centered Evaluative Assessment of Cardiac Events) study, we identified a nationally representative cohort of admissions for heart failure in a nationally representative sample of Chinese hospitals in 2015. Patients were classified into 1 of 3 groups according to their eligibility for spironolactone—“ideal” (left ventricular ejection fraction

Background Semaglutide holds the promise for weight loss and risk reduction. Less is known about racial and ethnic disparities in financial barriers among the semaglutide‐eligible population. Methods and Results We conducted a cross‐sectional analysis of adults aged 18 years or older using data from the National Health and Nutrition Examination Survey 2015 to 2020. We analyzed adults eligible for semaglutide based on Food and Drug Administration labeling and assessed financial barriers and social determinants of health among the eligible population overall and by race and ethnicity. A total of 13 711 adults were included in the final analysis. In 2015 to 2020, 51.1% (48.3%–53.2%) of US adults (≈112.5 million) met the Food and Drug Administration eligibility criteria for semaglutide. The percentage of adults eligible for semaglutide was highest among Black adults (56.6% [54.2%–59.1%]), followed by Hispanic adults (55.0% [52.8%–57.3%]). Among adults eligible for semaglutide, 11.9% (10.1%–13.6%) were uninsured, 13.3% (12.1%–14.5%) lacked a usual source of care, 33.6% (30.2%–36.9%) had low family income, and 38.9% (36.5%–41.3%) lacked higher education. Compared with White individuals, significantly larger proportions of Black and Hispanic individuals were uninsured, lacked a usual source of care, had low family income, or lacked higher education (P

Background This study aimed to compare percutaneous left atrial appendage occlusion (LAAO) with non‐vitamin K antagonist oral anticoagulants among patients with atrial fibrillation. Methods and Results Using a US administrative database, 562 850 patients with atrial fibrillation were identified, among whom 8397 were treated with LAAO and 554 453 were treated with non‐vitamin K antagonist oral anticoagulants between March 13, 2015 and December 31, 2018. Propensity score overlap weighting was used to balance baseline characteristics. The primary outcome was a composite end point of ischemic stroke or systemic embolism, major bleeding, and all‐cause mortality. The mean age was 76.4±7.6 years; 280 097 (49.8%) were female. Mean follow‐up was 1.5±1.0 years. LAAO was associated with no significant difference in the risk of the primary composite end point (hazard ratio [HR], 0.93 [0.84–1.03]), or the secondary outcomes including ischemic stroke/systemic embolism (HR, 1.07 [0.81–1.41]), and intracranial bleeding (HR, 1.08 [0.72–1.61]). LAAO was associated with a higher risk of major bleeding (HR, 1.22 [1.05–1.42], P=0.01) and a lower risk of mortality (HR, 0.73 [0.64–0.84], P

Abstract Background The electronic health record (EHR) holds the prospect of providing more complete and timely access to clinical information for biomedical research, quality assessments, and quality improvement compared to other data sources, such as administrative claims. In this study, we sought to assess the completeness and timeliness of structured diagnoses in the EHR compared to computed diagnoses for hypertension (HTN), hyperlipidemia (HLD), and diabetes mellitus (DM). Methods We determined the amount of time for a structured diagnosis to be recorded in the EHR from when an equivalent diagnosis could be computed from other structured data elements, such as vital signs and laboratory results. We used EHR data for encounters from January 1, 2012 through February 10, 2019 from an academic health system. Diagnoses for HTN, HLD, and DM were computed for patients with at least two observations above threshold separated by at least 30 days, where the thresholds were outpatient blood pressure of ≥ 140/90 mmHg, any low-density lipoprotein ≥ 130 mg/dl, or any hemoglobin A1c ≥ 6.5%, respectively. The primary measure was the length of time between the computed diagnosis and the time at which a structured diagnosis could be identified within the EHR history or problem list. Results We found that 39.8% of those with HTN, 21.6% with HLD, and 5.2% with DM did not receive a corresponding structured diagnosis recorded in the EHR. For those who received a structured diagnosis, a mean of 389, 198, and 166 days elapsed before the patient had the corresponding diagnosis of HTN, HLD, or DM, respectively, recorded in the EHR. Conclusions We found a marked temporal delay between when a diagnosis can be computed or inferred and when an equivalent structured diagnosis is recorded within the EHR. These findings demonstrate the continued need for additional study of the EHR to avoid bias when using observational data and reinforce the need for computational approaches to identify clinical phenotypes.

