Evaluating the robustness of an AI pathfinder application on eligibility criteria in multiple myeloma trials using real-world data and historical trials.

Background: Eligibility criteria are pivotal in achieving clinical trial success, enabling targeted patient enrollment while ensuring the trial safety. However, overly restrictive criteria hinder enrollment and study result generalizability. Broadening eligibility criteria enhances the trial inclusivity, diversity and enrollment pace. Liu et al. proposed an AI pathfinder method leveraging real-world data to broaden criteria without compromising efficacy and safety outcomes, demonstrating promise in non-small cell lung cancer trials. Aim: To assess the robustness of the methodology, considering diverse qualities of real-world data and to promote its application. Materials/Methods: We revised the AI pathfinder method, applied it to relapsed and refractory multiple myeloma trials and compared it using two real-world data sources. We modified the assessment and considered a bootstrap confidence interval of the AI pathfinder to enhance the decision robustness. Results & conclusion: Our findings confirmed the AI pathfinder's potential in identifying certain eligibility criteria, in other words, prior complications and laboratory tests for relaxation or removal. However, a robust quantitative assessment, accounting for trial variability and real-world data quality, is crucial for confident decision-making and prioritizing safety alongside efficacy.

Quantitative evaluation of the impact of relaxing eligibility criteria on the risk-benefit profile of drugs for lung cancer based on real-world data.

INTRODUCTION: Restrictive eligibility criteria in cancer drug trials result in low enrollment rates and limited population diversity. Relaxed eligibility criteria (REC) based on solid evidence is becoming necessary for stakeholders worldwide. However, the absence of high-quality, favorable evidence remains a major challenge. This study presents a protocol to quantitatively evaluate the impact of relaxing eligibility criteria in common non-small cell lung cancer (NSCLC) protocols in China, on the risk-benefit profile. This involves a detailed explanation of the rationale, framework, and design of REC. METHODS: To evaluate our REC in NSCLC drug trials, we will first construct a structured, cross-dimensional real-world NSCLC database using deep learning methods. We will then establish randomized virtual cohorts and perform benefit-risk assessment using Monte Carlo simulation and propensity matching. Shapley value will be utilized to quantitatively measure the effect of the change of each eligibility criterion on patient volume, clinical efficacy and safety. DISCUSSION: This study is one of the few that focuses on the problem of overly stringent eligibility criteria cancer drug clinical trials, providing quantitative evaluation of the effect of relaxing each NSCLC eligibility criterion. This study will not only provide scientific evidence for the rational design of population inclusion in lung cancer clinical trials, but also establish a data governance system, as well as a REC evaluation framework that can be generalized to other cancer studies.

Transforming diabetes care with continuous glucose monitoring: Rationale for expanded eligibility criteria.

ABSTRACT: Randomized trials and large retrospective database studies have shown that individuals treated with noninsulin therapies experience the same glycemic benefits from continuous glucose monitoring (CGM) use as those treated with more intensive treatment regimens. However, many policy makers and payers are reluctant to provide CGM coverage for these patients. Although the recent American Diabetes Association guidelines have taken an important first step in recommending that CGM should be offered to all adults treated with basal insulin who are capable of using it, clinicians should consider the acute and long-term consequences of persistent hyperglycemia in all of their patients with diabetes. This article describes how the use of the FreeStyle Libre 2 CGM System (Abbott Diabetes Care, Alameda, CA) improved glycemic outcomes and facilitates personalized diabetes care in two type 2 diabetes patients treated with noninsulin therapies.

Revisiting the lung cancer screening eligibility criteria to promote equity for Black individuals.

