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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.

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BACKGROUND: Cardiovascular devices account for one third of all Class I recalls, the U.S. Food and Drug Administration's (FDA) most severe designation, indicating a reasonable probability of "serious adverse health consequences or death." Understanding recalls and their causes is important for patient safety. OBJECTIVE: To characterize Class I recalls of cardiovascular devices and the clinical evidence supporting authorization. DESIGN: In this cross-sectional study, cardiovascular device recalls from 1 January 2013 through 31 December 2022 were identified using the FDA's annual log. Information about devices was extracted from publicly available FDA decision summaries. SETTING: The FDA Medical Device Recalls database. PARTICIPANTS: Cardiovascular devices with Class I recalls. MEASUREMENTS: Recalls were characterized by their causes and scope. Devices were characterized by their regulatory history (product code, special designations) and clinical evidence (premarket testing, postmarket surveillance). Clinical studies were analyzed for quality, including end point selection (clinical vs. surrogate, use of composites). RESULTS: From 2013 to 2022, there were 137 Class I recall events affecting 157 unique cardiovascular devices, of which 112 (71.3%) were moderate-risk 510(k) devices and 45 (28.7%) were high-risk premarket approval (PMA) devices. Recalls affected a median of 7649 units (IQR, 953 to 28 446) and were most commonly attributed to device design (43 [31.4%]). Forty-two (26.8%) devices had multiple Class I recalls. Thirty (19.1%) devices underwent premarket clinical testing (7 [6.2%] 510(k) devices, 17 [85.0%] PMA devices, and 6 [24.0%] PMA supplement devices). Most studies used surrogate (27 [79.4%]) and composite (24 [70.6%]) measures as primary end points. Twenty-two (48.9%) PMA devices had required postapproval studies, with 14 reporting delays. No 510(k) devices were subject to postmarket surveillance. LIMITATION: Details about clinical testing may be missing from FDA summaries. CONCLUSION: Cardiovascular devices with Class I recalls were infrequently subjected to premarket or postmarket testing, with recalls affecting thousands of patients annually. PRIMARY FUNDING SOURCE: None.

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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.

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IMPORTANCE: Hypertension poses a substantial public health challenge. Despite clinical practice guidelines for hypertension management, clinician adherence to these guidelines remains suboptimal. OBJECTIVE: To develop a taxonomy of suboptimal adherence scenarios for severe hypertension and identify barriers to guideline adherence. DESIGN, SETTING, and PARTICIPANTS: This qualitative content analysis using electronic health records (EHRs) of Yale New Haven Health System included participants who had at least 2 consecutive visits with markedly elevated blood pressure (BP; defined as at least 2 consecutive readings of systolic BP ≥160 mm Hg and diastolic BP ≥100 mm Hg) between January 1, 2013, and December 31, 2021, and no prescription for antihypertensive medication within a 90 days of the second BP measurement. Data analysis was conducted from January to December 2023. MAIN OUTCOMES AND MEASURES: The primary outcome was scenarios and influencing factors contributing to clinician nonadherence to the guidelines for hypertension management. A thematic analysis of EHR data was conducted to generate a pragmatic taxonomy of scenarios of suboptimal clinician guideline adherence in the management of severe hypertension. RESULTS: Of the 20 654 patients who met criteria, 200 were randomly selected and thematic saturation was reached after analyzing 100 patients (mean [SD] age at index visit, 66.5 [12.8] years; 50 female [50%]; 8 Black [8%]; 5 Hispanic or Latino [5%]; 85 White [85%]). Three content domains emerged: (1) clinician-related scenarios (defined as noninitiation or nonintensification of treatment due to issues relating to clinician intention, capability, or scope), which included 2 subcategories (did not address and diffusion of responsibility); (2) patient-related scenarios (defined as noninitiation or nonintensification of treatment due to patient behavioral considerations), which included 2 subcategories (patient nonadherence and patient preference); and (3) clinical complexity-related scenarios (defined as noninitiation or nonintensification of treatment due to clinical situational complexities), which included 3 subcategories (diagnostic uncertainty, maintenance of current intervention, and competing medical priorities). CONCLUSIONS AND RELEVANCE: In this qualitative study of EHR data, a taxonomy of suboptimal adherence scenarios for severe hypertension was developed and barriers to guideline adherence were identified. This pragmatic taxonomy lays the foundation for developing targeted interventions to improve clinician adherence to guidelines and patient outcomes.

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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.

