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Fibrinolytic agents catalyze the conversion of the inactive proenzyme plasminogen into the active protease plasmin, degrading fibrin within the thrombus and recanalizing occluded vessels. The history of these medications dates to the discovery of the first fibrinolytic compound, streptokinase, from bacterial cultures in 1933. Over time, researchers identified two other plasminogen activators in human samples, namely urokinase and tissue plasminogen activator (tPA). Subsequently, tPA was cloned using recombinant DNA methods to produce alteplase. Several additional derivatives of tPA, such as tenecteplase and reteplase, were developed to extend the plasma half-life of tPA. Over the past decades, fibrinolytic medications have been widely used to manage patients with venous and arterial thromboembolic events. Currently, alteplase is approved by the U.S. Food and Drug Administration (FDA) for use in patients with pulmonary embolism with hemodynamic compromise, ST-segment elevation myocardial infarction (STEMI), acute ischemic stroke, and central venous access device occlusion. Reteplase and tenecteplase have also received FDA approval for treating patients with STEMI. This review provides an overview of the historical background related to fibrinolytic agents and briefly summarizes their approved indications across various thromboembolic diseases. [ABSTRACT FROM AUTHOR]

Rationale: One quarter of Medicare beneficiaries hospitalized for chronic obstructive pulmonary disease (COPD) die within 1 year. Although overall mortality rates are higher among White patients with COPD, racial and ethnic differences in the vulnerable period following hospitalization are unknown. Objectives: To determine the association between race and ethnicity and mortality following COPD hospitalization and to evaluate the extent to which differences are explained by clinical, geographic, socioeconomic, and post–acute care factors among Medicare beneficiaries in the United States. Methods: In this retrospective cohort study of Medicare beneficiaries hospitalized for COPD exacerbation, we constructed Cox regression models for 1-year mortality accounting for hospital-level clustering; sequentially adjusting for clinical, geographic, neighborhood socioeconomic, and post–acute care characteristics; and stratifying by sex and individual socioeconomic status. Results: Among 244,624 hospitalizations, Medicare beneficiaries of racial and ethnic minority groups had a lower risk of dying within 1 year of hospitalization than those of White race (hazard ratios, 0.78 [95% confidence interval, 0.75–0.80] for Black patients, 0.79 [0.76–0.82] for Hispanic patients, and 0.82 [0.77–0.86] for others). Differences in visits to physicians, attendance of pulmonary rehabilitation, and discharge disposition explained some of the mortality gap among dual-eligible beneficiaries but not among non–dual-eligible beneficiaries. Conclusions: Medicare beneficiaries of White race are at greater risk of mortality following COPD hospitalization compared with beneficiaries of minority race and ethnicity groups. Our findings should be interpreted in the context of the selection of a hospitalized population and a potentially incomplete assessment of illness severity in administrative data, and warrant further investigation. [ABSTRACT FROM AUTHOR]