BACKGROUND: Early detection using low-dose computed tomography reduces lung-cancer-specific mortality by 20% among high-risk individuals. Blacks are less likely than Whites to meet lung cancer screening (LCS) criteria under both the former and the updated United States Preventive Services Task Force (USPSTF) guidelines. The purpose of this study was to assess racial disparities in LCS eligibility and to propose tailored eligibility criteria for Blacks to enable equitable screening rate between Whites and Blacks. METHODS: Data for this study were obtained from the Behavioral Risk Factor Surveillance System (2017-2021). 101,552 subjects were included in the final analysis. By employing a systematic approach, we sought cut-off points at which Blacks were equally likely as Whites to be eligible for LCS. We evaluated the minimum age and smoking pack-years for Blacks while we retained the 2021 USPSTF criteria for Whites. The final decision was based on the minimum Wald's Chi-square statistics. RESULTS: The model we employed identified cut-off points at which Blacks were equally likely as Whites to be eligible for LCS. Retaining the 2021 USPSTF criteria for Whites, the model discovered a new pair of points for Blacks by reducing the minimum age to 43 years and decreasing the cumulative number of cigarettes smoked to 15 pack-years. Based on these cut-off points, we created tailored criteria for Blacks. Under the tailored criteria, Blacks (OR: 1.00; 95 %CI: 0.88-1.14) had the same odds of eligibility for LCS as Whites. The odds of eligibility for LCS by sex under the tailored criteria did not differ significantly for Black men (OR: 1.02; 95 %CI: 0.85-1.24) and Black women (OR: 0.95; 95 %CI: 0.81-1.12) compared to their respective White counterparts. CONCLUSIONS: These tailored criteria for Blacks eliminate the disparities between Blacks and Whites in LCS eligibility. Future studies should test the sensitivity and specificity of these tailored criteria.

[Development of an artificial intelligence system to improve cancer clinical trial eligibility screening]. / Développement d'une solution d'intelligence artificielle pour améliorer le screening en recherche clinique.

INTRODUCTION: The recruitment step of all clinical trials is time consuming, harsh and generate extra costs. Artificial intelligence tools could improve recruitment in order to shorten inclusion phase. The objective was to assess the performance of an artificial intelligence driven tool (text mining, machine learning, classification…) for the screening and detection of patients, potentially eligible for recruitment in one of the clinical trials open at the "Institut de Cancérologie de Lorraine". METHODS: Computerized clinical data during the first medical consultation among patients managed in an anticancer center over the 2019-2023 period were used to study the performances of an artificial intelligence tool (SAS® Viya). Recall, precision and F1-score were used to determine the artificial intelligence algorithm effectiveness. Time saved on screening was determined by the difference between the time taken using the artificial intelligence-assisted method and that taken using the standard method in clinical trial participant screening. RESULTS: Out of 9876 patients included in the study, the artificial intelligence algorithm obtained the following scores: precision of 96 %, recall of 94 % and a 0.95 F1-score to detect patients with breast cancer (n=2039) and potentially eligible for inclusion in a clinical trial. The screening of 258 potentially eligible patient's files took 20s per file vs. 5min and 6s with standard method. DISCUSSION: This study suggests that artificial intelligence could yield sizable improvements over standard practices in several aspects of the patient screening process, as well as in approaches to feasibility, site selection, and trial selection.

Use of Race-Specific Equations in Pulmonary Function Tests Impedes Potential Eligibility for Care and Treatment of Pulmonary Fibrosis.

Rationale: The use of race-specific reference values to evaluate pulmonary function has long been embedded into clinical practice; however, there is a growing consensus that this practice may be inappropriate and that the use of race-neutral equations should be adopted to improve access to health care. Objectives: To evaluate whether the use of race-neutral equations to assess percent predicted forced vital capacity (FVC%pred) impacts eligibility for clinical trials, antifibrotic therapy, and referral for lung transplantation in Black, Hispanic/Latino, and White patients with interstitial lung disease (ILD). Methods: FVC%pred values for patients from the Pulmonary Fibrosis Foundation Patient Registry were calculated using race-specific (Hankinson and colleagues, 1999), race-agnostic (Global Lung Function Initiative [GLI]-2012), and race-neutral (GLI-2022 or GLI-Global) equations. Eligibility for ILD clinical trials (FVC%pred >45% and <90%), antifibrotic therapy (FVC%pred >55% and <82%), and lung transplantation referral (FVC%pred <70%) based on GLI-2022 and GLI-2012 equations were compared with those based on the Hankinson 1999 equation. Results: Baseline characteristics were available for 1,882 patients (Black, n = 104; Hispanic/Latino, n = 103; White, n = 1,675), and outcomes were evaluated in 1,531 patients with FVC%pred within ±90 days of registry enrollment (Black, n = 78; Hispanic/Latino, n = 72; White, n = 1,381). Black patients were younger at the time of consent and more likely to be female compared with Hispanic/Latino or White patients. Compared with GLI-2022, the Hankinson 1999 equation misclassified 22% of Black patients, 14% of Hispanic/Latino patients, and 12% of White patients for ILD clinical trial eligibility; 21% of Black patients, 17% of Hispanic/Latino patients, and 19% of White patients for antifibrotic therapy eligibility; and 6% of Black patients, 14% of Hispanic/Latino patients, and 12% of White patients for lung transplantation referral. Similar trends were observed when comparing the GLI-2012 and Hankinson 1999 equations. Conclusions: Misclassification of patients for critical interventions is highly prevalent when using the Hankinson 1999 equation and highlights the need to consider adopting the race-neutral GLI-2022 equation for enhanced accuracy and more equitable representation in pulmonary health care. Our results make a compelling case for reevaluating the use of race as a physiological variable and emphasize the pressing need for continuous innovation to ensure equal and optimal care for all patients regardless of their race or ethnicity. Clinical trial registered with www.clinicaltrials.gov (NCT02758808).