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Importance: The ways in which we access, acquire, and use data in clinical trials have evolved very little over time, resulting in a fragmented and inefficient system that limits the amount and quality of evidence that can be generated. Observations: Clinical trial design has advanced steadily over several decades. Yet the infrastructure for clinical trial data collection remains expensive and labor intensive and limits the amount of evidence that can be collected to inform whether and how interventions work for different patient populations. Meanwhile, there is increasing demand for evidence from randomized clinical trials to inform regulatory decisions, payment decisions, and clinical care. Although substantial public and industry investment in advancing electronic health record interoperability, data standardization, and the technology systems used for data capture have resulted in significant progress on various aspects of data generation, there is now a need to combine the results of these efforts and apply them more directly to the clinical trial data infrastructure. Conclusions and Relevance: We describe a vision for a modernized infrastructure that is centered around 2 related concepts. First, allowing the collection and rigorous evaluation of multiple data sources and types and, second, enabling the possibility to reuse health data for multiple purposes. We address the need for multidisciplinary collaboration and suggest ways to measure progress toward this goal.

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This cross-sectional study assesses the associations between patient and clinical factors and variations in time emergency department physicians spend using electronic health record (EHR) systems.

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BACKGROUND: Treating obesity may be a pathway to prevent and control hypertension. In the SURMOUNT-1 trial in people with obesity or overweight with weight-related complications, 72-week tirzepatide treatment led to clinically meaningful body weight and blood pressure reduction. Post hoc analyses were conducted to further explore the effects of tirzepatide on the pattern of blood pressure reduction and whether the effects were consistent across various subgroups. METHODS: The mixed effect for repeated measure model was used to compare changes in overall blood pressure, across demographic and clinical subgroups, baseline blood pressure subgroups and hypertension categories between SURMOUNT-1 participants randomised to treatment with tirzepatide and placebo. The association between weight changes and blood pressure and adverse events associated with low blood pressure were also evaluated by mediation analysis. RESULTS: Tirzepatide treatment was associated with a rapid decline in systolic and diastolic blood pressure over the first 24 weeks, followed by blood pressure stabilisation until the end of the observation period, resulting in a significant net reduction by 72 weeks of 6.8 mm Hg systolic and 4.2 mm Hg diastolic blood pressure versus placebo. Participants randomly assigned to any tirzepatide group were more likely than those assigned to placebo to have normal blood pressure at week 72 (58.0% vs 35.2%, respectively). The effects were broadly consistent across baseline blood pressure subgroups, shifting the blood pressure distribution curve to lower blood pressure levels. The mediation analysis indicated that weight loss explained 68% of the systolic and 71% of the diastolic blood pressure reduction. Low blood pressure adverse events were infrequent, but the rate was higher in the tirzepatide group. CONCLUSIONS: In these post hoc analyses, in participants with obesity or overweight, tirzepatide was associated with reduced blood pressure consistently across participant groups primarily via weight loss, with relatively few blood pressure-related adverse events. TRIAL REGISTRATION NUMBER: NCT04184622.

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OBJECTIVE: To evaluate the effectiveness of a clinical decision support system (CDSS) in improving the use of guideline accordant antihypertensive treatment in primary care settings in China. DESIGN: Pragmatic, open label, cluster randomised trial. SETTING: 94 primary care practices in four urban regions of China between August 2019 and July 2022: Luoyang (central China), Jining (east China), and Shenzhen (south China, including two regions). PARTICIPANTS: 94 practices were randomised (46 to CDSS, 48 to usual care). 12 137 participants with hypertension who used up to two classes of antihypertensives and had a systolic blood pressure <180 mm Hg and diastolic blood pressure <110 mm Hg were included. INTERVENTIONS: Primary care practices were randomised to use an electronic health record based CDSS, which recommended a specific guideline accordant regimen for initiation, titration, or switching of antihypertensive (the intervention), or to use the same electronic health record without CDSS and provide treatment as usual (control). MAIN OUTCOME MEASURES: The primary outcome was the proportion of hypertension related visits during which an appropriate (guideline accordant) treatment was provided. Secondary outcomes were the average reduction in systolic blood pressure and proportion of participants with controlled blood pressure (<140/90 mm Hg) at the last scheduled follow-up. Safety outcomes were patient reported antihypertensive treatment related events, including syncope, injurious fall, symptomatic hypotension or systolic blood pressure <90 mm Hg, and bradycardia. RESULTS: 5755 participants with 23 113 visits in the intervention group and 6382 participants with 27 868 visits in the control group were included. Mean age was 61 (standard deviation 13) years and 42.5% were women. During a median 11.6 months of follow-up, the proportion of visits at which appropriate treatment was given was higher in the intervention group than in the control group (77.8% (17 975/23 113) v 62.2% (17 328/27 868); absolute difference 15.2 percentage points (95% confidence interval (CI) 10.7 to 19.8); P<0.001; odds ratio 2.17 (95% CI 1.75 to 2.69); P<0.001). Compared with participants in the control group, those in the intervention group had a 1.6 mm Hg (95% CI -2.7 to -0.5) greater reduction in systolic blood pressure (-1.5 mm Hg v 0.3 mm Hg; P=0.006) and a 4.4 percentage point (95% CI -0.7 to 9.5) improvement in blood pressure control rate (69.0% (3415/4952) v 64.6% (3778/5845); P=0.07). Patient reported antihypertensive treatment related adverse effects were rare in both groups. CONCLUSIONS: Use of a CDSS in primary care in China improved the provision of guideline accordant antihypertensive treatment and led to a modest reduction in blood pressure. The CDSS offers a promising approach to delivering better care for hypertension, both safely and efficiently. TRIAL REGISTRATION: ClinicalTrials.gov NCT03636334.