Statins, which have anti-inflammatory and antithrombotic properties, could have effects in COVID-19 patients. Here, the authors find in a retrospective analysis of patients hospitalized with COVID-19 that antecedent statin use is associated with lower inpatient mortality.

Deep learning (DL) models have demonstrated state-of-the-art performance in the classification of diagnostic imaging in oncology. However, DL models for medical images can be compromised by adversarial images, where pixel values of input images are manipulated to deceive the DL model. To address this limitation, our study investigates the detectability of adversarial images in oncology using multiple detection schemes. Experiments were conducted on thoracic computed tomography (CT) scans, mammography, and brain magnetic resonance imaging (MRI). For each dataset we trained a convolutional neural network to classify the presence or absence of malignancy. We trained five DL and machine learning (ML)-based detection models and tested their performance in detecting adversarial images. Adversarial images generated using projected gradient descent (PGD) with a perturbation size of 0.004 were detected by the ResNet detection model with an accuracy of 100% for CT, 100% for mammogram, and 90.0% for MRI. Overall, adversarial images were detected with high accuracy in settings where adversarial perturbation was above set thresholds. Adversarial detection should be considered alongside adversarial training as a defense technique to protect DL models for cancer imaging classification from the threat of adversarial images.

Deep-learning methods for auto-segmenting brain images either segment one slice of the image (2D), five consecutive slices of the image (2.5D), or an entire volume of the image (3D). Whether one approach is superior for auto-segmenting brain images is not known. We compared these three approaches (3D, 2.5D, and 2D) across three auto-segmentation models (capsule networks, UNets, and nnUNets) to segment brain structures. We used 3430 brain MRIs, acquired in a multi-institutional study, to train and test our models. We used the following performance metrics: segmentation accuracy, performance with limited training data, required computational memory, and computational speed during training and deployment. The 3D, 2.5D, and 2D approaches respectively gave the highest to lowest Dice scores across all models. 3D models maintained higher Dice scores when the training set size was decreased from 3199 MRIs down to 60 MRIs. 3D models converged 20% to 40% faster during training and were 30% to 50% faster during deployment. However, 3D models require 20 times more computational memory compared to 2.5D or 2D models. This study showed that 3D models are more accurate, maintain better performance with limited training data, and are faster to train and deploy. However, 3D models require more computational memory compared to 2.5D or 2D models.

Background Complications are a measure of procedural quality, yet variation in complication rates following catheter ablation of atrial fibrillation (AF) among hospitals has not been systematically examined. We examined institutional variation in the risk‐standardized 30‐day complication rates (RSCRs) following AF ablation which may suggest variation in care quality. Methods and Results This cohort study included all patients >18 years old undergoing AF ablations from 2012 to 2017 in Australia and New Zealand. The primary outcome was procedure‐related complications occurring during the hospital stay and within 30 days of hospital discharge. We estimated the hospital‐specific risk‐standardized complication rates using a hierarchical generalized linear model. A total of 25 237 patients (mean age, 62.5±11.4 years; 30.2% women; median length of stay 1 day [interquartile range, 1–2 days]) were included. Overall, a complication occurred in 1400 (5.55%) patients (4.34% in hospital, 1.46% following discharge, and 0.25% experienced both). Bleeding (3.31%), pericardial effusion (0.74%), and infection (0.44%) were the most common complications while stroke/transient ischemic attack (0.24%), cardiorespiratory failure and shock (0.19%), and death (0.08%) occurred less frequently. Among 46 hospitals that performed ≥25 ablations during the study period, the crude complication rate varied from 0.00% to 21.43% (median, 5.74%). After adjustment for differences in patient and procedural characteristics, the median risk‐standardized complication rate was 5.50% (range, 2.89%–10.31%), with 10 hospitals being significantly different from the national average. Conclusions Procedure‐related complications occur in 5.55% of patients undergoing AF ablations, although the risk of complications varies 3‐fold among hospitals, which suggests potential disparities in care quality and the need for efforts to standardize AF ablation practices among hospitals.