Fabry-specific treatment in Australia: time to align eligibility criteria with international best practices.

BACKGROUND: Disease-specific therapy aims to improve symptoms, stabilise current disease and delay progression in patients with Fabry disease. In Australia, treatment access is subject to eligibility criteria initially established in 2004. Patients and their clinicians question why these criteria have remained unchanged despite significant progress in disease understanding. AIMS: Appraise the clinical quality of the Australian treatment access criteria. METHODS: The Fabry Australia Medical Advisory Committee (N = 6) used the Appraisal of Guidelines for REsearch and Evaluation Global Rating Scale (AGREE II GRS) to assess the clinical quality of the current treatment eligibility criteria. They reviewed the literature, developed 17 clinical statements to help guide reforms of the eligibility criteria and achieved consensus (achievement of ≥75% agreement in the range 5-7 on a 7-point Likert scale) through anonymous voting. The findings were applied to develop proposals for revised classification and treatment initiation criteria. RESULTS: The current treatment eligibility criteria underperformed on the AGREE II GRS. They are pragmatic but out-of-step with contemporary data. Consensus was achieved on all 17 proposed clinical statements. There was strong agreement to differentiate classical male Fabry patients to facilitate timelier access to Fabry-specific treatment. There was also agreement on the value of adopting relevant organ involvement criteria in classical female patients and patients with non-classical disease. CONCLUSIONS: Australian access criteria are misaligned with current clinical evidence. The clinical statements and proposed classification and initiation criteria should prompt discussions to support more equitable access to treatment and better align Australian practice with contemporary evidence and international guidelines.

AutoCriteria: a generalizable clinical trial eligibility criteria extraction system powered by large language models.

OBJECTIVES: We aim to build a generalizable information extraction system leveraging large language models to extract granular eligibility criteria information for diverse diseases from free text clinical trial protocol documents. We investigate the model's capability to extract criteria entities along with contextual attributes including values, temporality, and modifiers and present the strengths and limitations of this system. MATERIALS AND METHODS: The clinical trial data were acquired from https://ClinicalTrials.gov/. We developed a system, AutoCriteria, which comprises the following modules: preprocessing, knowledge ingestion, prompt modeling based on GPT, postprocessing, and interim evaluation. The final system evaluation was performed, both quantitatively and qualitatively, on 180 manually annotated trials encompassing 9 diseases. RESULTS: AutoCriteria achieves an overall F1 score of 89.42 across all 9 diseases in extracting the criteria entities, with the highest being 95.44 for nonalcoholic steatohepatitis and the lowest of 84.10 for breast cancer. Its overall accuracy is 78.95% in identifying all contextual information across all diseases. Our thematic analysis indicated accurate logic interpretation of criteria as one of the strengths and overlooking/neglecting the main criteria as one of the weaknesses of AutoCriteria. DISCUSSION: AutoCriteria demonstrates strong potential to extract granular eligibility criteria information from trial documents without requiring manual annotations. The prompts developed for AutoCriteria generalize well across different disease areas. Our evaluation suggests that the system handles complex scenarios including multiple arm conditions and logics. CONCLUSION: AutoCriteria currently encompasses a diverse range of diseases and holds potential to extend to more in the future. This signifies a generalizable and scalable solution, poised to address the complexities of clinical trial application in real-world settings.

Automated Matching of Patients to Clinical Trials: A Patient-Centric Natural Language Processing Approach for Pediatric Leukemia.

