Class I Recalls of Cardiovascular Devices Between 2013 and 2022 : A Cross-Sectional Analysis.

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.

Unveiling the Adoption and Barriers of Telemedicine in US Hospitals: A Comprehensive Analysis (2017-2022).

BACKGROUND: Telemedicine has emerged as a vital healthcare delivery model, especially pronounced during the COVID-19 pandemic. Our study uniquely focuses on an institutional lens, examining US hospitals to offer targeted policy implications. OBJECTIVE: To investigate the trend in telemedicine adoption across US hospitals from 2017 to 2022 and analyze the institutional challenges they encounter, particularly in the realm of electronic health information exchange. DESIGN: Cross-sectional study leveraging data from the American Hospital Association's (AHA) annual surveys for the years 2017 to 2021 and the 2022 AHA IT Supplement Survey. SETTING: The study includes a national sample of US hospitals, covering a diverse range of hospital types including large, nonprofit, teaching, and system-affiliated institutions. PARTICIPANTS: US hospitals form the study's participants, with a substantial response rate to the surveys. MAIN MEASURES: Key metrics include the number of telemedicine patient encounters, percentage of hospitals offering telemedicine services, and institutional challenges to electronic health information exchange. KEY RESULTS: Telemedicine encounters saw a 75% increase, growing from approximately 111.4 million in 2020 to nearly 194.4 million in 2021. The percentage of hospitals offering at least one form of telemedicine service went from 46% in 2017 to 72% in 2021. Larger, nonprofit, and teaching hospitals were more prone to telehealth adoption, without notable urban-rural disparities. While over 90% of hospitals allow patients to view and download medical records, only 41% permit online data submission. Importantly, 25% of hospitals identified Certified Health IT Developers such as EHR vendor as frequent culprits in information blocking, with cost being the primary obstacle. CONCLUSIONS: The findings underscore the rapid yet uneven adoption of telemedicine services in U.S. hospitals. The results point to the need for comprehensive policy interventions to address the challenges identified and realize telemedicine's full potential in healthcare delivery and resilience.

Discrepancies in the results reported for multiple sclerosis clinical trials: A comparison between ClinicalTrials.gov and peer-reviewed journals.

OBJECTIVE: We aimed to compare the results of phase III and IV clinical trials examining drugs to treat multiple sclerosis (MS) registered at ClinicalTrials.gov to those published in peer-reviewed journals. METHODS: After identifying trials registered at ClinicalTrials.gov, consecutive searches were conducted in PubMed, EMBASE and Google Scholar for matching publications. Information regarding participants and efficacy and safety results was extracted and compared. The degree of consistency was classified as 'concordant', 'discrepant' or 'not comparable'. The Kaplan-Meier method was used to model time to reporting. RESULTS: In total, 65 trials were appraised. The median time from completion to reporting was shorter for ClinicalTrials.gov (16.4 vs 27.3 months; p = 0.010). Information availability was generally higher in journals except for serious adverse events (SAEs) (86.2% vs 100.0%, p = 0.029) and their description (78.2% vs 100.0%, p < 0.001). However, 45 trials had at least one reporting discrepancy (69.2%). Three studies omitted one or more primary outcomes in the matching journal publication. Regarding safety results, the lowest consistencies were found for causes of death (60.0%) and description of SAEs (27.9%). CONCLUSION: Consulting both ClinicalTrials.gov and journals increases the accessibility to MS clinical trial results. Some data were frequently missing or disagreed between sources, raising concerns about transparency and generalizability of results.

Association of Race and Ethnicity With Postoperative Gabapentinoid and Opioid Prescribing Trends for Older Adults.

