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.

Cardiovascular Disease Risk Factor Control Following Release From Carceral Facilities: A Cross-Sectional Study.

BACKGROUND: Incarceration is a social determinant of cardiovascular health but is rarely addressed in clinical settings or public health prevention efforts. People who have been incarcerated are more likely to develop cardiovascular disease (CVD) at younger ages and have worse cardiovascular outcomes compared with the general population, even after controlling for traditional risk factors. This study aims to identify incarceration-specific factors that are associated with uncontrolled CVD risk factors to identify potential targets for prevention. METHODS AND RESULTS: Using data from JUSTICE (Justice-Involved Individuals Cardiovascular Disease Epidemiology), a prospective cohort study of individuals released from incarceration with CVD risk factors, we examine the unique association between incarceration-specific factors and CVD risk factor control. Participants (N=471), with a mean age of 45.0±10.8 (SD) years, were disproportionately from racially minoritized groups (79%), and poor (91%). Over half (54%) had at least 1 uncontrolled CVD risk factor at baseline. People released from jail, compared with prison, had lower Life's Essential 8 scores for blood pressure and smoking. Release from jail, as compared with prison, was associated with an increased odds of having an uncontrolled CVD risk factor, even after adjusting for age, race and ethnicity, gender, perceived stress, and life adversity score (adjusted odds ratio 1.62 [95% CI, 1.02-2.57]). DISCUSSION: Release from jail is associated with poor CVD risk factor control and requires tailored intervention, which is informative as states design and implement the Centers of Medicare & Medicaid Services Reentry 1115 waiver, which allows Medicaid to cover services before release from correctional facilities.

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.

Trends in use of intensive care during hospitalizations at the end-of-life among older adults with advanced cancer.

BACKGROUND: High-intensity end-of-life (EOL) care, marked by admission to intensive care units (ICUs) or in-hospital death, can be costly and burdensome. Recent trends in use of ICUs, life-sustaining treatments (LSTs), and noninvasive ventilation (NIV) during EOL hospitalizations among older adults with advanced cancer and patterns of in-hospital death are unknown. METHODS: We used SEER-Medicare data (2003-2017) to identify beneficiaries with advanced solid cancer (summary stage 7) who died within 3 years of diagnosis. We identified EOL hospitalizations (within 30 days of death), classifying them by increasing intensity of care into: (1) without ICU; (2) with ICU but without LST (invasive mechanical ventilation, tracheostomy, gastrostomy, acute dialysis) or NIV; (3) with ICU and NIV but without LST; and (4) with ICU and LST use. We constructed a multinomial regression model to evaluate trends in risk-adjusted hospitalization, overall and across hospitalization categories, adjusting for sociodemographics, cancer characteristics, comorbidities, and frailty. We evaluated trends in in-hospital death across categories. RESULTS: Of 226,263 Medicare beneficiaries with advanced cancer, 138,305 (61.1%) were hospitalized at EOL [Age, Mean (SD):77.9(7.1) years; 45.5% female]. Overall, EOL hospitalizations remained high throughout, from 78.1% (95% CI: 77.4, 78.7) in 2004 to 75.5% (95% CI: 74.5, 76.2) in 2017. Hospitalizations without ICU use decreased from 49.3% (95% CI: 48.5, 50.2) to 35.0% (95% CI: 34.2, 35.9) while hospitalizations with more intensive care increased, from 23.7% (95% CI: 23.0, 24.4) to 28.7% (95% CI: 27.9, 29.5) for ICU without LST or NIV, 0.8% (95% CI: 0.6, 0.9) to 3.8% (95% CI: 3.4, 4.1) for ICU with NIV but without LST, and 4.3% (95% CI: 4.0, 4.7) to 8.0% (95% CI: 7.5, 8.5) for ICU with LST use. Among those who experienced in-hospital death, the proportion receiving ICU care increased from 46.5% to 65.0%. CONCLUSIONS: Among older adults with advanced cancer, EOL hospitalization rates remained stable from 2004-2017. However, intensity of care during EOL hospitalizations increased as evidenced by increasing use of ICUs, LSTs, and NIV.

Incorporating Medicare Advantage Admissions Into the CMS Hospital-Wide Readmission Measure.