PURPOSE: Matching patients to clinical trials is cumbersome and costly. Attempts have been made to automate the matching process; however, most have used a trial-centric approach, which focuses on a single trial. In this study, we developed a patient-centric matching tool that matches patient-specific demographic and clinical information with free-text clinical trial inclusion and exclusion criteria extracted using natural language processing to return a list of relevant clinical trials ordered by the patient's likelihood of eligibility. MATERIALS AND METHODS: Records from pediatric leukemia clinical trials were downloaded from ClinicalTrials.gov. Regular expressions were used to discretize and extract individual trial criteria. A multilabel support vector machine (SVM) was trained to classify sentence embeddings of criteria into relevant clinical categories. Labeled criteria were parsed using regular expressions to extract numbers, comparators, and relationships. In the validation phase, a patient-trial match score was generated for each trial and returned in the form of a ranked list for each patient. RESULTS: In total, 5,251 discretized criteria were extracted from 216 protocols. The most frequent criterion was previous chemotherapy/biologics (17%). The multilabel SVM demonstrated a pooled accuracy of 75%. The text processing pipeline was able to automatically extract 68% of eligibility criteria rules, as compared with 80% in a manual version of the tool. Automated matching was accomplished in approximately 4 seconds, as compared with several hours using manual derivation. CONCLUSION: To our knowledge, this project represents the first open-source attempt to generate a patient-centric clinical trial matching tool. The tool demonstrated acceptable performance when compared with a manual version, and it has potential to save time and money when matching patients to trials.

Newborn Screening Conditions: Early Intervention and Probability of Developmental Delay.

OBJECTIVES: The purpose of this study is to explore which newborn screening (NBS) conditions are automatically eligible for early intervention (EI) across states and to determine the extent to which each disorder should automatically qualify for EI because of a high probability of developmental delay. METHODS: We examined each state's EI eligibility policy and reviewed the literature documenting developmental outcomes for each NBS condition. Using a novel matrix, we assessed the risk of developmental delay, medical complexity, and risk of episodic decompensation, revising the matrix iteratively until reaching consensus. Three NBS conditions (biotinidase deficiency, severe combined immunodeficiency, and propionic acidemia) are presented in detail as examples. RESULTS: Most states (88%) had Established Conditions lists to autoqualify children to EI. The average number of NBS conditions listed was 7.8 (range 0-34). Each condition appeared on average in 11.7 Established Conditions lists (range 2-29). After the literature review and consensus process, 29 conditions were likely to meet national criteria for an Established Condition. CONCLUSION: Despite benefiting from NBS and timely treatment, many children diagnosed with NBS conditions are at risk for developmental delays and significant medical complexity. The results demonstrate a need for more clarity and guidance regarding which children should qualify for EI. We suggest that most NBS conditions should automatically qualify based on the probability of resulting in a developmental delay. These findings suggest a future opportunity for collaboration between NBS and EI programs to create a consistent set of Established Conditions, potentially expediate referrals of eligible children, and streamline children's access to EI services.

Call to action: overcoming enrollment disparities in cancer clinical trials with modernized eligibility criteria.

Traditional clinical trial eligibility criteria restrict study populations, perpetuating enrollment disparities. We aimed to assess implementation of modernized eligibility criteria guidelines among pancreatic cancer (PC) clinical trials. Interventional PC trials in the United States since January 1, 2014, were identified via clinicaltrials.gov with December 31, 2017, as the transition for pre- and postguidance eras. Trials were assessed for guideline compliance and compared using Fisher exact test. In total, 198 trials were identified: 86 (43.4%) were pre- and 112 (56.6%) postguidance era. Improvements were seen in allowing patients with history of HIV (8.6% vs 43.8%; P < .0001), prior cancer (57.0% vs 72.3%; P = .034), or concurrent and/or stable cancer (2.1% vs 31.1%; P < .0001) to participate. Most (>95%) trials were compliant with laboratory reference ranges, QT interval corrected for heart rate (QTc) cutoffs, and rationalizing excluding prior therapies both pre- and postguidance eras. However, overall compliance with modernized criteria remains poor. We advocate for stakeholders to update protocols and scrutinize traditionally restrictive eligibility criteria.

A review of research on eligibility criteria for clinical trials.