BACKGROUND: Disparities in opioid prescribing by race/ethnicity have been described in many healthcare settings, with White patients being more likely to receive an opioid prescription than other races studied. As surgeons increase prescribing of nonopioid medications in response to the opioid epidemic, it is unknown whether postoperative prescribing disparities also exist for these medications, specifically gabapentinoids. METHODS: We conducted a retrospective cohort study using a 20% Medicare sample for 2013-2018. We included patients ≥66 years without prior gabapentinoid use who underwent one of 14 common surgical procedures. The primary outcome was the proportion of patients prescribed gabapentinoids at discharge among racial and ethnic groups. Secondary outcomes were days' supply of gabapentinoids, opioid prescribing at discharge, and oral morphine equivalent (OME) of opioid prescriptions. Trends over time were constructed by analyzing proportion of postoperative prescribing of gabapentinoids and opioids for each year. For trends by year by racial/ethnic groups, we ran a multivariable logistic regression with an interaction term of procedure year and racial/ethnic group. RESULTS: Of the 494,922 patients in the cohort (54% female, 86% White, 5% Black, 5% Hispanic, mean age 73.7 years), 3.7% received a new gabapentinoid prescription. Gabapentinoid prescribing increased over time for all groups and did not differ significantly among groups (P = 0.13). Opioid prescribing also increased, with higher proportion of prescribing to White patients than to Black and Hispanic patients in every year except 2014. CONCLUSIONS: We found no significant prescribing variation of gabapentinoids in the postoperative period between racial/ethnic groups. Importantly, we found that despite national attention to disparities in opioid prescribing, variation continues to persist in postoperative opioid prescribing, with a higher proportion of White patients being prescribed opioids, a difference that persisted over time.

Assessing Real-World Data From Electronic Health Records for Health Technology Assessment: The SUITABILITY Checklist: A Good Practices Report of an ISPOR Task Force.

This ISPOR Good Practices report provides a framework for assessing the suitability of electronic health records data for use in health technology assessments (HTAs). Although electronic health record (EHR) data can fill evidence gaps and improve decisions, several important limitations can affect its validity and relevance. The ISPOR framework includes 2 components: data delineation and data fitness for purpose. Data delineation provides a complete understanding of the data and an assessment of its trustworthiness by describing (1) data characteristics; (2) data provenance; and (3) data governance. Fitness for purpose comprises (1) data reliability items, ie, how accurate and complete the estimates are for answering the question at hand and (2) data relevance items, which assess how well the data are suited to answer the particular question from a decision-making perspective. The report includes a checklist specific to EHR data reporting: the ISPOR SUITABILITY Checklist. It also provides recommendations for HTA agencies and policy makers to improve the use of EHR-derived data over time. The report concludes with a discussion of limitations and future directions in the field, including the potential impact from the substantial and rapid advances in the diffusion and capabilities of large language models and generative artificial intelligence. The report's immediate audiences are HTA evidence developers and users. We anticipate that it will also be useful to other stakeholders, particularly regulators and manufacturers, in the future.

Feasibility of using real-world data to emulate substance use disorder clinical trials: a cross-sectional study.

INTRODUCTION: Real-world evidence is receiving considerable attention as a way to evaluate the efficacy and safety of medical products for substance use disorders (SUDs). However, the feasibility of using real-world data (RWD) to emulate clinical trials evaluating treatments for SUDs is uncertain. The aim of this study is to identify the number of clinical trials evaluating treatments for SUDs with reported results that could be feasibly emulated using observational data from contemporary insurance claims and/or electronic health record (EHR) data. METHODS: In this cross-sectional study, all phase 2-4 trials evaluating treatments for SUDs registered on ClinicalTrials.gov with reported results were identified. Each trial was evaluated to determine if the indications, interventions, at least 80% of eligibility criteria, comparators, and primary end points could be ascertained using contemporarily available administrative claims and/or structured EHR data. RESULTS: There were 272 SUD trials on ClinicalTrials.gov with reported results. Of these, when examining feasibility using contemporarily available administrative claims and/or structured EHR data, 262 (96.3%) had indications that were ascertainable; 194 (71.3%) had interventions that were ascertainable; 21 (7.7%) had at least 80% of eligibility criteria that were ascertainable; 17 (6.3%) had active comparators that were ascertainable; and 61 (22.4%) had primary end points that were ascertainable. In total, there were no trials for which all 5 characteristics were ascertainable using contemporarily available administrative claims and/or structured EHR data. When considering placebo comparators as ascertainable, there were 6 (2.2%) trials that had all 5 key characteristics classified as ascertainable from contemporarily available administrative claims and/or structured EHR data. CONCLUSIONS: No trials evaluating treatments for SUDs could be feasibly emulated using contemporarily available RWD, demonstrating a need for an increase in the resolution of data capture within a public health system to facilitate trial emulation.