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BACKGROUND: Internal tremors and vibrations are symptoms previously described as part of neurologic disorders but not fully described as a part of long COVID. This study compared pre-pandemic comorbidities, new-onset conditions, and long COVID symptoms between people with internal tremors and vibrations as part of their long COVID symptoms and people with long COVID but without these symptoms. METHODS: The Yale Listen to Immune, Symptom and Treatment Experiences Now (LISTEN) Study surveyed 423 adults who had long COVID between May 12, 2022 and June 1, 2023. The exposure variable was long COVID symptoms of internal tremors and vibrations. The outcome variables were demographic characteristics, pre-pandemic comorbidities, new-onset conditions, other symptoms, and quality of life. RESULTS: Among study participants with long COVID, median age was 46 years [IQR, 38-56]), 74% were female, 87% were Non-Hispanic White, and 158 (37%) reported "internal tremors, or buzzing/vibration" as a long COVID symptom. The 2 groups reported similar pre-pandemic comorbidities, but people with internal tremors reported worse health as measured by the Euro-QoL visual analogue scale (median: 40 points [IQR, 30-60] vs. 50 points [IQR, 35-62], P = 0.007) and had higher rates of new-onset mast cell disorders (11% [95% CI, 7.1-18] vs. 2.6% [1.2-5.6], P = 0.008) and neurologic conditions (22% [95% CI, 16-29] vs. 8.3% [5.4-12], P = 0.004). CONCLUSIONS: Among people with long COVID, those with internal tremors and vibrations had different conditions and symptoms and worse health status compared with others who had long COVID without these symptoms.

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This cross-sectional study assesses how frequently research articles published in the clinical journals with high impact factors are preprinted and whether preprinting is associated with changes in media attention and citation counts.

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Importance: Despite the availability of disease-modifying therapies, scalable strategies for heart failure (HF) risk stratification remain elusive. Portable devices capable of recording single-lead electrocardiograms (ECGs) can enable large-scale community-based risk assessment. Objective: To evaluate an artificial intelligence (AI) algorithm to predict HF risk from noisy single-lead ECGs. Design: Multicohort study. Setting: Retrospective cohort of individuals with outpatient ECGs in the integrated Yale New Haven Health System (YNHHS) and prospective population-based cohorts of UK Biobank (UKB) and Brazilian Longitudinal Study of Adult Health (ELSA-Brasil). Participants: Individuals without HF at baseline. Exposures: AI-ECG-defined risk of left ventricular systolic dysfunction (LVSD). Main Outcomes and Measures: Among individuals with ECGs, we isolated lead I ECGs and deployed a noise-adapted AI-ECG model trained to identify LVSD. We evaluated the association of the model probability with new-onset HF, defined as the first HF hospitalization. We compared the discrimination of AI-ECG against the pooled cohort equations to prevent HF (PCP-HF) score for new-onset HF using Harrel's C-statistic, integrated discrimination improvement (IDI), and net reclassification improvement (NRI). Results: There were 194,340 YNHHS patients (age 56 years [IQR, 41-69], 112,082 women [58%]), 42,741 UKB participants (65 years [59-71], 21,795 women [52%]), and 13,454 ELSA-Brasil participants (56 years [41-69], 7,348 women [55%]) with baseline ECGs. A total of 3,929 developed HF in YNHHS over 4.5 years (2.6-6.6), 46 in UKB over 3.1 years (2.1-4.5), and 31 in ELSA-Brasil over 4.2 years (3.7-4.5). A positive AI-ECG screen was associated with a 3- to 7-fold higher risk for HF, and each 0.1 increment in the model probability portended a 27-65% higher hazard across cohorts, independent of age, sex, comorbidities, and competing risk of death. AI-ECG's discrimination for new-onset HF was 0.725 in YNHHS, 0.792 in UKB, and 0.833 in ELSA-Brasil. Across cohorts, incorporating AI-ECG predictions in addition to PCP-HF resulted in improved Harrel's C-statistic (Δ=0.112-0.114), with an IDI of 0.078-0.238 and an NRI of 20.1%-48.8% for AI-ECG vs. PCP-HF. Conclusions and Relevance: Across multinational cohorts, a noise-adapted AI model with lead I ECGs as the sole input defined HF risk, representing a scalable portable and wearable device-based HF risk-stratification strategy.