The purpose of this paper is to systematically sort out and analyze the cutting-edge research on the eligibility criteria of clinical trials. Eligibility criteria are important prerequisites for the success of clinical trials. It directly affects the final results of the clinical trials. Inappropriate eligibility criteria will lead to insufficient recruitment, which is an important reason for the eventual failure of many clinical trials. We have investigated the research status of eligibility criteria for clinical trials on academic platforms such as arXiv and NIH. We have classified and sorted out all the papers we found, so that readers can understand the frontier research in this field. Eligibility criteria are the most important part of a clinical trial study. The ultimate goal of research in this field is to formulate more scientific and reasonable eligibility criteria and speed up the clinical trial process. The global research on the eligibility criteria of clinical trials is mainly divided into four main aspects: natural language processing, patient pre-screening, standard evaluation, and clinical trial query. Compared with the past, people are now using new technologies to study eligibility criteria from a new perspective (big data). In the research process, complex disease concepts, how to choose a suitable dataset, how to prove the validity and scientific of the research results, are challenges faced by researchers (especially for computer-related researchers). Future research will focus on the selection and improvement of artificial intelligence algorithms related to clinical trials and related practical applications such as databases, knowledge graphs, and dictionaries.

Text Classification of Cancer Clinical Trial Eligibility Criteria.

Automatic identification of clinical trials for which a patient is eligible is complicated by the fact that trial eligibility are stated in natural language. A potential solution to this problem is to employ text classification methods for common types of eligibility criteria. In this study, we focus on seven common exclusion criteria in cancer trials: prior malignancy, human immunodeficiency virus, hepatitis B, hepatitis C, psychiatric illness, drug/substance abuse, and autoimmune illness. Our dataset consists of 764 phase III cancer trials with these exclusions annotated at the trial level. We experiment with common transformer models as well as a new pre-trained clinical trial BERT model. Our results demonstrate the feasibility of automatically classifying common exclusion criteria. Additionally, we demonstrate the value of a pre-trained language model specifically for clinical trials, which yield the highest average performance across all criteria.

Implementing Modernized Eligibility Criteria in US National Cancer Institute Clinical Trials.

In 2018, the Cancer Therapy Evaluation Program (CTEP) at the US National Cancer Institute published new protocol template language that focused on organ function and prior and concurrent cancers in an effort to modernize eligibility criteria for cancer treatment trials. We conducted an analysis of CTEP-supported trials to evaluate the uptake and incorporation of the new language. The analysis included evaluation of 122 protocols approved in the years 2018-2020 for inclusion of the modernized eligibility criteria and consistency with new protocol template language related to 7 major eligibility criteria. These were cardiac function, liver function, kidney function, HIV status, prior and/or concurrent malignancies, treated and/or stable brain metastasis, and new and/or progressive brain metastases. Overall, CTEP trials evaluated in this period demonstrated that eligibility criteria were implemented to a relatively high degree ranging from a low of 54.1% for prior and/or concurrent malignancies to a high of 93.4% for eligibility criteria related to HIV infection. The findings demonstrate that modernized eligibility criteria can be successfully implemented but that consistent implementation requires sustained focused effort. As a result of these findings, CTEP began a new initiative in January 2022 that incorporates a specific review of eligibility criteria for new protocols to promote and improve consistency with the modernization effort.

A New Approach to Simplifying and Harmonizing Cancer Clinical Trials-Standardizing Eligibility Criteria.

Importance: Clinical trial sponsors rely on eligibility criteria to control the characteristics of patients in their studies, promote the safety of participants, and optimize the interpretation of results. However, in recent years, complex and often overly restrictive inclusion and exclusion criteria have created substantial barriers to patient access to novel therapies, hindered trial recruitment and completion, and limited generalizability of trial results. A LUNGevity Foundation working group developed a framework for lung cancer clinical trial eligibility criteria. The goals of this framework are to (1) simplify eligibility criteria, (2) facilitate stakeholders' (patients, clinicians, and sponsors) search for appropriate trials, and (3) harmonize trial populations to support intertrial comparisons of treatment effects. Observations: Clinicians and representatives from the pharmaceutical industry, the National Cancer Institute, the US Food and Drug Administration (FDA), the European Medicines Agency, and the LUNGevity Foundation undertook a process to identify and prioritize key items for inclusion in trial eligibility criteria. The group generated a prioritized library of terms to guide investigators and sponsors in the design of first-line, advanced non-small cell lung cancer clinical trials intended to support marketing application. These recommendations address disease stage and histologic features, enrollment biomarkers, performance status, organ function, brain metastases, and comorbidities. This effort forms the basis for a forthcoming FDA draft guidance for industry. Conclusions and Relevance: As an initial step, the recommended cross-trial standardization of eligibility criteria may harmonize trial populations. Going forward, by connecting diverse stakeholders and providing formal opportunity for public input, the emerging FDA draft guidance may also provide an opportunity to revise and simplify long-standing approaches to trial eligibility. This work serves as a prototype for similar efforts now underway for other cancers.