Introduction: Despite evidence supporting the benefits of marriage on cardiovascular health, the impact of marital/partner status on the long-term readmission of young acute myocardial infarction (AMI) survivors is less clear. We examined the association between marital/partner status and 1-year all-cause readmission and explored sex differences among young AMI survivors. Methods: Data were from the VIRGO study (Variation in Recovery: Role of Gender on Outcomes of Young AMI Patients), which enrolled young adults aged 18–55 years with AMI (2008–2012). The primary end point was all-cause readmission within 1 year of hospital discharge, obtained from medical records and patient interviews and adjudicated by a physician panel. We performed Cox proportional hazards models with sequential adjustment for demographic, socioeconomic, clinical, and psychosocial factors. Sex-marital/partner status interaction was also tested. Results: Of the 2,979 adults with AMI (2002 women [67.2%]; mean age 48 [interquartile range, 44–52] years), unpartnered individuals were more likely to experience all-cause readmissions compared with married/partnered individuals within the first year after hospital discharge (34.6% versus 27.2%, hazard ratio [HR] = 1.31; 95% confidence interval [CI], 1.15–1.49). The association attenuated but remained significant after adjustment for demographic and socioeconomic factors (adjusted HR, 1.16; 95% CI, 1.01–1.34), and it was not significant after further adjusting for clinical factors and psychosocial factors (adjusted HR, 1.10; 95%CI, 0.94–1.28). A sex-marital/partner status interaction was not significant (p = 0.69). Sensitivity analysis using data with multiple imputation and restricting outcomes to cardiac readmission yielded comparable results. Conclusions: In a cohort of young adults aged 18–55 years, unpartnered status was associated with 1.3-fold increased risk of all-cause readmission within 1 year of AMI discharge. Further adjustment for demographic, socioeconomic, clinical, and psychosocial factors attenuated the association, suggesting that these factors may explain disparities in readmission between married/partnered versus unpartnered young adults. Whereas young women experienced more readmission compared to similar-aged men, the association between marital/partner status and 1-year readmission did not vary by sex. [ABSTRACT FROM AUTHOR]

This document analyzes the variation in excess mortality rates across different states in the United States during the COVID-19 pandemic. It estimates the number of excess deaths and the potential number of deaths that could have been averted if all states had matched the lowest excess death rates. The findings suggest that a significant number of excess deaths could have potentially been prevented if all states had matched the rates of the ten states with the lowest excess mortality. The study also examines the correlation between excess mortality and vaccination rates, finding that the inverse correlation decreased over time. The document emphasizes the importance of reducing health disparities, increasing vaccination rates, and implementing effective measures during future outbreaks. [Extracted from the article]

The article offers information on the vast amount of data available about individuals' biology, behavior, environment, and health outcomes, which could address questions that matter most to patients. Topics include leveraging clinical data for personalized evidence, utilizing big data in the intensive care unit (ICU) for predictive analytics, multiomics approaches, and remote patient monitoring to advance treatment.

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 FROM AUTHOR]

Aim: We studied the association between neighbourhood material deprivation, a metric estimating inability to attain basic material needs, with outcomes and processes of care among incident heart failure patients in a universal healthcare system. Methods and results: In a population‐based retrospective study (2007–2019), we examined the association of material deprivation with 1‐year all‐cause mortality, cause‐specific hospitalization, and 90‐day processes of care. Using cause‐specific hazards regression, we quantified the relative rate of events after multiple covariate adjustment, stratifying by age ≤65 or ≥66 years. Among 395 763 patients (median age 76 [interquartile range 66–84] years, 47% women), there was significant interaction between age and deprivation quintile for mortality/hospitalization outcomes (p ≤ 0.001). Younger residents (age ≤65 years) of the most versus least deprived neighbourhoods had higher hazards of all‐cause death (hazard ratio [HR] 1.19, 95% confidence interval [CI] 1.10–1.29]) and cardiovascular hospitalization (HR 1.29 [95% CI 1.19–1.39]). Older individuals (≥66 years) in the most deprived neighbourhoods had significantly higher hazard of death (HR 1.11 [95% CI 1.08–1.14]) and cardiovascular hospitalization (HR 1.13 [95% CI 1.09–1.18]) compared to the least deprived. The magnitude of the association between deprivation and outcomes was amplified in the younger compared to the older age group. More deprived individuals in both age groups had a lower hazard of cardiology visits and advanced cardiac imaging (all p < 0.001), while the most deprived of younger ages were less likely to undergo implantable cardioverter‐defibrillator/cardiac resynchronization therapy‐pacemaker implantation (p = 0.023), compared to the least deprived. Conclusion: Patients with newly‐diagnosed heart failure residing in the most deprived neighbourhoods had worse outcomes and reduced access to care than those less deprived. [ABSTRACT FROM AUTHOR]