Importance: Medicare Advantage (MA) enrollment is rapidly expanding, yet Centers for Medicare & Medicaid Services (CMS) claims-based hospital outcome measures, including readmission rates, have historically included only fee-for-service (FFS) beneficiaries. Objective: To assess the outcomes of incorporating MA data into the CMS claims-based FFS Hospital-Wide All-Cause Unplanned Readmission (HWR) measure. Design, Setting, and Participants: This cohort study assessed differences in 30-day unadjusted readmission rates and demographic and risk adjustment variables for MA vs FFS admissions. Inpatient FFS and MA administrative claims data were extracted from the Integrated Data Repository for all admissions for Medicare beneficiaries from July 1, 2018, to June 30, 2019. Measure reliability and risk-standardized readmission rates were calculated for the FFS and MA cohort vs the FFS-only cohort, overall and within specialty subgroups (cardiorespiratory, cardiovascular, medicine, surgery, neurology), then changes in hospital performance quintiles were assessed after adding MA admissions. Main Outcome and Measure: Risk-standardized readmission rates. Results: The cohort included 11 029 470 admissions (4 077 633 [37.0%] MA; 6 044 060 [54.8%] female; mean [SD] age, 77.7 [8.2] years). Unadjusted readmission rates were slightly higher for MA vs FFS admissions (15.7% vs 15.4%), yet comorbidities were generally lower among MA beneficiaries. Test-retest reliability for the FFS and MA cohort was higher than for the FFS-only cohort (0.78 vs 0.73) and signal-to-noise reliability increased in each specialty subgroup. Mean hospital risk-standardized readmission rates were similar for the FFS and MA cohort and FFS-only cohorts (15.5% vs 15.3%); this trend was consistent across the 5 specialty subgroups. After adding MA admissions to the FFS-only HWR measure, 1489 hospitals (33.1%) had their performance quintile ranking changed. As their proportion of MA admissions increased, more hospitals experienced a change in their performance quintile ranking (147 hospitals [16.3%] in the lowest quintile of percentage MA admissions; 408 [45.3%] in the highest). The combined cohort added 63 hospitals eligible for public reporting and more than 4 million admissions to the measure. Conclusions and Relevance: In this cohort study, adding MA admissions to the HWR measure was associated with improved measure reliability and precision and enabled the inclusion of more hospitals and beneficiaries. After MA admissions were included, 1 in 3 hospitals had their performance quintile changed, with the greatest shifts among hospitals with a high percentage of MA admissions.

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.

Cost-effectiveness of bevacizumab therapy in the care of patients with hereditary hemorrhagic telangiectasia.

ABSTRACT: No US Food and Drug Administration- or European Medicines Agency-approved therapies exist for bleeding due to hereditary hemorrhagic telangiectasia (HHT), the second-most common inherited bleeding disorder worldwide. The current standard of care (SOC) includes iron and red cell supplementation, alongside the necessary hemostatic procedures, none of which target underlying disease pathogenesis. Recent evidence has demonstrated that bleeding pathophysiology is amenable to systemic antiangiogenic therapy with the anti-vascular endothelial growth factor bevacizumab. Despite its high cost, the addition of longitudinal bevacizumab to the current SOC may reduce overall health care resource use and improve patient quality of life. We conducted, to our knowledge, the first cost-effectiveness analysis of IV bevacizumab in patients with HHT with the moderate-to-severe phenotype, comparing bevacizumab added to SOC vs SOC alone. The primary outcome was the incremental net monetary benefit (iNMB) reported over a lifetime time horizon and across accepted willingness-to-pay thresholds, in US dollar per quality-adjusted life year (QALY). Bevacizumab therapy accrued 9.3 QALYs while generating $428 000 in costs, compared with 8.3 QALYs and $699 000 in costs accrued in the SOC strategy. The iNMB of bevacizumab therapy vs the SOC was $433 000. No parameter variation and no scenario analysis, including choice of iron supplementation product, changed the outcome of bevacizumab being a cost-saving strategy. Bevacizumab therapy also saved patients an average of 133 hours spent receiving HHT-specific care per year of life. In probabilistic sensitivity analysis, bevacizumab was favored in 100% of all 10 000 Monte Carlo iterations across base-case and all scenario analyses. Bevacizumab should be considered for more favorable formulary placement in the care of patients with moderate-to-severe HHT.

Use of Open Science Practices in Surgical Journals.

This cross-sectional study assesses the level of adoption of 5 new tools that promote high quality and transparency in surgical research.

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.

National Trends in Racial and Ethnic Disparities in Use of Recommended Therapies in Adults with Atherosclerotic Cardiovascular Disease, 1999-2020.