Combining human and machine intelligence for clinical trial eligibility querying.

OBJECTIVE: To combine machine efficiency and human intelligence for converting complex clinical trial eligibility criteria text into cohort queries. MATERIALS AND METHODS: Criteria2Query (C2Q) 2.0 was developed to enable real-time user intervention for criteria selection and simplification, parsing error correction, and concept mapping. The accuracy, precision, recall, and F1 score of enhanced modules for negation scope detection, temporal and value normalization were evaluated using a previously curated gold standard, the annotated eligibility criteria of 1010 COVID-19 clinical trials. The usability and usefulness were evaluated by 10 research coordinators in a task-oriented usability evaluation using 5 Alzheimer's disease trials. Data were collected by user interaction logging, a demographic questionnaire, the Health Information Technology Usability Evaluation Scale (Health-ITUES), and a feature-specific questionnaire. RESULTS: The accuracies of negation scope detection, temporal and value normalization were 0.924, 0.916, and 0.966, respectively. C2Q 2.0 achieved a moderate usability score (3.84 out of 5) and a high learnability score (4.54 out of 5). On average, 9.9 modifications were made for a clinical study. Experienced researchers made more modifications than novice researchers. The most frequent modification was deletion (5.35 per study). Furthermore, the evaluators favored cohort queries resulting from modifications (score 4.1 out of 5) and the user engagement features (score 4.3 out of 5). DISCUSSION AND CONCLUSION: Features to engage domain experts and to overcome the limitations in automated machine output are shown to be useful and user-friendly. We concluded that human-computer collaboration is key to improving the adoption and user-friendliness of natural language processing.

Vaccine Administration in Children's Hospitals.

OBJECTIVES: To examine inpatient vaccine delivery across a national sample of children's hospitals. METHODS: We conducted a retrospective cohort study examining vaccine administration at 49 children's hospitals in the Pediatric Health Information System database. Children <18 years old admitted between July 1, 2017, and June 30, 2019, and age eligible for vaccinations were included. We determined the proportion of hospitalizations with ≥1 dose of any vaccine type administered overall and by hospital, the type of vaccines administered, and the demographic characteristics of children who received vaccines. We calculated adjusted hospital-level rates for each vaccine type by hospital. We used logistic and linear regression models to examine characteristics associated with vaccine administration. RESULTS: There were 1 185 667 children and 1 536 340 hospitalizations included. The mean age was 5.5 years; 18% were non-Hispanic Black, and 55% had public insurance. There were ≥1 vaccine doses administered in 12.9% (95% confidence interval: 12.8-12.9) of hospitalizations, ranging from 1% to 45% across hospitals. The most common vaccines administered were hepatitis B and influenza. Vaccine doses other than the hepatitis B birth dose and influenza were administered in 1.9% of hospitalizations. Children had higher odds of receiving a vaccine dose other than the hepatitis B birth dose or influenza if they were <2 months old, had public insurance, were non-Hispanic Black race, were medically complex, or had a length of stay ≥3 days. CONCLUSIONS: In this national study, few hospitalizations involved vaccine administration with substantial variability across US children's hospitals. Efforts to standardize inpatient vaccine administration may represent an opportunity to increase childhood vaccine coverage.

Transcatheter Edge-to-Edge Repair in COAPT-Ineligible Patients: Incidence and Predictors of 2-Year Good Outcome.