Use of electronic health records to characterize patients with uncontrolled hypertension in two large health system networks.

BACKGROUND: Improving hypertension control is a public health priority. However, consistent identification of uncontrolled hypertension using computable definitions in electronic health records (EHR) across health systems remains uncertain. METHODS: In this retrospective cohort study, we applied two computable definitions to the EHR data to identify patients with controlled and uncontrolled hypertension and to evaluate differences in characteristics, treatment, and clinical outcomes between these patient populations. We included adult patients (≥ 18 years) with hypertension (based on either ICD-10 codes of hypertension or two elevated blood pressure [BP] measurements) receiving ambulatory care within Yale-New Haven Health System (YNHHS; a large US health system) and OneFlorida Clinical Research Consortium (OneFlorida; a Clinical Research Network comprised of 16 health systems) between October 2015 and December 2018. We identified patients with controlled and uncontrolled hypertension based on either a single BP measurement from a randomly selected visit or all BP measurements recorded between hypertension identification and the randomly selected visit). RESULTS: Overall, 253,207 and 182,827 adults at YNHHS and OneFlorida were identified as having hypertension. Of these patients, 83.1% at YNHHS and 76.8% at OneFlorida were identified using ICD-10-CM codes, whereas 16.9% and 23.2%, respectively, were identified using elevated BP measurements (≥ 140/90 mmHg). A total of 24.1% of patients at YNHHS and 21.6% at OneFlorida had both diagnosis code for hypertension and elevated blood pressure measurements. Uncontrolled hypertension was observed among 32.5% and 43.7% of patients at YNHHS and OneFlorida, respectively. Uncontrolled hypertension was disproportionately higher among Black patients when compared with White patients (38.9% versus 31.5% in YNHHS; p < 0.001; 49.7% versus 41.2% in OneFlorida; p < 0.001). Medication prescription for hypertension management was more common in patients with uncontrolled hypertension when compared with those with controlled hypertension (overall treatment rate: 39.3% versus 37.3% in YNHHS; p = 0.04; 42.2% versus 34.8% in OneFlorida; p < 0.001). Patients with controlled and uncontrolled hypertension had similar incidence rates of deaths, CVD events, and healthcare visits at 3, 6, 12, and 24 months. The two computable definitions generated consistent results. CONCLUSIONS: While the current EHR systems are not fully optimized for disease surveillance and stratification, our findings illustrate the potential of leveraging EHR data to conduct digital population surveillance in the realm of hypertension management.

Trustworthy and ethical AI-enabled cardiovascular care: a rapid review.

BACKGROUND: Artificial intelligence (AI) is increasingly used for prevention, diagnosis, monitoring, and treatment of cardiovascular diseases. Despite the potential for AI to improve care, ethical concerns and mistrust in AI-enabled healthcare exist among the public and medical community. Given the rapid and transformative recent growth of AI in cardiovascular care, to inform practice guidelines and regulatory policies that facilitate ethical and trustworthy use of AI in medicine, we conducted a literature review to identify key ethical and trust barriers and facilitators from patients' and healthcare providers' perspectives when using AI in cardiovascular care. METHODS: In this rapid literature review, we searched six bibliographic databases to identify publications discussing transparency, trust, or ethical concerns (outcomes of interest) associated with AI-based medical devices (interventions of interest) in the context of cardiovascular care from patients', caregivers', or healthcare providers' perspectives. The search was completed on May 24, 2022 and was not limited by date or study design. RESULTS: After reviewing 7,925 papers from six databases and 3,603 papers identified through citation chasing, 145 articles were included. Key ethical concerns included privacy, security, or confidentiality issues (n = 59, 40.7%); risk of healthcare inequity or disparity (n = 36, 24.8%); risk of patient harm (n = 24, 16.6%); accountability and responsibility concerns (n = 19, 13.1%); problematic informed consent and potential loss of patient autonomy (n = 17, 11.7%); and issues related to data ownership (n = 11, 7.6%). Major trust barriers included data privacy and security concerns, potential risk of patient harm, perceived lack of transparency about AI-enabled medical devices, concerns about AI replacing human aspects of care, concerns about prioritizing profits over patients' interests, and lack of robust evidence related to the accuracy and limitations of AI-based medical devices. Ethical and trust facilitators included ensuring data privacy and data validation, conducting clinical trials in diverse cohorts, providing appropriate training and resources to patients and healthcare providers and improving their engagement in different phases of AI implementation, and establishing further regulatory oversights. CONCLUSION: This review revealed key ethical concerns and barriers and facilitators of trust in AI-enabled medical devices from patients' and healthcare providers' perspectives. Successful integration of AI into cardiovascular care necessitates implementation of mitigation strategies. These strategies should focus on enhanced regulatory oversight on the use of patient data and promoting transparency around the use of AI in patient care.