Background Reports on medium and long-term sequelae of SARS-CoV-2 infections largely lack quantification of incidence and relative risk. We describe the rationale and methods of the Innovative Support for Patients with SARS-CoV-2 Registry (INSPIRE) that combines patient-reported outcomes with data from digital health records to understand predictors and impacts of SARS-CoV-2 infection. Methods INSPIRE is a prospective, multicenter, longitudinal study of individuals with symptoms of SARS-CoV-2 infection in eight regions across the US. Adults are eligible for enrollment if they are fluent in English or Spanish, reported symptoms suggestive of acute SARS-CoV-2 infection, and if they are within 42 days of having a SARS-CoV-2 viral test (i.e., nucleic acid amplification test or antigen test), regardless of test results. Recruitment occurs in-person, by phone or email, and through online advertisement. A secure online platform is used to facilitate the collation of consent-related materials, digital health records, and responses to self-administered surveys. Participants are followed for up to 18 months, with patient-reported outcomes collected every three months via survey and linked to concurrent digital health data; follow-up includes no in-person involvement. Our planned enrollment is 4,800 participants, including 2,400 SARS-CoV-2 positive and 2,400 SARS-CoV-2 negative participants (as a concurrent comparison group). These data will allow assessment of longitudinal outcomes from SARS-CoV-2 infection and comparison of the relative risk of outcomes in individuals with and without infection. Patient-reported outcomes include self-reported health function and status, as well as clinical outcomes including health system encounters and new diagnoses. Results Participating sites obtained institutional review board approval. Enrollment and follow-up are ongoing. Conclusions This study will characterize medium and long-term sequelae of SARS-CoV-2 infection among a diverse population, predictors of sequelae, and their relative risk compared to persons with similar symptomatology but without SARS-CoV-2 infection. These data may inform clinical interventions for individuals with sequelae of SARS-CoV-2 infection.

Background Whether there are sex differences in hemodynamic profiles among people with elevated blood pressure is not well understood and could guide personalization of treatment. Methods and results We described the clinical and hemodynamic characteristics of adults with elevated blood pressure in China using impedance cardiography. We included 45,082 individuals with elevated blood pressure (defined as systolic blood pressure of ≥130 mmHg or a diastolic blood pressure of ≥80 mmHg), of which 35.2% were women. Overall, women had a higher mean systolic blood pressure than men (139.0 [±15.7] mmHg vs 136.8 [±13.8] mmHg, PConclusions Our findings indicate that there are sex differences in hypertension phenotype, but that sex alone is insufficient to infer an individual’s profile.

Background Data on health status outcomes after spontaneous coronary artery dissection (SCAD) are limited. Methods and findings Using the Variation in Recovery: Role of Gender on Outcomes of Young AMI Patients (VIRGO) study we compared patients with SCAD and other acute myocardial infarction (AMI) at presentation (baseline), 1-month, and-12 months using standardized health status instruments. Among 3572 AMI patients ≤ 55 years, 67 had SCAD. SCAD patients were younger (median age (IQR) 45 (40.5–51) years vs. 48 (44–52) in other AMI, p = 0.003), more often female (92.5% vs. 66.6%), have college education (73.1% vs. 51.7%) and household income >$100,000 (43.3% vs. 17.7% (All pConclusions SCAD patients fare marginally better than other AMI patients on most health status instruments and have similar 12-month health status recovery. Better pre-event health status suggests a need to modify exercise prescriptions and cardiac rehabilitation protocols to better assist this physically active population to recover.

Abstract Background To estimate, prior to finalization of claims, the national monthly numbers of admissions and rates of 30-day readmissions and post-discharge observation-stays for Medicare fee-for-service beneficiaries hospitalized with acute myocardial infarction (AMI), heart failure (HF), or pneumonia. Methods The centers for Medicare & Medicaid Services (CMS) Integrated Data Repository, including the Medicare beneficiary enrollment database, was accessed in June 2015, February 2017, and February 2018. We evaluated patterns of delay in Medicare claims accrual, and used incomplete, non-final claims data to develop and validate models for real-time estimation of admissions, readmissions, and observation stays. Results These real-time reporting models accurately estimate, within 2 months from admission, the monthly numbers of admissions, 30-day readmission and observation-stay rates for patients with AMI, HF, or pneumonia. Conclusions This work will allow CMS to track the impact of policy decisions in real time and enable hospitals to better monitor their performance nationally.