Importance: Despite efforts to improve the quality of care for patients with atherosclerotic cardiovascular disease (ASCVD), it is unclear whether the US has made progress in reducing racial and ethnic differences in utilization of guideline-recommended therapies for secondary prevention. Objective: To evaluate 21-year trends in racial and ethnic differences in utilization of guideline-recommended pharmacological medications and lifestyle modifications among US adults with ASCVD. Design, Setting, and Participants: This cross-sectional study includes data from the National Health and Nutrition Examination Survey between 1999 and 2020. Eligible participants were adults aged 18 years or older with a history of ASCVD. Data were analyzed between March 2022 and May 2023. Exposure: Self-reported race and ethnicity. Main Outcome and Measures: Rates and racial and ethnic differences in the use of guideline-recommended pharmacological medications and lifestyle modifications. Results: The study included 5218 adults with a history of ASCVD (mean [SD] age, 65.5 [13.2] years, 2148 women [weighted average, 44.2%]), among whom 1170 (11.6%) were Black, 930 (7.7%) were Hispanic or Latino, and 3118 (80.7%) were White in the weighted sample. Between 1999 and 2020, there was a significant increase in total cholesterol control and statin use in all racial and ethnic subgroups, and in angiotensin-converting enzyme inhibitor (ACEI) and angiotensin receptor blocker (ARB) utilization in non-Hispanic White individuals and Hispanic and Latino individuals (Hispanic and Latino individuals: 17.12 percentage points; 95% CI, 0.37-37.88 percentage points; P = .046; non-Hispanic White individuals: 12.14 percentage points; 95% CI, 6.08-18.20 percentage points; P < .001), as well as smoking cessation within the Hispanic and Latino population (-27.13 percentage points; 95% CI, -43.14 to -11.12 percentage points; P = .002). During the same period, the difference in smoking cessation between Hispanic and Latino individuals and White individuals was reduced (-24.85 percentage points; 95% CI, -38.19 to -11.51 percentage points; P < .001), but racial and ethnic differences for other metrics did not change significantly. Notably, substantial gaps persisted between current care and optimal care throughout the 2 decades of data analyzed. In the period of 2017 to 2020, optimal regimens were observed in 47.4% (95% CI, 39.3%-55.4%), 48.7% (95% CI, 36.7%-60.6%), and 53.0% (95% CI, 45.6%-60.4%) of Black, Hispanic and Latino, and White individuals, respectively. Conclusions and Relevance: In this cross-sectional study of US adults with ASCVD, significant disparities persisted between current care and optimal care, surpassing any differences observed among demographic groups. These findings highlight the critical need for sustained efforts to bridge these gaps and achieve better outcomes for all patients, regardless of their racial and ethnic backgrounds.

Distinguishing features of long COVID identified through immune profiling.

Post-acute infection syndromes may develop after acute viral disease1. Infection with SARS-CoV-2 can result in the development of a post-acute infection syndrome known as long COVID. Individuals with long COVID frequently report unremitting fatigue, post-exertional malaise, and a variety of cognitive and autonomic dysfunctions2-4. However, the biological processes that are associated with the development and persistence of these symptoms are unclear. Here 275 individuals with or without long COVID were enrolled in a cross-sectional study that included multidimensional immune phenotyping and unbiased machine learning methods to identify biological features associated with long COVID. Marked differences were noted in circulating myeloid and lymphocyte populations relative to the matched controls, as well as evidence of exaggerated humoral responses directed against SARS-CoV-2 among participants with long COVID. Furthermore, higher antibody responses directed against non-SARS-CoV-2 viral pathogens were observed among individuals with long COVID, particularly Epstein-Barr virus. Levels of soluble immune mediators and hormones varied among groups, with cortisol levels being lower among participants with long COVID. Integration of immune phenotyping data into unbiased machine learning models identified the key features that are most strongly associated with long COVID status. Collectively, these findings may help to guide future studies into the pathobiology of long COVID and help with developing relevant biomarkers.

Clinical trial data sharing: a cross-sectional study of outcomes associated with two U.S. National Institutes of Health models.

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.

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.

Open science practices in research published in surgical journals: A cross-sectional study.

Open science practices are research tools used to improve research quality and transparency. These practices have been used by researchers in various medical fields, though the usage of these practices in the surgical research ecosystem has not been quantified. In this work, we studied the use of open science practices in general surgery journals. Eight of the highest-ranked general surgery journals by SJR2 were chosen and their author guidelines were reviewed. From each journal, 30 articles published between January 1, 2019 and August 11, 2021 were randomly chosen and analyzed. Five open science practices were measured (preprint publication prior to peer-reviewed publication, use of Equator guidelines, study protocol preregistration prior to peer-reviewed publication, published peer review, and public accessibility of data, methods, and/or code). Across all 240 articles, 82 (34%) used one or more open science practices. Articles in the International Journal of Surgery showed greatest use of open science practices, with a mean of 1.6 open science practices compared to 0.36 across the other journals (p<.001). Adoption of open science practices in surgical research remains low, and further work is needed to increase utilization of these tools.

Sex-Related Differences in Patient Characteristics, Risk Factors, and Symptomatology in Older Adults with Pulmonary Embolism: Findings from the SERIOUS-PE Study.