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%, pone-sample t-test = 0.001; SPRINT: −17.6% ± 3.6%, pone-sample t-test < 0.001), while preserving the original average treatment effect (IRIS: hazard ratio of 0.73 ± 0.01 for pioglitazone vs placebo, vs 0.76 in the original trial; SPRINT: hazard ratio of 0.72 ± 0.01 for intensive vs standard systolic blood pressure, vs 0.75 in the original trial; all simulations with Cox regression-derived p value of < 0.01 for the effect of the intervention on the respective primary outcome). This adaptive framework has the potential to maximize RCT enrollment efficiency. [ABSTRACT FROM AUTHOR]

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 FROM AUTHOR]

Background and Aims Early diagnosis of aortic stenosis (AS) is critical to prevent morbidity and mortality but requires skilled examination with Doppler imaging. This study reports the development and validation of a novel deep learning model that relies on two-dimensional (2D) parasternal long axis videos from transthoracic echocardiography without Doppler imaging to identify severe AS, suitable for point-of-care ultrasonography. Methods and results In a training set of 5257 studies (17 570 videos) from 2016 to 2020 [Yale-New Haven Hospital (YNHH), Connecticut], an ensemble of three-dimensional convolutional neural networks was developed to detect severe AS, leveraging self-supervised contrastive pretraining for label-efficient model development. This deep learning model was validated in a temporally distinct set of 2040 consecutive studies from 2021 from YNHH as well as two geographically distinct cohorts of 4226 and 3072 studies, from California and other hospitals in New England, respectively. The deep learning model achieved an area under the receiver operating characteristic curve (AUROC) of 0.978 (95% CI: 0.966, 0.988) for detecting severe AS in the temporally distinct test set, maintaining its diagnostic performance in geographically distinct cohorts [0.952 AUROC (95% CI: 0.941, 0.963) in California and 0.942 AUROC (95% CI: 0.909, 0.966) in New England]. The model was interpretable with saliency maps identifying the aortic valve, mitral annulus, and left atrium as the predictive regions. Among non-severe AS cases, predicted probabilities were associated with worse quantitative metrics of AS suggesting an association with various stages of AS severity. Conclusion This study developed and externally validated an automated approach for severe AS detection using single-view 2D echocardiography, with potential utility for point-of-care screening. [ABSTRACT FROM AUTHOR]

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 FROM AUTHOR]

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 FROM AUTHOR]

Key Points: Question: How many excess deaths and years of potential life lost for the Black population, compared with the White population, occurred in the United States from 1999 through 2020? Findings: Based on Centers for Disease Control and Prevention data, excess deaths and years of potential life lost persisted throughout the period, with initial progress followed by stagnation of improvement and substantial worsening in 2020. The Black population had 1.63 million excess deaths, representing more than 80 million years of potential life lost over the study period. Meaning: After initial progress, excess mortality and years of potential life lost among the US Black population stagnated and then worsened, indicating a need for new approaches. Importance: Amid efforts in the US to promote health equity, there is a need to assess recent progress in reducing excess deaths and years of potential life lost among the Black population compared with the White population. Objective: To evaluate trends in excess mortality and years of potential life lost among the Black population compared with the White population. Design, setting, and participants: Serial cross-sectional study using US national data from the Centers for Disease Control and Prevention from 1999 through 2020. We included data from non-Hispanic White and non-Hispanic Black populations across all age groups. Exposures: Race as documented in the death certificates. Main outcomes and measures: Excess age-adjusted all-cause mortality, cause-specific mortality, age-specific mortality, and years of potential life lost rates (per 100 000 individuals) among the Black population compared with the White population. Results: From 1999 to 2011, the age-adjusted excess mortality rate declined from 404 to 211 excess deaths per 100 000 individuals among Black males (P for trend <.001). However, the rate plateaued from 2011 through 2019 (P for trend =.98) and increased in 2020 to 395—rates not seen since 2000. Among Black females, the rate declined from 224 excess deaths per 100 000 individuals in 1999 to 87 in 2015 (P for trend <.001). There was no significant change between 2016 and 2019 (P for trend =.71) and in 2020 rates increased to 192—levels not seen since 2005. The trends in rates of excess years of potential life lost followed a similar pattern. From 1999 to 2020, the disproportionately higher mortality rates in Black males and females resulted in 997 623 and 628 464 excess deaths, respectively, representing a loss of more than 80 million years of life. Heart disease had the highest excess mortality rates, and the excess years of potential life lost rates were largest among infants and middle-aged adults. Conclusions and relevance: Over a recent 22-year period, the Black population in the US experienced more than 1.63 million excess deaths and more than 80 million excess years of life lost when compared with the White population. After a period of progress in reducing disparities, improvements stalled, and differences between the Black population and the White population worsened in 2020. This study focuses on differences in mortality and years of potential life lost between Black and White populations to understand recent trends. [ABSTRACT FROM AUTHOR]