Abstract Machine learning (ML) techniques have become ubiquitous and indispensable for solving intricate problems in most disciplines. To determine the extent of funding for clinical research projects applying ML techniques by the National Institutes of Health (NIH) in 2017, we searched the NIH Research Portfolio Online Reporting Tools Expenditures and Results (RePORTER) system using relevant keywords. We identified 535 projects, which together received a total of $264 million, accounting for 2% of the NIH extramural budget for clinical research.

Physical activity and social support are associated with better outcomes after surviving acute myocardial infarction (AMI), and greater walkability has been associated with activity and support. We used data from the SILVER-AMI study (November 2014–June 2017), a longitudinal cohort of community-living adults ≥ 75 years hospitalized for AMI to assess associations of neighborhood walkability with health outcomes, and to assess whether physical activity and social support mediate this relationship, if it exists. We included data from 1345 participants who were not bedbound, were discharged home, and for whom we successfully linked walkability scores (from Walk Score®) for their home census block. Our primary outcome was hospital-free survival time (HFST) at six months after discharge; secondary outcomes included physical and mental health at six months, assessed using SF-12. Physical activity and social support were measured at baseline. Covariates included cognition, functioning, comorbidities, participation in rehabilitation or physical therapy, and demographics. We employed survival analysis to examine associations between walkability and HFST, before and after adjustment for covariates; we repeated analyses using linear regression with physical and mental health as outcomes. In adjusted models, walkability was not associated with physical health (ß = 0.010; 95% CI: −0.027, 0.047), mental health (ß = −0.08; 95% CI: −0.175, −0.013), or HFST (ß = 0.008; 95% CI: −0.023, 0.009). Social support was associated with mental health in adjusted models. Neighborhood walkability was not predictive of outcomes among older adults with existing coronary disease, suggesting that among older adults, mobility limitations may supercede neighborhood walkability.

Background Despite its clinical significance, the risk of severe infection requiring hospitalization among outpatients with severe acute respiratory syndrome coronavirus 2 infection who receive angiotensin‐converting enzyme (ACE) inhibitors and angiotensin receptor blockers (ARBs) remains uncertain. Methods and Results In a propensity score–matched outpatient cohort (January–May 2020) of 2263 Medicare Advantage and commercially insured individuals with hypertension and a positive outpatient SARS‐CoV‐2, we determined the association of ACE inhibitors and ARBs with COVID‐19 hospitalization. In a concurrent inpatient cohort of 7933 hospitalized with COVID‐19, we tested their association with in‐hospital mortality. The robustness of the observations was assessed in a contemporary cohort (May–August). In the outpatient study, neither ACE inhibitors (hazard ratio [HR], 0.77; 0.53–1.13, P=0.18) nor ARBs (HR, 0.88; 0.61–1.26, P=0.48) were associated with hospitalization risk. ACE inhibitors were associated with lower hospitalization risk in the older Medicare group (HR, 0.61; 0.41–0.93, P=0.02), but not the younger commercially insured group (HR, 2.14; 0.82–5.60, P=0.12; P‐interaction 0.09). Neither ACE inhibitors nor ARBs were associated with lower hospitalization risk in either population in the validation cohort. In the primary inpatient study cohort, neither ACE inhibitors (HR, 0.97; 0.81–1.16; P=0.74) nor ARBs (HR, 1.15; 0.95–1.38, P=0.15) were associated with in‐hospital mortality. These observations were consistent in the validation cohort. Conclusions ACE inhibitors and ARBs were not associated with COVID‐19 hospitalization or mortality. Despite early evidence for a potential association between ACE inhibitors and severe COVID‐19 prevention in older individuals, the inconsistency of this observation in recent data argues against a role for prophylaxis.