Rapid, accurate publication and dissemination of clinical trial results: benefits and challenges.

Large-scale clinical trials are essential in cardiology and require rapid, accurate publication, and dissemination. Whereas conference presentations, press releases, and social media disseminate information quickly and often receive considerable coverage by mainstream and healthcare media, they lack detail, may emphasize selected data, and can be open to misinterpretation. Preprint servers speed access to research manuscripts while awaiting acceptance for publication by a journal, but these articles are not formally peer-reviewed and sometimes overstate the findings. Publication of trial results in a major journal is very demanding but the use of existing checklists can help accelerate the process. In case of rejection, procedures such as easing formatting requirements and possibly carrying over peer-review to other journals could speed resubmission. Secondary publications can help maximize benefits from clinical trials; publications of secondary endpoints and subgroup analyses further define treatment effects and the patient populations most likely to benefit. These rely on data access, and although data sharing is becoming more common, many challenges remain. Beyond publication in medical journals, there is a need for wider knowledge dissemination to maximize impact on clinical practice. This might be facilitated through plain language summary publications. Social media, websites, mainstream news outlets, and other publications, although not peer-reviewed, are important sources of medical information for both the public and for clinicians. This underscores the importance of ensuring that the information is understandable, accessible, balanced, and trustworthy. This report is based on discussions held on December 2021, at the 18th Global Cardiovascular Clinical Trialists meeting, involving a panel of editors of some of the top medical journals, as well as members of the lay press, industry, and clinical trialists.

Associations Between Surrogate Markers and Clinical Outcomes for Nononcologic Chronic Disease Treatments.

Importance: Surrogate markers are increasingly used as primary end points in clinical trials supporting drug approvals. Objective: To systematically summarize the evidence from meta-analyses, systematic reviews and meta-analyses, and pooled analyses (hereafter, meta-analyses) of clinical trials examining the strength of association between treatment effects measured using surrogate markers and clinical outcomes in nononcologic chronic diseases. Data sources: The Food and Drug Administration (FDA) Adult Surrogate Endpoint Table and MEDLINE from inception to March 19, 2023. Study Selection: Three reviewers selected meta-analyses of clinical trials; meta-analyses of observational studies were excluded. Data Extraction and Synthesis: Two reviewers extracted correlation coefficients, coefficients of determination, slopes, effect estimates, or results from meta-regression analyses between surrogate markers and clinical outcomes. Main Outcomes and Measures: Correlation coefficient or coefficient of determination, when reported, was classified as high strength (r ≥ 0.85 or R2 ≥ 0.72); primary findings were otherwise summarized. Results: Thirty-seven surrogate markers listed in FDA's table and used as primary end points in clinical trials across 32 unique nononcologic chronic diseases were included. For 22 (59%) surrogate markers (21 chronic diseases), no eligible meta-analysis was identified. For 15 (41%) surrogate markers (14 chronic diseases), at least 1 meta-analysis was identified, 54 in total (median per surrogate marker, 2.5; IQR, 1.3-6.0); among these, median number of trials and patients meta-analyzed was 18.5 (IQR, 12.0-43.0) and 90 056 (IQR, 20 109-170 014), respectively. The 54 meta-analyses reported 109 unique surrogate marker-clinical outcome pairs: 59 (54%) reported at least 1 r or R2, 10 (17%) of which reported at least 1 classified as high strength, whereas 50 (46%) reported slopes, effect estimates, or results of meta-regression analyses only, 26 (52%) of which reported at least 1 statistically significant result. Conclusions and Relevance: Most surrogate markers used as primary end points in clinical trials to support FDA approval of drugs treating nononcologic chronic diseases lacked high-strength evidence of associations with clinical outcomes from published meta-analyses.