Objective Nonexercise algorithms are cost-effective methods to estimate cardiorespiratory fitness (CRF), but the existing models have limitations in generalizability and predictive power. This study aims to improve the nonexercise algorithms using machine learning (ML) methods and data from US national population surveys. Materials and Methods We used the 1999–2004 data from the National Health and Nutrition Examination Survey (NHANES). Maximal oxygen uptake (VO2 max), measured through a submaximal exercise test, served as the gold standard measure for CRF in this study. We applied multiple ML algorithms to build 2 models: a parsimonious model using commonly available interview and examination data, and an extended model additionally incorporating variables from Dual-Energy X-ray Absorptiometry (DEXA) and standard laboratory tests in clinical practice. Key predictors were identified using Shapley additive explanation (SHAP). Results Among the 5668 NHANES participants in the study population, 49.9% were women and the mean (SD) age was 32.5 years (10.0). The light gradient boosting machine (LightGBM) had the best performance across multiple types of supervised ML algorithms. Compared with the best existing nonexercise algorithms that could be applied to the NHANES, the parsimonious LightGBM model (RMSE: 8.51 ml/kg/min [95% CI: 7.73–9.33]) and the extended LightGBM model (RMSE: 8.26 ml/kg/min [95% CI: 7.44–9.09]) significantly reduced the error by 15% and 12% (P  < .001 for both), respectively. Discussion The integration of ML and national data source presents a novel approach for estimating cardiovascular fitness. This method provides valuable insights for cardiovascular disease risk classification and clinical decision-making, ultimately leading to improved health outcomes. Conclusion Our nonexercise models provide improved accuracy in estimating VO2 max within NHANES data as compared to existing nonexercise algorithms. [ABSTRACT FROM AUTHOR]

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 < 0.001). The prevalence of atrial fibrillation was higher in the slow progression group; there was no significant between-group difference in the prevalence of other comorbidities. The rapid progression group had a significantly higher AVR rate (HR 3.4 [2.4–4.8], P < 0.001); there was no between-group difference in mortality (HR 0.7 [0.5–1.0]; P = 0.079). Leveraging longitudinal echocardiographic data, we identified two distinct groups of patients with moderate AS: slow and rapid progression. A higher initial MPG (≥ 24 mmHg) was associated with more rapid progression of AS and higher rates of AVR, thus indicating the predictive value of MPG in management of the disease. [ABSTRACT FROM AUTHOR]