Abstract Background Availability of nursing home care has declined and national efforts have been initiated to improve the quality of nursing home care in the U.S. Yet, data are limited on whether there are geographic variations in declines of availability and quality of nursing home care, and whether variations persist over time. We sought to assess geographic variation in availability and quality of nursing home care. Methods Retrospective study using Medicaid/Medicare-certified nursing home data from the Centers for Medicare & Medicaid Services, 1996–2016. Outcomes were 1) availability of all nursing home care (1996–2016), measured by the number of Medicaid/Medicare-certified beds for a given county per 100,000 population aged ≥65 years, regardless of nursing home star rating; 2) availability of 5-star nursing home care, measured by the number of Medicaid/Medicare-certified beds provided by 5-star nursing homes; and 3) utilization of nursing home beds, defined as the rate of occupied Medicaid/Medicare-certified beds among the total Medicaid/Medicare-certified beds. Results From 1999 to 2016, availability of all nursing home care declined from 4882 (standard deviation: 931) to 3480 (912) beds, per 100,000 population aged ≥65 years. Persistent geographic variation in availability of nursing home care was observed; the correlation coefficient of county-specific availabilities from 1996 to 2016 was 0.78 (95% CI 0.77–0.79). From 2011 to 2016, availability of 5-star nursing home beds increased from 658 (303) to 895 (661) per 100,000 population aged ≥65 years. The correlation coefficient for county-specific availabilities from 2011 to 2016 was 0.54 (95% CI 0.51–0.56). Availability and quality of nursing home care were not highly correlated. In 2016, the correlation coefficient for county-specific availabilities between all nursing home and 5-star nursing home beds was 0.33 (95% CI 0.30–0.36). From 1996 to 2016, the utilization of certified beds declined from 78.5 to 72.2%. This decline was consistent across all census divisions, but most pronounced in the Mountain division and less in the South-Atlantic division. Conclusion We observed persistent geographic variations in availability and quality of nursing home care. Availability of all nursing home care declined but availability of 5-star nursing home care increased. Availability and quality of nursing home care were not highly correlated.

Abstract Background Efforts to decrease hospitalization costs could increase post-acute care costs. This effect could undermine initiatives to reduce overall episode costs and have implications for the design of health care under alternative payment models. Methods Among Medicare fee-for-service beneficiaries aged ≥65 years hospitalized with acute myocardial infarction (AMI) between July 2010 and June 2013 in the Premier Healthcare Database, we studied the association of in-hospital and post-acute care resource utilization and outcomes by in-hospital cost tertiles. Results Among patients with AMI at 326 hospitals, the median (range) of each hospital’s mean per-patient in-hospital risk-standardized cost (RSC) for the low, medium, and high cost tertiles were $16,257 ($13,097–$17,648), $18,544 ($17,663–$19,875), and $21,831 ($19,923–$31,296), respectively. There was no difference in the median (IQR) of risk-standardized post-acute payments across cost-tertiles: $5014 (4295-6051), $4980 (4349-5931) and $4922 (4056-5457) for the low (n = 90), medium (n = 98), and high (n = 86) in-hospital RSC tertiles (p = 0.21), respectively. In-hospital and 30-day mortality rates did not differ significantly across the in-hospital RSC tertiles; however, 30-day readmission rates were higher at hospitals with higher in-hospital RSCs: median = 17.5, 17.8, and 18.0% at low, medium, and high in-hospital RSC tertiles, respectively (p = 0.005 for test of trend across tertiles). Conclusions In our study of patients hospitalized with AMI, greater resource utilization during the hospitalization was not associated with meaningful differences in costs or mortality during the post-acute period. These findings suggest that it may be possible for higher cost hospitals to improve efficiency in care without increasing post-acute care utilization or worsening outcomes.

Abstract Background Mandatory trial registration, and later results reporting, were proposed to mitigate selective clinical trial publication and outcome reporting. The Food and Drug Administration (FDA) Amendments Act (FDAAA) was enacted by Congress on September 27, 2007, requiring the registration of all non-phase I clinical trials involving FDA-regulated medical interventions and results reporting for approved drugs. The association between FDAAA enactment and the registration, results reporting, and publication bias of neuropsychiatric trials has not been studied. Methods We conducted a retrospective cohort study of all efficacy trials supporting FDA new drug approvals between 2005 to 2014 for neuropsychiatric indications. Trials were categorized as pre- or post-FDAAA based on initiation and/or completion dates. The main outcomes were the proportions of trials registered and reporting results in ClinicalTrials.gov, and the degree of publication bias, estimated using the relative risks pre- and post-FDAAA of both the publication of positive vs non-positive trials, as well as of publication of positive vs non-positive trials without misleading interpretations. Registration and results reporting proportions were compared pre- and post-FDAAA using the two-tailed Fisher exact test, and the degrees of publication bias were compared by calculating the ratio of relative risks (RRR) for each period. Results The FDA approved 37 new drugs for neuropsychiatric indications between 2005 and 2014 on the basis of 142 efficacy trials, of which 101 were pre-FDAAA and 41 post-FDAAA. Post-FDAAA trials were significantly more likely to be registered (100% vs 64%; p