BACKGROUND: COAPT-trial entry criteria are useful to identify patients with better outcomes after transcatheter edge-to-edge repair (TEER). However, up to one-half of real-world patients with secondary mitral regurgitation (SMR) undergoing TEER do not meet these highly selective criteria and no study has formally investigated them. The aim of this study was to evaluate the predictors of good outcome after TEER in COAPT-ineligible patients. METHODS: All consecutive patients with SMR and heart failure (HF) treated with MitraClip at 3 European centres were retrospectively screened. The presence of at least 1 COAPT exclusion criterion was used to define a COAPT-ineligible profile, allowing the inclusion in the study population. Freedom from all-cause death or HF hospitalisation was evaluated at 2-year follow-up (primary end point). RESULTS: A total of 305 patients (47%) had a COAPT-ineligible profile. An overall 58% rate of all-cause death or HF hospitalisation was detected at 2 years. Patients with a single COAPT exclusion criterion experienced fewer adverse events than those with multiple criteria (55% vs 69%). At multivariable Cox regression analysis, New York Heart Association functional class II, younger age (< 75 years), lower serum creatinine (< 2 mg/dL), lower left ventricular end-diastolic volume (< 240 mL), and the absence of hemodynamic instability, atrial fibrillation, and chronic obstructive pulmonary disease were independently associated with good outcome. CONCLUSIONS: In this real-world series of patients with SMR undergoing TEER, a COAPT-ineligible profile was common. The presence of only 1 COAPT exclusion criterion or the absence of hemodynamic instability were associated with the most favourable outcomes.

Changes in Racial and Ethnic Disparities in Access to Care and Health Among US Adults at Age 65 Years.

Importance: Medicare provides nearly universal health insurance to individuals at age 65 years. How eligibility for Medicare affects racial and ethnic disparities in access to care and health is poorly understood. Objective: To assess the association of Medicare with racial and ethnic disparities in access to care and health. Design, Setting, and Participants: This cross-sectional study uses regression discontinuity to compare racial and ethnic disparities before and after age 65 years, the age at which US adults are eligible for Medicare. There are a total of 2 434 320 respondents in the Behavioral Risk Factor Surveillance System and 44 587 state-age-year observations in the US Centers for Disease Control and Prevention Wide-Ranging Online Data for Epidemiologic Research Data (eg, the mortality rate for individuals age 63 years in New York in 2017) from January 2008 to December 2018. The data were analyzed between February and May 2021. Exposures: Eligibility for Medicare at age 65 years. Main Outcomes and Measures: Proportions of respondents with health insurance, as well as self-reported health and mortality. To examine access, whether respondents had a usual source of care, encountered cost-related barriers to care, or received influenza vaccines was assessed. Results: Of 2 434 320 participants, 192 346 were Black individuals, 104 294 were Hispanic individuals, and 892 177 were men. Immediately after age 65 years, insurance coverage increased more for Black respondents (from 86.3% to 95.8% or 9.5 percentage points; 95% CI, 7.6-11.4) and Hispanic respondents (from 77.4% to 91.3% or 13.9 percentage points; 95% CI, 12.0-15.8) than White respondents (from 92.0% to 98.5% or 6.5 percentage points; 95% CI, 6.1-7.0). This was associated with a 53% reduction compared with the size of the disparity between White and Black individuals before age 65 years (5.7% to 2.7% or 3.0 percentage points; 95% CI, 0.9-5.1; P = .003) and a 51% reduction compared with the size of the disparity between White and Hispanic individuals before age 65 years (14.6% to 7.2% or 7.4 percentage points; 95% CI, 5.3-9.5; P < .001). Medicare eligibility was associated with narrowed disparities between White and Hispanic individuals in access to care, lowering disparities in access to a usual source of care from 10.5% to 7.5% (P = .05), cost-related barriers to care from 11.4% to 6.9% (P < .001), and influenza vaccination rates from 8.1% to 3.3% (P = .01). For disparities between White and Black individuals, access to a usual source of care before and after age 65 years was not significantly different: 1.2% to 0.0% (P = .24), cost-related barriers to care from 5.8% to 4.3% (P = .22), and influenza vaccinations from 11.0% to 10.3% (P = .60). The share of people in poor self-reported health decreased by 3.8 percentage points for Hispanic respondents, 2.6 percentage points for Black respondents, and 0.2 percentage points for White respondents. Mortality-related disparities at age 65 years were unchanged. Medicare's association with reduced disparities largely persisted after the US Affordable Care Act took effect in 2014. Conclusions and Relevance: In this cross-sectional study that uses a regression discontinuity design, eligibility for Medicare at age 65 years was associated with marked reductions in racial and ethnic disparities in insurance coverage, access to care, and self-reported health.