Simple Summary: While deep learning has become a powerful tool in analysis of cancer imaging, deep learning models have potential vulnerabilities that pose security threats in the setting of clinical implementation. One weakness of deep learning models is that they can be deceived by adversarial images, which are manipulated images that have pixels intentionally perturbed to alter the output of the deep learning model. Recent research has shown that adversarial detection models can differentiate adversarial images from normal images to protect deep learning models from attack. We compared the effectiveness of different adversarial detection schemes, using three cancer imaging datasets (computed tomography, mammography, and magnetic resonance imaging). We found that that the detection schemes demonstrate strong performance overall but exhibit limited efficacy in detecting a subset of adversarial images. We believe our findings provide a useful basis in the application of adversarial defenses to deep learning models for medical images in oncology. 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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Key Points: Question: When medical devices that had been cleared under the FDA's 510(k) pathway are subject to Class I recalls, how often did the predicates for these devices also have a history of recalls? Findings: In this cross-sectional analysis of 156 cases of 510(k)–authorized devices with Class I recalls from 2017 through 2021, 44.1% used predicates with Class I recalls. In addition, 48.1% of these devices were subsequently used as predicates to authorize descendant devices later subject to Class I recalls. The risk of a Class I recall was 6.40 times higher for descendants that used predicates with Class I recalls than for devices using Class I recall–free predicates. Meaning: Stronger safeguards are needed to prevent problematic predicate selection and ensure patient safety. Importance: In the US, nearly all medical devices progress to market under the 510(k) pathway, which uses previously authorized devices (predicates) to support new authorizations. Current regulations permit manufacturers to use devices subject to a Class I recall—the FDA's most serious designation indicating a high probability of adverse health consequences or death—as predicates for new devices. The consequences for patient safety are not known. Objective: To determine the risk of a future Class I recall associated with using a recalled device as a predicate device in the 510(k) pathway. Design and Setting: In this cross-sectional study, all 510(k) devices subject to Class I recalls from January 2017 through December 2021 (index devices) were identified from the FDA's annual recall listings. Information about predicate devices was extracted from the Devices@FDA database. Devices authorized using index devices as predicates (descendants) were identified using a regulatory intelligence platform. A matched cohort of predicates was constructed to assess the future recall risk from using a predicate device with a Class I recall. Main Outcomes and Measures: Devices were characterized by their regulatory history and recall history. Risk ratios (RRs) were calculated to compare the risk of future Class I recalls between devices descended from predicates with matched controls. Results: Of 156 index devices subject to Class I recall from 2017 through 2021, 44 (28.2%) had prior Class I recalls. Predicates were identified for 127 index devices, with 56 (44.1%) using predicates with a Class I recall. One hundred four index devices were also used as predicates to support the authorization of 265 descendant devices, with 50 index devices (48.1%) authorizing a descendant with a Class I recall. Compared with matched controls, devices authorized using predicates with Class I recalls had a higher risk of subsequent Class I recall (6.40 [95% CI, 3.59-11.40]; P<.001). Conclusions and Relevance: Many 510(k) devices subjected to Class I recalls in the US use predicates with a known history of Class I recalls. These devices have substantially higher risk of a subsequent Class I recall. Safeguards for the 510(k) pathway are needed to prevent problematic predicate selection and ensure patient safety. This cross-sectional study evaluates the frequency of new devices receiving US Food and Drug Administration approval through its 510(k) pathway based on predicate devices that had been subject to a Class 1 recall. [ABSTRACT FROM AUTHOR]

Background: Medicaid, the primary source of insurance coverage for disadvantaged Americans, was originally designed as a temporary safety-net program. No studies have used long-run data to assess the recent use of the program by beneficiaries.Objective: To assess patterns of short- and long-term enrollment among beneficiaries, using a 10-year longitudinal panel of Michigan Medicaid eligibility data.Design: Primary analyses assessing trends in Medicaid enrollment among cohorts of existing and new beneficiaries.Setting: Administrative records from Michigan Medicaid for the period 2011 to 2020.Participants: 3.97 million Medicaid beneficiaries.Measurements: Short- and long-term enrollment in the program.Results: The sample includes 3.97 million unique beneficiaries enrolled at some point between 2011 and 2020. Among a cohort of 1.23 million beneficiaries enrolled in 2011, over half (53%) were also enrolled in Medicaid in June 2020, spending, on average, two-thirds of that period (67%) on Medicaid. These beneficiaries, however, experienced substantial lapses in coverage, as only 25% were continuously enrolled throughout the period. Enrollment was less stable when assessed from the perspective of newly enrolled beneficiaries, of whom only 37% remained enrolled at the end of the study period.Limitation: Primary estimates from a single state.Conclusion: For many beneficiaries, Medicaid has served as their primary source of coverage for at least a decade. This pattern would justify increasing investments in the program to improve long-term health outcomes.Primary Funding Source: Self-funded. [ABSTRACT FROM AUTHOR]