Background Many communities have implemented systems of regionalized care to improve access to timely care for patients with ST‐segment–elevation myocardial infarction. However, patients who are ultimately diagnosed with non–ST‐segment–elevation myocardial infarctions (NSTEMIs) may also be affected, and the impact of regionalization programs on NSTEMI treatment and outcomes is unknown. We set out to determine the effects of ST‐segment–elevation myocardial infarction regionalization schemes on treatment and outcomes of patients diagnosed with NSTEMIs. Methods and Results The cohort included all patients receiving care in emergency departments diagnosed with an NSTEMI at all nonfederal hospitals in California from January 1, 2005 to September 30, 2015. Data were analyzed using a difference‐in‐differences approach. The main outcomes were 1‐year mortality and angiography within 3 days of the index admission. A total of 293 589 patients with NSTEMIs received care in regionalized and nonregionalized communities. Over the study period, rates of early angiography increased by 0.5 and mortality decreased by 0.9 percentage points per year among the overall population (95% CI, 0.4–0.6 and −1.0 to −0.8, respectively). Regionalization was not associated with early angiography (−0.5%; 95% CI, −1.1 to 0.1) or death (0.2%; 95% CI, −0.3 to 0.8). Conclusions ST‐segment–elevation myocardial infarction regionalization programs were not statistically associated with changes in guideline‐recommended early angiography or changes in risk of death for patients with NSTEMI. Increases in the proportion of patients with NSTEMI who underwent guideline‐directed angiography and decreases in risk of mortality were accounted for by secular trends unrelated to regionalization policies.

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.

Background Despite investments to improve quality of emergency care for patients with acute myocardial infarction (AMI), few studies have described national, real‐world trends in AMI care in the emergency department (ED). We aimed to describe trends in the epidemiology and quality of AMI care in US EDs over a recent 11‐year period, from 2005 to 2015. Methods and Results We conducted an observational study of ED visits for AMI using the National Hospital Ambulatory Medical Care Survey, a nationally representative probability sample of US EDs. AMI visits were classified as ST‐segment–elevation myocardial infarction (STEMI) and non‐STEMI. Outcomes included annual incidence of AMI, median ED length of stay, ED disposition type, and ED administration of evidence‐based medications. Annual ED visits for AMI decreased from 1 493 145 in 2005 to 581 924 in 2015. Estimated yearly incidence of ED visits for STEMI decreased from 1 402 768 to 315 813. The proportion of STEMI sent for immediate, same‐hospital catheterization increased from 12% to 37%. Among patients with STEMI sent directly for catheterization, median ED length of stay decreased from 62 to 37 minutes. ED administration of antithrombotic and nonaspirin antiplatelet agents rose for STEMI (23%–31% and 10%–27%, respectively). Conclusions National, real‐world trends in the epidemiology of AMI in the ED parallel those of clinical registries, with decreases in AMI incidence and STEMI proportion. ED care processes for STEMI mirror evolving guidelines that favor high‐intensity antiplatelet therapy, early invasive strategies, and regionalization of care.

In the current era of patient empowerment and precision medicine, access to timely information is critical to decision-making. Unfortunately, we currently lack patient-specific, real-time data about clinical presentation, risk of thrombotic or hemorrhagic events, key risk factors, and adverse outcomes in patients with venous thromboembolism (VTE). Accordingly, the R egistro I nformatizado E nfermedad T rombo E mbólica (RIETE) investigators developed a tool to provide an open-source, real-time graphic representation of VTE-related data derived from over 90 000 patients with confirmed VTE. This information is intended to facilitate discussion in the informed decision-making process. The current article describes the aims, rationale, methods, and ongoing and future efforts of the real-time VTE infographics developed by the RIETE registry collaborators.