Measuring Equity in Readmission as a Distinct Assessment of Hospital Performance.

Importance: Equity is an essential domain of health care quality. The Centers for Medicare & Medicaid Services (CMS) developed 2 Disparity Methods that together assess equity in clinical outcomes. Objectives: To define a measure of equitable readmissions; identify hospitals with equitable readmissions by insurance (dual eligible vs non-dual eligible) or patient race (Black vs White); and compare hospitals with and without equitable readmissions by hospital characteristics and performance on accountability measures (quality, cost, and value). Design, Setting, and Participants: Cross-sectional study of US hospitals eligible for the CMS Hospital-Wide Readmission measure using Medicare data from July 2018 through June 2019. Main Outcomes and Measures: We created a definition of equitable readmissions using CMS Disparity Methods, which evaluate hospitals on 2 methods: outcomes for populations at risk for disparities (across-hospital method); and disparities in care within hospitals' patient populations (within-a-single-hospital method). Exposures: Hospital patient demographics; hospital characteristics; and 3 measures of hospital performance-quality, cost, and value (quality relative to cost). Results: Of 4638 hospitals, 74% served a sufficient number of dual-eligible patients, and 42% served a sufficient number of Black patients to apply CMS Disparity Methods by insurance and race. Of eligible hospitals, 17% had equitable readmission rates by insurance and 30% by race. Hospitals with equitable readmissions by insurance or race cared for a lower percentage of Black patients (insurance, 1.9% [IQR, 0.2%-8.8%] vs 3.3% [IQR, 0.7%-10.8%], P < .01; race, 7.6% [IQR, 3.2%-16.6%] vs 9.3% [IQR, 4.0%-19.0%], P = .01), and differed from nonequitable hospitals in multiple domains (teaching status, geography, size; P < .01). In examining equity by insurance, hospitals with low costs were more likely to have equitable readmissions (odds ratio, 1.57 [95% CI, 1.38-1.77), and there was no relationship between quality and value, and equity. In examining equity by race, hospitals with high overall quality were more likely to have equitable readmissions (odds ratio, 1.14 [95% CI, 1.03-1.26]), and there was no relationship between cost and value, and equity. Conclusion and Relevance: A minority of hospitals achieved equitable readmissions. Notably, hospitals with equitable readmissions were characteristically different from those without. For example, hospitals with equitable readmissions served fewer Black patients, reinforcing the role of structural racism in hospital-level inequities. Implementation of an equitable readmission measure must consider unequal distribution of at-risk patients among hospitals.

Physician responses to apple watch-detected irregular rhythm alerts.

BACKGROUND: While the US Food and Drug Administration (FDA) has cleared smartwatch software for detecting atrial fibrillation (AF), there is lack of guidance on management by physicians. We sought to evaluate the approach to management of Apple Watch alerts for AF by physicians and assess whether respondent and case characteristics were associated with their approach. METHODS: We conducted a case-based survey of physicians practicing primary care, emergency medicine, and cardiology at 2 large academic centers (Yale and University of California San Francisco) between September and December 2021. Cases described asymptomatic patients receiving Apple Watch AF alerts; cases varied in sex, race, medical history, and notification frequency. We evaluated physician responses among prespecified diagnostic testing, referral, and treatment options. RESULTS: We emailed 636 physicians, of whom 95 (14.9%) completed the survey, including 39 primary care, 25 emergency medicine, and 31 cardiology physicians. Among a total of 192 cases (16 unique scenarios), physicians selected at least one diagnostic test in 191 (99.5%) cases and medications in 48 (25.0%). Physicians in primary care, emergency medicine, and cardiology reported varying preference for patient referral (14%, 30%, and 16%, respectively; P=.048), rhythm monitoring (84%, 46%, and 94%, respectively; P<.001), measurement of BNP (8%, 20%, and 2%; P=.003), and use of antiarrhythmics (16%, 4%, and 23%; P=.023). There were few physician differences in reported practices across patient demographics (sex and race), clinical complexity, and alert frequency of the clinical case. CONCLUSIONS: In hypothetical cases of patients presenting without clinical symptoms, physicians opted for further diagnostic testing and often to medical intervention based on Apple Watch irregular rhythm notifications. There was also considerable variation across physician specialties, suggesting a need for uniform clinical practice guidelines. Additional study is required before irregular rhythm notifications should be used in clinical settings.