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 FROM AUTHOR]

The author talk about ChatGPT, an artificial intelligence (AI) chatbot that processes and generates natural language text. They discuss the number of users garnered by ChatGPT, its irrevocably progressive attitude which is said to be particularly striking about the program's response, and concerns expressed by physicians and other scientists over some of ChatGPT's blindspots.

Importance: Patient safety is a US national priority, yet lacks a comprehensive assessment of progress over the past decade.Objective: To determine the change in the rate of adverse events in hospitalized patients.Design, Setting, and Participants: This serial cross-sectional study used data from the Medicare Patient Safety Monitoring System from 2010 to 2019 to assess in-hospital adverse events in patients. The study included 244 542 adult patients hospitalized in 3156 US acute care hospitals across 4 condition groups from 2010 through 2019: acute myocardial infarction (17%), heart failure (17%), pneumonia (21%), and major surgical procedures (22%); and patients hospitalized from 2012 through 2019 for all other conditions (22%).Exposures: Adults aged 18 years or older hospitalized during each included calendar year.Main Outcomes and Measures: Information on adverse events (abstracted from medical records) included 21 measures across 4 adverse event domains: adverse drug events, hospital-acquired infections, adverse events after a procedure, and general adverse events (hospital-acquired pressure ulcers and falls). The outcomes were the total change over time for the observed and risk-adjusted adverse event rates in the subpopulations.Results: The study sample included 190 286 hospital discharges combined in the 4 condition-based groups of acute myocardial infarction, heart failure, pneumonia, and major surgical procedures (mean age, 68.0 [SD, 15.9] years; 52.6% were female) and 54 256 hospital discharges for the group including all other conditions (mean age, 57.7 [SD, 20.7] years; 59.8% were female) from 3156 acute care hospitals across the US. From 2010 to 2019, the total change was from 218 to 139 adverse events per 1000 discharges for acute myocardial infarction, from 168 to 116 adverse events per 1000 discharges for heart failure, from 195 to 119 adverse events per 1000 discharges for pneumonia, and from 204 to 130 adverse events per 1000 discharges for major surgical procedures. From 2012 to 2019, the rate of adverse events for all other conditions remained unchanged at 70 adverse events per 1000 discharges. After adjustment for patient and hospital characteristics, the annual change represented by relative risk in all adverse events per 1000 discharges was 0.94 (95% CI, 0.93-0.94) for acute myocardial infarction, 0.95 (95% CI, 0.94-0.96) for heart failure, 0.94 (95% CI, 0.93-0.95) for pneumonia, 0.93 (95% CI, 0.92-0.94) for major surgical procedures, and 0.97 (95% CI, 0.96-0.99) for all other conditions. The risk-adjusted adverse event rates declined significantly in all patient groups for adverse drug events, hospital-acquired infections, and general adverse events. For patients in the major surgical procedures group, the risk-adjusted rates of events after a procedure declined significantly.Conclusions and Relevance: In the US between 2010 and 2019, there was a significant decrease in the rates of adverse events abstracted from medical records for patients admitted for acute myocardial infarction, heart failure, pneumonia, and major surgical procedures and there was a significant decrease in the adjusted rates of adverse events between 2012 and 2019 for all other conditions. Further research is needed to understand the extent to which these trends represent a change in patient safety. [ABSTRACT FROM AUTHOR]