Background The digital transformation of medical data provides opportunities to perform digital population health surveillance and identify people inadequately managed in usual care. We leveraged the electronic health records of a large health system to identify patients with markedly elevated blood pressure and characterize their follow‐up care pattern. Methods and Results We included 373 861 patients aged 18 to 85 years, who had at least 1 outpatient encounter in the Yale New Haven Health System between January 2013 and December 2017. We described the prevalence and follow‐up pattern of patients with at least 1 systolic blood pressure (SBP) ≥160 mm Hg or diastolic blood pressure (DBP) ≥100 mm Hg and patients with at least 1 SBP ≥180 mm Hg or DBP ≥120 mm Hg. Of 373 861 patients included, 56 909 (15.2%) had at least 1 SBP ≥160 mm Hg or DBP ≥100 mm Hg, and 10 476 (2.8%) had at least 1 SBP ≥180 mm Hg or DBP ≥120 mm Hg. Among patients with SBP ≥160 mm Hg or DBP ≥100 mm Hg, only 28.3% had a follow visit within 1 month (time window of follow‐up recommended by the guideline) and 19.9% subsequently achieved control targets (SBP

Background: Although increased age is associated with higher systolic blood pressure (SBP) in general, there may be variation across individuals in how SBP changes over time. The goal of this paper is to identify heterogeneity in SBP trajectories among young adults with similar initial values and identify personal characteristics associated with different trajectory patterns. This may have important implications for prevention and prognosis. Methods: A cohort of 12,468 individuals aged 18–35 years in the Qingdao Port Cardiovascular Health Study in China was followed yearly during 2000–2011. Individuals were categorized into three strata according to their baseline SBP: ≤110 mmHg, 111–130 mmHg, and >130 mmHg. Within each stratum, group-based trajectory analyses were conducted to identify distinct SBP trajectory patterns, and their association with sociodemographic and baseline health characteristics was assessed by ordinal logistic regression. Results: Five distinct groups of individuals exhibiting divergent patterns of increasing, stable or decreasing SBP trends were identified within each stratum. This is a first report to identify a subgroup with decreasing trend in SBP. Individuals with more advanced age, having less than high school education, family history of cardiovascular diseases, greater body mass index, greater waist circumference, and hyperlipidemia at baseline were more likely to experience trajectories of higher SBP within each stratum. Conclusions: The diverging trajectories among young adults with similar initial SBP highlight the need for prevention and feasibility of effective blood pressure control, while the identified risk factors may inform targeted interventions.

Background Sex differences in health status outcomes after percutaneous coronary intervention among patients without acute myocardial infarction are not well described. Methods and Results A total of 2237 patients (33.4% women) without acute myocardial infarction undergoing percutaneous coronary intervention were enrolled from 39 Chinese tertiary hospitals in the PEACE (China Patient‐centered Evaluative Assessment of Cardiac Events) prospective percutaneous coronary intervention study. Data were collected immediately before and 1 year following percutaneous coronary intervention. Health status was measured using the disease‐specific Seattle Angina Questionnaire (SAQ) Angina Frequency and Quality of Life domains, as well as the SAQ Summary Score. Among the study population, women were older, more often single, had lower levels of education, and had a higher prevalence of cardiac risk factors such as hypertension and diabetes mellitus. Women had lower mean 1‐year SAQ Angina Frequency scores (mean±SD, 91.0±17.3 versus 93.9±13.3; P

Background Patients who survive acute myocardial infarction (AMI) are at high risk for recurrence. We determined whether rehospitalizations after AMI further increased risk of recurrent AMI. Methods and Results The study included Medicare fee‐for‐service patients aged ≥65 years discharged alive after AMI from acute‐care hospitals in fiscal years 2009–2014. The outcome was recurrent AMI within 1 year of the index AMI. The Clinical Classifications Software (CCS) was used to classify rehospitalizations into disease categories. A Cox regression model was fit accounting for CCS‐specific hospitalizations as time‐varying variables and patient characteristics at discharge for the index AMI, adjusting for the competing risk of death. The rate of 1‐year recurrent AMI was 5.3% (95% CI, 5.27%–5.41%), and median (interquartile range) time from discharge to recurrent AMI was 115 (34–230) days. Eleven disease categories (diabetes mellitus, anemia, hypertension, coronary atherosclerosis, chest pain, heart failure, pneumonia, chronic obstructive pulmonary disease, gastrointestinal hemorrhage, renal failure, complication of implant or graft) were associated with increased risk of recurrent AMI. Septicemia was associated with lower recurrence risk. Hazard ratios ranged from 1.6 (95% CI, 1.55–1.70, heart failure) to 1.1 (95% CI, 1.04–1.25, pneumonia) to 0.6 (95% CI, 0.58–0.71, septicemia). Conclusions Patient risk of recurrent AMI changed based on the occurrence of hospitalizations after the index AMI. Improving post–acute care to prevent unplanned rehospitalizations, especially rehospitalizations for chronic diseases, and extending the focus of outcomes measures to condition‐specific rehospitalizations within 30 days and beyond is important for the secondary prevention of AMI.