Sex-specific factors are implicated in pulmonary embolism (PE) presentation in young patients, as indicated by increased risk in pregnancy. Whether sex differences exist in PE presentation, comorbidities, and symptomatology in older adults, the age group in which most PEs occur, remains unknown. We identified older adults (aged ≥65 years) with PE in a large international PE registry replete with information about relevant clinical characteristics (RIETE registry, 2001-2021). To provide national data from the United States, we assessed sex differences in clinical characteristics and risk factors of Medicare beneficiaries with PE (2001-2019). The majority of older adults with PE in RIETE (19,294/33,462, 57.7%) and in the Medicare database (551,492/948,823, 58.7%) were women. Compared with men, women with PE less frequently had atherosclerotic diseases, lung disease, cancer, or unprovoked PE, but more frequently had varicose veins, depression, prolonged immobility, or history of hormonal therapy (p < 0.001 for all). Women less often presented with chest pain (37.3 vs. 40.6%) or hemoptysis (2.4 vs. 5.6%) but more often with dyspnea (84.6 vs. 80.9%) (p < 0.001 for all). Measures of clot burden, PE risk stratification, and use of imaging modalities were comparable between women and men. PE is more common in elderly women than in men. Cancer and cardiovascular disease are more common in men, whereas transient provoking factors including trauma, immobility, or hormone therapy are more common in elderly women with PE. Whether such differences correlate with disparities in treatment or differences in short- or long-term clinical outcomes warrants further investigation.

Comparing Detection Schemes for Adversarial Images against Deep Learning Models for Cancer Imaging.

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.

Characterization of multi-domain postoperative recovery trajectories after cardiac surgery using a digital platform.

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.

Racial and Ethnic Disparities in Financial Barriers Among Overweight and Obese Adults Eligible for Semaglutide in the United States.

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 (≈43.3 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<0.001 for all). Conclusions Many Americans who were eligible for semaglutide were likely to be unable to afford the medication. Among the eligible population, a larger proportion of Black and Hispanic adults had financial barriers than other subgroups.

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.

Clinical Presentation and Short- and Long-term Outcomes in Patients With Isolated Distal Deep Vein Thrombosis vs Proximal Deep Vein Thrombosis in the RIETE Registry.

Importance: Insufficient data exist about the clinical presentation, short-term, and long-term outcomes of patients with isolated distal deep vein thrombosis (IDDVT), that is, thrombosis in infrapopliteal veins without proximal extension or pulmonary embolism (PE). Objective: To determine the clinical characteristics, short-term, and 1-year outcomes in patients with IDDVT and to compare the outcomes in unadjusted and multivariable adjusted analyses with patients who had proximal DVT. Design, Setting, and Participants: This was a multicenter, international cohort study in participating sites of the Registro Informatizado Enfermedad Tromboembólica (RIETE) registry conducted from March 1, 2001, through February 28, 2021. Patients included in this study had IDDVT. Patients with proximal DVT were identified for comparison. Patients were excluded if they had a history of asymptomatic DVT, upper-extremity DVT, coexisting PE, or COVID-19 infection. Main Outcomes and Measures: Primary outcomes were 90-day and 1-year mortality, 1-year major bleeding, and 1-year venous thromboembolism (VTE) deterioration, which was defined as subsequent development of proximal DVT or PE. Results: A total of 33 897 patients were identified with isolated DVT (without concomitant PE); 5938 (17.5%) had IDDVT (mean [SD] age, 61 [17] years; 2975 male patients [50.1%]), and 27 959 (82.5%) had proximal DVT (mean [SD] age, 65 [18] years; 14 315 male patients [51.2%]). Compared with individuals with proximal DVT, those with IDDVT had a lower comorbidity burden but were more likely to have had recent surgery or to have received hormonal therapy. Patients with IDDVT had lower risk of 90-day mortality compared with those with proximal DVT (odds ratio [OR], 0.47; 95% CI, 0.40-0.55). Findings were similar in 1-year unadjusted analyses (hazard ratio [HR], 0.52; 95% CI, 0.46-0.59) and adjusted analyses (HR, 0.72; 95% CI, 0.64-0.82). Patients with IDDVT had a lower 1-year hazard of VTE deterioration (HR, 0.83; 95% CI, 0.69-0.99). In 1-year adjusted analyses of patients without an adverse event within the first 3 months, IDDVT was associated with lower risk of VTE deterioration (adjusted HR, 0.48; 95% CI, 0.24-0.97). By 1-year follow-up, symptoms or signs of postthrombotic syndrome were less common in patients with IDDVT (47.6% vs 60.5%). Conclusions and Relevance: Results of this cohort study suggest that patients with IDDVT had a less ominous prognosis compared with patients with proximal DVT. Such differences were likely multifactorial, including the differences in demographics, risk factors, comorbidities, particularly for all-cause mortality, and a potential association of thrombus location with VTE deterioration and postthrombotic syndrome. Randomized clinical trials are needed to assess the optimal long-term management of IDDVT.