Identifying treatment heterogeneity in atrial fibrillation using a novel causal machine learning method.

BACKGROUND: Lifelong oral anticoagulation is recommended in patients with atrial fibrillation (AF) to prevent stroke. Over the last decade, multiple new oral anticoagulants (OACs) have expanded the number of treatment options for these patients. While population-level effectiveness of OACs has been compared, it is unclear if there is variability in benefit and risk across patient subgroups. METHODS: We analyzed claims and medical data for 34,569 patients who initiated a nonvitamin K antagonist oral anticoagulant (non-vitamin K antagonist oral anticoagulant (NOAC); apixaban, dabigatran, and rivaroxaban) or warfarin for nonvalvular AF between 08/01/2010 and 11/29/2017 from the OptumLabs Data Warehouse. A machine learning (ML) method was applied to match different OAC groups on several baseline variables including, age, sex, race, renal function, and CHA2DS2 -VASC score. A causal ML method was then used to discover patient subgroups characterizing the head-to-head treatment effects of the OACs on a primary composite outcome of ischemic stroke, intracranial hemorrhage, and all-cause mortality. RESULTS: The mean age, number of females and white race in the entire cohort of 34,569 patients were 71.2 (SD, 10.7) years, 14,916 (43.1%), and 25,051 (72.5%) respectively. During a mean follow-up of 8.3 (SD, 9.0) months, 2,110 (6.1%) of patients experienced the composite outcome, of whom 1,675 (4.8%) died. The causal ML method identified 5 subgroups with variables favoring apixaban over dabigatran; 2 subgroups favoring apixaban over rivaroxaban; 1 subgroup favoring dabigatran over rivaroxaban; and 1 subgroup favoring rivaroxaban over dabigatran in terms of risk reduction of the primary endpoint. No subgroup favored warfarin and most dabigatran vs warfarin users favored neither drug. The variables that most influenced favoring one subgroup over another included Age, history of ischemic stroke, thromboembolism, estimated glomerular filtration rate, Race, and myocardial infarction. CONCLUSIONS: Among patients with AF treated with a NOAC or warfarin, a causal ML method identified patient subgroups with differences in outcomes associated with OAC use. The findings suggest that the effects of OACs are heterogeneous across subgroups of AF patients, which could help personalize the choice of OAC. Future prospective studies are needed to better understand the clinical impact of the subgroups with respect to OAC selection.

Evidence of publication bias in multiple sclerosis clinical trials: a comparative analysis of published and unpublished studies registered in ClinicalTrials.gov.

BACKGROUND: Complete and timely publication of clinical trials ensures that patients and the medical community are fully informed when making treatment decisions. The aim of this study is to assess the publication of phase III and IV clinical trials on multiple sclerosis (MS) drugs that have been carried out between 2010 and 2019 and to identify the factors associated with their publication in peer-reviewed journals. METHODS: An advanced search in ClinicalTrials.gov was performed and consecutive searches in PubMed, EMBASE and Google Scholar were conducted looking for the associated publications of all completed trials. Study design characteristics, results and other relevant information were extracted. Data was analysed following a case-control design. Clinical trials with associated publications in peer-reviewed journals were the cases and unpublished trials were the controls. A multivariate logistic regression analysis was performed to identify factors associated with trial publication. RESULTS: One hundred and fifty clinical trials were included in the analysis. Ninety-six of them (64.0%) were published in peer-reviewed journals. In the multivariate analysis, factors associated with trial publication were a favourable primary outcome (OR 12.49, 95% CI 1.28 to 122.29) and reaching the originally estimated sample size (OR 41.97, 95% CI 1.96 to 900.48), while those associated with a lower odds of publication were having 20% or more patients lost to follow-up (OR 0.03, 95% CI 0.01 to 0.52) and evaluating drugs intended to improve treatment tolerability (OR 0.01, 95% CI 0.00 to 0.74). CONCLUSIONS: Phase III and IV clinical trials on MS drugs are prone to under-reporting and publication bias. Efforts must be made to promote a complete and accurate dissemination of data in MS clinical research.

The Changing Medical Publishing Industry: Economics, Expansion, and Equity.