Background More than 600 000 coronary stents are implanted during percutaneous coronary interventions (PCIs) annually in the United States. Because no real‐world surveillance system exists to monitor their long‐term safety, claims data are often used for this purpose. The extent to which adverse events identified with claims data can be reasonably attributed to a specific medical device is uncertain. Methods and Results We used deterministic matching to link the NCDR (National Cardiovascular Data Registry) CathPCI Registry to Medicare fee‐for‐service claims for patients aged ≥65 years who underwent PCI with drug‐eluting stents (DESs) between July 1, 2009 and December 31, 2013. We identified subsequent PCIs within 1 year of the index procedure in Medicare claims as potential safety events. We linked these subsequent PCIs back to the NCDR CathPCI Registry to ascertain how often the revascularization could be reasonably attributed to the same coronary artery as the index PCI (ie, target vessel revascularization). Of 415 306 DES placements in 368 194 patients, 33 174 repeat PCIs were identified in Medicare claims within 1 year. Of these, 28 632 (86.3%) could be linked back to the NCDR CathPCI Registry; 16 942 (51.1% of repeat PCIs) were target vessel revascularizations. Of these, 8544 (50.4%) were within a previously placed DES: 7652 for in‐stent restenosis and 1341 for stent thrombosis. Of 16 176 patients with a claim for acute myocardial infarction in the follow‐up period, 4446 (27.5%) were attributed to the same coronary artery in which the DES was implanted during the index PCI (ie, target vessel myocardial infarction). Of 24 288 patients whose death was identified in claims data, 278 (1.1%) were attributed to the same coronary artery in which the DES was implanted during the index PCI. Conclusions Most repeat PCIs following DES stent implantation identified in longitudinal claims data could be linked to real‐world registry data, but only half could be reasonably attributed to the same coronary artery as the index procedure. Attribution among those with acute myocardial infarction or who died was even less frequent. Safety signals identified using claims data alone will require more in‐depth examination to accurately assess stent safety.

Background Improving postoperative recovery is important, with a national focus on postacute care, but the volume and quality of evidence in this area are not well characterized. We conducted a systematic review to characterize studies on postoperative recovery after adult cardiac surgery using patient‐reported outcome measures. Methods and Results From MEDLINE and Web of Science, studies were included if they prospectively assessed postoperative recovery on adult patients undergoing cardiac surgery using patient‐reported outcome measures. Six recovery domains were defined by prior literature: nociceptive symptoms, mental health, physical function, activities of daily living, sleep, and cognitive function. Of the 3432 studies, 105 articles met the inclusion criteria. The studies were small (median sample size, 119), and mostly conducted in single‐center settings (n=81; 77%). Study participants were predominantly men (71%) and white (88%). Coronary artery bypass graft was included in 93% (n=98). Studies commonly selected for elective cases (n=56; 53%) and patients with less comorbidity (n=67; 64%). Median follow‐up duration was 91 (interquartile range, 42–182) days. Studies most commonly assessed 1 domain (n=42; 40%). The studies also varied in the instruments used and differed in their reporting approach. Studies commonly excluded patients who died during the follow‐up period (n=48; 46%), and 45% (n=47) did not specify how those patients were analyzed. Conclusions Studies of postoperative patient‐reported outcome measures are low in volume, most often single site without external validation, varied in their approach to missing data, and narrow in the domains and diversity of patients. The evidence base for postoperative patient‐reported outcome measures needs to be strengthened.

Background Characterizing and assessing the prevalence, awareness, and treatment patterns of patients with isolated diastolic hypertension (IDH) can generate new knowledge and highlight opportunities to improve their care. Methods and Results We used data from the China PEACE (Patient‐centered Evaluative Assessment of Cardiac Events) Million Persons Project, which screened 2 351 035 participants aged 35 to 75 years between 2014 and 2018. IDH was defined as systolic and diastolic blood pressure of 28 kg/m2, consumed alcohol, had diabetes mellitus, and prior cardiovascular events were more likely to have IDH (all P28 kg/m2, higher income, diabetes mellitus, prior cardiovascular events, and Central or Eastern region (all P