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Importance: Older adults with socioeconomic disadvantage develop a greater burden of disability after critical illness than those without socioeconomic disadvantage. The delivery of in-hospital rehabilitation that can mitigate functional decline may be influenced by social determinants of health (SDOH). Whether rehabilitation delivery differs by SDOH during critical illness hospitalization is not known. Objective: To evaluate whether SDOH are associated with the delivery of skilled rehabilitation during critical illness hospitalization among older adults. Design, Setting, and Participants: This cohort study used data from the National Health and Aging Trends Study linked with Medicare claims (2011-2018). Participants included older adults hospitalized with a stay in the intensive care unit (ICU). Data were analyzed from August 2022 to September 2023. Exposures: Dual eligibility for Medicare and Medicaid, education, income, limited English proficiency (LEP), and rural residence. Main Outcome and Measures: The primary outcome was delivery of physical therapy (PT) and/or occupational therapy (OT) during ICU hospitalization, characterized as any in-hospital PT or OT and rate of in-hospital PT or OT, calculated as total number of units divided by length of stay. Results: In the sample of 1618 ICU hospitalizations (median [IQR] patient age, 81.0 [75.0-86.0] years; 842 [52.0%] female), 371 hospitalizations (22.9%) were among patients with dual Medicare and Medicaid eligibility, 523 hospitalizations (32.6%) were among patients with less than high school education, 320 hospitalizations (19.8%) were for patients with rural residence, and 56 hospitalizations (3.5%) were among patients with LEP. A total of 1076 hospitalized patients (68.5%) received any PT or OT, with a mean rate of 0.94 (95% CI, 0.86-1.02) units/d. After adjustment for age, sex, prehospitalization disability, mechanical ventilation, and organ dysfunction, factors associated with lower odds of receipt of PT or OT included dual Medicare and Medicaid eligibility (adjusted odds ratio, 0.70 [95% CI, 0.50-0.97]) and rural residence (adjusted odds ratio, 0.65 [95% CI, 0.48-0.87]). LEP was associated with a lower rate of PT or OT (adjusted rate ratio, 0.55 [95% CI, 0.32-0.94]). Conclusions and Relevance: These findings highlight the need to consider SDOH in efforts to promote rehabilitation delivery during ICU hospitalization and to investigate factors underlying inequities in this practice.

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BACKGROUND: Individuals with long COVID lack evidence-based treatments and have difficulty participating in traditional site-based trials. Our digital, decentralized trial investigates the efficacy and safety of nirmatrelvir/ritonavir, targeting viral persistence as a potential cause of long COVID. METHODS: The PAX LC trial (NCT05668091) is a Phase 2, 1:1 randomized, double-blind, superiority, placebo-controlled trial in 100 community-dwelling, highly symptomatic adult participants with long COVID residing in the 48 contiguous US states to determine the efficacy, safety, and tolerability of 15 days of nirmatrelvir/ritonavir compared with placebo/ritonavir. Participants are recruited via patient groups, cultural ambassadors, and social media platforms. Medical records are reviewed through a platform facilitating participant-mediated data acquisition from electronic health records nationwide. During the drug treatment, participants complete daily digital diaries using a web-based application. Blood draws for eligibility and safety assessments are conducted at or near participants' homes. The study drug is shipped directly to participants' homes. The primary endpoint is the PROMIS-29 Physical Health Summary Score difference between baseline and Day 28, evaluated by a mixed model repeated measure analysis. Secondary endpoints include PROMIS-29 (Mental Health Summary Score and all items), Modified GSQ-30 with supplemental symptoms questionnaire, COVID Core Outcome Measures for Recovery, EQ-5D-5L (Utility Score and all items), PGIS 1 and 2, PGIC 1 and 2, and healthcare utilization. The trial incorporates immunophenotyping to identify long COVID biomarkers and treatment responders. CONCLUSION: The PAX LC trial uses a novel decentralized design and a participant-centric approach to test a 15-day regimen of nirmatrelvir/ritonavir for long COVID.