Medical journal publishing has changed dramatically over the past decade. The shift from print to electronic distribution altered the industry's economic model. This was followed by open access mandates from funding organizations and the subsequent imposition of article processing charges on authors. The medical publishing industry is large and while there is variation across journals, it is overall highly profitable. As journals have moved to digital dissemination, advertising revenues decreased and publishers shifted some of the losses onto authors by way of article processing charges. The number of open access journals has increased substantially in recent years. The open access model presents an equity paradox; while it liberates scientific knowledge for the consumer, it presents barriers to those who produce research. This emerging "pay-to-publish" system offers advantages to authors who work in countries and at institutes with more resources. Finally, the medical publishing industry represents an unusual business model; the people who provide both the content and the external peer review receive no payment from the publisher, who generates revenue from the content. The very unusual economic model of this industry makes it vulnerable to disruptive change. The economic model of medical publishing is rapidly evolving and this will lead to disruption of the industry. These changes will accelerate dissemination of science and may lead to a shift away from lower-impact journals towards pre-print servers.

Press Releases of Drug-Related Randomized Trial Results Prior to Publication in High-Impact Journals: an Observational Study.

IMPORTANCE: Results from high-profile randomized controlled trials (RCTs) are routinely reported through press release months prior to peer-reviewed publication. There are potential benefits to press releases (e.g., knowledge dissemination, ensuring regulatory compliance), but also potential drawbacks (e.g., selective reporting, positive "spin"). OBJECTIVE: To characterize the practice of press release predating the publication of a drug-related RCT in a peer-reviewed journal ("preemptive press release"), including factors associated with this practice. DESIGN, SETTING, AND PARTICIPANTS: We systematically reviewed all RCTs of medications published between 2015 and 2019 in the New England Journal of Medicine (NEJM), Journal of the American Medical Association (JAMA), and Lancet. Press releases were identified using a systematic search of the grey literature (e.g., press release databases, study sponsor websites). An RCT was considered to have a preemptive press release if the press release was published at least three months (90 days) prior to the date of publication in a peer-reviewed journal. MAIN OUTCOMES AND MEASURES: Presence of preemptive press release, defined as a press-release at least 90 days prior to the date of publication in a peer-reviewed journal. As secondary measures for dissemination, we also assessed citation count and Altmetric score. RESULTS: We identified 988 RCTs, of which 172 (17%) had a press release published at least 90 days before the date of peer-reviewed publication. Press releases were published a median of 246 days (interquartile range [IQR] 169-366 days) before publication in a peer-reviewed journal. In the multivariable logistic regression model, the strongest predictor of having a preemptive press release was funding by a pharmaceutical company (odds ratio 13, 95% CI 7, 25). Approximately 85% of RCTs with preemptive press releases had a positive primary outcome and, concordantly, 81% of the corresponding press releases had a positive headline. Multivariable regression models identified studies with a preemptive press release had a similar Altmetric score (median - 15, 95% CI - 33, 12) and higher median citation count (median 22 [95% CI 10 to 33] compared to studies without a preemptive press release. CONCLUSIONS AND RELEVANCE: Preemptive press releases were common, most often issued for trials funded by a pharmaceutical company, and typically preceded publication in a peer-reviewed journal by approximately eight months.

Assessing the use of observational methods and real-world data to emulate ongoing randomized controlled trials.

BACKGROUND/AIMS: There has been growing interest in better understanding the potential of observational research methods in medical product evaluation and regulatory decision-making. Previously, we used linked claims and electronic health record data to emulate two ongoing randomized controlled trials, characterizing the populations and results of each randomized controlled trial prior to publication of its results. Here, our objective was to compare the populations and results from the emulated trials with those of the now-published randomized controlled trials. METHODS: This study compared participants' demographic and clinical characteristics and study results between the emulated trials, which used structured data from OptumLabs Data Warehouse, and the published PRONOUNCE and GRADE trials. First, we examined the feasibility of implementing the baseline participant characteristics included in the published PRONOUNCE and GRADE trials' using real-world data and classified each variable as ascertainable, partially ascertainable, or not ascertainable. Second, we compared the emulated trials and published randomized controlled trials for baseline patient characteristics (concordance determined using standardized mean differences <0.20) and results of the primary and secondary endpoints (concordance determined by direction of effect estimates and statistical significance). RESULTS: The PRONOUNCE trial enrolled 544 participants, and the emulated trial included 2226 propensity score-matched participants. In the PRONOUNCE trial publication, one of the 32 baseline participant characteristics was listed as an exclusion criterion on ClinicalTrials.gov but was ultimately not used. Among the remaining 31 characteristics, 9 (29.0%) were ascertainable, 11 (35.5%) were partially ascertainable, and 10 (32.2%) were not ascertainable using structured data from OptumLabs. For one additional variable, the PRONOUNCE trial did not provide sufficient detail to allow its ascertainment. Of the nine variables that were ascertainable, values in the emulated trial and published randomized controlled trial were discordant for 6 (66.7%). The primary endpoint of time from randomization to the first major adverse cardiovascular event and secondary endpoints of nonfatal myocardial infarction and stroke were concordant between the emulated trial and published randomized controlled trial. The GRADE trial enrolled 5047 participants, and the emulated trial included 7540 participants. In the GRADE trial publication, 8 of 34 (23.5%) baseline participant characteristics were ascertainable, 14 (41.2%) were partially ascertainable, and 11 (32.4%) were not ascertainable using structured data from OptumLabs. For one variable, the GRADE trial did not provide sufficient detail to allow for ascertainment. Of the eight variables that were ascertainable, values in the emulated trial and published randomized controlled trial were discordant for 4 (50.0%). The primary endpoint of time to hemoglobin A1c ≥7.0% was mostly concordant between the emulated trial and the published randomized controlled trial. CONCLUSION: Despite challenges, observational methods and real-world data can be leveraged in certain important situations for a more timely evaluation of drug effectiveness and safety in more diverse and representative patient populations.

Use of Recalled Devices in New Device Authorizations Under the US Food and Drug Administration's 510(k) Pathway and Risk of Subsequent Recalls.

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.

Association of Psychiatric Diagnoses and Medicaid Coverage with Length of Stay Among Inpatients Discharged to Skilled Nursing Facilities.

BACKGROUND: Inpatients with psychiatric diagnoses often require higher levels of care in skilled nursing facilities (SNFs) and are more likely to be covered by Medicaid, which reimburses SNFs at significantly lower rates than Medicare and commercial payors. OBJECTIVE: To characterize factors affecting length of stay in inpatients discharged to SNFs. DESIGN: A retrospective cross-sectional study design using 2016-2018 data from National Inpatient Sample. PARTICIPANTS: Inpatients aged ≥ 40 who were discharged to SNFs. EXPOSURES: Primary discharge diagnosis (medical, psychiatric, or substance use) and primary payor. MAIN OUTCOMES AND MEASURES: Length of stay, categorized non-exclusively as >3 days, >7 days, or > 14 days. RESULTS: Among 9,821,155 inpatient discharges to SNFs between 2016 and 2018, 95.7% had medical primary discharge diagnoses, 3.3% psychiatric diagnoses, and 1.0% substance use diagnoses; Medicare was the most common primary payor (83.3%), followed by private insurance (7.9%), Medicaid (6.6%), and others (2.2%). Median length of stay for all patients was 5.0 days (interquartile range [IQR], 3.0-8.0), 5.0 (IQR, 3.0-8.0) for those with medical diagnoses, 8.0 (IQR, 4.0-15.0) for psychiatric diagnoses, and 5.0 (IQR, 3.0-8.0) for substance use diagnoses. After multivariable adjustment, compared to patients with medical diagnoses, patients with psychiatric diagnoses were more likely to have hospital stays > 3, > 7, and > 14 days, respectively (p < 0.001). Compared to Medicare patients, Medicaid patients were more likely to have hospital stays > 3, > 7, and > 14 days, respectively (p < 0.001). Compared to patients with medical diagnoses, those with psychiatric diagnoses were also more likely to have lengths of stay 1 times, 1.5 times, and 2 times greater than the national geometric mean length of stay for that diagnosis-related group (p < 0.001). CONCLUSIONS: Patients discharged to SNFs after inpatient hospitalization for psychiatric diagnoses and with Medicaid coverage were more likely to have longer lengths of stay than patients with medical diagnoses and those with Medicare coverage, respectively.